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1
Lei, X., Y. Wang, and T. Guo. "DOWNSCALING OF SMAP SOIL MOISTURE PRODUCT BY DATA FUSION WITH VIIRS LST/EVI PRODUCT." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W5-2021 (December 23, 2021): 355–60. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w5-2021-355-2021.
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Abstract. Soil moisture is an essential variable of environment and climate change, which affects the energy and water exchange between soil and atmosphere. The estimation of soil moisture is thus very important in geoscience, while at same time also challenging. Satellite remote sensing provides an efficient way for large-scale soil moisture distribution mapping, and microwave remote sensing satellites/sensors, such as Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer (AMSR), and Soil Moisture Active Passive (SMAP) satellite, are widely used to retrieve soil moisture in a global scale. However, most microwave products have relatively coarse resolution (tens of kilometres), which limits their application in regional hydrological simulation and disaster prevention. In this study, the SMAP soil moisture product with spatial resolution of 9km is downscaled to 750m by fusing with VIIRS optical products. The LST-EVI triangular space pattern provides the physical foundation for the microwave-optical data fusion, so that the downscaled soil moisture product not only matches well with the original SMAP product, but also presents more detailed distribution patterns compared with the original dataset. The results show a promising prospect to use the triangular method to produce finer soil moisture datasets (within 1 km) from the coarse soil moisture product.
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Liu, Y. Y., R. M. Parinussa, W. A. Dorigo, R. A. M. de Jeu, W. Wagner, A. I. J. M. van Dijk, M. F. McCabe, and J. P. Evans. "Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals." Hydrology and Earth System Sciences Discussions 7, no. 5 (September 2, 2010): 6699–724. http://dx.doi.org/10.5194/hessd-7-6699-2010.
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Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved retrievals of surface soil moisture variations at global scales. Here we propose a technique to take advantage of retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates over sparse-to-moderately vegetated areas to obtain an improved soil moisture product. To do this, absolute soil moisture values from AMSR-E and relative soil moisture derived from ASCAT are rescaled against a reference land surface model date set using a cumulative distribution function (CDF) matching approach. While this technique imposes the bias of the reference to the rescaled satellite products, it adjusts both satellite products to the same range and almost preserves the correlation between satellite products and in situ measurements. Comparisons with in situ data demonstrated that over the regions where the correlation coefficient between rescaled AMSR-E and ASCAT is above 0.65 (hereafter referred to as transitional regions), merging the different satellite products together increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT are respectively used in the merged product. Thus the merged product carries the advantages of better spatial coverage overall and increased number of observations particularly for the transitional regions. The combination approach developed in this study has the potential to be applied to existing microwave satellites as well as to new microwave missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.
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Liu, Y. Y., R. M. Parinussa, W. A. Dorigo, R. A. M. De Jeu, W. Wagner, A. I. J. M. van Dijk, M. F. McCabe, and J. P. Evans. "Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals." Hydrology and Earth System Sciences 15, no. 2 (February 1, 2011): 425–36. http://dx.doi.org/10.5194/hess-15-425-2011.
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Abstract. Combining information derived from satellite-based passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a methodology that takes advantage of the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates to produce an improved soil moisture product. First, volumetric soil water content (m3 m−3) from AMSR-E and degree of saturation (%) from ASCAT are rescaled against a reference land surface model data set using a cumulative distribution function matching approach. While this imposes any bias of the reference on the rescaled satellite products, it adjusts them to the same range and preserves the dynamics of original satellite-based products. Comparison with in situ measurements demonstrates that where the correlation coefficient between rescaled AMSR-E and ASCAT is greater than 0.65 ("transitional regions"), merging the different satellite products increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT, respectively, are used for the merged product. Therefore the merged product carries the advantages of better spatial coverage overall and increased number of observations, particularly for the transitional regions. The combination method developed has the potential to be applied to existing microwave satellites as well as to new missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.
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Rabin, Robert M., and Timothy J. Schmit. "Estimating Soil Wetness from the GOES Sounder." Journal of Atmospheric and Oceanic Technology 23, no. 7 (July 1, 2006): 991–1003. http://dx.doi.org/10.1175/jtech1895.1.
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Abstract In this note, the relationship between the observed daytime rise in surface radiative temperature, derived from the Geostationary Operational Environmental Satellites (GOES) sounder clear-sky data, and modeled soil moisture is explored over the continental United States. The motivation is to provide an infrared (IR) satellite–based index for soil moisture, which has a higher resolution than possible with the microwave satellite data. The daytime temperature rise is negatively correlated with soil moisture in most areas. Anomalies in soil moisture and daytime temperature rise are also negatively correlated on monthly time scales. However, a number of exceptions to this correlation exist, particularly in the western states. In addition to soil moisture, the capacity of vegetation to generate evapotranspiration influences the amount of daytime temperature rise as sensed by the satellite. In general, regions of fair to poor vegetation health correspond to the relatively high temperature rise from the satellite. Regions of favorable vegetation match locations of lower-than-average temperature rise.
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Zhu, Hongchun, Zhilin Zhang, and Aifeng Lv. "Evaluation of Satellite-Derived Soil Moisture in Qinghai Province Based on Triple Collocation." Water 12, no. 5 (May 2, 2020): 1292. http://dx.doi.org/10.3390/w12051292.
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Evaluating the reliability of satellite-based and reanalysis soil moisture products is very important in soil moisture research. The traditional methods of evaluating soil moisture products rely on the verification of satellite inversion data and ground observation; however, the ground measurement data is often difficult to obtain. The triple collocation (TC) method can be used to evaluate the accuracy of a product without obtaining the ground measurement data. This study focused on the whole of Qinghai Province, China (31°–40° N, 89°–103° E), and used the TC method to obtain the error variance for satellite-based soil moisture data, the signal-to-noise ratio (SNR) of the same data, and the correlation between the same data and the ground-truth soil moisture, using passive satellite products: Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity (SMOS), Fengyun-3B Microwave Radiation Imager (FY3B), Fengyun-3C Microwave Radiation Imager (FY3C), and Advanced Microwave Scanning Radiometer 2 (AMSR2); an active satellite product Advanced Scatterometer (ASCAT), and reanalysis data Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system. The TC results for the passive satellite data were then compared with the satellite-derived enhanced vegetation index (EVI) to explore the influence of vegetation coverage on the results. The following conclusions are drawn: (1) for the SMAP, SMOS, FY3B, FY3C, and AMSR2 satellite data, the spatial distributions of the TC-derived error variance, the SNR of the satellite-derived soil moisture, and the correlation coefficient between the satellite-derived and ground-truth soil moisture, were all relatively similar, which indirectly verified the reliability of the TC method; and (2) SMOS data have poor applicability for the estimation of soil moisture in Qinghai Province due to their insufficient detection capability in the Qaidam area, high error variance (median 0.0053), high SNR (median 0.43), and low correlation coefficient with ground-truth soil moisture (median 0.57).
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Barrett, Damian J., and Luigi J. Renzullo. "On the Efficacy of Combining Thermal and Microwave Satellite Data as Observational Constraints for Root-Zone Soil Moisture Estimation." Journal of Hydrometeorology 10, no. 5 (October 1, 2009): 1109–27. http://dx.doi.org/10.1175/2009jhm1043.1.
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Abstract Data assimilation applications require the development of appropriate mathematical operators to relate model states to satellite observations. Two such “observation” operators were developed and used to examine the conditions under which satellite microwave and thermal observations provide effective constraints on estimated soil moisture. The first operator uses a two-layer surface energy balance (SEB) model to relate root-zone moisture with top-of-canopy temperature. The second couples SEB and microwave radiative transfer models to yield top-of-atmosphere brightness temperature from surface layer moisture content. Tangent linear models for these operators were developed to examine the sensitivity of modeled observations to variations in soil moisture. Assuming a standard deviation in the observed surface temperature of 0.5 K and maximal model sensitivity, the error in the analysis moisture content decreased by 11% for a background error of 0.025 m3 m−3 and by 29% for a background error of 0.05 m3 m−3. As the observation error approached 2 K, the assimilation of individual surface temperature observations provided virtually no constraint on estimates of soil moisture. Given the range of published errors on brightness temperature, microwave satellite observations were always a strong constraint on soil moisture, except under dense forest and in relatively dry soils. Under contrasting vegetation cover and soil moisture conditions, orthogonal information contained in thermal and microwave observations can be used to improve soil moisture estimation because limited constraint afforded by one data type is compensated by strong constraint from the other data type.
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Yang, Kun, Toshio Koike, Ichirow Kaihotsu, and Jun Qin. "Validation of a Dual-Pass Microwave Land Data Assimilation System for Estimating Surface Soil Moisture in Semiarid Regions." Journal of Hydrometeorology 10, no. 3 (June 1, 2009): 780–93. http://dx.doi.org/10.1175/2008jhm1065.1.
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Abstract This study examines the capability of a new microwave land data assimilation system (LDAS) for estimating soil moisture in semiarid regions, where soil moisture is very heterogeneous. This system assimilates the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) 6.9- and 18.7-GHz brightness temperatures into a land surface model (LSM), with a radiative transfer model as an observation operator. To reduce errors caused by uncertainties of system parameters, the LDAS uses a dual-pass assimilation algorithm, with a calibration pass to estimate major model parameters from satellite data and an assimilation pass to estimate the near-surface soil moisture. Validation data of soil moisture were collected in a Mongolian semiarid region. Results show that (i) the LDAS-estimated soil moistures are comparable to areal averages of in situ measurements, though the measured soil moistures were highly variable from site to site; (ii) the LSM-simulated soil moistures show less biases when the LSM uses LDAS-calibrated parameter values instead of default parameter values, indicating that the satellite-based calibration does contribute to soil moisture estimations; and (iii) compared to the LSM, the LDAS produces more robust and reliable soil moisture when forcing data become worse. The lower sensitivity of the LDAS output to precipitation is particularly encouraging for applying this system to regions where precipitation data are prone to errors.
