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Journal articles on the topic 'Irrigation, Land Surface Model, Remote Sensing, Data Assimilation'

Author: Grafiati

Published: 10 March 2023

Last updated: 31 July 2025

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1

Fan, Xingwang, Yanyu Lu, Yongwei Liu, Tingting Li, Shangpei Xun, and Xiaosong Zhao. "Validation of Multiple Soil Moisture Products over an Intensive Agricultural Region: Overall Accuracy and Diverse Responses to Precipitation and Irrigation Events." Remote Sensing 14, no. 14 (2022): 3339. http://dx.doi.org/10.3390/rs14143339.

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Remote sensing and land surface models promote the understanding of soil moisture dynamics by means of multiple products. These products differ in data sources, algorithms, model structures and forcing datasets, complicating the selection of optimal products, especially in regions with complex land covers. This study compared different products, algorithms and flagging strategies based on in situ observations in Anhui province, China, an intensive agricultural region with diverse landscapes. In general, models outperform remote sensing in terms of valid data coverage, metrics against observati

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Han, X., H. J. H. Franssen, R. Rosolem, R. Jin, X. Li, and H. Vereecken. "Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China." Hydrology and Earth System Sciences 19, no. 1 (2015): 615–29. http://dx.doi.org/10.5194/hess-19-615-2015.

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Abstract. The recent development of the non-invasive cosmic-ray soil moisture sensing technique fills the gap between point-scale soil moisture measurements and regional-scale soil moisture measurements by remote sensing. A cosmic-ray probe measures soil moisture for a footprint with a diameter of ~ 600 m (at sea level) and with an effective measurement depth between 12 and 76 cm, depending on the soil humidity. In this study, it was tested whether neutron counts also allow correcting for a systematic error in the model forcings. A lack of water management data often causes systematic input er

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Han, X., H. J. Hendricks Franssen, R. Rosolem, R. Jin, X. Li, and H. Vereecken. "Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray neutrons and land surface temperature: a study in the Heihe catchment, China." Hydrology and Earth System Sciences Discussions 11, no. 7 (2014): 9027–66. http://dx.doi.org/10.5194/hessd-11-9027-2014.

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Abstract. The recent development of the non-invasive cosmic-ray soil moisture sensing technique fills the gap between point scale soil moisture measurements and regional scale soil moisture measurements by remote sensing. A cosmic-ray probe measures soil moisture for a footprint with a diameter of ~600 m (at sea level) and with an effective measurement depth between 12 and 76 cm, depending on the soil humidity. In this study, it was tested whether neutron counts also allow to correct for a systematic error in the model forcings. Lack of water management data often cause systematic input errors

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Modanesi, Sara, Christian Massari, Alexander Gruber, et al. "Optimizing a backscatter forward operator using Sentinel-1 data over irrigated land." Hydrology and Earth System Sciences 25, no. 12 (2021): 6283–307. http://dx.doi.org/10.5194/hess-25-6283-2021.

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Abstract. Worldwide, the amount of water used for agricultural purposes is rising, and the quantification of irrigation is becoming a crucial topic. Because of the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large-scale land surface models (LSMs) is improving, but it is still hampered by the lack of information about dynamic crop rotations, or the extent of irrigated areas, and the mostly unknown timing and amount of irrigati

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Kumar, S. V., C. D. Peters-Lidard, J. A. Santanello, et al. "Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes." Hydrology and Earth System Sciences 19, no. 11 (2015): 4463–78. http://dx.doi.org/10.5194/hess-19-4463-2015.

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Abstract. Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a huma

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Kumar, S. V., C. D. Peters-Lidard, J. A. Santanello, et al. "Evaluating the utility of satellite soil moisture retrievals over irrigated areas and the ability of land data assimilation methods to correct for unmodeled processes." Hydrology and Earth System Sciences Discussions 12, no. 6 (2015): 5967–6009. http://dx.doi.org/10.5194/hessd-12-5967-2015.

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Abstract. The Earth's land surface is characterized by tremendous natural heterogeneity and human engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human induced modifications to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a

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7

Sun, Yule, Quanming Liu, Chunjuan Wang, Qi Liu, and Zhongyi Qu. "Improving Soil Moisture Estimation by Integrating Remote Sensing Data into HYDRUS-1D Using an Ensemble Kalman Filter Approach." Agriculture 15, no. 12 (2025): 1320. https://doi.org/10.3390/agriculture15121320.

