Статті в журналах з теми "Soil moisture Australia Measurement"
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1
Ladson, A. R., J. R. Lander, A. W. Western, R. B. Grayson, and Lu Zhang. "Estimating extractable soil moisture content for Australian soils from field measurements." Soil Research 44, no. 5 (2006): 531. http://dx.doi.org/10.1071/sr04180.
Повний текст джерелаАнотація:
The amount of water that can be stored in soil and evaporated or actively used by plants is a key parameter in hydrologic models and is important for crop and pasture production. Often, the active soil moisture store is estimated from laboratory measurements of soil properties. An alternative approach, described in this paper, is to estimate the extractable soil moisture capacity from direct measurements of soil moisture content in the field. A time series of soil moisture values, over the depth of the soil, shows the actual changes in water content. The difference between the wettest and driest profiles is an estimate of the extractable soil moisture storage. We have gathered data on extractable soil water capacity for 180 locations over Australia and have compared our values with published results from the Atlas of Australian Soils (AAS), derived from profile descriptions and pedo-transfer functions. Our results show that data from the AAS provide a useful lower bound for measured extractable soil moisture storage, but of the sites examined, 42% had values >2 times those in the AAS. In part, this was because total soil depths were underestimated in the AAS results compared with the active depths from the measured data. Active depths are strongly related to vegetation type.
2
Kodikara, J., P. Rajeev, D. Chan, and C. Gallage. "Soil moisture monitoring at the field scale using neutron probe." Canadian Geotechnical Journal 51, no. 3 (March 2014): 332–45. http://dx.doi.org/10.1139/cgj-2012-0113.
Повний текст джерелаАнотація:
Measurement of the moisture variation in soils is required for geotechnical design and research because soil properties and behavior can vary as moisture content changes. The neutron probe, which was developed more than 40 years ago, is commonly used to monitor soil moisture variation in the field. This study reports a full-scale field monitoring of soil moisture using a neutron moisture probe for a period of more than 2 years in the Melbourne (Australia) region. On the basis of soil types available in the Melbourne region, 23 sites were chosen for moisture monitoring down to a depth of 1500 mm. The field calibration method was used to develop correlations relating the volumetric moisture content and neutron counts. Observed results showed that the deepest “wetting front” during the wet season was limited to the top 800 to 1000 mm of soil whilst the top soil layer down to about 550 mm responded almost immediately to the rainfall events. At greater depths (550 to 800 mm and below 800 mm), the moisture variations were relatively low and displayed predominantly periodic fluctuations. This periodic nature was captured with Fourier analysis to develop a cyclic moisture model on the basis of an analytical solution of a one-dimensional moisture flow equation for homogeneous soils. It is argued that the model developed can be used to predict the soil moisture variations as applicable to buried structures such as pipes.
3
Jing, Wenlong, Jia Song, and Xiaodan Zhao. "Validation of ECMWF Multi-Layer Reanalysis Soil Moisture Based on the OzNet Hydrology Network." Water 10, no. 9 (August 23, 2018): 1123. http://dx.doi.org/10.3390/w10091123.
Повний текст джерелаАнотація:
Soil moisture reanalysis products can provide soil water information for the surface and root zone soil layers, which are significant for understanding the water cycle and climate change. However, the accuracy of multi-layer soil moisture datasets obtained from reanalysis products remains unclear in some areas. In this study, we evaluated the root zone soil moisture of the ERA-Interim soil moisture product, as well as the surface soil moisture based on in situ measurements from the OzNet hydrological measurement network over southeast Australia. In general, the ERA-Interim soil moisture product presents good agreement with in situ soil moisture values and can nicely reflect time variations, with correlation coefficient (R) values in the range of 0.73 to 0.84 and unbiased root mean square difference (ubRMSD) values from 0.035 m3·m−3 to 0.060 m3·m−3. Although the ERA-Interim soil moisture also can reflect temporal dynamics of soil moisture at root zone layer at depths of 28–100 cm, low correlations were found in winter. In addition, the ERA-Interim soil moisture product overestimates in situ measurements at depths of 0–7 cm and 7–28 cm, whereas the product shows underestimated values compared with in situ soil moisture at the root zone of 28–100 cm. Consequently, the ERA-Interim soil moisture product has both high absolute and temporal accuracy at depths of 7–28 cm, and the ERA-Interim soil moisture product can nicely capture temporal dynamics at all the evaluated soil level depths, except for the depth of 28–100 cm during the winter months. The contributions of terrain, vegetation cover, and soil texture to the model error were addressed by feature importance estimations using the random forest (RF) algorithm. Results indicate that terrain features may have an impact on the model errors. It is clear that the accuracy of the ERA-Interim soil moisture can be improved by adjusting the assimilation scheme, and the results of this study are expected to provide a comprehensive understanding of the model errors and references for optimizing the model.
4
Tian, Siyuan, Luigi J. Renzullo, Albert I. J. M. van Dijk, Paul Tregoning, and Jeffrey P. Walker. "Global joint assimilation of GRACE and SMOS for improved estimation of root-zone soil moisture and vegetation response." Hydrology and Earth System Sciences 23, no. 2 (February 21, 2019): 1067–81. http://dx.doi.org/10.5194/hess-23-1067-2019.
Повний текст джерелаАнотація:
Abstract. The lack of direct measurement of root-zone soil moisture poses a challenge to the large-scale prediction of ecosystem response to variation in soil water. Microwave remote sensing capability is limited to measuring moisture content in the uppermost few centimetres of soil. The GRACE (Gravity Recovery and Climate Experiment) mission detected the variability in storage within the total water column. However, root-zone soil moisture cannot be separated from GRACE-observed total water storage anomalies without ancillary information on surface water and groundwater changes. In this study, GRACE total water storage anomalies and SMOS near-surface soil moisture observations were jointly assimilated into a hydrological model globally to better estimate the impact of changes in root-zone soil moisture on vegetation vigour. Overall, the accuracy of root-zone soil moisture estimates through the joint assimilation of surface soil moisture and total water storage retrievals showed improved consistency with ground-based soil moisture measurements and satellite-observed greenness when compared to open-loop estimates (i.e. without assimilation). For example, the correlation between modelled and in situ measurements of root-zone moisture increased by 0.1 (from 0.48 to 0.58) and 0.12 (from 0.53 to 0.65) on average for grasslands and croplands, respectively. Improved correlations were found between vegetation greenness and soil water storage on both seasonal variability and anomalies over water-limited regions. Joint assimilation results show a more severe deficit in soil water anomalies in eastern Australia, southern India and eastern Brazil over the period of 2010 to 2016 than the open-loop, consistent with the satellite-observed vegetation greenness anomalies. The assimilation of satellite-observed water content contributes to more accurate knowledge of soil water availability, providing new insights for monitoring hidden water stress and vegetation conditions.
5
Dang, Y. P., R. C. Dalal, M. J. Pringle, A. J. W. Biggs, S. Darr, B. Sauer, J. Moss, J. Payne, and D. Orange. "Electromagnetic induction sensing of soil identifies constraints to the crop yields of north-eastern Australia." Soil Research 49, no. 7 (2011): 559. http://dx.doi.org/10.1071/sr11199.
Повний текст джерелаАнотація:
Salinity, sodicity, acidity, and phytotoxic concentrations of chloride (Cl–) in soil are major constraints to crop production in many soils of north-eastern Australia. Soil constraints vary both spatially across the landscape and vertically within the soil profile. Identification of the spatial variability of these constraints will allow farmers to tune management to the potential of the land, which will, in turn, bring economic benefit. For three cropping fields in Australia’s northern grains region, we used electromagnetic induction with an EM38, which measures apparent electrical conductivity of the soil (ECa) and soil sampling to identify potential management classes. Soil Cl– and soluble Na+ concentrations, EC of the saturated extract (ECse), and soil moisture were the principal determinants of the variation of ECa, measured both at the drained upper limit of moisture (UL) and at the lower limit (LL) of moisture extracted by the crop. Grain yield showed a strong negative relation with ECa at both UL and LL, although it was stronger for the latter. We arrive at a framework to estimate the monetary value of site-specific management options, through: (i) identification of potential management classes formed from ECa at LL; (ii) measurement of soil attributes generally associated with soil constraints in the region; (iii) grain yield monitoring; and (iv) simple on-farm experiments. Simple on-farm experiments suggested that, for constrained areas, matching fertiliser application to realistic yield potential, coupled to gypsum amelioration, could potentially benefit growers by AU$14–46/ha.year (fertiliser) and $207/ha.3 years (gypsum).
6
Jana, Raghavendra B., Ali Ershadi, and Matthew F. McCabe. "Examining the relationship between intermediate-scale soil moisture and terrestrial evaporation within a semi-arid grassland." Hydrology and Earth System Sciences 20, no. 10 (September 30, 2016): 3987–4004. http://dx.doi.org/10.5194/hess-20-3987-2016.
Повний текст джерелаАнотація:
Abstract. Interactions between soil moisture and terrestrial evaporation affect water cycle behaviour and responses between the land surface and the atmosphere across scales. With strong heterogeneities at the land surface, the inherent spatial variability in soil moisture makes its representation via point-scale measurements challenging, resulting in scale mismatch when compared to coarser-resolution satellite-based soil moisture or evaporation estimates. The Cosmic Ray Neutron Probe (CRNP) was developed to address such issues in the measurement and representation of soil moisture at intermediate scales. Here, we present a study to assess the utility of CRNP soil moisture observations in validating model evaporation estimates. The CRNP soil moisture product from a pasture in the semi-arid central west region of New South Wales, Australia, was compared to evaporation derived from three distinct approaches, including the Priestley–Taylor (PT-JPL), Penman–Monteith (PM-Mu), and Surface Energy Balance System (SEBS) models, driven by forcing data from local meteorological station data and remote sensing retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Pearson's correlations, quantile–quantile (Q–Q) plots, and analysis of variance (ANOVA) were used to qualitatively and quantitatively evaluate the temporal distributions of soil moisture and evaporation over the study site. The relationships were examined against nearly 2 years of observation data, as well as for different seasons and for defined periods of analysis. Results highlight that while direct correlations of raw data were not particularly instructive, the Q–Q plots and ANOVA illustrate that the root-zone soil moisture represented by the CRNP measurements and the modelled evaporation estimates reflect similar distributions under most meteorological conditions. The PT-JPL and PM-Mu model estimates performed contrary to expectation when high soil moisture and cold temperatures were present, while SEBS model estimates displayed a disconnect from the soil moisture distribution in summers with long dry spells. Importantly, no single evaporation model matched the statistical distribution of the measured soil moisture for the entire period, highlighting the challenges in effectively capturing evaporative flux response within changing landscapes. One of the outcomes of this work is that the analysis points to the feasibility of using intermediate-scale soil moisture measurements to evaluate gridded estimates of evaporation, exploiting the independent, yet physically linked nature of these hydrological variables.
7
Cai, Jiangtao, Tiexi Chen, Qingyun Yan, Xin Chen, and Renjie Guo. "The Spatial-Temporal Characteristics of Soil Moisture and Its Persistence over Australia in the Last 20 Years." Water 14, no. 4 (February 16, 2022): 598. http://dx.doi.org/10.3390/w14040598.