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Wang, Guojie, Xiaowen Ma, Daniel Fiifi Tawia Hagan, Robin van der Schalie, Giri Kattel, Waheed Ullah, Liangliang Tao, Lijuan Miao, and Yi Liu. "Towards Consistent Soil Moisture Records from China’s FengYun-3 Microwave Observations." Remote Sensing 14, no. 5 (March 2, 2022): 1225. http://dx.doi.org/10.3390/rs14051225.
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Soil moisture plays an essential role in the land-atmosphere interface. It has become necessary to develop quality large-scale soil moisture data from satellite observations for relevant applications in climate, hydrology, agriculture, etc. Specifically, microwave-based observations provide more consistent land surface records because they are unhindered by cloud conditions. The recent microwave radiometers onboard FY-3B, FY-3C and FY-3D satellites launched by China’s Meteorological Administration (CMA) extend the number of available microwave observations, covering late 2011 up until the present. These microwave observations have the potential to provide consistent global soil moisture records to date, filling the data gaps where soil moisture estimates are missing in the existing records. Along these lines, we studied the FY-3C to understand its added value due to its unique time of observation in a day (ascending: 22:15, descending: 10:15) absent from the existing satellite soil moisture records. Here, we used the triple collocation technique to optimize a benchmark retrieval model of land surface temperature (LST) tailored to the observation time of FY3C, by evaluating various soil moisture scenarios obtained with different bias-imposed LSTs from 2014 to 2016. The globally optimized LST was used as an input for the land parameter retrieval model (LPRM) algorithm to obtain optimized global soil moisture estimates. The obtained FY-3C soil moisture observations were evaluated with global in situ and reanalysis datasets relative to FY3B soil moisture products to understand their differences and consistencies. We found that the RMSEs of their anomalies were mostly concentrated between 0.05 and 0.15 m3 m−3, and correlation coefficients were between 0.4 and 0.7. The results showed that the FY-3C ascending data could better capture soil moisture dynamics than the FY-3B estimates. Both products were found to consistently complement the skill of each other over space and time globally. Finally, a linear combination approach that maximizes temporal correlations merged the ascending and descending soil moisture observations separately. The results indicated that superior soil moisture estimates are obtained from the combined product, which provides more reliable global soil moisture records both day and night. Therefore, this study aims to show that there is merit to the combined usage of the two FY-3 products, which will be extended to the FY-3D, to fill the gap in existing long-term global satellite soil moisture records.
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Mavrovic, Alex, Renato Pardo Lara, Aaron Berg, François Demontoux, Alain Royer, and Alexandre Roy. "Soil dielectric characterization during freeze–thaw transitions using L-band coaxial and soil moisture probes." Hydrology and Earth System Sciences 25, no. 3 (March 4, 2021): 1117–31. http://dx.doi.org/10.5194/hess-25-1117-2021.
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Abstract. Soil microwave permittivity is a crucial parameter in passive microwave retrieval algorithms but remains a challenging variable to measure. To validate and improve satellite microwave data products, precise and reliable estimations of the relative permittivity (εr=ε/ε0=ε′-jε′′; unitless) of soils are required, particularly for frozen soils. In this study, permittivity measurements were acquired using two different instruments: the newly designed open-ended coaxial probe (OECP) and the conventional Stevens HydraProbe. Both instruments were used to characterize the permittivity of soil samples undergoing several freeze–thaw cycles in a laboratory environment. The measurements were compared to soil permittivity models. The OECP measured frozen (εfrozen′=[3.5; 6.0], εfrozen′′=[0.46; 1.2]) and thawed (εthawed′=[6.5; 22.8], εthawed′′=[1.43; 5.7]) soil microwave permittivity. We also demonstrate that cheaper and widespread soil permittivity probes operating at lower frequencies (i.e., Stevens HydraProbe) can be used to estimate microwave permittivity given proper calibration relative to an L-band (1–2 GHz) probe. This study also highlighted the need to improve dielectric soil models, particularly during freeze–thaw transitions. There are still important discrepancies between in situ and modeled estimates and no current model accounts for the hysteresis effect shown between freezing and thawing processes, which could have a significant impact on freeze–thaw detection from satellites.
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Zhu, Liming, Huifeng Wu, Min Li, Chaoyin Dou, and A.-Xing Zhu. "Estimation of Irrigation Water Use by Using Irrigation Signals from SMAP Soil Moisture Data." Agriculture 13, no. 9 (August 29, 2023): 1709. http://dx.doi.org/10.3390/agriculture13091709.
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Accurate irrigation water-use data are essential to agricultural water resources management and optimal allocation. The obscuration presented by ground cover in farmland and the subjectivity of irrigation-related decision-making processes mean that effectively identifying regional irrigation water use remains a critical problem to be solved. In view of the advantages of satellite microwave remote sensing in monitoring soil moisture, previous studies have proposed a method for estimating irrigation water use using the satellite microwave remote sensing of soil moisture. However, the method is affected by false irrigation signals from soil moisture increases caused by non-irrigation factors, causing irrigation water use to be overestimated. Therefore, the purpose of this study is to improve the estimation of irrigation water use in drylands by using irrigation signals from SMAP soil moisture data. In this paper, the irrigation water use in Henan Province is estimated by using the irrigation signals from SMAP (soil moisture active and passive) soil moisture data. Firstly, a method for recognizing irrigation signals in soil moisture data obtained by microwave satellite remote sensing was used. Then, an estimation model of the amount of irrigation water (SM2Rainfall model) was built on each data pixel of the satellite microwave remote sensing of soil moisture. Finally, the amount of irrigation water utilized in Henan Province was estimated by combining the irrigation signals and irrigation water-use estimation model, and the results were evaluated. According to the findings, this study improved the estimation accuracy of irrigation water use by using the irrigation signals in Henan Province. The result of this study is of great importance to accurately obtain irrigation water use in the region.
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Parinussa, Robert M., Thomas R. H. Holmes, Niko Wanders, Wouter A. Dorigo, and Richard A. M. de Jeu. "A Preliminary Study toward Consistent Soil Moisture from AMSR2." Journal of Hydrometeorology 16, no. 2 (April 1, 2015): 932–47. http://dx.doi.org/10.1175/jhm-d-13-0200.1.
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Abstract A preliminary study toward consistent soil moisture products from the Advanced Microwave Scanning Radiometer 2 (AMSR2) is presented. Its predecessor, the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), has provided Earth scientists with a consistent and continuous global soil moisture dataset. A major challenge remains to achieve synergy between these soil moisture datasets, which is hampered by the lack of an overlapping observation period of the sensors. Here, observations of the multifrequency microwave radiometer on board the Tropical Rainfall Measuring Mission (TRMM) satellite were used to improve consistency between AMSR-E and AMSR2. Several scenarios to achieve synergy between the AMSR-E and AMSR2 soil moisture products were evaluated. The novel soil moisture retrievals from C-band observations, a frequency band that is lacking on board the TRMM satellite, are also presented. A global comparison of soil moisture retrievals against ERA-Interim soil moisture demonstrates the need for an intercalibration procedure. Several different scenarios based on filtering were tested, and the impact on the soil moisture retrievals was evaluated against two independent reference soil moisture datasets (reanalysis and in situ soil moisture) that cover both individual observation periods of the AMSR-E and AMSR2 sensors. Results show a high degree of consistency between both satellite products and two independent reference products for the soil moisture products retrieved from X-band observations. Care should be taken in the interpretation of the presented soil moisture products, and future research is needed to further align the AMSR2 and AMSR-E sensor calibrations.
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Ghilain, Nicolas, Alirio Arboleda, Okke Batelaan, Jonas Ardö, Isabel Trigo, Jose-Miguel Barrios, and Francoise Gellens-Meulenberghs. "A New Retrieval Algorithm for Soil Moisture Index from Thermal Infrared Sensor On-Board Geostationary Satellites over Europe and Africa and Its Validation." Remote Sensing 11, no. 17 (August 21, 2019): 1968. http://dx.doi.org/10.3390/rs11171968.
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Monitoring soil moisture at the Earth’surface is of great importance for drought early warnings. Spaceborne remote sensing is a keystone in monitoring at continental scale, as satellites can make observations of locations which are scarcely monitored by ground-based techniques. In recent years, several soil moisture products for continental scale monitoring became available from the main space agencies around the world. Making use of sensors aboard polar satellites sampling in the microwave spectrum, soil moisture can be measured and mapped globally every few days at a spatial resolution as fine as 25 km. However, complementarity of satellite observations is a crucial issue to improve the quality of the estimations provided. In this context, measurements within the visible and infrared from geostationary satellites provide information on the surface from a totally different perspective. In this study, we design a new retrieval algorithm for daily soil moisture monitoring based only on the land surface temperature observations derived from the METEOSAT second generation geostationary satellites. Soil moisture has been retrieved from the retrieval algorithm for an eight years period over Europe and Africa at the SEVIRI sensor spatial resolution (3 km at the sub-satellite point). The results, only available for clear sky and partly cloudy conditions, are for the first time extensively evaluated against in-situ observations provided by the International Soil Moisture Network and FLUXNET at sites across Europe and Africa. The soil moisture retrievals have approximately the same accuracy as the soil moisture products derived from microwave sensors, with the most accurate estimations for semi-arid regions of Europe and Africa, and a progressive degradation of the accuracy towards northern latitudes of Europe. Although some possible improvements can be expected by a better use of other products derived from SEVIRI, the new approach developped and assessed here is a valuable alternative to microwave sensors to monitor daily soil moisture at the resolution of few kilometers over entire continents and could reveal a good complementarity to an improved monitoring system, as the algorithm can produce surface soil moisture with less than 1 day delay over clear sky and non-steady cloudy conditions (over 10% of the time).