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Reliable soil moisture projections are critical for optimizing crop productivity and water savings in irrigation in arid and semi-arid regions. However, capturing their spatial and temporal variability is difficult when using individual observations, modeling, or satellite-based methods. Here, we present an integrated framework that combines satellite-derived soil moisture estimates, ground-based observations, the HYDRUS-1D vadose zone model, and the ensemble Kalman filter (EnKF) data assimilation method to improve soil moisture simulations over saline-affected farmland in the Hetao irrigation

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Ouaadi, Nadia, Lionel Jarlan, Saïd Khabba, Jamal Ezzahar, Michel Le Page, and Olivier Merlin. "Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture into the FAO-56 Approach in the South Mediterranean Region." Remote Sensing 13, no. 14 (2021): 2667. http://dx.doi.org/10.3390/rs13142667.

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Agricultural water use represents more than 70% of the world’s freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a pa

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Chang, Hongfang, Jiabing Cai, Baozhong Zhang, Zheng Wei, and Di Xu. "Early Yield Forecasting of Maize by Combining Remote Sensing Images and Field Data with Logistic Models." Remote Sensing 15, no. 4 (2023): 1025. http://dx.doi.org/10.3390/rs15041025.

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Early forecasting of crop yield from field to region is important for stabilizing markets and safeguarding food security. Producing a precise forecasting result with fewer inputs is an ongoing goal for the large-area yield evaluation. We present one approach of yield prediction for maize that was explored by incorporating remote-sensing-derived land surface temperature (LST) and field in-season data into a series of logistic models with only a few parameters. Continuous observation data of maize were utilized to calibrate and validate the corresponding logistic models for regional biomass esti

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Khan, Ihsan Ullah, Mudassar Iqbal, Zeshan Ali, Abu Bakar Arshed, Mo Wang, and Rana Muhammad Adnan. "Evaluation and Mapping of Snow Characteristics Using Remote Sensing Data in Astore River Basin, Pakistan." Atmosphere 16, no. 5 (2025): 550. https://doi.org/10.3390/atmos16050550.

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Being an agricultural country, Pakistan requires lots of water for irrigation. A major portion of its water resources is located in the upper indus basin (UIB). The snowmelt runoff generated from high-altitude areas of the UIB provides inflow into the Indus river system that boosts the water supply. Snow accumulation during the winter period in the highlands in the watershed(s) becomes a source of water inflow during the snow-melting period, which is described according to characteristics like snow depth, snow density, and snow water equivalent. Snowmelt water release (SWE) and snowmelt water

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Massoud, Elias C., Zhen Liu, Amin Shaban, and Mhamad Hage. "Groundwater Depletion Signals in the Beqaa Plain, Lebanon: Evidence from GRACE and Sentinel-1 Data." Remote Sensing 13, no. 5 (2021): 915. http://dx.doi.org/10.3390/rs13050915.

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Regions with high productivity of agriculture, such as the Beqaa Plain, Lebanon, often rely on groundwater supplies for irrigation demand. Recent reports have indicated that groundwater consumption in this region has been unsustainable, and quantifying rates of groundwater depletion has remained a challenge. Here, we utilize 15 years of data (June 2002–April 2017) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission to show Total Water Storage (TWS) changes in Lebanon’s Beqaa Plain. We then obtain complimentary information on various hydrologic cycle variables, such as so

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Xu, Xiaoyong, Jonathan Li, and Bryan A. Tolson. "Progress in integrating remote sensing data and hydrologic modeling." Progress in Physical Geography: Earth and Environment 38, no. 4 (2014): 464–98. http://dx.doi.org/10.1177/0309133314536583.

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Remote sensing and hydrologic modeling are two key approaches to evaluate and predict hydrology and water resources. Remote sensing technologies, due to their ability to offer large-scale spatially distributed observations, have opened up new opportunities for the development of fully distributed hydrologic and land-surface models. In general, remote sensing data can be applied to land-surface and hydrologic modeling through three strategies: model inputs (basin information, boundary conditions, etc.), parameter estimation (model calibration), and state estimation (data assimilation). There ha

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Margulis, Steven A., Jongyoun Kim, and Terri Hogue. "A Comparison of the Triangle Retrieval and Variational Data Assimilation Methods for Surface Turbulent Flux Estimation." Journal of Hydrometeorology 6, no. 6 (2005): 1063–72. http://dx.doi.org/10.1175/jhm451.1.