Повний текст джерелаАнотація:
Persistence is an important feature of soil moisture, which affects many important processes such as land–air interaction and ecohydrological processes. Soil moisture datasets from reanalysis, remote-sensing observations and land surface models have been widely used in various ecohydrological studies, however, due to the complexity of hydrological processes, the essential features of soil moisture such as spatial-temporal characteristics and persistence still need to be further quantified. This study focused on the Australia region and used in situ observation from fourteen International Soil Moisture Network sites to evaluate soil moisture from six gridded products, including satellite remote-sensing records (ESA CCI), output of reanalysis (ERA5-Land) and land surface models (GLDAS and GLEAM). High correlation coefficients between observations and the other soil moisture datasets were gotten. Regional averaged inter-annual variations of soil moisture were relatively large with some dry periods (2002–2010, 2013–2016) and wet periods (2011–2012) indicated by these gridded products. General coherent spatial patterns were found in long-term soil moisture with large differences in the lateral inflow area of the Great Artesian Basin. The coefficient of variation of these soil moisture datasets generally decreased from northwest to southeast, but the enhanced vegetation index coefficient of variation was larger in the southwest corner, northeast (non-coastal areas) and the lateral inflow area. Persistence calculated from various soil moisture datasets had quite large differences compared with measurements. Meanwhile, little coherence was gotten among different surface soil moisture datasets, the persistence of deep soil moisture seemed to be significantly overestimated. Therefore, models still need to improve the temporal characteristics with the persistence rather than the correlation coefficient.
8
Dharssi, I., K. J. Bovis, B. Macpherson, and C. P. Jones. "Operational assimilation of ASCAT surface soil wetness at the Met Office." Hydrology and Earth System Sciences 15, no. 8 (August 31, 2011): 2729–46. http://dx.doi.org/10.5194/hess-15-2729-2011.
Повний текст джерелаАнотація:
Abstract. Currently, no extensive, near real time, global soil moisture observation network exists. Therefore, the Met Office global soil moisture analysis scheme has instead used observations of screen temperature and humidity. A number of new space-borne remote sensing systems, operating at microwave frequencies, have been developed that provide a more direct retrieval of surface soil moisture. These systems are attractive since they provide global data coverage and the horizontal resolution is similar to weather forecasting models. Several studies show that measurements of normalised backscatter (surface soil wetness) from the Advanced Scatterometer (ASCAT) on the meteorological operational (MetOp) satellite contain good quality information about surface soil moisture. This study describes methods to convert ASCAT surface soil wetness measurements to volumetric surface soil moisture together with bias correction and quality control. A computationally efficient nudging scheme is used to assimilate the ASCAT volumetric surface soil moisture data into the Met Office global soil moisture analysis. This ASCAT nudging scheme works alongside a soil moisture nudging scheme that uses observations of screen temperature and humidity. Trials, using the Met Office global Unified Model, of the ASCAT nudging scheme show a positive impact on forecasts of screen temperature and humidity for the tropics, North America and Australia. A comparison with in-situ soil moisture measurements from the US also indicates that assimilation of ASCAT surface soil wetness improves the soil moisture analysis. Assimilation of ASCAT surface soil wetness measurements became operational during July 2010.
9
Dharssi, I., K. Bovis, B. Macpherson, and C. Jones. "Operational assimilation of ASCAT surface soil wetness at the Met Office." Hydrology and Earth System Sciences Discussions 8, no. 2 (April 29, 2011): 4313–54. http://dx.doi.org/10.5194/hessd-8-4313-2011.
Повний текст джерелаАнотація:
Abstract. Currently, no extensive global soil moisture observation network exists. Therefore, the Met Office global soil moisture analysis scheme has instead used observations of screen temperature and humidity. A number of new space-borne remote sensing systems, operating at microwave frequencies, have been developed that provide a more direct retrieval of surface soil moisture. These systems are attractive since they provide global data coverage and the horizontal resolution is similar to weather forecasting models. Several studies show that measurements of normalised backscatter (surface soil wetness) from the Advanced Scatterometer (ASCAT) on the meteorological operational (MetOp) satellite contain good quality information about surface soil moisture. This note describes methods to convert ASCAT surface soil wetness measurements to volumetric surface soil moisture together with bias correction and quality control. A computationally efficient nudging scheme is used to assimilate the ASCAT volumetric surface soil moisture data into the Met Office global soil moisture analysis. This ASCAT nudging scheme works alongside a soil moisture nudging scheme that uses observations of screen temperature and humidity. Trials, using the Met Office global Unified Model, of the ASCAT nudging scheme show a positive impact on forecasts of screen temperature and humidity for the tropics, North America and Australia. A comparison with in-situ soil moisture measurements from the US also indicates that assimilation of ASCAT surface soil wetness improves the soil moisture analysis. Assimilation of ASCAT surface soil wetness measurements became operational during July 2010.
10
Suman, Swati, Prashant K. Srivastava, George P. Petropoulos, Dharmendra K. Pandey, and Peggy E. O’Neill. "Appraisal of SMAP Operational Soil Moisture Product from a Global Perspective." Remote Sensing 12, no. 12 (June 19, 2020): 1977. http://dx.doi.org/10.3390/rs12121977.
Повний текст джерелаАнотація:
Space-borne soil moisture (SM) satellite products such as those available from Soil Moisture Active Passive (SMAP) offer unique opportunities for global and frequent monitoring of SM and also to understand its spatiotemporal variability. The present study investigates the performance of the SMAP L4 SM product at selected experimental sites across four continents, namely North America, Europe, Asia and Australia. This product provides global scale SM estimates at 9 km × 9 km spatial resolution at daily intervals. For the product evaluation, co-orbital in situ SM measurements were used, acquired at 14 test sites in North America, Europe, and Australia belonging to the International Soil Moisture Network (ISMN) and local networks in India. The satellite SM estimates of up to 0–5 cm soil layer were compared against collocated ground measurements using a series of statistical scores. Overall, the best performance of the SMAP product was found in North America (RMSE = 0.05 m3/m3) followed by Australia (RMSE = 0.08 m3/m3), Asia (RMSE = 0.09 m3/m3) and Europe (RMSE = 0.14 m3/m3). Our findings provide important insights into the spatiotemporal variability of the specific operational SM product in different ecosystems and environments. This study also furnishes an independent verification of this global product, which is of international interest given its suitability for a wide range of practical and research applications.
11
McJannet, David, Aaron Hawdon, Brett Baker, Luigi Renzullo, and Ross Searle. "Multiscale soil moisture estimates using static and roving cosmic-ray soil moisture sensors." Hydrology and Earth System Sciences 21, no. 12 (December 1, 2017): 6049–67. http://dx.doi.org/10.5194/hess-21-6049-2017.
Повний текст джерелаАнотація:
Abstract. Soil moisture plays a critical role in land surface processes and as such there has been a recent increase in the number and resolution of satellite soil moisture observations and the development of land surface process models with ever increasing resolution. Despite these developments, validation and calibration of these products has been limited because of a lack of observations on corresponding scales. A recently developed mobile soil moisture monitoring platform, known as the rover, offers opportunities to overcome this scale issue. This paper describes methods, results and testing of soil moisture estimates produced using rover surveys on a range of scales that are commensurate with model and satellite retrievals. Our investigation involved static cosmic-ray neutron sensors and rover surveys across both broad (36 × 36 km at 9 km resolution) and intensive (10 × 10 km at 1 km resolution) scales in a cropping district in the Mallee region of Victoria, Australia. We describe approaches for converting rover survey neutron counts to soil moisture and discuss the factors controlling soil moisture variability. We use independent gravimetric and modelled soil moisture estimates collected across both space and time to validate rover soil moisture products. Measurements revealed that temporal patterns in soil moisture were preserved through time and regression modelling approaches were utilised to produce time series of property-scale soil moisture which may also have applications in calibration and validation studies or local farm management. Intensive-scale rover surveys produced reliable soil moisture estimates at 1 km resolution while broad-scale surveys produced soil moisture estimates at 9 km resolution. We conclude that the multiscale soil moisture products produced in this study are well suited to future analysis of satellite soil moisture retrievals and finer-scale soil moisture models.
12
Lee, Junchan, Sunil Bisnath, Regina S. K. Lee, and Narin Gavili Kilane. "Computation Approach for Quantitative Dielectric Constant from Time Sequential Data Observed by CYGNSS Satellites." Remote Sensing 13, no. 11 (May 21, 2021): 2032. http://dx.doi.org/10.3390/rs13112032.
Повний текст джерелаАнотація:
This paper describes a computation method for obtaining dielectric constant using Global Navigation Satellite System reflectometry (GNSS-R) products. Dielectric constant is a crucial component in the soil moisture retrieval process using reflected GNSS signals. The reflectivity for circular polarized signals is combined with the dielectric constant equation that is used for radiometer observations. Data from the Cyclone Global Navigation Satellite System (CYGNSS) mission, an eight-nanosatellite constellation for GNSS-R, are used for computing dielectric constant. Data from the Soil Moisture Active Passive (SMAP) mission are used to measure the soil moisture through its radiometer, and they are considered as a reference to confirm the accuracy of the new dielectric constant calculation method. The analyzed locations have been chosen that correspond to sites used for the calibration and validation of the SMAP soil moisture product using in-situ measurement data. The retrieved results, especially in the case of a specular point around Yanco, Australia, show that the estimated results track closely to the soil moisture results, and the Root Mean Square Error (RMSE) in the estimated dielectric constant is approximately 5.73. Similar results can be obtained when the specular point is located near the Texas Soil Moisture Network (TxSON), USA. These results indicate that the analysis procedure is well-defined, and it lays the foundation for obtaining quantitative soil moisture content using the GNSS reflectometry results. Future work will include applying the computation product to determine the characteristics that will allow for the separation of coherent and incoherent signals in delay Doppler maps, as well as to develop local soil moisture models.
13
Tong, Cheng, Hongquan Wang, Ramata Magagi, Kalifa Goïta, Luyao Zhu, Mengying Yang, and Jinsong Deng. "Soil Moisture Retrievals by Combining Passive Microwave and Optical Data." Remote Sensing 12, no. 19 (September 28, 2020): 3173. http://dx.doi.org/10.3390/rs12193173.
Повний текст джерелаАнотація:
This paper aims to retrieve the temporal dynamics of soil moisture from 2015 to 2019 over an agricultural site in Southeast Australia using the Soil Moisture Active Passive (SMAP) brightness temperature. To meet this objective, two machine learning approaches, Random Forest (RF), Support Vector Machine (SVM), as well as a statistical Ordinary Least Squares (OLS) model were established, with the auxiliary data including the 16-day composite MODIS NDVI (MOD13Q1) and Surface Temperature (ST). The entire data were divided into two parts corresponding to ascending (6:00 p.m. local time) and descending (6:00 a.m. local time) orbits of SMAP overpasses. Thus, the three models were trained using the descending data acquired during the five years (2015 to 2019), and validated using the ascending product of the same period. Consequently, three different temporal variations of the soil moisture were obtained based on the three models. To evaluate their accuracies, the retrieved soil moisture was compared against the SMAP level-2 soil moisture product, as well as to in-situ ground station data. The comparative results show that the soil moisture obtained using the OLS, RF and SVM algorithms are highly correlated to the SMAP level-2 product, with high coefficients of determination (R2OLS = 0.981, R2SVM = 0.943, R2RF = 0.983) and low RMSE (RMSEOLS = 0.016 cm3/cm3, RMSESVM = 0.047 cm3/cm3, RMSERF = 0.016 cm3/cm3). Meanwhile, the estimated soil moistures agree with in-situ station data across different years (R2OLS = 0.376~0.85, R2SVM = 0.376~0.814, R2RF = 0.39~0.854; RMSEOLS = 0.049~0.105 cm3/cm3, RMSESVM = 0.073~0.1 cm3/cm3, RMSERF = 0.047~0.102 cm3/cm3), but an overestimation issue is observed for high vegetation conditions. The RF algorithm outperformed the SVM and OLS, in terms of the agreement with the ground measurements. This study suggests an alternative soil moisture retrieval scheme, in complementary to the SMAP baseline algorithm, for a fast soil moisture retrieval.
14
Peischl, S., J. P. Walker, C. Rüdiger, N. Ye, Y. H. Kerr, E. Kim, R. Bandara, and M. Allahmoradi. "The AACES field experiments: SMOS calibration and validation across the Murrumbidgee River catchment." Hydrology and Earth System Sciences 16, no. 6 (June 22, 2012): 1697–708. http://dx.doi.org/10.5194/hess-16-1697-2012.