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Xu, Yaping, Cuiling Liu, Lei Wang, and Lei Zou. "Exploring the Spatial Autocorrelation in Soil Moisture Networks: Analysis of the Bias from Upscaling the Texas Soil Observation Network (TxSON)." Water 15, no. 1 (December 27, 2022): 87. http://dx.doi.org/10.3390/w15010087.
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Microwave remote sensing such as soil moisture active passive (SMAP) can provide soil moisture data for agricultural and hydrological studies. However, the scales between station-measured and satellite-measured products are quite different, as stations measure on a point scale while satellites have a much larger footprint (e.g., 9 km). Consequently, the validation for soil moisture products, especially inter-comparison between these two types of observations, is quite a challenge. Spatial autocorrelation among the stations could be a contribution of bias, which impacts the dense soil moisture networks when compared with satellite soil moisture products. To examine the effects of spatial autocorrelation to soil moisture upscaling models, this study proposes a spatial analysis approach for soil moisture ground observation upscaling and Thiessen polygon-based block kriging (TBP kriging) and compares the results with three other methods typically used in the current literature: arithmetic average, Thiessen polygon, and Gaussian-weighted average. Using the Texas Soil Observation Network (TxSON) as ground observation, this methodology detects spatial autocorrelation in the distribution of the stations that exist in dense soil moisture networks and improved the spatial modeling accuracy when carrying out upscaling tasks. The study concluded that through TBP kriging the minimum root-mean-square deviation (RMSD) is given where spatial autocorrelation takes place in the soil moisture stations. Through TBP kriging, the station-measured and satellite-measured soil moisture products are more comparable.
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Zhang, Xiaohu, Jianxiu Qiu, Guoyong Leng, Yongmin Yang, Quanzhou Gao, Yue Fan, and Jiashun Luo. "The Potential Utility of Satellite Soil Moisture Retrievals for Detecting Irrigation Patterns in China." Water 10, no. 11 (October 24, 2018): 1505. http://dx.doi.org/10.3390/w10111505.
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Climate change and anthropogenic activities, including agricultural irrigation have significantly altered the global and regional hydrological cycle. However, human-induced modification to the natural environment is not well represented in land surface models (LSMs). In this study, we utilize microwave-based soil moisture products to aid the detection of under-represented irrigation processes throughout China. The satellite retrievals used in this study include passive microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and its successor AMSR2, active microwave observations from the Advanced Scatterometer (ASCAT), and the blended multi-sensor soil moisture product from the European Space Agency (i.e., ESA CCI product). We first conducted validations of the three soil moisture retrievals against in-situ observations (collected from the nationwide agro-meteorological network) in irrigated areas in China. It is found that compared to the conventional Spearman’s rank correlation and Pearson correlation coefficients, entropy-based mutual information is more suitable for evaluating soil moisture anomalies induced by irrigation. In general, around 60% of uncertainties in the anomaly of “ground truth” time series can be resolved by soil moisture retrievals, with ASCAT outperforming the others. Following this, the potential utility of soil moisture retrievals in mapping irrigation patterns in China is investigated by examining the difference in probability distribution functions (detected by two-sample Kolmogorov-Smirnov test) between soil moisture retrievals and benchmarks of the numerical model ERA-Interim without considering the irrigation process. Results show that microwave remote sensing provides a promising alternative to detect the under-represented irrigation process against the reference LSM ERA-Interim. Specifically, the highest performance in detecting irrigation intensity is found when using ASCAT in Huang-Huai-Hai Plain, followed by advanced microwave scanning radiometer (AMSR) and ESA CCI. Compared to ASCAT, the irrigation detection capabilities of AMSR exhibit higher discrepancies between descending and ascending orbits, since the soil moisture retrieval algorithm of AMSR is based on surface temperature and, thus, more affected by irrigation practices. This study provides insights into detecting the irrigation extent using microwave-based soil moisture with aid of LSM simulations, which has great implications for numerical model development and agricultural managements across the country.
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Blyth, K. "An Assessment of the Capabilities of the ERS Satellites' Active Microwave Instruments for Monitoring Soil Moisture Change." Hydrology and Earth System Sciences 1, no. 1 (March 31, 1997): 159–74. http://dx.doi.org/10.5194/hess-1-159-1997.
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Abstract. The launch of the European Remote sensing Satellite (ERS-1) in July 1991 represented an important turning point in the development of Earth observation as it was the first of a series of satellites which would carry high resolution active microwave (radar) sensors which could operate through the thickest cloudeover and provide continuity of data for at least a decade. This was of particular relevance to hydrological applications, such as soil moisture monitoring, which generally require frequent satellite observations to monitor changes in state. ERS-1 and its successor ERS-2 carry the active microwave instrument (AMI) which operates in 3 modes (synthetic aperture radar, wind scatterometer and wave seatterometer) together with the radar altimeter which may all be useful for the observation of soil moisture. This paper assesses the utility of these sensors through a comprehensive review of work in this field. Two approaches to soil moisture retrieval are identified: 1) inversion modelling, where the physical effects of vegetation and soil roughness on radar backscatter are quantified through the use of multi-frequency and/or multi-polarization sensors and 2) change detection where these effects are normalized through frequent satellite observation, the residual effects being attributed to short-term changes in soil moisture. Both approaches will be better supported by the future European Envisat-l satellite which will provide both multi-polarization SAR and low resolution products which should facilitate more frequent temporal observation.
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Gruhier, C., P. de Rosnay, S. Hasenauer, T. Holmes, R. de Jeu, Y. Kerr, E. Mougin, et al. "Soil moisture active and passive microwave products: intercomparison and evaluation over a Sahelian site." Hydrology and Earth System Sciences 14, no. 1 (January 22, 2010): 141–56. http://dx.doi.org/10.5194/hess-14-141-2010.
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Abstract. This paper presents a comparison and an evaluation of five soil moisture products based on satellite-based passive and active microwave measurements. Products are evaluated for 2005–2006 against ground measurements obtained from the soil moisture network deployed in Mali (Sahel) in the framework of the African Monsoon Multidisciplinary Analysis project. It is shown that the accuracy of the soil moisture products is sensitive to the retrieval approach as well as to the sensor type (active or passive) and to the signal frequency (from 5.6 GHz to 18.8 GHz). The spatial patterns of surface soil moisture are compared between the different products at meso-scale (14.5° N–17.5° N and 2° W–1° W). A general good consistency between the different satellite soil moisture products is shown in terms of meso-scale spatial distribution, in particular after convective rainfall occurrences. Comparison to ground measurement shows that although soil moisture products obtained from satellite generally over-estimate soil moisture values during the dry season, most of them capture soil moisture temporal variations in good agreement with ground station measurements.
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Nambiar, Manoj K., Jaison Thomas Ambadan, Tracy Rowlandson, Paul Bartlett, Erica Tetlock, and Aaron A. Berg. "Comparing the Assimilation of SMOS Brightness Temperatures and Soil Moisture Products on Hydrological Simulation in the Canadian Land Surface Scheme." Remote Sensing 12, no. 20 (October 16, 2020): 3405. http://dx.doi.org/10.3390/rs12203405.
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Soil moisture is a key variable used to describe water and energy exchanges at the land surface/atmosphere interface. Therefore, there is widespread interest in the use of soil moisture retrievals from passive microwave satellites. In the assimilation of satellite soil moisture data into land surface models, two approaches are commonly used. In the first approach brightness temperature (TB) data are assimilated, while in the second approach retrieved soil moisture (SM) data from the satellite are assimilated. However, there is not a significant body of literature comparing the differences between these two approaches, and it is not known whether there is any advantage in using a particular approach over the other. In this study, TB and SM L2 retrieval products from the Soil Moisture and Ocean Salinity (SMOS) satellite are assimilated into the Canadian Land Surface Scheme (CLASS), for improved soil moisture estimation over an agricultural region in Saskatchewan. CLASS is the land surface component of the Canadian Earth System Model (CESM), and the Canadian Seasonal and Interannual Prediction System (CanSIPS). Our results indicated that assimilating the SMOS products improved the soil moisture simulation skill of the CLASS. Near surface soil moisture assimilation also resulted in improved forecasts of root zone soil moisture (RZSM) values. Although both techniques resulted in improved forecasts of RZSM, assimilation of TB resulted in the superior estimates.
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Gruhier, C., P. de Rosnay, S. Hasenauer, T. Holmes, R. de Jeu, Y. Kerr, E. Mougin, et al. "Soil moisture active and passive microwave products: intercomparison and evaluation over a Sahelian site." Hydrology and Earth System Sciences Discussions 6, no. 4 (August 5, 2009): 5303–39. http://dx.doi.org/10.5194/hessd-6-5303-2009.
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Abstract. This paper presents a comparison and an evaluation of five soil moisture products based on satellite-based passive and active microwave measurements. Products are evaluated for 2005–2006 against ground measurements obtained from the soil moisture network deployed in Mali (Sahel) in the framework of the African Monsoon Multidisciplinary Analysis project. It is shown that the accuracy of the soil moisture products is sensitive to the retrieval approach as well as to the sensor type (active or passive) and to the signal frequency (from 5.6 GHz to 18.8 GHz). The spatial patterns of surface soil moisture are compared between the different products at meso-scale (14.5° N–17.5° N and 2° W–1° W). A general good consistency between the different satellite soil moisture products is shown in terms of meso-scale spatial distribution, in particular after convective rainfall occurrences. Soil moisture values provided by the different products are compared to ground measurements time series. Although soil moisture products obtained from satellite generally over-estimate soil moisture values during the dry season, most of them capture soil moisture temporal variations in good agreement with ground station measurements.
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Gao, Huilin, Eric F. Wood, Matthias Drusch, and Matthew F. McCabe. "Copula-Derived Observation Operators for Assimilating TMI and AMSR-E Retrieved Soil Moisture into Land Surface Models." Journal of Hydrometeorology 8, no. 3 (June 1, 2007): 413–29. http://dx.doi.org/10.1175/jhm570.1.