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Abstract Future operational frameworks for estimating surface turbulent fluxes over the necessary spatial and temporal scales will undoubtedly require the use of remote sensing products. Techniques used to estimate surface fluxes from radiometric surface temperature generally fall into two categories: retrieval-based and data assimilation approaches. Up to this point, there has been little comparison between retrieval- and assimilation-based techniques. In this note, the triangle retrieval method is compared to a variational data assimilation approach for estimating surface turbulent fluxes fr

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Meng, Chunlei, Chaolin Zhang, and Ronglin Tang. "Variational Estimation of Land–Atmosphere Heat Fluxes and Land Surface Parameters Using MODIS Remote Sensing Data." Journal of Hydrometeorology 14, no. 2 (2013): 608–21. http://dx.doi.org/10.1175/jhm-d-12-028.1.

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Abstract A variational data assimilation algorithm for assimilating the land surface temperature (LST) into the Common Land Model (CLM) was implemented using the land surface energy balance equation as the adjoint physical constraint. In this data assimilation algorithm, the evaporative fractions of the soil and canopy were adjusted according to the remotely sensed surface temperature observations. This paper developed a very simple analytical algorithm to characterize the errors’ weighting matrices in the cost function. The leaf area index (LAI) retrieved from the Moderate Resolution Imaging

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Benavides Pinjosovsky, Hector Simon, Sylvie Thiria, Catherine Ottlé, Julien Brajard, Fouad Badran, and Pascal Maugis. "Variational assimilation of land surface temperature within the ORCHIDEE Land Surface Model Version 1.2.6." Geoscientific Model Development 10, no. 1 (2017): 85–104. http://dx.doi.org/10.5194/gmd-10-85-2017.

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Abstract. The SECHIBA module of the ORCHIDEE land surface model describes the exchanges of water and energy between the surface and the atmosphere. In the present paper, the adjoint semi-generator software called YAO was used as a framework to implement a 4D-VAR assimilation scheme of observations in SECHIBA. The objective was to deliver the adjoint model of SECHIBA (SECHIBA-YAO) obtained with YAO to provide an opportunity for scientists and end users to perform their own assimilation. SECHIBA-YAO allows the control of the 11 most influential internal parameters of the soil water content, by o

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Marshall, M., K. Tu, C. Funk, et al. "Combining surface reanalysis and remote sensing data for monitoring evapotranspiration." Hydrology and Earth System Sciences Discussions 9, no. 2 (2012): 1547–87. http://dx.doi.org/10.5194/hessd-9-1547-2012.

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Abstract. Climate change is expected to have the greatest impact on the world's poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled actual evapotranspiration (AET), a key input in continental-scale hydrologic models. In this study, a model driven by dynamic canopy AET was combined with the Global Land Data Assimilation System r

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Karishma, C. G., Balaji Kannan, K. Nagarajan, S. Panneerselvam, and S. Pazhanivelan. "Spatial and temporal estimation of actual evapotranspiration of lower Bhavani basin, Tamil Nadu using Surface Energy Balance Algorithm for Land Model." Journal of Applied and Natural Science 14, no. 2 (2022): 566–74. http://dx.doi.org/10.31018/jans.v14i2.3412.

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Estimating evapotranspiration's spatiotemporal variance is critical for regional water resource management and allocation, including irrigation scheduling, drought monitoring, and forecasting. The Surface Energy Balance Algorithm for Land (SEBAL) method can be used to estimate spatio-temporal variations in evapotranspiration (ET) using remote sensing-based variables like Land Surface Temperature (LST), Normalized Difference Vegetation Index (NDVI), surface albedo, transmittance, and surface emissivity. The main aim of the study was to evaluate the actual evapotranspiration for the lower Bhavan

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Duan, Hao, Qiuju Li, Haowei Xu, and Liqi Cao. "Coupled Calculation of Soil Moisture Content and PML Model Based on Data Assimilation in the Hetao Irrigation District." Atmosphere 15, no. 3 (2024): 340. http://dx.doi.org/10.3390/atmos15030340.