Повний текст джерелаАнотація:
Abstract. Following the launch of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission on 2 November 2009, SMOS soil moisture products need to be rigorously validated at the satellite's approximately 45 km scale and disaggregation techniques for producing maps with finer resolutions tested. The Australian Airborne Cal/val Experiments for SMOS (AACES) provide the basis for one of the most comprehensive assessments of SMOS data world-wide by covering a range of topographic, climatic and land surface variability within an approximately 500 × 100 km2 study area, located in South-East Australia. The AACES calibration and validation activities consisted of two extensive field experiments which were undertaken across the Murrumbidgee River catchment during the Australian summer and winter season of 2010, respectively. The datasets include airborne L-band brightness temperature, thermal infrared and multi-spectral observations at 1 km resolution, as well as extensive ground measurements of near-surface soil moisture and ancillary data, such as soil temperature, soil texture, surface roughness, vegetation water content, dew amount, leaf area index and spectral characteristics of the vegetation. This paper explains the design and data collection strategy of the airborne and ground component of the two AACES campaigns and presents a preliminary analysis of the field measurements including the application and performance of the SMOS core retrieval model on the diverse land surface conditions captured by the experiments. The data described in this paper are publicly available from the website: http://www.moisturemap.monash.edu.au/aaces.
15
Dorigo, W. A., W. Wagner, R. Hohensinn, S. Hahn, C. Paulik, A. Xaver, A. Gruber, et al. "The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements." Hydrology and Earth System Sciences 15, no. 5 (May 30, 2011): 1675–98. http://dx.doi.org/10.5194/hess-15-1675-2011.
Повний текст джерелаАнотація:
Abstract. In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International Soil Moisture Network (ISMN; http://www.ipf.tuwien.ac.at/insitu) was initiated to serve as a centralized data hosting facility where globally available in situ soil moisture measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their soil moisture datasets with the ISMN on a voluntary and no-cost basis. Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. These will assist the user in correctly interpreting the soil moisture data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status May 2011), the ISMN contains data of 19 networks and more than 500 stations located in North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that both the number of stations and the time period covered by the existing stations are still growing. Hence, it will become an increasingly important resource for validating and improving satellite-derived soil moisture products and studying climate related trends. As the ISMN is animated by the scientific community itself, we invite potential networks to enrich the collection by sharing their in situ soil moisture data.
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Dorigo, W. A., W. Wagner, R. Hohensinn, S. Hahn, C. Paulik, M. Drusch, S. Mecklenburg, P. van Oevelen, A. Robock, and T. Jackson. "The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements." Hydrology and Earth System Sciences Discussions 8, no. 1 (February 3, 2011): 1609–63. http://dx.doi.org/10.5194/hessd-8-1609-2011.
Повний текст джерелаАнотація:
Abstract. In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International Soil Moisture Network (ISMN; http://www.ipf.tuwien.ac.at/insitu) was initiated to serve as a centralized data hosting facility where globally available in situ soil moisture measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their soil moisture datasets with the ISMN on a voluntary and no-cost basis. Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. These will assist the user in correctly interpreting the soil moisture data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status January 2011), the ISMN contains data of 16 networks and more than 500 stations located in the North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that both the number of stations and the time period covered by the existing stations are still growing. Hence, it will become an increasingly important resource for validating and improving satellite-derived soil moisture products and studying climate related trends. As the ISMN is animated by the scientific community itself, we invite potential networks to enrich the collection by sharing their in situ soil moisture data.
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De Lannoy, Gabriëlle J. M., and Rolf H. Reichle. "Global Assimilation of Multiangle and Multipolarization SMOS Brightness Temperature Observations into the GEOS-5 Catchment Land Surface Model for Soil Moisture Estimation." Journal of Hydrometeorology 17, no. 2 (February 1, 2016): 669–91. http://dx.doi.org/10.1175/jhm-d-15-0037.1.
Повний текст джерелаАнотація:
Abstract Multiangle and multipolarization L-band microwave observations from the Soil Moisture Ocean Salinity (SMOS) mission are assimilated into the Goddard Earth Observing System Model, version 5 (GEOS-5), using a spatially distributed ensemble Kalman filter. A variant of this system is also used for the Soil Moisture Active Passive (SMAP) Level 4 soil moisture product. The assimilation involves a forward simulation of brightness temperatures (Tb) for various incidence angles and polarizations and an inversion of the differences between Tb forecasts and observations into updates to modeled surface and root-zone soil moisture, as well as surface soil temperature. With SMOS Tb assimilation, the unbiased root-mean-square difference between simulations and gridcell-scale in situ measurements in a few U.S. watersheds during the period from 1 July 2010 to 1 July 2014 is 0.034 m3 m−3 for both surface and root-zone soil moisture. A validation against gridcell-scale measurements and point-scale measurements from sparse networks in the United States, Australia, and Europe demonstrates that the assimilation improves both surface and root-zone soil moisture results over the open-loop (no assimilation) estimates in areas with limited vegetation and terrain complexity. At the global scale, the assimilation of SMOS Tb introduces mean absolute increments of 0.004 m3 m−3 to the profile soil moisture content and 0.7 K to the surface soil temperature. The updates induce changes to energy fluxes and runoff amounting to about 15% of their respective temporal standard deviation.
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Stanley, John N., David W. Lamb, Gregory Falzon, and Derek A. Schneider. "Apparent electrical conductivity (ECa) as a surrogate for neutron probe counts to measure soil moisture content in heavy clay soils (Vertosols)." Soil Research 52, no. 4 (2014): 373. http://dx.doi.org/10.1071/sr13142.
Повний текст джерелаАнотація:
Site-specific measurements of the apparent electrical conductivity (ECa) of soil using the EM38 were correlated with near-simultaneous neutron probe readings over periods of moisture extraction by an irrigated cotton crop. Thirty sites were monitored from three ECa zones within a 96-ha field of grey Vertosol soil 30 km west of Moree, New South Wales, Australia. This study differs from previous approaches by reporting the effect on ECa of a wetting front (irrigation) reaching a single ECa measurement point in a field and by using polyethylene neutron probe access tubes so that the EM38 could be operated directly over the same site measured by a neutron probe. We report strong correlations (r = 0.94) between neutron probe counts (CRR) averaged to a depth of 40 or 60 cm and ECa from an EM38 held in the vertical mode 20 cm above the soil surface. All combinations of EM sensor height (0–1.2 m) to neutron probe measurement depth (0.2–1.4 m) returned correlations >0.85. The relationship between CCR and ECa was linear for the purposes of estimating water content over a range of background ECa levels. More critical modelling suggested a slight curve (logarithmic model) fitted best. The range of surface-surveyed ECa from the start of irrigation (refill point) to fully irrigated (full point) was ~27 mS m–1 for this Vertosol, where surface ECa readings typically ranged from 50 to 200 mS m–1. We suggest that the calibration of ECa to CRR might be effected by a two-point measurement of the soil, namely at both upper (field capacity) and lower (wilting point) ECa values, and a site-specific calibration template generated by extending these point measures to whole-field surveys.
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Deng, Khidir, Salim Lamine, Andrew Pavlides, George Petropoulos, Prashant Srivastava, Yansong Bao, Dionissios Hristopulos, and Vasileios Anagnostopoulos. "Operational Soil Moisture from ASCAT in Support of Water Resources Management." Remote Sensing 11, no. 5 (March 9, 2019): 579. http://dx.doi.org/10.3390/rs11050579.
Повний текст джерелаАнотація:
This study provides the results of an extensive investigation of the Advanced Scaterometter (ASCAT) surface soil moisture global operational product accuracy across three continents (United States of America (USA), Europe, and Australia). ASCAT predictions of surface soil moisture were compared against near concurrent in situ measurements from the FLUXNET observational network. A total of nine experimental sites were used to assess the accuracy of ASCAT Surface Soil Moisture (ASCAT SSM) predictions for two complete years of observations (2010, 2011). Results showed a generally reasonable agreement between the ASCAT product and the in situ soil moisture measurements in the 0–5 cm soil moisture layer. The Root Mean Square Error (RMSE) was below 0.135 m3 m−3 at all of the sites. With a few exceptions, Pearson’s correlation coefficient was above 45%. Grassland, shrublands, and woody savanna land cover types exhibited satisfactory agreement in all the sites analyzed (RMSE ranging from 0.05 to 0.13 m3 m−3). Seasonal performance was tested, but no definite conclusion can be made with statistical significance at this time, as the seasonal results varied from continent to continent and from year to year. However, the satellite and in situ measurements for Needleleaf forests were practically uncorrelated (R = −0.11 and −0.04). ASCAT predictions overestimated the observed values at all of the sites in Australia. A positive bias of approximately 0.05 m3 m−3 was found with respect to the observed values that were in the range 0–0.3 m3 m−3. Better agreement was observed for the grassland sites in most cases (RMSE ranging from 0.09 to 0.10 m3 m−3 and R from 0.46 to 0.90). Our results provide supportive evidence regarding the potential value of the ASCAT global operational product for meso-scale studies and the relevant practical applications. A key contribution of this study is a comprehensive evaluation of ASCAT product soil moisture estimates at different sites around the globe. These sites represent a variety of climatic, environmental, biome, and topographical conditions.
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Malbéteau, Y., O. Merlin, G. Balsamo, S. Er-Raki, S. Khabba, J. P. Walker, and L. Jarlan. "Toward a Surface Soil Moisture Product at High Spatiotemporal Resolution: Temporally Interpolated, Spatially Disaggregated SMOS Data." Journal of Hydrometeorology 19, no. 1 (January 1, 2018): 183–200. http://dx.doi.org/10.1175/jhm-d-16-0280.1.
Повний текст джерелаАнотація:
Abstract High spatial and temporal resolution surface soil moisture is required for most hydrological and agricultural applications. The recently developed Disaggregation based on Physical and Theoretical Scale Change (DisPATCh) algorithm provides 1-km-resolution surface soil moisture by downscaling the 40-km Soil Moisture Ocean Salinity (SMOS) soil moisture using Moderate Resolution Imaging Spectroradiometer (MODIS) data. However, the temporal resolution of DisPATCh data is constrained by the temporal resolution of SMOS (a global coverage every 3 days) and further limited by gaps in MODIS images due to cloud cover. This paper proposes an approach to overcome these limitations based on the assimilation of the 1-km-resolution DisPATCh data into a simple dynamic soil model forced by (inaccurate) precipitation data. The performance of the approach was assessed using ground measurements of surface soil moisture in the Yanco area in Australia and the Tensift-Haouz region in Morocco during 2014. It was found that the analyzed daily 1-km-resolution surface soil moisture compared slightly better to in situ data for all sites than the original disaggregated soil moisture products. Over the entire year, assimilation increased the correlation coefficient between estimated soil moisture and ground measurements from 0.53 to 0.70, whereas the mean unbiased RMSE (ubRMSE) slightly decreased from 0.07 to 0.06 m3 m−3 compared to the open-loop force–restore model. The proposed assimilation scheme has significant potential for large-scale applications over semiarid areas, since the method is based on data available at the global scale together with a parsimonious land surface model.
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Tao, Liangliang, Dongryeol Ryu, Andrew Western, and Sun-Gu Lee. "Comparison of KOMPSAT-5 and Sentinel-1 Radar Data for Soil Moisture Estimations Using a New Semi-Empirical Model." Remote Sensing 14, no. 16 (August 19, 2022): 4042. http://dx.doi.org/10.3390/rs14164042.