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Abstract Assimilating soil moisture from satellite remote sensing into land surface models (LSMs) has potential for improving model predictions by providing real-time information at large scales. However, the majority of the research demonstrating this potential has been limited to datasets based on either airborne data or synthetic observations. The limited availability of satellite-retrieved soil moisture and the observed qualitative difference between satellite-retrieved and modeled soil moisture has posed challenges in demonstrating the potential over large regions in actual applications. Comparing modeled and satellite-retrieved soil moisture fields shows systematic differences between their mean values and between their dynamic ranges, and these systematic differences vary with satellite sensors, retrieval algorithms, and LSMs. This investigation focuses on generating observation operators for assimilating soil moisture into LSMs using a number of satellite–model combinations. The remotely sensed soil moisture products come from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the NASA/Earth Observing System (EOS) Advanced Microwave Scanning Radiometer (AMSR-E). The soil moisture model predictions are from the Variable Infiltration Capacity (VIC) hydrological model; the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); and the NCEP North American Regional Reanalysis (NARR). For this analysis, the satellite and model data are over the southern Great Plains region from 1998 to 2003 (1998–2002 for ERA-40). Previous work on observation operators used the matching of cumulative distributions to transform satellite-retrieved soil moisture into modeled soil moisture, which implied perfect correlations between the ranked values. In this paper, a bivariate statistical approach, based on copula distributions, is employed for representing the joint distribution between retrieved and modeled soil moisture, allowing for a quantitative estimation of the uncertainty in modeled soil moisture when merged with a satellite retrieval. The conditional probability distribution of model-based soil moisture conditioned on a satellite retrieval forms the basis for the soil moisture observation operator. The variance of these conditional distributions for different retrieval algorithms, LSMs, and locations provides an indication of the information content of satellite retrievals in assimilation. Results show that the operators vary by season and by land surface model, with the satellite retrievals providing more information in summer [July–August (JJA)] and fall [September–November (SON)] than winter [December–February (DJF)] or spring [March–May (MAM)] seasons. Also, the results indicate that the value of satellite-retrieved soil moisture is most useful to VIC, followed by ERA-40 and then NARR.
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Dabrowska-Zielinska, K., M. Budzynska, W. Kowalik, and K. Turlej. "Soil moisture and evapotranspiration of wetlands vegetation habitats retrieved from satellite images." Hydrology and Earth System Sciences Discussions 7, no. 4 (August 19, 2010): 5929–55. http://dx.doi.org/10.5194/hessd-7-5929-2010.
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Abstract. The research has been carried out in Biebrza Ramsar Convention test site situated in the N-E part of Poland. Data from optical and microwave satellite images have been analysed and compared to the detailed soil-vegetation ground truth measurements conducted during the satellite overpasses. Satellite data applied for the study include: ENVISAT.ASAR, ENVISAT.MERIS, ALOS.PALSAR, ALOS.AVNIR-2, ALOS.PRISM, TERRA.ASTER, and NOAA.AVHRR. Optical images have been used for classification of wetlands vegetation habitats and vegetation surface roughness expressed by LAI. Also, heat fluxes have been calculated using NOAA.AVHRR data and meteorological data. Microwave images have been used for the assessment of soil moisture. For each of the classified wetlands vegetation habitats the relationship between soil moisture and backscattering coefficient has been examined, and the best combination of microwave variables (wave length, incidence angle, polarization) has been used for mapping and monitoring of soil moisture. The results of this study give possibility to improve models of water cycle over wetlands ecosystems by adding information about soil moisture and surface heat fluxes derived from satellite images. Such information is very essential for better protection of the European sensitive wetland ecosystems. ENVISAT and ALOS images have been obtained from ESA for AO ID 122 and AOALO.3742 projects.
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Volchek, A. A., and D. O. Petrov. "SOURCES OF GLOBAL SCALE SOIL MOISTURE MONITORING DATA BY SATELLITE BASED REMOTE SENSING OF EARTH’S SURFACE." Hydrometeorology and ecology 100, no. 1 (2021): 36–41. http://dx.doi.org/10.54668/2789-6323-2021-100-1-36-41.
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A review of modern tools of global monitoring of soil moisture by means of remote sensing of the Earth’s surface is presented. The characteristic features of the use of orbital radiometers and radars of C, X and L microwave bands for estimating the volumetric soil moisture at a depth of 5 cm and the root layer of vegetation are considered. A review of the capabilities of satellite gravimetry to assess the land water equivalent thickness is made. A number of sources have been proposed for obtaining estimates of soil water content from satellite based radiometric devices and orbital gravimetric systems. Based on the analysis of scientific research papers, the complexity of monitoring the level of fire danger indices in forests is shown, and the prospects of assessing soil moisture in agricultural regions using microwave orbital instruments are demonstrated, and the adequacy of calculating the moisture content in soil at a depth of up to one meter using satellite gravimetry is described.
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THAPLIYAL, P. K., B. M. RAO, P. K. PAL, and H. P. DAS. "Potential of IRS-P4 microwave radiometer data for soil moisture estimation over India." MAUSAM 54, no. 1 (January 18, 2022): 277–86. http://dx.doi.org/10.54302/mausam.v54i1.1512.
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Soil moisture at different temporal and spatial scales is very important for various applications. At smaller spatial scales it has importance for the agro-meteorological applications, whereas at large spatial scales it is an important boundary parameter in the numerical prediction models of atmosphere for monthly to seasonal time-scale integrations. Frequent in situ global measurements of soil moisture at these spatial scales are virtually impossible because large heterogeneity of soil types makes these observations highly expensive and time consuming. Satellite based microwave radiometers can provide indirect estimates of soil moisture at resolutions compatible to that of climate models (50-100 km). In this paper the potential of Multi-frequency Scanning Microwave Radiometer (MSMR) onboard Indian satellite IRS-P4 is assessed for large area averaged soil moisture estimation. These are compared with the weekly-observed in situ soil moisture data over a few observatories of India Meteorological Department (IMD).
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Pellarin, T., T. Tran, J. M. Cohard, S. Galle, J. P. Laurent, P. de Rosnay, and T. Vischel. "Hourly soil moisture mapping over West Africa using AMSR-E observations and a satellite-based rainfall product." Hydrology and Earth System Sciences Discussions 6, no. 3 (June 3, 2009): 4035–64. http://dx.doi.org/10.5194/hessd-6-4035-2009.
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Abstract. This paper provides an original and simple methodology to map surface soil moisture with a fine temporal and spatial resolution over large areas based on a satellite rainfall accumulation product and soil microwave emission measurements at C-band. The first motivation of this study was to obtain high temporal frequency (~1 h) in order to study the possible feedback mechanisms between soil moisture and convection in West Africa. The use of soil moisture maps derived from satellite microwave measurements was not possible due to the low (at best daily) temporal resolution. Thus, a rainfall accumulation product based on Meteosat geostationary satellite measurements was used together with a simple Antecedent Precipitation Index (API) model to produce soil moisture map at the 10×10 km2 and 30 min resolution. Due to uncertainties on the satellite-based rainfall accumulation product, derived soil moisture maps were found to be erroneous. An assimilation technique based on AMSR-E C-band measurements into a microwave emission model was developed. The assimilation technique described in this study consists of modulating the rainfall accumulation estimate between two successive AMSR-E brightness temperatures (TB) measurements in order to match simulated and observed TB. When a rainfall event happens, the initial rainfall accumulation estimate is modulated using a multiplicative factor ranging from 0 to 7. The best solution is given by the rainfall rate which minimizes the difference between observed and simulated TB. Ground-based soil moisture measurements obtained at three sites in Niger, Mali and Benin were used to assess the methodology which was found to improve the soil moisture estimates over the three sites.
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Hagan, Daniel Fiifi Tawia, Guojie Wang, Seokhyeon Kim, Robert M. Parinussa, Yi Liu, Waheed Ullah, Asher Samuel Bhatti, Xiaowen Ma, Tong Jiang, and Buda Su. "Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging." Remote Sensing 12, no. 13 (July 7, 2020): 2164. http://dx.doi.org/10.3390/rs12132164.
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In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency’s essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration’s (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth, the microwave radiometer imager (MWRI) onboard China’s Fengyun-3B (FY3B) satellite, is absent in the ECV scheme. Here, the normalized soil moisture products are merged based on their availability within the study period. Evaluation of the merged product demonstrated that the correlations and unbiased root mean square differences were improved over the whole period. Compared to ECV, the merged product from this scheme performed better over dense and sparsely vegetated regions. Additionally, the trends in the parent inputs are preserved in the merged data. Further analysis of FY3B’s contribution to the merging scheme showed that it is as dependable as the widely used AMSR2, as it contributed significantly to the improvements in the merged product.
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Edokossi, Komi, Andres Calabia, Shuanggen Jin, and Iñigo Molina. "GNSS-Reflectometry and Remote Sensing of Soil Moisture: A Review of Measurement Techniques, Methods, and Applications." Remote Sensing 12, no. 4 (February 12, 2020): 614. http://dx.doi.org/10.3390/rs12040614.
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The understanding of land surface-atmosphere energy exchange is extremely important for predicting climate change and weather impacts, particularly the influence of soil moisture content (SMC) on hydrometeorological and ecological processes, which are also linked to human activities. Unfortunately, traditional measurement methods are expensive and cumbersome over large areas, whereas measurements from satellite active and passive microwave sensors have shown advantages for SMC monitoring. Since the launch of the first passive microwave satellite in 1978, more and more progresses have been made in monitoring SMC from satellites, e.g., the Soil Moisture Active and Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions in the last decade. Recently, new methods using signals of opportunity have been emerging, highlighting the Global Navigation Satellite Systems-Reflectometry (GNSS-R), which has wide applications in Earth’s surface remote sensing due to its numerous advantages (e.g., revisiting time, global coverage, low cost, all-weather measurements, and near real-time) when compared to the conventional observations. In this paper, a detailed review on the current SMC measurement techniques, retrieval approaches, products, and applications is presented, particularly the new and promising GNSS-R technique. Recent advances, future prospects and challenges are given and discussed.