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Most Penman-Monteith-Leuning (PML) evapotranspiration (ET) modeling studies are dominated by consideration of meteorological, energy, and land use information, etc., but the dynamic coupling of soil moisture content (SM), especially in terms of improving accuracy through assimilation, lacks sufficient attention. This paper proposes a research framework for the dynamic coupling simulation of PML model and SM based on data assimilation, i.e., the remote sensing monitored SM is combined with soil evaporation of PML to obtain high-precision time-continuous SM data through data assimilation; simult

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Tian, Yingze, Tongren Xu, Fei Chen, Xinlei He, and Shi Li. "Can Data Assimilation Improve Short-Term Prediction of Land Surface Variables?" Remote Sensing 14, no. 20 (2022): 5172. http://dx.doi.org/10.3390/rs14205172.

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Data assimilation methods have been used to improve the performances of land surface models by integrating remote sensing and in situ measurements. However, the impact of data assimilation on improving the forecast of land surface variables has not been well studied, which is essential for weather and hydrology forecasting. In this study, a multi-pass land data assimilation scheme (MLDAS) based on the Noah-MP model was used to predict short-term land surface variables (e.g., sensible heat fluxes (H), latent heat fluxes (LE), and surface soil moisture (SM)) by jointly assimilating soil moisture

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Strebel, Lukas, Heye R. Bogena, Harry Vereecken, and Harrie-Jan Hendricks Franssen. "Coupling the Community Land Model version 5.0 to the parallel data assimilation framework PDAF: description and applications." Geoscientific Model Development 15, no. 2 (2022): 395–411. http://dx.doi.org/10.5194/gmd-15-395-2022.

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Abstract. Land surface models are important for improving our understanding of the Earth system. They are continuously improving and becoming better in representing the different land surface processes, e.g., the Community Land Model version 5 (CLM5). Similarly, observational networks and remote sensing operations are increasingly providing more data, e.g., from new satellite products and new in situ measurement sites, with increasingly higher quality for a range of important variables of the Earth system. For the optimal combination of land surface models and observation data, data assimilati

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Li, Suosuo, Yuanpu Liu, Yongjie Pan, Zhe Li, and Shihua Lyu. "Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China." Remote Sensing 13, no. 17 (2021): 3409. http://dx.doi.org/10.3390/rs13173409.

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Land-surface characteristics (LSCs) and land-soil moisture conditions can modulate energy partition at the land surface, impact near-surface atmosphere conditions, and further affect land–atmosphere interactions. This study investigates the effect of land-surface-characteristic parameters (LSCPs) including albedo, leaf-area index (LAI), and soil moisture (SM) on hot weather by in East China using the numerical model. Simulations using the Weather Research and Forecasting (WRF) Model were conducted for a hot weather event with a high spatial resolution of 1 km in domain 3 by using ERA-Interim f

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Durand, Michael, and Steven A. Margulis. "Feasibility Test of Multifrequency Radiometric Data Assimilation to Estimate Snow Water Equivalent." Journal of Hydrometeorology 7, no. 3 (2006): 443–57. http://dx.doi.org/10.1175/jhm502.1.

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Abstract A season-long, point-scale radiometric data assimilation experiment is performed in order to test the feasibility of snow water equivalent (SWE) estimation. Synthetic passive microwave observations at Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) frequencies and synthetic broadband albedo observations are assimilated simultaneously in order to update snowpack states in a land surface model using the ensemble Kalman filter (EnKF). The effects of vegetation and atmosphere are included in the radiative transfer model (R

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Montaldo, Nicola, Andrea Gaspa, and Roberto Corona. "Multiscale Assimilation of Sentinel and Landsat Data for Soil Moisture and Leaf Area Index Predictions Using an Ensemble-Kalman-Filter-Based Assimilation Approach in a Heterogeneous Ecosystem." Remote Sensing 14, no. 14 (2022): 3458. http://dx.doi.org/10.3390/rs14143458.