Повний текст джерелаАнотація:
X-band KOMPSAT-5 provides a good perspective for soil moisture retrieval at high-spatial resolution over arid and semi-arid areas. In this paper, an intercomparison of KOMPSAT-5 and C-band Sentinel-1 radar data in soil moisture retrieval was conducted over agricultural fields in Wimmera, Victoria, Australia. Optical images from Sentinel-2 were also used to calculate the scattering contribution of vegetation. This study employed a new semi-empirical vegetation scattering model with a linear association of soil moisture with observed backscatter coefficient and vegetation indices. The Combined Vegetation Index (CVI) was proposed and first used to parameterize vegetation water content. As a result, the vegetation scattering model was developed to monitor soil moisture based on remotely sensed data and ground measurements. Application of the algorithm over dryland wheat field sites demonstrated that the estimated satellite-based soil moisture contents have good linear relationships with the ground measurements. The correlation coefficients (R) are 0.862 and 0.616, and the root mean square errors (RMSEs) have the values of 0.020 cm3/cm3 and 0.032 cm3/cm3 at X- and C-bands, respectively. Furthermore, the validation results also indicated that X-band provided higher consistent accuracy for soil moisture inversion than C-band. These results showed significant promise in retrieving soil moisture using KOMPSAT-5 and Sentinel-1 remotely sensed data at high-spatial resolution over agricultural fields, with subsequent uses for crop growth and yield estimation.
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Peischl, S., J. P. Walker, C. Rüdiger, N. Ye, Y. H. Kerr, E. Kim, R. Bandara, and M. Allahmoradi. "The AACES field experiments: SMOS calibration and validation across the Murrumbidgee River catchment." Hydrology and Earth System Sciences Discussions 9, no. 3 (March 2, 2012): 2763–95. http://dx.doi.org/10.5194/hessd-9-2763-2012.
Повний текст джерелаАнотація:
Abstract. Following the launch of the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission on 2 November 2009, SMOS products need to be rigorously validated at the satellite's approximately 45 km scale, and disaggregation techniques for maps with finer resolutions tested. The Australian Airborne Cal/val Experiments for SMOS (AACES) provide the basis for one of the most comprehensive assessments of SMOS data world-wide by covering a range of topographic, climatic and land surface variability within an approximately 500 × 100 km2 study area, located in South-East Australia. The AACES calibration and validation activities consisted of two extensive field experiments which were undertaken across the Murrumbidgee River catchment during the Australian summer and winter season of 2010, respectively. The data sets include airborne L-band brightness temperature, thermal infrared and multi-spectral observations at 1 km resolution, as well as extensive ground measurements of near-surface soil moisture and ancillary data, such as soil temperature, soil texture, surface roughness, vegetation water content, dew amount, leaf area index and spectral characteristics of the vegetation. This paper explains the design and data collection strategy of the airborne and ground component of the two AACES campaigns and presents a preliminary analysis of the field measurements including the application and performance of the SMOS core retrieval model on the diverse land surface conditions captured by the experiments. The data described in this paper are publicly available from the website: http://www.moisturemap.monash.edu.au/aaces.
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Brockwell, J., Catherine M. Evans, Alison M. Bowman, and Alison McInnes. "Distribution, frequency of occurrence and symbiotic properties of the Australian native legume Trigonella suavissima Lindl. and its associated root-nodule bacteria." Rangeland Journal 32, no. 4 (2010): 395. http://dx.doi.org/10.1071/rj09080.
Повний текст джерелаАнотація:
Trigonella suavissima Lindl. is an Australian native legume belonging to the tribe Trifolieae. It is an ephemeral species that is widely distributed in the arid interior of the continent where it occurs, following periodic inundation, on clay soils of the watercourse country of the Channel Country (far-western Queensland, north-east South Australia and north-western New South Wales). T. suavissima is the only member of its tribe that is endemic to Australia. Likewise, its root-nodule bacteria (Sinorhizobium sp.) may be the only member of its taxonomic group (S. meliloti, S. medicae) that is an Australian native. The distribution and frequency of occurrence of T. suavissima and the size of soil populations (density) of Sinorhizobium were monitored at 64 locations along inland river systems of the Channel Country. Measurements were made of (i) the nitrogen-fixing effectiveness of the symbioses between T. suavissima and strains of its homologous Sinorhizobium and (ii) the nitrogen-fixing effectiveness of the symbioses between legumes symbiotically related to T. suavissima and diverse strains of Sinorhizobium. It was concluded that the distribution and frequency of occurrence of T. suavissima is soil related. The species is most widespread on fine-textured clay soils with deep, self-mulching surfaces and high moisture-holding capacity. By contrast, the occurrence of T. suavissima is sporadic in the upper reaches of the inland river systems where the soils are poorly structured clays with lower moisture-holding capacity. Sinorhizobium is most abundant where the plant is most common. The nitrogen-fixing symbioses between T. suavissima and strains of Sinorhizobium isolated from soils across the region were consistently effective and often highly effective. Some of these strains fixed a little nitrogen with lucerne (Medicago sativa L.). T. suavissima also had some symbiotic (nitrogen-fixing) affinity with an exotic Trigonella (T. arabica Del.). The economic value of T. suavissima (and its symbiosis with Sinorhizobium) to the beef industry in the Channel Country is discussed.
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Kibirige, Daniel, and Endre Dobos. "Off-Site Calibration Approach of EnviroScan Capacitance Probe to Assist Operational Field Applications." Water 13, no. 6 (March 19, 2021): 837. http://dx.doi.org/10.3390/w13060837.
Повний текст джерелаАнотація:
Soil water content or soil moisture content is considered one of the most critical properties of the soil for crop production, irrigation, and environmental studies. The technical development of soil moisture measurement devices is swift, but calibration among field conditions is still not entirely resolved. Accurate calibration requires samples taken right next to the sensor that disturbs the site and changes the soil conditions. Real field operation requires the probe to represent larger areas that have undisturbed soils around the probe. These would describe the parcel’s general soil conditions and start providing data from the time of installation. This study aimed to compare several potential solutions for off-site calibration of an operational EnviroScan sensor (Sentek Technologies, Stepney South, Australia). The performances of the default and soil texture-specific equations provided by the manufacturer were compared with the field and laboratory calibration approaches. Two statistical parameters, coefficient of determination (R2) and root square mean error (RMSE) was used to determine logarithmic model results. The results show that the default calibration equations in all three classes have relatively low performances with RMSE values of around 10–15 and R2 values ranging from 0.4 to 0.8. However, significant refinement was achieved by selecting texture-specific equations from the manufacturer’s libraries. The soil texture-specific equations of the EnviroScan often yielded quite satisfactory results, with RMSEs ranging between 2 and 4. Similar RMSE values were achieved from the laboratory calibration exercises, but the reapplication potential of these equations was often questionable due to the severely changed soil conditions of the laboratory processed soil compared to the field soil conditions.
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Gupta, V. V. S. R., M. M. Roper, and D. K. Roget. "Potential for non-symbiotic N2-fixation in different agroecological zones of southern Australia." Soil Research 44, no. 4 (2006): 343. http://dx.doi.org/10.1071/sr05122.
Повний текст джерелаАнотація:
Nitrogen fixation by symbiotic and non-symbiotic bacteria can be a significant source of nitrogen in cropping systems. However, contributions from non-symbiotic nitrogen fixation (NSNF) are dependent on available carbon in the soil and environmental conditions (soil moisture and temperature). In Australia, measurements of NSNF have been made in the field by quantifying nitrogenase activity. These studies have included determinations of the moisture and temperature requirements for NSNF and for crop residue decomposition that supplies carbon to NSNF bacteria. Other studies have determined the N input by NSNF using N budget calculations. These data together with information about carbon supply and environmental conditions were used to estimate potential NSNF in the cropping zones of southern Australia. Using the ArcviewGIS Spatial Analyst (v3.1), maps of Australia showing estimates of NSNF in different cropping zones as determined by rainfall and temperature or carbon availability were generated. In Western Australia (represented by Wongan Hills) and South Australia (represented by Avon), where summers are dry, estimates of NSNF were generally low (10–15 kg N/ha from January to June) due to limitations of soil moisture. In New South Wales, particularly in the north where summer rainfall patterns develop (represented by Gunnedah), the warm, moist conditions produced higher estimates of NSNF (totaling 32–38 kg N/ha from January to June). In this region, the majority of estimated NSNF occurred in January and February leading to the depletion of carbon supplies and reduced NSNF in autumn (March–June). Information about potential supplies of N from NSNF across the cropping zones should be useful for researchers to select and study areas that are most likely to benefit from NSNF. It should also help agronomists and extension officers explain changes in N status within paddocks or within specific farming systems and to provide more accurate advice on N fertiliser requirements, particularly in low-input farming systems.
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Fang, Li, Xiwu Zhan, Jifu Yin, Jicheng Liu, Mitchell Schull, Jeffrey P. Walker, Jun Wen, et al. "An Intercomparison Study of Algorithms for Downscaling SMAP Radiometer Soil Moisture Retrievals." Journal of Hydrometeorology 21, no. 8 (August 1, 2020): 1761–75. http://dx.doi.org/10.1175/jhm-d-19-0034.1.
Повний текст джерелаАнотація:
AbstractIn the past decade, a variety of algorithms have been introduced to downscale passive microwave soil moisture observations. Some exploit the soil moisture information from optical/thermal sensing of land surface temperature (LST) and vegetation dynamics while others use active microwave (radar) observations. In this study, downscaled soil moisture data at 9- or 1-km resolution from several algorithms are intercompared against in situ soil moisture measurements to determine their reliability in an operational system. The finescale satellite data used here for downscaling the coarse-scale SMAP data are observations of LST from the Geostationary Operational Environmental Satellite (GOES) and vegetation index (VI) from the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) for the warm seasons in 2015 and 2016. Three recently developed downscaling algorithms are evaluated and compared: a simple regression algorithm based on 9-km thermal inertial data, a data mining approach called regression tree based on 9- and 1-km LST and VI, and the NASA SMAP enhanced 9-km soil moisture product algorithm. Seven sets of in situ soil moisture data from intensive networks were used for validation, including 1) the CREST-SMART network in Millbrook, New York; 2) Walnut Gulch Watershed in Arizona; 3) Little Washita Watershed in Oklahoma; 4) Fort Cobb Reservoir Experimental Watersheds in Oklahoma; 5) Little River Watershed in Georgia; 6) the Tibetan Plateau network in China, and 7) the OzNet in Australia. Soil moisture measurements of the in situ networks were upscaled to the corresponding SMAP reference pixels at 9 km and used to assess the accuracy of downscaled products at a 9-km scale. Results revealed that the downscaled 9-km soil moisture products generally outperform the 36-km product for most in situ datasets. The linear regression algorithm using the thermal sensing based evaporative stress index (ESI) had the best agreement with the in situ measurements from networks in the contiguous United States according to the site-by-site comparison. In addition, the inertial thermal linear regression method demonstrated the lowest unbiased RMSE when comparing to the matched-up in situ datasets as well. In general, this method is promising for operational generation of fine-resolution soil moisture data product.
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Albergel, C., W. Dorigo, R. H. Reichle, G. Balsamo, P. de Rosnay, J. Muñoz-Sabater, L. Isaksen, R. de Jeu, and W. Wagner. "Skill and Global Trend Analysis of Soil Moisture from Reanalyses and Microwave Remote Sensing." Journal of Hydrometeorology 14, no. 4 (August 1, 2013): 1259–77. http://dx.doi.org/10.1175/jhm-d-12-0161.1.