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Khvostov, I. V. "Microwave Satellite Systems for Hydrological Monitoring." Izvestiya of Altai State University, no. 1(111) (March 6, 2020): 52–57. http://dx.doi.org/10.14258/izvasu(2020)1-07.
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This paper considers existing and promising satellite microwave radiometry systems suitable for the evaluation of geophysical (hydrological) parameters of atmosphere, ocean, and land. A comparative analysis is provided for data sets available for end-users. Algorithms and tools for processing and visualization of satellite data are discussed. The capabilities of modern satellite systems to perform specific tasks of remote sensing are described using the example of a river flood in the Altai region in 2014. Monitoring soil moisture of upper layers of soil on floodplains combined with meteorological forecasts allows assessment of the probability of river flooding at certain areas using values of maximum soil moisture capacity. The effect of changes in the physical properties of ice during its destruction is discussed. This effect has been discovered by analyzing the dynamics of daily satellite measurements of brightness temperatures. It can be considered as a harbinger of ice condition changes of large freshwater bodies. The analysis of brightness temperature seasonal variations is presented using the example of Lake Big Bear (Canada).
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Zhan, W., M. Pan, N. Wanders, and E. F. Wood. "Correction of real-time satellite precipitation with satellite soil moisture observations." Hydrology and Earth System Sciences Discussions 12, no. 6 (June 16, 2015): 5749–87. http://dx.doi.org/10.5194/hessd-12-5749-2015.
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Abstract. Rainfall and soil moisture are two key elements in modeling the interactions between the land surface and the atmosphere. Accurate and high-resolution real-time precipitation is crucial for monitoring and predicting the on-set of floods, and allows for alert and warning before the impact becomes a disaster. Assimilation of remote sensing data into a flood-forecasting model has the potential to improve monitoring accuracy. Space-borne microwave observations are especially interesting because of their sensitivity to surface soil moisture and its change. In this study, we assimilate satellite soil moisture retrievals using the Variable Infiltration Capacity (VIC) land surface model, and a dynamic assimilation technique, a particle filter, to adjust the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) real-time precipitation estimates. We compare updated precipitation with real-time precipitation before and after adjustment and with NLDAS gauge-radar observations. Results show that satellite soil moisture retrievals provide additional information by correcting errors in rainfall bias. High accuracy soil moisture retrievals, when merged with precipitation, generally increase both rainfall frequency and intensity, and are most effective in the correction of rainfall under dry to normal surface condition while limited/negative improvement is seen over wet/saturated surfaces. Errors from soil moisture, mixed among the real signal, may generate a false rainfall signal approximately 2 mm day−1 and thus lower the precipitation accuracy after adjustment.
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Wagner, Wolfgang, Günter Blöschl, Paolo Pampaloni, Jean-Christophe Calvet, Bizzarro Bizzarri, Jean-Pierre Wigneron, and Yann Kerr. "Operational readiness of microwave remote sensing of soil moisture for hydrologic applications." Hydrology Research 38, no. 1 (February 1, 2007): 1–20. http://dx.doi.org/10.2166/nh.2007.029.
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Microwave remote sensing of soil moisture has been an active area of research since the 1970s but has yet found little use in operational applications. Given recent advances in retrieval algorithms and the approval of a dedicated soil moisture satellite, it is time to re-assess the potential of various satellite systems to provide soil moisture information for hydrologic applications in an operational fashion. This paper reviews recent progress made with retrieving surface soil moisture from three types of microwave sensors – radiometers, Synthetic Aperture Radars (SARs), and scatterometers. The discussion focuses on the operational readiness of the different techniques, considering requirements that are typical for hydrological applications. It is concluded that operational coarse-resolution (25–50 km) soil moisture products can be expected within the next few years from radiometer and scatterometer systems, while scientific and technological breakthroughs are still needed for operational soil moisture retrieval at finer scales (<1 km) from SAR. Also, further research on data assimilation methods is needed to make best use of the coarse-resolution surface soil moisture data provided by radiometer and scatterometer systems in a hydrologic context and to fully assess the value of these data for hydrological predictions.
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Gao, H., E. F. Wood, T. J. Jackson, M. Drusch, and R. Bindlish. "Using TRMM/TMI to Retrieve Surface Soil Moisture over the Southern United States from 1998 to 2002." Journal of Hydrometeorology 7, no. 1 (February 1, 2006): 23–38. http://dx.doi.org/10.1175/jhm473.1.
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Abstract Passive microwave remote sensing has been recognized as a potential method for measuring soil moisture. Combined with field observations and hydrological modeling brightness temperatures can be used to infer soil moisture states and fluxes in real time at large scales. However, operationally acquiring reliable soil moisture products from satellite observations has been hindered by three limitations: suitable low-frequency passive radiometric sensors that are sensitive to soil moisture and its changes; a retrieval model (parameterization) that provides operational estimates of soil moisture from top-of-atmosphere (TOA) microwave brightness temperature measurements at continental scales; and suitable, large-scale validation datasets. In this paper, soil moisture is retrieved across the southern United States using measurements from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) X-band (10.65 GHz) radiometer with a land surface microwave emission model (LSMEM) developed by the authors. Surface temperatures required for the retrieval algorithm were obtained from the Variable Infiltration Capacity (VIC) hydrological model using North American Land Data Assimilation System (NLDAS) forcing data. Because of the limited information content on soil moisture in the observed brightness temperatures over regions characterized by heavy vegetation, active precipitation, snow, and frozen ground, quality control flags for the retrieved soil moisture are provided. The resulting retrieved soil moisture database will be available through the NASA Goddard Space Flight Center (GSFC) Distributed Active Archive Center (DAAC) at a 1/8° spatial resolution across the southern United States for the 5-yr period of January 1998 through December 2002. Initial comparisons with in situ observations obtained from the Oklahoma Mesonet resulted in seasonal correlation coefficients exceeding 0.7 for half of the time covered by the dataset. The dynamic range of the satellite-derived soil moisture dataset is considerably higher compared to the in situ data. The spatial pattern of the TMI soil moisture product is consistent with the corresponding precipitation fields.
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Parinussa, R. M., T. R. H. Holmes, M. T. Yilmaz, and W. T. Crow. "The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations." Hydrology and Earth System Sciences 15, no. 10 (October 17, 2011): 3135–51. http://dx.doi.org/10.5194/hess-15-3135-2011.
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Abstract. For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the current Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and WindSat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and WindSat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and WindSat to obtain coincident surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer and therefore lack an instrument suited to estimate the physical temperature of the Earth. Instead, soil moisture algorithms from these new generation satellites rely on ancillary sources of surface temperature (e.g. re-analysis or near real time data from weather prediction centres). A consequence of relying on such ancillary data is the need for temporal and spatial interpolation, which may introduce uncertainties. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the Rvalue data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature output on the accuracy of WindSat and AMSR-E based surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of MERRA land surface temperature instead of Ka-band radiometric land surface temperature leads to a relative decrease in skill (on average 9.7%) of soil moisture anomaly estimates. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates show a relative increase in skill (on average 13.7%) when using MERRA land surface temperature. In addition, a pre-processing technique to shift phase of the modelled surface temperature is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a very high correlation (R2 = 0.95) and consistency between the two evaluation techniques lends further credibility to the obtained results.
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Zhan, W., M. Pan, N. Wanders, and E. F. Wood. "Correction of real-time satellite precipitation with satellite soil moisture observations." Hydrology and Earth System Sciences 19, no. 10 (October 22, 2015): 4275–91. http://dx.doi.org/10.5194/hess-19-4275-2015.
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Abstract. Rainfall and soil moisture are two key elements in modeling the interactions between the land surface and the atmosphere. Accurate and high-resolution real-time precipitation is crucial for monitoring and predicting the onset of floods, and allows for alert and warning before the impact becomes a disaster. Assimilation of remote sensing data into a flood-forecasting model has the potential to improve monitoring accuracy. Space-borne microwave observations are especially interesting because of their sensitivity to surface soil moisture and its change. In this study, we assimilate satellite soil moisture retrievals using the Variable Infiltration Capacity (VIC) land surface model, and a dynamic assimilation technique, a particle filter, to adjust the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) real-time precipitation estimates. We compare updated precipitation with real-time precipitation before and after adjustment and with NLDAS gauge-radar observations. Results show that satellite soil moisture retrievals provide additional information by correcting errors in rainfall bias. The assimilation is most effective in the correction of medium rainfall under dry to normal surface conditions, while limited/negative improvement is seen over wet/saturated surfaces. On the other hand, high-frequency noises in satellite soil moisture impact the assimilation by increasing rainfall frequency. The noise causes larger uncertainty in the false-alarmed rainfall over wet regions. A threshold of 2 mm day−1 soil moisture change is identified and applied to the assimilation, which masked out most of the noise.
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Kojima, Yuki, Kazuo Oki, Kosuke Noborio, and Masaru Mizoguchi. "Estimating Soil Moisture Distributions across Small Farm Fields with ALOS/PALSAR." International Scholarly Research Notices 2016 (July 26, 2016): 1–8. http://dx.doi.org/10.1155/2016/4203783.
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The ALOS (advanced land observing satellite) has an active microwave sensor, PALSAR (phased array L-band synthetic aperture radar), which has a fine resolution of 6.5 m. Because of the fine resolution, PALSAR provides the possibility of estimating soil moisture distributions in small farmlands. Making such small-scale estimates has not been available with traditional satellite remote sensing techniques. In this study, the relationship between microwave backscattering coefficient (σ) measured with PALSAR and ground-based soil moisture was determined to investigate the performance of PALSAR for estimating soil moisture distribution in a small-scale farmland. On the ground at a cabbage field in Japan in 2008, the soil moisture distribution of multiple soil layers was measured using time domain reflectometry when the ALOS flew over the field. Soil moisture in the 0–20 cm soil layer showed the largest correlation coefficient with σ (r=0.403). The σ values also showed a strong correlation with the ground surface coverage ratio by cabbage plants. Our results suggested that PALSAR could estimate soil moisture distribution of the 0–20 cm soil layer across a bare field and a crop coverage ratio when crops were planted.