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Data assimilation techniques allow researchers to optimally merge remote sensing observations in ecohydrological models, guiding them for improving land surface fluxes predictions. Presently, freely available remote sensing products, such as those of Sentinel 1 radar, Landsat 8 sensors, and Sentinel 2 sensors, allow the monitoring of land surface variables (e.g., radar backscatter for soil moisture and the normalized difference vegetation index (NDVI) and for leaf area index (LAI)) at unprecedentedly high spatial and time resolutions, appropriate for heterogeneous ecosystems, typical of semiar

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Campo, L., F. Castelli, D. Entekhabi, and F. Caparrini. "Land-atmosphere interactions in an high resolution atmospheric simulation coupled with a surface data assimilation scheme." Natural Hazards and Earth System Sciences 9, no. 5 (2009): 1613–24. http://dx.doi.org/10.5194/nhess-9-1613-2009.

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Abstract. A valid tool for the retrieving of the turbulent fluxes that characterize the surface energy budget is constituted by the remote sensing of land surface states. In this study sequences of satellite-derived observations (from SEVIRI sensors aboard the Meteosat Second Generation) of Land Surface Temperature have been used as input in a data assimilation scheme in order to retrieve parameters that describe energy balance at the ground surface in the Tuscany region, in central Italy, during summer 2005. A parsimonious 1-D multiscale variational assimilation procedure has been followed, t

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Toma, NS, DH Samuel, and A. Tena. "Assessment on irrigation system performance of sugarcane farm using remote sensing at lower Omo basin, Ethiopia." African Journal of Food, Agriculture, Nutrition and Development 22, no. 112 (2022): 20993–1018. http://dx.doi.org/10.18697/ajfand.112.21555.

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This study was aimed at assessing the irrigation system performance at Omo Kuraz Sugar Cane Development Project using data from remote sensing and meteorological stations. To analyze the distribution of evapotranspiration over the treatment area, the SEBAL (Surface Energy Balance Algorithm) model was used to evaluate the evapotranspiration (ET) rate for sugarcane at the lower Omo River Basin. Surface energy balance algorithm input like NDVI, Land surface temperature, TOA albedo and emissivity was calculated from Land Lat 8 image using the ENVI software. The data were collected from the farm si

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Hao, Dalei, Gautam Bisht, Karl Rittger, et al. "Evaluation of E3SM land model snow simulations over the western United States." Cryosphere 17, no. 2 (2023): 673–97. http://dx.doi.org/10.5194/tc-17-673-2023.

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Abstract. Seasonal snow has crucial impacts on climate, ecosystems, and humans, but it is vulnerable to global warming. The land component (ELM) of the Energy Exascale Earth System Model (E3SM) mechanistically simulates snow processes from accumulation, canopy interception, compaction, and snow aging to melt. Although high-quality field measurements, remote sensing snow products, and data assimilation products with high spatio-temporal resolution are available, there has been no systematic evaluation of the snow properties and phenology in ELM. This study comprehensively evaluates ELM snow sim

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Zhao, Haoteng, Liping Di, and Ziheng Sun. "WaterSmart-GIS: A Web Application of a Data Assimilation Model to Support Irrigation Research and Decision Making." ISPRS International Journal of Geo-Information 11, no. 5 (2022): 271. http://dx.doi.org/10.3390/ijgi11050271.

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Irrigation is the primary consumer of freshwater by humans and accounts for over 70% of all annual water use. However, due to the shortage of open critical information in agriculture such as soil, precipitation, and crop status, farmers heavily rely on empirical knowledge to schedule irrigation and tend to excessive irrigation to ensure crop yields. This paper presents WaterSmart-GIS, a web-based geographic information system (GIS), to collect and disseminate near-real-time information critical for irrigation scheduling, such as soil moisture, evapotranspiration, precipitation, and humidity, t

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Toride, Sawada, Aida, and Koike. "Toward High-Resolution Soil Moisture Monitoring by Combining Active-Passive Microwave and Optical Vegetation Remote Sensing Products with Land Surface Model." Sensors 19, no. 18 (2019): 3924. http://dx.doi.org/10.3390/s19183924.