Повний текст джерелаАнотація:
Abstract In situ soil moisture measurements from 2007 to 2010 for 196 stations from five networks across the world (United States, France, Spain, China, and Australia) are used to determine the reliability of three soil moisture products: (i) a revised version of the ECMWF Interim Re-Analysis (ERA-Interim; ERA-Land); (ii) a revised version of the Modern-Era Retrospective Analysis for Research and Applications (MERRA) reanalysis from NASA (MERRA-Land); and (iii) a new, microwave-based multisatellite surface soil moisture dataset (SM-MW). Evaluation of the time series and anomalies from a moving monthly mean shows a good performance of the three products in capturing the annual cycle of surface soil moisture and its short-term variability. On average, correlations (95% confidence interval) are 0.66 (±0.038), 0.69 (±0.038), and 0.60 (±0.061) for ERA-Land, MERRA-Land, and SM-MW. The two reanalysis products also capture the root-zone soil moisture well; on average, correlations are 0.68 (±0.035) and 0.73 (±0.032) for ERA-Land and MERRA-Land, respectively. Global trends analysis for 1988–2010 suggests a decrease of surface soil moisture contents (72% of significant trends are negative, i.e., drying) for ERA-Land and an increase in surface soil moisture (59% of significant trends are positive, i.e., wetting) for MERRA-Land. As the spatial extent and fractions of significant trends in both products differ, the trend reflected in the majority of grid points within different climate classes was investigated and compared to that of SM-MW. The latter is dominated by negative significant trends (73.2%) and is more in line with ERA-Land. For both reanalysis products, trends for the upper layer of soil are confirmed in the root-zone soil moisture (first meter of soil).
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Massari, Christian, Luca Brocca, Thierry Pellarin, Gab Abramowitz, Paolo Filippucci, Luca Ciabatta, Viviana Maggioni, Yann Kerr, and Diego Fernandez Prieto. "A daily 25 km short-latency rainfall product for data-scarce regions based on the integration of the Global Precipitation Measurement mission rainfall and multiple-satellite soil moisture products." Hydrology and Earth System Sciences 24, no. 5 (May 26, 2020): 2687–710. http://dx.doi.org/10.5194/hess-24-2687-2020.
Повний текст джерелаАнотація:
Abstract. Rain gauges are unevenly spaced around the world with extremely low gauge density over developing countries. For instance, in some regions in Africa the gauge density is often less than one station per 10 000 km2. The availability of rainfall data provided by gauges is also not always guaranteed in near real time or with a timeliness suited for agricultural and water resource management applications, as gauges are also subject to malfunctions and regulations imposed by national authorities. A potential alternative is satellite-based rainfall estimates, yet comparisons with in situ data suggest they are often not optimal. In this study, we developed a short-latency (i.e. 2–3 d) rainfall product derived from the combination of the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) Early Run (IMERG-ER) with multiple-satellite soil-moisture-based rainfall products derived from ASCAT (Advanced Scatterometer), SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active and Passive) L3 (Level 3) satellite soil moisture (SM) retrievals. We tested the performance of this product over four regions characterized by high-quality ground-based rainfall datasets (India, the conterminous United States, Australia and Europe) and over data-scarce regions in Africa and South America by using triple-collocation (TC) analysis. We found that the integration of satellite SM observations with in situ rainfall observations is very beneficial with improvements of IMERG-ER up to 20 % and 40 % in terms of correlation and error, respectively, and a generalized enhancement in terms of categorical scores with the integrated product often outperforming reanalysis and ground-based long-latency datasets. We also found a relevant overestimation of the rainfall variability of GPM-based products (up to twice the reference value), which was significantly reduced after the integration with satellite soil-moisture-based rainfall estimates. Given the importance of a reliable and readily available rainfall product for water resource management and agricultural applications over data-scarce regions, the developed product can provide a valuable and unique source of rainfall information for these regions.
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Sun, Xi, Jie Li, Donald Cameron, and Gregory Moore. "On the Use of Sap Flow Measurements to Assess the Water Requirements of Three Australian Native Tree Species." Agronomy 12, no. 1 (December 27, 2021): 52. http://dx.doi.org/10.3390/agronomy12010052.
Повний текст джерелаАнотація:
The measurement of sap movement in xylem sapwood tissue using heat pulse velocity sap flow instruments has been commonly used to estimate plant transpiration. In this study, sap flow sensors (SFM1) based on the heat ratio method (HRM) were used to assess the sap flow performance of three different tree species located in the eastern suburbs of Melbourne, Australia over a 12-month period. A soil moisture budget profile featuring potential evapotranspiration and precipitation was developed to indicate soil moisture balance while the soil-plant-atmosphere continuum was examined at the study site using data obtained from different monitoring instruments. The comparison of sap flow volume for the three species clearly showed that the water demand of Corymbia maculata was the highest when compared to Melaleuca styphelioides and Lophostemon confertus and the daily sap flow volume on the north side of the tree on average was 63% greater than that of the south side. By analysing the optimal temperature and vapour pressure deficit (VPD) for transpiration for all sampled trees, it was concluded that the Melaleuca styphelioides could better cope with hotter and drier weather conditions.
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Liu, Y. Y., J. P. Evans, M. F. McCabe, R. A. M. de Jeu, A. I. J. M. van Dijk, and H. Su. "Influence of cracking clays on satellite estimated and model simulated soil moisture." Hydrology and Earth System Sciences 14, no. 6 (June 18, 2010): 979–90. http://dx.doi.org/10.5194/hess-14-979-2010.
Повний текст джерелаАнотація:
Abstract. Vertisols are clay soils that are common in the monsoonal and dry warm regions of the world. One of the characteristics of these soil types is to form deep cracks during periods of extended dry, resulting in significant variation of the soil and hydrologic properties. Understanding the influence of these varying soil properties on the hydrological behavior of the system is of considerable interest, particularly in the retrieval or simulation of soil moisture. In this study we compare surface soil moisture (θ in m3 m−3) retrievals from AMSR-E using the VUA-NASA (Vrije Universiteit Amsterdam in collaboration with NASA) algorithm with simulations from the Community Land Model (CLM) over vertisol regions of mainland Australia. For the three-year period examined here (2003–2005), both products display reasonable agreement during wet periods. During dry periods however, AMSR-E retrieved near surface soil moisture falls below values for surrounding non-clay soils, while CLM simulations are higher. CLM θ are also higher than AMSR-E and their difference keeps increasing throughout these dry periods. To identify the possible causes for these discrepancies, the impacts of land use, topography, soil properties and surface temperature used in the AMSR-E algorithm, together with vegetation density and rainfall patterns, were investigated. However these do not explain the observed θ responses. Qualitative analysis of the retrieval model suggests that the most likely reason for the low AMSR-E θ is the increase in soil porosity and surface roughness resulting from cracking of the soil. To quantitatively identify the role of each factor, more in situ measurements of soil properties that can represent different stages of cracking need to be collected. CLM does not simulate the behavior of cracking soils, including the additional loss of moisture from the soil continuum during drying and the infiltration into cracks during rainfall events, which results in overestimated θ when cracks are present. The hydrological influence of soil physical changes are expected to propagate through the modeled system, such that modeled infiltration, evaporation, surface temperature, surface runoff and groundwater recharge should be interpreted with caution over these soil types when cracks might be present. Introducing temporally dynamic roughness and soil porosity into retrieval algorithms and adding a "cracking clay" module into models are expected to improve the representation of vertisol hydrology.
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Zhao, Y., Y. Z. Wang, Z. H. Xu, and L. Fu. "Impacts of prescribed burning on soil greenhouse gas fluxes in a suburban native forest of south-eastern Queensland, Australia." Biogeosciences 12, no. 21 (November 3, 2015): 6279–90. http://dx.doi.org/10.5194/bg-12-6279-2015.
Повний текст джерелаАнотація:
Abstract. Prescribed burning is a forest management practice that is widely used in Australia to reduce the risk of damaging wildfires. Prescribed burning can affect both carbon (C) and nitrogen (N) cycling in the forest and thereby influence the soil-atmosphere exchange of major greenhouse gases, i.e. carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). To quantify the impact of a prescribed burning (conducted on 27 May 2014) on greenhouse gas exchange and the potential controlling mechanisms, we carried out a series of field measurements before (August 2013) and after (August 2014 and November 2014) the fire. Gas exchange rates were determined in four replicate plots which were burned during the combustion and in another four adjacent unburned plots located in green islands, using a set of static chambers. Surface soil properties including temperature, pH, moisture, soil C and N pools were also determined either by in situ measurement or by analysing surface 10 cm soil samples. All of the chamber measurements indicated a net sink of atmospheric CH4, with mean CH4 uptake ranging from 1.15 to 1.99 mg m−2 d−1. Prescribed burning significantly enhanced CH4 uptake as indicated by the significant higher CH4 uptake rates in the burned plots measured in August 2014. In the following 3 months, the CH4 uptake rate was recovered to the pre-burning level. Mean CO2 emission from the forest soils ranged from 2721.76 to 7113.49 mg m−2 d−1. The effect of prescribed burning on CO2 emission was limited within the first 3 months, as no significant difference was observed between the burned and the adjacent unburned plots in both August and November 2014. The CO2 emissions showed more seasonal variations, rather than the effects of prescribed burning. The N2O emission in the plots was quite low, and no significant impact of prescribed burning was observed. The changes in understory plants and litter layers, surface soil temperature, C and N substrate availability and microbial activities, following the prescribed burning, were the factors that controlled the greenhouse gas exchanges. Our results suggested that the low-intensity prescribed burning would decrease soil CO2 emission and increase CH4 uptake, but this effect would be present within a relatively short period. Only slight changes in the surface soil properties during the combustion and very limited impacts of prescribed burning on the mineral soils supported the rapid recovery of the greenhouse gas exchange rates.
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Zhao, Y., Y. Z. Wang, Z. H. Xu, and L. Fu. "Impacts of prescribed burning on soil greenhouse gas fluxes in a suburban native forest of south-eastern Queensland, Australia." Biogeosciences Discussions 12, no. 13 (July 9, 2015): 10679–706. http://dx.doi.org/10.5194/bgd-12-10679-2015.
Повний текст джерелаАнотація:
Abstract. Prescribed burning is a forest management practice that is widely used in Australia to reduce the risk of damaging wildfires. It can affect both carbon (C) and nitrogen (N) cycling in the forest and thereby influence the soil–atmosphere exchange of major greenhouse gases, i.e. carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O). To quantify the impact of a prescribed burning (conducted on 27 May 2014) on greenhouse gas exchange and the potential controlling mechanisms, we carried out a series of field measurements before (August 2013) and after (August 2014 and November 2014) the fire. Gas exchange rates were determined at 4 replicate sites which were burned during the combustion and another 4 adjacent unburned sites located in green islands, using a set of static chambers. Surface soil properties including temperature, pH, moisture, soil C and N pools were also determined either by in situ measurement or by analysing surface 10 cm soil samples. All of the chamber measurements indicated a net sink of atmospheric CH4, with mean CH4 uptake ranging from 1.15 to 1.99 mg m−2 day−1. The burning significantly enhanced CH4 uptake as indicated by the significant higher CH4 uptake rates at the burned sites measured in August 2014. While within the next 3 months the CH4 uptake rate was recovered to pre-burning levels. Mean CO2 emission from forest soils ranged from 2721.76 to 7113.49 mg m−2 day−1. The effect of prescribed burning on CO2 emission was limited within the first 3 months, as no significant difference was observed between the burned and the adjacent unburned sites in both August and November 2014. The temporal dynamics of the CO2 emission presented more seasonal variations, rather than burning effects. The N2O emission at the studied sites was quite low, and no significant impact of burning was observed. The changes in understory plants and litter layers, surface soil temperature, C and N substrate availability and microbial activities, resulting from the burning, were the factors that controlled the greenhouse gas exchanges. Our results suggested that the low intensity prescribed burning would decrease soil CO2 emission and increase CH4 uptake, however, this effect would be present within a relative short period. Only slight changes in the surface soil during the combustion and very limited damages in the mineral soils supported the quick recovery of the greenhouse gas exchange rates.
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Daniells, Ian G. "Hardsetting soils: a review." Soil Research 50, no. 5 (2012): 349. http://dx.doi.org/10.1071/sr11102.