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Maggioni, Viviana, Rolf H. Reichle, and Emmanouil N. Anagnostou. "The Efficiency of Assimilating Satellite Soil Moisture Retrievals in a Land Data Assimilation System Using Different Rainfall Error Models." Journal of Hydrometeorology 14, no. 1 (February 1, 2013): 368–74. http://dx.doi.org/10.1175/jhm-d-12-0105.1.
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Abstract The efficiency of assimilating near-surface soil moisture retrievals from Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) observations in a Land Data Assimilation System (LDAS) is assessed using satellite rainfall forcing and two different satellite rainfall error models: a complex, multidimensional satellite rainfall error model (SREM2D) and the simpler (control) model (CTRL) used in the NASA Goddard Earth Observing System Model, version 5 LDAS. For the study domain of Oklahoma, LDAS soil moisture estimates improve over the satellite retrievals and the open-loop (no assimilation) land surface model estimates, exhibiting higher daily anomaly correlation coefficients (e.g., 0.36 in the open loop, 0.38 in the AMSR-E, and 0.50 in LDAS for surface soil moisture). The LDAS soil moisture estimates also match the performance of a benchmark model simulation forced with high-quality radar precipitation. Compared to using the CTRL rainfall error model in LDAS, using the more complex SREM2D exhibits only slight improvements in soil moisture estimates.
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Sawada, Yohei. "Quantifying Drought Propagation from Soil Moisture to Vegetation Dynamics Using a Newly Developed Ecohydrological Land Reanalysis." Remote Sensing 10, no. 8 (July 30, 2018): 1197. http://dx.doi.org/10.3390/rs10081197.
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Despite the importance of the interaction between soil moisture and vegetation dynamics to understand the complex nature of drought, few land reanalyses explicitly simulate vegetation growth and senescence. In this study, I provide a new land reanalysis which explicitly simulates the interaction between sub-surface soil moisture and vegetation dynamics by the sequential assimilation of satellite microwave brightness temperature observations into a land surface model (LSM). Assimilating satellite microwave brightness temperature observations improves the skill of a LSM to simultaneously simulate soil moisture and the seasonal cycle of leaf area index (LAI). By analyzing soil moisture and LAI simulated by this new land reanalysis, I identify the drought events which significantly damage LAI on the climatological day-of-year of the LAI’s seasonal peak and quantify drought propagation from soil moisture to LAI in the global snow-free region. On average, soil moisture in the shallow soil layers (0–0.45 m) quickly recovers from the drought condition before the climatological day-of-year of the LAI’s seasonal peak while soil moisture in the deeper soil layer (1.05–2.05 m) and LAI recover from the drought condition approximately 100 days after the climatological day-of-year of the LAI’s seasonal peak.
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Miralles, Diego G., Wade T. Crow, and Michael H. Cosh. "Estimating Spatial Sampling Errors in Coarse-Scale Soil Moisture Estimates Derived from Point-Scale Observations." Journal of Hydrometeorology 11, no. 6 (December 1, 2010): 1423–29. http://dx.doi.org/10.1175/2010jhm1285.1.
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Abstract The validation of satellite surface soil moisture products requires comparisons between point-scale ground observations and footprint-scale (>100 km2) retrievals. In regions containing a limited number of measurement sites per footprint, some of the observed difference between the retrievals and ground observations is attributable to spatial sampling error and not the intrinsic error of the satellite retrievals themselves. Here, a triple collocation (TC) approach is applied to footprint-scale soil moisture products acquired from passive microwave remote sensing, land surface modeling, and a single ground-based station with the goal of the estimating (and correcting for) spatial sampling error in footprint-scale soil moisture estimates derived from the ground station. Using these three soil moisture products, the TC approach is shown to estimate point-to-footprint soil moisture sampling errors to within 0.0059 m3 m−3 and enhance the ability to validate satellite footprint-scale soil moisture products using existing low-density ground networks.
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Baldwin, Douglas, Salvatore Manfreda, Henry Lin, and Erica A. H. Smithwick. "Estimating Root Zone Soil Moisture Across the Eastern United States with Passive Microwave Satellite Data and a Simple Hydrologic Model." Remote Sensing 11, no. 17 (August 27, 2019): 2013. http://dx.doi.org/10.3390/rs11172013.
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Root zone soil moisture (RZSM) affects many natural processes and is an important component of environmental modeling, but it is expensive and challenging to monitor for relatively small spatial extents. Satellite datasets offer ample spatial coverage of near-surface soil moisture content at up to a daily time-step, but satellite-derived data products are currently too coarse in spatial resolution to use directly for many environmental applications, such as those for small catchments. This study investigated the use of passive microwave satellite soil moisture data products in a simple hydrologic model to provide root zone soil moisture estimates across a small catchment over a two year time period and the Eastern U.S. (EUS) at a 1 km resolution over a decadal time-scale. The physically based soil moisture analytical relationship (SMAR) was calibrated and tested with the Advanced Microwave Scanning Radiometer (AMSRE), Soil Moisture Ocean Salinity (SMOS), and Soil Moisture Active Passive (SMAP) data products. The SMAR spatial model relies on maps of soil physical properties and was first tested at the Shale Hills experimental catchment in central Pennsylvania. The model met a root mean square error (RMSE) benchmark of 0.06 cm3 cm−3 at 66% of the locations throughout the catchment. Then, the SMAR spatial model was calibrated at up to 68 sites (SCAN and AMERIFLUX network sites) that monitor soil moisture across the EUS region, and maps of SMAR parameters were generated for each satellite data product. The average RMSE for RZSM estimates from each satellite data product is <0.06 cm3 cm−3. Lastly, the 1 km EUS regional RZSM maps were tested with data from the Shale Hills, which was set aside for validating the regional SMAR, and the RMSE between the RZSM predictions and the catchment average is 0.042 cm3 cm−3. This study offers a promising approach for generating long time-series of regional RZSM maps with the same spatial resolution of soil property maps.
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Zheng, Weizhong, Xiwu Zhan, Jicheng Liu, and Michael Ek. "A Preliminary Assessment of the Impact of Assimilating Satellite Soil Moisture Data Products on NCEP Global Forecast System." Advances in Meteorology 2018 (June 10, 2018): 1–12. http://dx.doi.org/10.1155/2018/7363194.
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It is well documented that soil moisture has a strong impact on precipitation forecasts of numerical weather prediction models. Several microwave satellite soil moisture retrieval data products have also been available for applications. However, these observational data products have not been employed in any operational numerical weather or climate prediction models. In this study, a preliminary test of assimilating satellite soil moisture data products from the NOAA-NESDIS Soil Moisture Operational Product System (SMOPS) into the NOAA-NCEP Global Forecast System (GFS) is conducted. Using the ensemble Kalman filter (EnKF) introduced in recent year publications and implemented in the GFS, the multiple satellite blended daily global soil moisture data from SMOPS for the month of April 2012 are assimilated into the GFS. The forecasts of surface variables, anomaly correlations of isobar heights, and precipitation forecast skills of the GFS with and without the soil moisture data assimilation are assessed. The surface and deep layer soil moisture estimates of the GFS after the satellite soil moisture assimilation are found to have slightly better agreement with the ground soil moisture measurements at dozens of sites across the continental United States (CONUS). Forecasts of surface humidity and air temperature, 500 hPa height anomaly correlations, and the precipitation forecast skill demonstrated certain level of improvements after the soil moisture assimilation against those without the soil moisture assimilation. However, the methodology for the soil moisture data assimilation into operational GFS runs still requires further development efforts and tests.
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Naeimi, Vahid, Zoltan Bartalis, and Wolfgang Wagner. "ASCAT Soil Moisture: An Assessment of the Data Quality and Consistency with the ERS Scatterometer Heritage." Journal of Hydrometeorology 10, no. 2 (April 1, 2009): 555–63. http://dx.doi.org/10.1175/2008jhm1051.1.
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Abstract This article presents a first comparison between remotely sensed surface soil moisture retrieved with the European Remote Sensing Satellite-2 (ERS-2) scatterometer (SCAT) and the corresponding product provided by the Advanced Scatterometer (ASCAT) on board Meteorological Operation satellite (MetOp), the first of a series of three satellites providing, among other things, continuity of global soil moisture observations using active microwave techniques for the next 15 yr. Three months of collocated 2007 data were used from the SCAT and ASCAT, limited to two study regions with different land cover composition. The result of the assessment is satisfactory and ensures consistency of migrating soil moisture retrieval from the long-term SCAT dataset to ASCAT measurements. The influence of a shift of observation incidence angle ranges between the two instrument generations was not found to be significant for the soil moisture retrieval. The correlation coefficients (R) between two relative soil moisture (normalized water content) datasets compared in different incidence angle ranges are around 0.90 with root-mean-square error (RMSE) values in the order of 8.5. Results are expected to improve slightly further once the calibration of the ASCAT instrument is finalized.
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Peng, J., J. Niesel, and A. Loew. "Evaluation of soil moisture downscaling using a simple thermal based proxy – the REMEDHUS network (Spain) example." Hydrology and Earth System Sciences Discussions 12, no. 8 (August 31, 2015): 8505–51. http://dx.doi.org/10.5194/hessd-12-8505-2015.