Повний текст джерелаАнотація:
Hardsetting soils have been defined as soils that set to a hard, structureless mass during drying and are thereafter difficult or impossible to cultivate until the profile is rewetted. Soil strength increases rapidly as the soil dries, and so seedlings must grow quickly before soil strength becomes too high for root growth or shoot emergence. Recent work on the mechanisms of hardsetting confirms that aggregate disruption through slaking and dispersion on wetting leads to coalescence. Bridging by dispersed particles under matric potential makes a soil hardset. Failure to recover from a coalesced state as the soil dries leaves it with a massive structure. This paper reviews the worldwide occurrence of hardsetting soils, the evolution of definitions of hardsetting, and the use of those definitions in soil classification with particular emphasis on Australia. Measurement of hardsetting includes methods such as visual score of slaking and dispersion, penetration resistance, fall-cone penetration, dispersion, fractions of soil organic matter, friability index, modulus of rupture, and a particular use of the soil water retention curve. Overcoming problems associated with hardsetting soils and their ongoing management is difficult. Further work is needed on the reasons for variable responses to tillage, no tillage, and pasture. Modifying soil texture has limited application, and increasing soil organic matter under cropping is difficult in low-rainfall areas. Polymers have been shown to be beneficial. Mulching maintains higher soil moisture and therefore a softer surface, while biochar shows inconsistent effects. Controlled traffic is a key to reducing recompaction. Management of a hardsetting soil must include the whole rotation, including when to till, when to crop, and when to graze or not.
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Bell, M., N. Seymour, G. R. Stirling, A. M. Stirling, L. Van Zwieten, T. Vancov, G. Sutton, and P. Moody. "Impacts of management on soil biota in Vertosols supporting the broadacre grains industry in northern Australia." Soil Research 44, no. 4 (2006): 433. http://dx.doi.org/10.1071/sr05137.
Повний текст джерелаАнотація:
The grain-producing regions of northern New South Wales and southern and central Queensland are characterised by cropping systems that are strongly dependent on stored soil moisture rather than in-crop rainfall, and tillage systems that are increasingly reliant on zero or minimum tillage. Crops are grown relatively infrequently and crop rotations are dominated by winter and summer grains (wheat [Triticum aestivum L.] and sorghum [Sorghum bicolor L. Moench], respectively), with smaller areas of grain legumes and cotton (Gossypium hirsutum L.). The grey, black, and brown Vertosols represent the more productive soils in the region under rainfed cropping, and are the focus of work reported in this study. Soil samples were collected from surface soils (0–0.30 m) across the region, utilising sites of long term tillage and residue management studies, fertiliser trials, and commercial fields to enable an assessment of the impact of various management practices on soil biological properties. A number of biological and biochemical parameters were measured (microbial biomass C, total organic C and labile C fractions, total C and N, microbial activity using FDA, cellulase activity, free living nematodes, total DNA and fatty acid profiles), and the response of wheat, sorghum, and chickpea (Cicer arietinum L.) to steam pasteurisation was assessed in glasshouse bioassays. The objective was to obtain an indication of the biological status of grain-growing soils and assess the impact of biological constraints in soils from different regions and management systems. Results showed that biological activity in cropped soils was consistently low relative to other land uses in northern Australia, with management practices like stubble retention and adoption of zero tillage producing relatively small benefits. In the case of zero tillage, many of these benefits were confined to the top 0.05 m of the soil profile. Fallowing to recharge soil moisture reserves significantly reduced all soil biological parameters, while pasture leys produced consistent positive benefits. Breaking a long fallow with a short duration grain or brown manure crop significantly moderated the negative effects of a long bare fallow on soil biology. Use of inorganic N and P fertilisers produced minimal effects on soil biota, with the exception of one component of the free-living nematode community (the Dorylaimida). The glasshouse bioassays provided consistent evidence that soil biota were constraining growth of both grain crops (sorghum and wheat) but not the grain legume (chickpea). The biota associated with this constraint have not yet been identified, but effects were consistent across the region and were not associated with the presence of any known pathogen or correlated with any of the measured soil biological or biochemical properties. Further work to confirm the existence and significance of these constraints under field conditions is needed. None of the measured biological or biochemical parameters consistently changed in response to management practices, while conflicting conclusions could sometimes be drawn from different measurements on the same soil sample. This highlights the need for further work on diagnostic tools to quantify soil biological communities, and suggests there is no clear link between measured changes in soil biological communities and economically or ecologically important soil attributes.
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Liu, Yangxiaoyue, Yaping Yang, and Xiafang Yue. "Evaluation of Satellite-Based Soil Moisture Products over Four Different Continental In-Situ Measurements." Remote Sensing 10, no. 7 (July 23, 2018): 1161. http://dx.doi.org/10.3390/rs10071161.
Повний текст джерелаАнотація:
Global, near-real-time satellite-based soil moisture (SM) datasets have been developed over recent decades. However, there has been a lack of comparison among different passing times, retrieving algorithms, and sensors between SM products over various regions. In this study, we assessed seven types of SM products (AMSR_A, AMSR_D, ECV_A, ECV_C, ECV_P, SMOS_A, and SMOS_D) over four different continental in-situ networks in North America, the Tibetan Plateau, Western Europe, and Southeastern Australia. Bias, R, root mean square error (RMSE), unbiased root mean square difference (ubRMSD), anomalies, and anomalies R were calculated to explore the agreement between satellite-based SM and in-situ measurements. Taylor diagrams were drawn for an inter-comparison. The results showed that (1) ECV_C was superior both in characterizing the SM temporal variation tendency and absolute value, while ECV_A produced numerous abnormal values over all validation regions. ECV_P was able to basically express the SM variation tendency, except for a few overestimations and underestimations. (2) The ascending data (AMSR_A, SMOS_A) generally outperformed the corresponding descending data (AMSR_D, SMOS_D). (3) AMSR exceeded SMOS in terms of the coefficient of correlation. (4) The validation result of SMOS_D over the NAN and OZN networks was unsatisfactory, with a rather poor correlation for both original data and anomalies.
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Rains, Dominik, Xujun Han, Hans Lievens, Carsten Montzka, and Niko E. C. Verhoest. "SMOS brightness temperature assimilation into the Community Land Model." Hydrology and Earth System Sciences 21, no. 11 (November 28, 2017): 5929–51. http://dx.doi.org/10.5194/hess-21-5929-2017.
Повний текст джерелаАнотація:
Abstract. SMOS (Soil Moisture and Ocean Salinity mission) brightness temperatures at a single incident angle are assimilated into the Community Land Model (CLM) across Australia to improve soil moisture simulations. Therefore, the data assimilation system DasPy is coupled to the local ensemble transform Kalman filter (LETKF) as well as to the Community Microwave Emission Model (CMEM). Brightness temperature climatologies are precomputed to enable the assimilation of brightness temperature anomalies, making use of 6 years of SMOS data (2010–2015). Mean correlation R with in situ measurements increases moderately from 0.61 to 0.68 (11 %) for upper soil layers if the root zone is included in the updates. A reduced improvement of 5 % is achieved if the assimilation is restricted to the upper soil layers. Root-zone simulations improve by 7 % when updating both the top layers and root zone, and by 4 % when only updating the top layers. Mean increments and increment standard deviations are compared for the experiments. The long-term assimilation impact is analysed by looking at a set of quantiles computed for soil moisture at each grid cell. Within hydrological monitoring systems, extreme dry or wet conditions are often defined via their relative occurrence, adding great importance to assimilation-induced quantile changes. Although still being limited now, longer L-band radiometer time series will become available and make model output improved by assimilating such data that are more usable for extreme event statistics.
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Macdonald, Ben C. T., Graeme D. Schwenke, Annabelle McPherson, Clarence Mercer, Jonathan Baird, and Gunasekhar Nachimuthu. "Soil water deficit effects on soil inorganic nitrogen in alternate-furrow flood irrigated Australian cotton production systems." Soil Research 60, no. 2 (November 4, 2021): 137–46. http://dx.doi.org/10.1071/sr20223.
Повний текст джерелаАнотація:
Context Predicting the nitrogen (N) mineralisation from soil organic matter is a key aid to fertiliser decision-making and improving the N fertiliser use efficiency of a crop. Aims and methods Field experiments were conducted to assess the amount of inorganic N derived from soil organic matter mineralisation over two seasons (2017–2018 and 2018–2019) across treatments differing in irrigation frequency and amount. During both seasons, the plant line soil in each treatment was sequentially sampled at each irrigation event. Key results There was an effect of the soil water deficit on the measured accumulated soil inorganic N derived from mineralisation in both measurement years. It was observed that soil inorganic N accumulated in the plant line rather than in other hillside and furrow positions for all soil moisture deficit treatments in both years. In 2017–2018, N accumulated in the plant was significantly greater than the measured accumulated inorganic N (0–300 mm). Conclusions and implications The sequential soil sampling approach was challenging in irrigated systems and we propose a hybrid measurement of pre-season available soil N and/or plant N uptake in nil N fertiliser plots as a means of estimating N derived from soil organic matter mineralisation.
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Kinsela, Andrew S., O. Tom Denmead, Bennett C. T. Macdonald, Michael D. Melville, Jason K. Reynolds, and Ian White. "Field-based measurements of sulfur gas emissions from an agricultural coastal acid sulfate soil, eastern Australia." Soil Research 49, no. 6 (2011): 471. http://dx.doi.org/10.1071/sr11089.
Повний текст джерелаАнотація:
The emissions of biogenic hydrogen sulfide (H2S) and sulfur dioxide (SO2) play important roles in the global atmospheric sulfur (S) cycle. Field-based investigations using ultraviolet fluorescence spectroscopy show that drained acid sulfate soils (ASS) are a potentially unaccounted source of biogenic H2S and SO2. Significant diurnal variations were evident in SO2 fluxes, with average daytime measurements 9.3–16.5-fold greater than night-time emissions. Similar diurnal patterns in H2S fluxes were observed but proved statistically insignificant. The results from simultaneously collected micrometeorological measurements suggest that emissions of SO2 and H2S are most likely occurring via different processes. The SO2 fluxes are closely linked to surface soil temperature and moisture content, whereas H2S is constantly emitted from the land surface at the two study sites. Drained ASS are most likely mapped as agricultural lands rather than drained backswamps. Therefore, these areas are likely to be assigned H2S and SO2 flux values of zero in greenhouse gas species inventories. These findings suggest a need to expand these measurements to other drained ASS areas to refine regional (and possibly global) atmospheric S budgets. Further research is necessary to elucidate the sources of measured S compounds, and specifically whether they are limited to individual agricultural drainage patterns in ASS.
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Page, K. L., R. C. Dalal, and R. J. Raison. "The impact of harvesting native forests on vegetation and soil C stocks, and soil CO2, N2O and CH4 fluxes." Australian Journal of Botany 59, no. 7 (2011): 654. http://dx.doi.org/10.1071/bt11207.
Повний текст джерелаАнотація:
Australia’s harvested native forests are extremely diverse in terms of species-mix, disturbance history and ecology, forest productivity and C storage. Our understanding of the effects of harvesting on C storage and greenhouse gas (GHG) emissions from these systems is incomplete, and this paper consolidates current Australian knowledge, places this in a global context, and identifies areas requiring further study. The uptake of CO2 and the re-accumulation of forest C stocks after harvesting or other disturbance is largely dependent on forest primary production. However, in Australian native forests, knowledge of rates of primary production for the diverse range of species and management practices present is poor. Soil respiration rates following harvest have also been largely unquantified for Australian systems. It is essential that both these parameters are quantified if estimates of net ecosystem production (NEP) are to be made. It is generally acknowledged that harvested forests have a negative NEP, and thus are sources of C, immediately following harvest, but attain a positive NEP as the forest regrows and photosynthetic capacity increases. The magnitude and time course of these changes are largely unknown for most Australian forest systems. In addition, little data are available to quantify the effect on soil C storage, and where estimates have been made these are often subject to methodological uncertainty and are thus highly contentious. Following harvest, the changes that occur to soil structure, moisture content, and N cycling may also influence CH4 and N2O flux, although these fluxes also remain largely unquantified in harvested Australian forests. Given the significant changes to NEP, CH4 and N2O fluxes observed after forest harvest in international studies, it is expected that GHG fluxes would typically increase from Australian native forests following harvest, and then slowly decrease over time as biomass accumulates, and N2O and CH4 fluxes return to background levels. However, it is currently difficult to quantify the magnitude and time course of these changes due to a lack of both gas flux and primary production measurements. Clearly, further research effort to quantify these parameters throughout Australia is required in order to obtain a more reliable picture of the effects of harvesting and other disturbances on forest GHG balance.