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Abstract. Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution in the order of tens of kilometers, which are too coarse for many regional hydrological applications such as agriculture monitoring and drought predication. Therefore, various downscaling methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of the simple Vegetation Temperature Condition Index (VTCI) downscaling scheme over a dense soil moisture observational network (REMEDHUS) in Spain. Firstly, the optimized VTCI was determined through sensitivity analyses of VTCI to surface temperature, vegetation index, cloud, topography and land cover heterogeneity, using data from MODIS and MSG SEVIRI. Then the downscaling scheme was applied to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture observations, spatial pattern comparison, as well as seasonal and land use analyses show that the downscaling method can significantly improve the spatial details of CCI soil moisture while maintain the accuracy of CCI soil moisture. The accuracy level is comparable to other downscaling methods that were also validated against REMEDHUS network. Furthermore, slightly better performance of MSG SEVIRI over MODIS was observed, which suggests the high potential of applying geostationary satellite for downscaling soil moisture in the future. Overall, considering the simplicity, limited data requirements and comparable accuracy level to other complex methods, the VTCI downscaling method can facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.
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Lakshmi, Venkat. "Remote Sensing of Soil Moisture." ISRN Soil Science 2013 (March 7, 2013): 1–33. http://dx.doi.org/10.1155/2013/424178.
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Soil moisture is an important variable in land surface hydrology as it controls the amount of water that infiltrates into the soil and replenishes the water table versus the amount that contributes to surface runoff and to channel flow. However observations of soil moisture at a point scale are very sparse and observing networks are expensive to maintain. Satellite sensors can observe large areas but the spatial resolution of these is dependent on microwave frequency, antenna dimensions, and height above the earth’s surface. The higher the sensor, the lower the spatial resolution and at low elevations the spacecraft would use more fuel. Higher spatial resolution requires larger diameter antennas that in turn require more fuel to maintain in space. Given these competing issues most passive radiometers have spatial resolutions in 10s of kilometers that are too coarse for catchment hydrology applications. Most local applications require higher-spatial-resolution soil moisture data. Downscaling of the data requires ancillary data and model products, all of which are used here to develop high-spatial-resolution soil moisture for catchment applications in hydrology. In this paper the author will outline and explain the methodology for downscaling passive microwave estimation of soil moisture.
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Parinussa, R. M., T. R. H. Holmes, and W. T. Crow. "The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations." Hydrology and Earth System Sciences Discussions 8, no. 4 (July 11, 2011): 6683–719. http://dx.doi.org/10.5194/hessd-8-6683-2011.
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Abstract. For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the modern Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and Windsat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and Windsat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and Windsat to obtain surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer. Because of this, ancillary – and potentially less accurate – sources of surface temperature information (e.g. re-analysis data from operational weather prediction centers) must be sought to produce surface soil moisture retrievals. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the R value data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature predictions on the accuracy of Windsat and AMSR-E surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of Ka-band radiometric land surface temperature leads to better soil moisture anomaly estimates compared to those retrieved using MERRA land surface temperature predictions. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates retrieved using MERRA land surface temperature are superior. In addition, the surface temperature phase shifting approach is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a high degree of consistency is noted between evaluation results produced by the TC and Rvalue soil moisture verification approaches.
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Mehraban, Abouzar, Yansong Bao, Emmanuel Yeboah, Benedicta Akua Sarfo, Michael Kpakpo Allotey, Charafa El Rhadiouini, Ben Emunah Aikins, et al. "Dust Outbreaks across East Iran: Application of Multi-Source Remote Sensing Data (AMSR-E and FengYun3-MWRI) on the Effects of Soil Moisture." Journal of Geography, Environment and Earth Science International 27, no. 9 (July 25, 2023): 1–18. http://dx.doi.org/10.9734/jgeesi/2023/v27i9702.
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One of the most significant hydro-meteorological and agricultural variables is soil moisture, yet measuring it remains a difficult task. Due to the significant spatial fluctuation of soil moisture, it is difficult to quantify it in a particular spot or field across a sizable region. Despite the thermal band's limitations in assessing soil moisture, MODIS and AVHRR, which are inappropriate were utilized in this investigation. The study examined the impact of soil moisture on dust outbreak. Soil moisture in the study domain was monitored using field techniques and the hybrid model. It combined multi-sourced remote sensing data, obtained from AMSER-E and FY-3 satellites. AMSER-E satellite measures the light temperature in five frequencies ranging from 6. 9 to 89 GHz based on data obtained from AMSER-E. Findings revealed areas with a spatial scale of 25 km2 has a 12-hour time step or variability in dust storm, thereby influencing soil moisture content within the zone of study. In addition to introducing acceptable potentials of the passive microwave band for accurate and applied monitoring of the soil moisture, the present results are viewed as a reliable source for studies on drought in time scale. The study shows that Zabol in Sistan has the highest annual average of 80.7 dust storm days. Soil moisture estimates serve a great deal for preparing soil moisture maps and the evaluation of temporal and spatial variations of soil moisture in study region to address issues related to dust storms.
In order to identify the areas affected by dust storms and understand how dust particles are dispersed in the Sistan region, satellite image processing was employed using MODIS 1 sensor images obtained from the TERRA satellite.
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Champagne, Catherine, Andrew Davidson, Patrick Cherneski, Jessika L’Heureux, and Trevor Hadwen. "Monitoring Agricultural Risk in Canada Using L-Band Passive Microwave Soil Moisture from SMOS." Journal of Hydrometeorology 16, no. 1 (February 1, 2015): 5–18. http://dx.doi.org/10.1175/jhm-d-14-0039.1.
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Abstract Soil moisture from Soil Moisture Ocean Salinity (SMOS) passive microwave satellite data was assessed as an information source for identifying regions experiencing climate-related agricultural risk for a period from 2010 to 2013. Both absolute soil moisture and soil moisture anomalies compared to a 4-yr SMOS satellite baseline were used in the assessment. The 4-yr operational period of SMOS was wetter than the 30-yr climate normal in many locations, particularly in the late summer for most regions and in the spring for the province of Manitoba. This leads to a somewhat unrepresentative baseline that skews anomaly measures at different parts of the growing season. SMOS soil moisture does, however, show a clear trend where extremes are present, with drier-than-average conditions during periods that drought and dry soil risks were identified and wetter-than-average conditions when flooding and excess moisture were present. Areas where extreme weather events caused crop losses were identifiable using SMOS soil moisture, both at the provincial and regional scales. The variability in soil moisture between at-risk areas and normal areas is very small but consistent, both geographically and over time, making SMOS a good real-time indicator for risk assessment.
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Balas, Duda B., Mukesh Kumar Tiwari, and Gautam R. Patel. "Estimation of Surface and Subsurface Soil Moisture Using Microwave Remote Sensing: A Typical Analysis." International Journal of Environment and Climate Change 13, no. 10 (August 31, 2023): 1804–16. http://dx.doi.org/10.9734/ijecc/2023/v13i102836.
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Accurate measurement and monitoring of surface and subsurface soil moisture is essential for understanding hydrological processes, crop growth modeling, crop water requirement, and climate studies. Accurate measurement of the soil moisture content (SMC) in the root zone is essential for precise irrigation authority and plant water stress evaluation. However, the existing passive microwave satellite missions, Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), that operate at L-band, can only estimate the top 5 cm of soil moisture. Microwave remote sensing has proven to be a valuable tool for non-invasive soil moisture estimation. This research aims to investigate and develop a methodology for estimating surface and subsurface soil moisture using microwave data from Sentinel-1. The study was conducted to establish the relationship between surface & the backscatter coefficient derived using the Sentinel-1 SAR microwave remote sensing satellite imagery, and relationship between surface and subsurface soil moisture at different depths, in the Godhra region. Two seasons namely summer (Zaid) and monsoon (Kharif) were taken into consideration to build up the relationship between surface soil moisture and co-polarization backscatter coefficient ( For the summer (Zaid) and monsoon (Kharif) seasons, the co-polarization backscatter coefficient ( and surface soil moisture (0-5, cm) were found to have a correlation in terms of R2 as 0.91 and 0.90, respectively. The study explores the relationship between microwave signals and surface soil moisture content (0-5, cm) and then the relationship between surface soil moisture and soil moisture at various depths were also modeled thereby contributing to improved soil moisture estimation techniques and applications. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, and 41-60 cm depths were found to be 0.60, 0.51, and 0.46, respectively, in the summer (Zaid) season. The value of the coefficient of determination (R2) of surface soil moisture (0-5, cm) to subsurface soil moisture at 6-20 cm, 21-40 cm, 41-60 cm, 61-80 cm, and 81-100 cm depths were found to be 0.83, 0.61, 0.51, 0.26, and 0.13, respectively. According to the study, it is observed that the relationship between co-polarization backscatter coefficient ( and soil moisture weakens as the depth of soil moisture increases. Overall, the regression models developed between the co-polarization backscatter coefficient ( and surface soil moisture showed very good results, whereas the regression models developed between the surface soil moisture and soil moisture at various depths showed reasonably acceptable results up to the depth of 60 cm. The findings in the present study suggest that Sentinel-1A C-band SAR data can be used to estimate surface soil moisture. It is also shown in this study that the surface soil moisture can be correlated with the subsurface soil moisture up to the depth of 60 cm, satisfactorily using regression equations.
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Gheybi, Fatemeh, Parivash Paridad, Farid Faridani, Ali Farid, Alonso Pizarro, Mauro Fiorentino, and Salvatore Manfreda. "Soil Moisture Monitoring in Iran by Implementing Satellite Data into the Root-Zone SMAR Model." Hydrology 6, no. 2 (May 28, 2019): 44. http://dx.doi.org/10.3390/hydrology6020044.