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Bolland, MDA, and MJ Baker. "Increases in soil water content decrease the residual value of superphosphate." Australian Journal of Experimental Agriculture 27, no. 4 (1987): 571. http://dx.doi.org/10.1071/ea9870571.
Повний текст джерелаАнотація:
In pot experiments, levels of superphosphate incorporated through the whole soil were incubated for 30 days in 2 lateritic soils from south-western Australia at 1 of the following 5 soil water contents: air-dry for 30 days, at field capacity for 10 or 30 days, and flooded for 10 or 30 days. The soils were then air-dried for 30 days and the residual value of the superphosphate relative to freshly applied superphosphate was measured using 30-day-old triticale (x Triticosecale cv. Tyalla) and wheat (Triticum aestivum cv. Gamenya) plants. Soil samples were collected just before sowing from each pot for measurement of bicarbonate-extractable phosphorus (P) levels which were compared with the DM yield of plant tops. For all treatments there was a common relationship between the P concentration (data not shown) or P content in the tops and the DM yield. This shows that the treatments can be considered as different dilutions of the same fertiliser. Less P was taken up by plants as the moisture content and period of contact with moist soil increased, and this limited yield. The effectiveness of superphosphate incubated in dry soil was similar to the effectiveness of freshly applied superphosphate. Incubation in moist soil reduced the effectiveness of superphosphate for plant growth, by about 50% for soils incubated at field capacity and 70% for flooded incubated soils. As calculated from the P content of plant tops, the effectiveness of superphosphate incubated in dry soil was similar to the effectiveness of freshly applied superphosphate, and the effectiveness of superphosphate decreased by about 55% for soils incubated at field capacity and 75% for flooded incubated soils. The amount of P extracted by sodium bicarbonate from soil sampled just before sowing was also influenced by the incubation treatments, and decreased in the following order: freshly applied = air dry incubated > field capacity incubated > flooded incubated. Thus the bicarbonate-soluble P extracted from the soil qualitatively paralleled the yield results. However, compared with the yield results, the decreases were not as marked. When the bicarbonate-extracted P results were compared with subsequent yields of triticale or wheat, separate calibration curves were required for the different incubation treatments.
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Müller, J. F., T. Stavrakou, S. Wallens, I. De Smedt, M. Van Roozendael, M. J. Potosnak, J. Rinne, B. Munger, A. Goldstein, and A. B. Guenther. "Global isoprene emissions estimated using MEGAN, ECMWF analyses and a detailed canopy environment model." Atmospheric Chemistry and Physics Discussions 7, no. 6 (November 1, 2007): 15373–407. http://dx.doi.org/10.5194/acpd-7-15373-2007.
Повний текст джерелаАнотація:
Abstract. The global emissions of isoprene are calculated at 0.5° resolution for each year between 1995 and 2006, based on the MEGAN (Model of Emissions of Gases and Aerosols from Nature) version 2 model (Guenther et al., 2006) and a detailed multi-layer canopy environment model for the calculation of leaf temperature and visible radiation fluxes. The calculation is driven by meteorological fields – air temperature, cloud cover, downward solar irradiance, windspeed, volumetric soil moisture in 4 soil layers – provided by analyses of the European Centre for Medium-Range Weather Forecasts (ECMWF). The estimated annual global isoprene emission ranges between 374 Tg (in 1996) and 449 Tg (in 1998 and 2005), for an average of ca. 410 Tg/year over the whole period, i.e. about 30% less than the standard MEGAN estimate (Guenther et al., 2006). This difference is due, to a large extent, to the impact of the soil moisture stress factor, which is found here to decrease the global emissions by more than 20%. In qualitative agreement with past studies, high annual emissions are found to be generally associated with El Niño events. The emission inventory is evaluated against flux measurement campaigns at Harvard forest (Massachussets) and Tapajós in Amazonia, showing that the model can capture quite well the short-term variability of emissions, but that it fails to reproduce the observed seasonal variation at the tropical rainforest site, with largely overestimated wet season fluxes. The comparison of the HCHO vertical columns calculated by a chemistry and transport model (CTM) with HCHO distributions retrieved from space provides useful insights on tropical isoprene emissions. For example, the relatively low emissions calculated over Western Amazonia (compared to the corresponding estimates in the inventory of Guenther et al., 1995) are validated by the excellent agreement found between the CTM and HCHO data over this region. The parameterized impact of the soil moisture stress on isoprene emissions is found to reduce the model/data bias over Australia, but it leads to underestimated emissions near the end of the dry season over subtropical Africa.
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Müller, J. F., T. Stavrakou, S. Wallens, I. De Smedt, M. Van Roozendael, M. J. Potosnak, J. Rinne, B. Munger, A. Goldstein, and A. B. Guenther. "Global isoprene emissions estimated using MEGAN, ECMWF analyses and a detailed canopy environment model." Atmospheric Chemistry and Physics 8, no. 5 (March 6, 2008): 1329–41. http://dx.doi.org/10.5194/acp-8-1329-2008.
Повний текст джерелаАнотація:
Abstract. The global emissions of isoprene are calculated at 0.5° resolution for each year between 1995 and 2006, based on the MEGAN (Model of Emissions of Gases and Aerosols from Nature) version 2 model (Guenther et al., 2006) and a detailed multi-layer canopy environment model for the calculation of leaf temperature and visible radiation fluxes. The calculation is driven by meteorological fields – air temperature, cloud cover, downward solar irradiance, windspeed, volumetric soil moisture in 4 soil layers – provided by analyses of the European Centre for Medium-Range Weather Forecasts (ECMWF). The estimated annual global isoprene emission ranges between 374 Tg (in 1996) and 449 Tg (in 1998 and 2005), for an average of ca. 410 Tg/year over the whole period, i.e. about 30% less than the standard MEGAN estimate (Guenther et al., 2006). This difference is due, to a large extent, to the impact of the soil moisture stress factor, which is found here to decrease the global emissions by more than 20%. In qualitative agreement with past studies, high annual emissions are found to be generally associated with El Niño events. The emission inventory is evaluated against flux measurement campaigns at Harvard forest (Massachussets) and Tapajós in Amazonia, showing that the model can capture quite well the short-term variability of emissions, but that it fails to reproduce the observed seasonal variation at the tropical rainforest site, with largely overestimated wet season fluxes. The comparison of the HCHO vertical columns calculated by a chemistry and transport model (CTM) with HCHO distributions retrieved from space provides useful insights on tropical isoprene emissions. For example, the relatively low emissions calculated over Western Amazonia (compared to the corresponding estimates in the inventory of Guenther et al., 1995) are validated by the excellent agreement found between the CTM and HCHO data over this region. The parameterized impact of the soil moisture stress on isoprene emissions is found to reduce the model/data bias over Australia, but it leads to underestimated emissions near the end of the dry season over subtropical Africa.
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Ciabatta, Luca, Christian Massari, Luca Brocca, Alexander Gruber, Christoph Reimer, Sebastian Hahn, Christoph Paulik, Wouter Dorigo, Richard Kidd, and Wolfgang Wagner. "SM2RAIN-CCI: a new global long-term rainfall data set derived from ESA CCI soil moisture." Earth System Science Data 10, no. 1 (February 8, 2018): 267–80. http://dx.doi.org/10.5194/essd-10-267-2018.
Повний текст джерелаАнотація:
Abstract. Accurate and long-term rainfall estimates are the main inputs for several applications, from crop modeling to climate analysis. In this study, we present a new rainfall data set (SM2RAIN-CCI) obtained from the inversion of the satellite soil moisture (SM) observations derived from the ESA Climate Change Initiative (CCI) via SM2RAIN (Brocca et al., 2014). Daily rainfall estimates are generated for an 18-year long period (1998–2015), with a spatial sampling of 0.25° on a global scale, and are based on the integration of the ACTIVE and the PASSIVE ESA CCI SM data sets.The quality of the SM2RAIN-CCI rainfall data set is evaluated by comparing it with two state-of-the-art rainfall satellite products, i.e. the Tropical Measurement Mission Multi-satellite Precipitation Analysis 3B42 real-time product (TMPA 3B42RT) and the Climate Prediction Center Morphing Technique (CMORPH), and one modeled data set (ERA-Interim). A quality check is carried out on a global scale at 1° of spatial sampling and 5 days of temporal sampling by comparing these products with the gauge-based Global Precipitation Climatology Centre Full Data Daily (GPCC-FDD) product. SM2RAIN-CCI shows relatively good results in terms of correlation coefficient (median value > 0.56), root mean square difference (RMSD, median value < 10.34 mm over 5 days) and bias (median value < −14.44 %) during the evaluation period. The validation has been carried out at original resolution (0.25°) over Europe, Australia and five other areas worldwide to test the capabilities of the data set to correctly identify rainfall events under different climate and precipitation regimes.The SM2RAIN-CCI rainfall data set is freely available at https://doi.org/10.5281/zenodo.846259.
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Brocca, Luca, Paolo Filippucci, Sebastian Hahn, Luca Ciabatta, Christian Massari, Stefania Camici, Lothar Schüller, Bojan Bojkov, and Wolfgang Wagner. "SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations." Earth System Science Data 11, no. 4 (October 22, 2019): 1583–601. http://dx.doi.org/10.5194/essd-11-1583-2019.
Повний текст джерелаАнотація:
Abstract. Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products suffer from space and time inconsistency due to the non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent “bottom-up” approach that exploits satellite soil moisture observations for estimating rainfall through the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall data record as a single polar orbiting satellite sensor is used. Here we exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp) satellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System programme. The continuity of the scatterometer sensor is ensured until the mid-2040s through the MetOp Second Generation Programme. Therefore, by applying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-term rainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a 12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data record is assessed on a regional scale through comparison with high-quality ground networks in Europe, the United States, India, and Australia. Moreover, an assessment on a global scale is provided by using the triple-collocation (TC) technique allowing us also to compare these data with the latest, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), the Early Run version of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the gauge-based Global Precipitation Climatology Centre (GPCC) products. Results show that the SM2RAIN–ASCAT rainfall data record performs relatively well at both a regional and global scale, mainly in terms of root mean square error (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT data record provides performance better than IMERG and GPCC in data-scarce regions of the world, such as Africa and South America. In these areas, we expect larger benefits in using SM2RAIN–ASCAT for hydrological and agricultural applications. The limitations of the SM2RAIN–ASCAT data record consist of the underestimation of peak rainfall events and the presence of spurious rainfall events due to high-frequency soil moisture fluctuations that might be corrected in the future with more advanced bias correction techniques. The SM2RAIN–ASCAT data record is freely available at https://doi.org/10.5281/zenodo.3405563 (Brocca et al., 2019) (recently extended to the end of August 2019).
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Winter, Klaus, and Joseph A. M. Holtum. "Induction and reversal of crassulacean acid metabolism in Calandrinia polyandra: effects of soil moisture and nutrients." Functional Plant Biology 38, no. 7 (2011): 576. http://dx.doi.org/10.1071/fp11028.