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Monitoring Surface Soil Moisture (SSM) and Root Zone Soil Moisture (RZSM) dynamics at the regional scale is of fundamental importance to many hydrological and ecological studies. This need becomes even more critical in arid and semi-arid regions, where there are a lack of in situ observations. In this regard, satellite-based Soil Moisture (SM) data is promising due to the temporal resolution of acquisitions and the spatial coverage of observations. Satellite-based SM products are only able to estimate moisture from the soil top layer; however, linking SSM with RZSM would provide valuable information on land surface-atmosphere interactions. In the present study, satellite-based SSM data from Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Soil Moisture Active Passive (SMAP) are first compared with the few available SM in situ observations, and are then coupled with the Soil Moisture Analytical Relationship (SMAR) model to estimate RZSM in Iran. The comparison between in situ SM observations and satellite data showed that the SMAP satellite products provide more accurate description of SSM with an average correlation coefficient (R) of 0.55, root-mean-square error (RMSE) of 0.078 m3 m−3 and a Bias of 0.033 m3 m−3. Thereafter, the SMAP satellite products were coupled with SMAR model, providing a description of the RZSM with performances that are strongly influenced by the misalignment between point and pixel processes measured in the preliminary comparison of SSM data.
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Peng, J., J. Niesel, and A. Loew. "Evaluation of soil moisture downscaling using a simple thermal-based proxy – the REMEDHUS network (Spain) example." Hydrology and Earth System Sciences 19, no. 12 (December 3, 2015): 4765–82. http://dx.doi.org/10.5194/hess-19-4765-2015.
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Abstract. Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution on the order of tens of kilometers, which are too coarse for many regional hydrological applications such as agriculture monitoring and drought prediction. Therefore, various downscaling methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to investigate the validity and robustness of the simple vegetation temperature condition index (VTCI) downscaling scheme over a dense soil moisture observational network (REMEDHUS) in Spain. First, the optimized VTCI was determined through sensitivity analyses of VTCI to surface temperature, vegetation index, cloud, topography, and land cover heterogeneity, using data from Moderate Resolution Imaging Spectroradiometer~(MODIS) and MSG SEVIRI (METEOSAT Second Generation – Spinning Enhanced Visible and Infrared Imager). Then the downscaling scheme was applied to improve the spatial resolution of the European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) soil moisture, which is a merged product based on both active and passive microwave observations. The results from direct validation against soil moisture observations, spatial pattern comparison, as well as seasonal and land use analyses show that the downscaling method can significantly improve the spatial details of CCI soil moisture while maintaining the accuracy of CCI soil moisture. The accuracy level is comparable to other downscaling methods that were also validated against the REMEDHUS network. Furthermore, slightly better performance of MSG SEVIRI over MODIS was observed, which suggests the high potential of applying a geostationary satellite for downscaling soil moisture in the future. Overall, considering the simplicity, limited data requirements and comparable accuracy level to other complex methods, the VTCI downscaling method can facilitate relevant hydrological applications that require high spatial and temporal resolution soil moisture.
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AlJassar, Hala K., Marouane Temimi, Dara Entekhabi, Peter Petrov, Hussain AlSarraf, Panagiotis Kokkalis, and Nair Roshni. "Forward Simulation of Multi-Frequency Microwave Brightness Temperature over Desert Soils in Kuwait and Comparison with Satellite Observations." Remote Sensing 11, no. 14 (July 11, 2019): 1647. http://dx.doi.org/10.3390/rs11141647.
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In this study, we address the variations of bare soil surface microwave brightness temperatures and evaluate the performance of a dielectric mixing model over the desert of Kuwait. We use data collected in a field survey and data obtained from NASA Soil Moisture Active Passive (SMAP), European Space Agency Soil Moisture and Ocean Salinity (SMOS), Advanced Microwave Scanning Radiometer 2 (AMSR2), and Special Sensor Microwave/Imager (SSM/I). In situ measurements are collected during two intensive field campaigns over bare, flat, and homogeneous soil terrains in the desert of Kuwait. Despite the prevailing dry desert environment, a large range of soil moisture values was monitored, due to precedent rain events and subsequent dry down. The mean relative difference (MRD) is within the range of ±0.005 m3·m−3 during the two sampling days. This reflects consistency of soil moisture in space and time. As predicted by the model, the higher frequency channels (18 to 19 GHz) demonstrate reduced sensitivity to surface soil moisture even in the absence of vegetation, topography and heterogeneity. In the 6.9 to 10.7 GHz range, only the horizontal polarization is sensitive to surface soil moisture. Instead, at the frequency of 1.4 GHz, both polarizations are sensitive to soil moisture and span a large dynamic range as predicted by the model. The error statistics of the difference between observed satellite brightness temperature (Tb) (excluding SMOS data due to radio frequency interference, RFI) and simulated brightness temperatures (Tbs) show values of Root Mean Square Deviation (RMSD) of 5.05 K at vertical polarization and 4.88 K at horizontal polarization. Such error could be due to the performance of the dielectric mixing model, soil moisture sampling depth and the impact of parametrization of effective temperature and roughness.
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Blank, Daniel, Annette Eicker, Laura Jensen, and Andreas Güntner. "A global analysis of water storage variations from remotely sensed soil moisture and daily satellite gravimetry." Hydrology and Earth System Sciences 27, no. 13 (July 4, 2023): 2413–35. http://dx.doi.org/10.5194/hess-27-2413-2023.
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Abstract. Water storage changes in the soil can be observed on a global scale with different types of satellite remote sensing. While active or passive microwave sensors are limited to the upper few centimeters of the soil, satellite gravimetry can detect changes in the terrestrial water storage (TWS) in an integrative way, but it cannot distinguish between storage variations in different compartments or soil depths. Jointly analyzing both data types promises novel insights into the dynamics of subsurface water storage and of related hydrological processes. In this study, we investigate the global relationship of (1) several satellite soil moisture products and (2) non-standard daily TWS data from the Gravity Recovery and Climate Experiment/Follow-On (GRACE/GRACE-FO) satellite gravimetry missions on different timescales. The six soil moisture products analyzed in this study differ in the post-processing and the considered soil depth. Level 3 surface soil moisture data sets of the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions are compared to post-processed Level 4 data products (surface and root zone soil moisture) and the European Space Agency Climate Change Initiative (ESA CCI) multi-satellite product. On a common global 1∘ grid, we decompose all TWS and soil moisture data into seasonal to sub-monthly signal components and compare their spatial patterns and temporal variability. We find larger correlations between TWS and soil moisture for soil moisture products with deeper integration depths (root zone vs. surface layer) and for Level 4 data products. Even for high-pass filtered sub-monthly variations, significant correlations of up to 0.6 can be found in regions with a large, high-frequency storage variability. A time shift analysis of TWS versus soil moisture data reveals the differences in water storage dynamics with integration depth.
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van der Molen, M. K., R. A. M. de Jeu, W. Wagner, I. R. van der Velde, P. Kolari, J. Kurbatova, A. Varlagin, et al. "The effect of assimilating satellite-derived soil moisture data in SiBCASA on simulated carbon fluxes in Boreal Eurasia." Hydrology and Earth System Sciences 20, no. 2 (February 3, 2016): 605–24. http://dx.doi.org/10.5194/hess-20-605-2016.
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Abstract. Boreal Eurasia is a region where the interaction between droughts and the carbon cycle may have significant impacts on the global carbon cycle. Yet the region is extremely data sparse with respect to meteorology, soil moisture, and carbon fluxes as compared to e.g. Europe. To better constrain our vegetation model SiBCASA, we increase data usage by assimilating two streams of satellite-derived soil moisture. We study whether the assimilation improved SiBCASA's soil moisture and its effect on the simulated carbon fluxes. By comparing to unique in situ soil moisture observations, we show that the passive microwave soil moisture product did not improve the soil moisture simulated by SiBCASA, but the active data seem promising in some aspects. The match between SiBCASA and ASCAT soil moisture is best in the summer months over low vegetation. Nevertheless, ASCAT failed to detect the major droughts occurring between 2007 and 2013. The performance of ASCAT soil moisture seems to be particularly sensitive to ponding, rather than to biomass. The effect on the simulated carbon fluxes is large, 5–10 % on annual GPP and TER, tens of percent on local NEE, and 2 % on area-integrated NEE, which is the same order of magnitude as the inter-annual variations. Consequently, this study shows that assimilation of satellite-derived soil moisture has potentially large impacts, while at the same time further research is needed to understand under which conditions the satellite-derived soil moisture improves the simulated soil moisture.
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van der Molen, M. K., R. A. M. de Jeu, W. Wagner, I. R. van der Velde, P. Kolari, J. Kurbatova, A. Varlagin, et al. "The effect of assimilating satellite derived soil moisture in SiBCASA on simulated carbon fluxes in Boreal Eurasia." Hydrology and Earth System Sciences Discussions 12, no. 9 (September 4, 2015): 9003–54. http://dx.doi.org/10.5194/hessd-12-9003-2015.
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Abstract. Boreal Eurasia is a region where the interaction between droughts and the carbon cycle may have significant impacts on the global carbon cycle. Yet the region is extremely data sparse with respect to meteorology, soil moisture and carbon fluxes as compared to e.g. Europe. To better constrain our vegetation model SiBCASA, we increase data usage by assimilating two streams of satellite derived soil moisture. We study if the assimilation improved SiBCASA's soil moisture and its effect on the simulated carbon fluxes. By comparing to unique in situ soil moisture observations, we show that the passive microwave soil moisture product did not improve the soil moisture simulated by SiBCASA, but the active data seem promising in some aspects. The match between SiBCASA and ASCAT soil moisture is best in the summer months over low vegetation. Nevertheless, ASCAT failed to detect the major droughts occurring between 2007 and 2013. The performance of ASCAT soil moisture seems to be particularly sensitive to ponding, rather than to biomass. The effect on the simulated carbon fluxes is large, 5–10% on annual GPP and TER, and tens of percent on local NEE, and 2% on area-integrated NEE, which is the same order of magnitude as the inter-annual variations. Consequently, this study shows that assimilation of satellite derived soil moisture has potentially large impacts, while at the same time further research is needed to understand under which conditions the satellite derived soil moisture improves the simulated soil moisture.