Повний текст джерелаАнотація:
Calandrinia polyandra Benth. (Montiaceae), an annual succulent herb endemic to Australia, is an exemplary facultative crassulacean acid metabolism (CAM) plant as demonstrated by continuous whole-plant lifetime CO2 exchange measurements under controlled conditions in the laboratory. Reduced soil water availability induced a shift from solely daytime CO2 fixation to dark CO2 fixation. The shift from C3 photosynthesis to CAM was reversible either upon rewatering alone, or upon a combination of rewatering and addition of nutrients. These observations highlight the role of edaphic conditions in controlling CAM expression in a plant that has the option of fixing CO2 either during the day or during the night, providing further evidence that this extreme form of photosynthetic plasticity is primarily controlled by the environment rather than plant ontogeny. The stimulating effect of soil nutrients on CO2 fixation in the light and its negative effect on dark CO2 fixation have not been described previously and deserve further attention. In the most widely used CAM model system, the halophytic Mesembryanthemum crystallinum L., CAM is typically induced by high salinity, and some metabolic responses may be CAM-unrelated and related to salt stress per se. C. polyandra could be an excellent complementary system for studying the biochemical and molecular foundations of CAM because drought stress elicits a complete C3 to CAM transition.
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Monk, Khalia, Elise-Andrée Guérette, Clare Paton-Walsh, Jeremy D. Silver, Kathryn M. Emmerson, Steven R. Utembe, Yang Zhang, et al. "Evaluation of Regional Air Quality Models over Sydney and Australia: Part 1—Meteorological Model Comparison." Atmosphere 10, no. 7 (July 4, 2019): 374. http://dx.doi.org/10.3390/atmos10070374.
Повний текст джерелаАнотація:
The ability of meteorological models to accurately characterise regional meteorology plays a crucial role in the performance of photochemical simulations of air pollution. As part of the research funded by the Australian government’s Department of the Environment Clean Air and Urban Landscape hub, this study set out to complete an intercomparison of air quality models over the Sydney region. This intercomparison would test existing modelling capabilities, identify any problems and provide the necessary validation of models in the region. The first component of the intercomparison study was to assess the ability of the models to reproduce meteorological observations, since it is a significant driver of air quality. To evaluate the meteorological component of these air quality modelling systems, seven different simulations based on varying configurations of inputs, integrations and physical parameterizations of two meteorological models (the Weather Research and Forecasting (WRF) and Conformal Cubic Atmospheric Model (CCAM)) were examined. The modelling was conducted for three periods coinciding with comprehensive air quality measurement campaigns (the Sydney Particle Studies (SPS) 1 and 2 and the Measurement of Urban, Marine and Biogenic Air (MUMBA)). The analysis focuses on meteorological variables (temperature, mixing ratio of water, wind (via wind speed and zonal wind components), precipitation and planetary boundary layer height), that are relevant to air quality. The surface meteorology simulations were evaluated against observations from seven Bureau of Meteorology (BoM) Automatic Weather Stations through composite diurnal plots, Taylor plots and paired mean bias plots. Simulated vertical profiles of temperature, mixing ratio of water and wind (via wind speed and zonal wind components) were assessed through comparison with radiosonde data from the Sydney Airport BoM site. The statistical comparisons with observations identified systematic overestimations of wind speeds that were more pronounced overnight. The temperature was well simulated, with biases generally between ±2 °C and the largest biases seen overnight (up to 4 °C). The models tend to have a drier lower atmosphere than observed, implying that better representations of soil moisture and surface moisture fluxes would improve the subsequent air quality simulations. On average the models captured local-scale meteorological features, like the sea breeze, which is a critical feature driving ozone formation in the Sydney Basin. The overall performance and model biases were generally within the recommended benchmark values (e.g., ±1 °C mean bias in temperature, ±1 g/kg mean bias of water vapour mixing ratio and ±1.5 m s−1 mean bias of wind speed) except at either end of the scale, where the bias tends to be larger. The model biases reported here are similar to those seen in other model intercomparisons.
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Zhang, Yuxi, Jeffrey P. Walker, Valentijn R. N. Pauwels, and Yuval Sadeh. "Assimilation of Wheat and Soil States into the APSIM-Wheat Crop Model: A Case Study." Remote Sensing 14, no. 1 (December 24, 2021): 65. http://dx.doi.org/10.3390/rs14010065.
Повний текст джерелаАнотація:
Optimised farm crop productivity requires careful management in response to the spatial and temporal variability of yield. Accordingly, combination of crop simulation models and remote sensing data provides a pathway for providing the spatially variable information needed on current crop status and the expected yield. An ensemble Kalman filter (EnKF) data assimilation framework was developed to assimilate plant and soil observations into a prediction model to improve crop development and yield forecasting. Specifically, this study explored the performance of assimilating state observations into the APSIM-Wheat model using a dataset collected during the 2018/19 wheat season at a farm near Cora Lynn in Victoria, Australia. The assimilated state variables include (1) ground-based measurements of Leaf Area Index (LAI), soil moisture throughout the profile, biomass, and soil nitrate-nitrogen; and (2) remotely sensed observations of LAI and surface soil moisture. In a baseline scenario, an unconstrained (open-loop) simulation greatly underestimated the wheat grain with a relative difference (RD) of −38.3%, while the assimilation constrained simulations using ground-based LAI, ground-based biomass, and remotely sensed LAI were all found to improve the RD, reducing it to −32.7%, −9.4%, and −7.6%, respectively. Further improvements in yield estimation were found when: (1) wheat states were assimilated in phenological stages 4 and 5 (end of juvenile to flowering), (2) plot-specific remotely sensed LAI was used instead of the field average, and (3) wheat phenology was constrained by ground observations. Even when using parameters that were not accurately calibrated or measured, the assimilation of LAI and biomass still provided improved yield estimation over that from an open-loop simulation.
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Gupta, Surya, Tomislav Hengl, Peter Lehmann, Sara Bonetti, and Dani Or. "SoilKsatDB: global database of soil saturated hydraulic conductivity measurements for geoscience applications." Earth System Science Data 13, no. 4 (April 15, 2021): 1593–612. http://dx.doi.org/10.5194/essd-13-1593-2021.
Повний текст джерелаАнотація:
Abstract. The saturated soil hydraulic conductivity (Ksat) is a key parameter in many hydrological and climate models. Ksat values are primarily determined from basic soil properties and may vary over several orders of magnitude. Despite the availability of Ksat datasets in the literature, significant efforts are required to combine the data before they can be used for specific applications. In this work, a total of 13 258 Ksat measurements from 1908 sites were assembled from the published literature and other sources, standardized (i.e., units made identical), and quality checked in order to obtain a global database of soil saturated hydraulic conductivity (SoilKsatDB). The SoilKsatDB covers most regions across the globe, with the highest number of Ksat measurements from North America, followed by Europe, Asia, South America, Africa, and Australia. In addition to Ksat, other soil variables such as soil texture (11 584 measurements), bulk density (11 262 measurements), soil organic carbon (9787 measurements), moisture content at field capacity (7382), and wilting point (7411) are also included in the dataset. To show an application of SoilKsatDB, we derived Ksat pedotransfer functions (PTFs) for temperate regions and laboratory-based soil properties (sand and clay content, bulk density). Accurate models can be fitted using a random forest machine learning algorithm (best concordance correlation coefficient (CCC) equal to 0.74 and 0.72 for temperate area and laboratory measurements, respectively). However, when these Ksat PTFs are applied to soil samples obtained from tropical climates and field measurements, respectively, the model performance is significantly lower (CCC = 0.49 for tropical and CCC = 0.10 for field measurements). These results indicate that there are significant differences between Ksat data collected in temperate and tropical regions and Ksat measured in the laboratory or field. The SoilKsatDB dataset is available at https://doi.org/10.5281/zenodo.3752721 (Gupta et al., 2020) and the code used to extract the data from the literature and the applied random forest machine learning approach are publicly available under an open data license.
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Bird, P. R., T. T. Jackson, G. A. Kearney, and K. W. Williams. "Effect of two tree windbreaks on adjacent pastures in south-western Victoria, Australia." Australian Journal of Experimental Agriculture 42, no. 6 (2002): 809. http://dx.doi.org/10.1071/ea02016.
Повний текст джерелаАнотація:
The effects of 2 tree windbreaks on pasture production in adjoining paddocks were assessed over 4 years in a cool-temperate climate, perennial pasture area in south-western Victoria, Australia. The Willandra windbreak was 2 rows of direct-sown black wattle (Acacia mearnsii) aligned south-east–north-west. The Helm View windbreak was 4 rows of mixed Eucalyptus, Casuarina and Acacia spp., aligned east–west. Pastures at both sites had been sown to perennial ryegrass and subterranean clover. Wind direction data indicated that the north-eastern and south-western paddocks at Willandra were sheltered 39 and 28% of the time, respectively. The northern and southern paddocks at Helm View were sheltered 34 and 42% of the time, respectively. The relationship of rainfall with wind speed and direction was examined to indicate the potential of windbreaks of various orientations to protect livestock. The windbreaks had clear effects on pasture production in the competition zone along the margins of the windbreak. Average pasture production at Willandra in the zone 0.5–0.75 H (where H is the windbreak height) was 69% of open yield; production in the 0.9–1.5 H zone was 96%. At Helm View, production in the zone 0.7–1.0 H was 74% of open yield, with 81% in the zone 1.1–1.5 H. Averaged over all years, both windbreaks had no effect on pasture growth in the 2–10 H sheltered zone, although in some years there were small significant growth increases at parts of that zone in certain paddocks. Periodic soil moisture measurements at Willandra showed that water use was greater in the competition zone near the windbreak, with a difference in summer water content of the 1.2 m profile of at least 20 mm between soil 4.6 m from the windbreak and further away. The difference was due to greater water use from the deepest part of the profile. No other effects of the windbreak on soil water content were observed.
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Choudhury, Malini Roy, Jack Christopher, Armando Apan, Scott Chapman, Neal Menzies, and Yash Dang. "Integrated High-Throughput Phenotyping with High Resolution Multispectral, Hyperspectral and 3D Point Cloud Techniques for Screening Wheat Genotypes on Sodic Soils." Proceedings 36, no. 1 (April 8, 2020): 206. http://dx.doi.org/10.3390/proceedings2019036206.
Повний текст джерелаАнотація:
Wheat production in southern Queensland, Australia is adversely affected by soil sodicity. Crop phenotyping could be useful to improve productivity in such soils. This research focused on adapting high-throughput phenotyping of crop biophysical attributes to monitor crop health, nutrient deficiencies and plant moisture availability. We conducted an aerial and ground-based campaign during several wheat growing stages to capture crop information for 18 wheat genotypes at a moderately sodic site near Goondiwindi in southern Queensland. Three techniques were employed (multispectral, hyperspectral, and 3D point cloud) to monitor crop characteristics and predict biomass and yield. Spectral information and vegetation indices (VI) such as, normalized different vegetation index (NDVI), modified soil adjusted vegetation index (MSAVI), and leaf area index (LAI) were derived from multispectral imagery and compared with ground-based agronomic data for biomass, leaf area, and yield. Significant correlations were observed between NDVI and yield (R2 = 0.81), LAI (R2 = 0.74), and biomass (R2 = 0.65). Partial least square regression (PLS-R) modelling using hyperspectral spectroscopy data provided crop yield predictions that correlated significantly with observed yield (R2 = 0.65). The 3D point cloud technique was effective with comparison to in field manual measurements of crop architectural traits height and foliage cover (e.g., for height R2 = 0.73). For, this study multispectral techniques showed a greater potential to predict biomass and yield of wheat genotypes under moderately sodic soils than hyperspectral and 3D point cloud techniques. In future, the genotypes will be tested under more severely sodic soils to monitor crop performance and predicting yield.