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The assimilation of radiometer and synthetic aperture radar (SAR) data is a promising recent technique to downscale soil moisture products, yet it requires land surface parameters and meteorological forcing data at a high spatial resolution. In this study, we propose a new downscaling approach, named integrated passive and active downscaling (I-PAD), to achieve high spatial and temporal resolution soil moisture datasets over regions without detailed soil data. The Advanced Microwave Scanning Radiometer (AMSR-E) and Phased Array-type L-band SAR (PALSAR) data are combined through a dual-pass lan

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Lakshmi, Venkat, Seungbum Hong, Eric E. Small, and Fei Chen. "The influence of the land surface on hydrometeorology and ecology: new advances from modeling and satellite remote sensing." Hydrology Research 42, no. 2-3 (2011): 95–112. http://dx.doi.org/10.2166/nh.2011.071.

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The importance of land surface processes has long been recognized in hydrometeorology and ecology for they play a key role in climate and weather modeling. However, their quantification has been challenging due to the complex nature of the land surface amongst other reasons. One of the difficult parts in the quantification is the effect of vegetation that are related to land surface processes such as soil moisture variation and to atmospheric conditions such as radiation. This study addresses various relational investigations among vegetation properties such as Normalized Difference Vegetation

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Arsenault, Kristi R., Grey S. Nearing, Shugong Wang, Soni Yatheendradas, and Christa D. Peters-Lidard. "Parameter Sensitivity of the Noah-MP Land Surface Model with Dynamic Vegetation." Journal of Hydrometeorology 19, no. 5 (2018): 815–30. http://dx.doi.org/10.1175/jhm-d-17-0205.1.

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Abstract The Noah land surface model with multiple parameterization options (Noah-MP) includes a routine for the dynamic simulation of vegetation carbon assimilation and soil carbon decomposition processes. To use remote sensing observations of vegetation to constrain simulations from this model, it is necessary first to understand the sensitivity of the model to its parameters. This is required for efficient parameter estimation, which is both a valuable way to use observations and also a first or concurrent step in many state-updating data assimilation procedures. We use variance decompositi

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Knorr, Wolfgang, Matthew Williams, Tea Thum, et al. "A comprehensive land-surface vegetation model for multi-stream data assimilation, D&B v1.0." Geoscientific Model Development 18, no. 7 (2025): 2137–59. https://doi.org/10.5194/gmd-18-2137-2025.

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Abstract. Advances in Earth observation capabilities mean that there is now a multitude of spatially resolved data sets available that can support the quantification of water and carbon pools and fluxes at the land surface. However, such quantification ideally requires efficient synergistic exploitation of those data, which in turn requires carbon and water land-surface models with the capability to simultaneously assimilate several such data streams. The present article discusses the requirements for such a model and presents one such model based on the combination of the existing Data Assimi

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Zhou, Hongkui, Guangpo Geng, Jianhua Yang, Hao Hu, Li Sheng, and Weidong Lou. "Improving Soil Moisture Estimation via Assimilation of Remote Sensing Product into the DSSAT Crop Model and Its Effect on Agricultural Drought Monitoring." Remote Sensing 14, no. 13 (2022): 3187. http://dx.doi.org/10.3390/rs14133187.

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Accurate knowledge of soil moisture is crucial for agricultural drought monitoring. Data assimilation has proven to be a promising technique for improving soil moisture estimation, and various studies have been conducted on soil moisture data assimilation based on land surface models. However, crop growth models, which are ideal tools for agricultural simulation applications, are rarely used for soil moisture assimilation. Moreover, the role of data assimilation in agricultural drought monitoring is seldom investigated. In the present work, we assimilated the European Space Agency (ESA) Climat

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Fang, Bin, Venkat Lakshmi, Rajat Bindlish, and Thomas Jackson. "AMSR2 Soil Moisture Downscaling Using Temperature and Vegetation Data." Remote Sensing 10, no. 10 (2018): 1575. http://dx.doi.org/10.3390/rs10101575.

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Soil moisture (SM) applications in terrestrial hydrology require higher spatial resolution soil moisture products than those provided by passive microwave remote sensing instruments (grid resolution of 9 km or larger). In this investigation, an innovative algorithm that uses visible/infrared remote sensing observations to downscale Advanced Microwave Scanning Radiometer 2 (AMSR2) coarse spatial resolution SM products was developed and implemented for use with data provided by the Advanced Microwave Scanning Radiometer 2 (AMSR2). The method is based on using the Normalized Difference Vegetation

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I. Ali, S. R. Ahmad, W. K. Awan, and S. Rafiq. "CROP WATER CONSUMPTION MODELLING AT OUTLET LEVEL BY USING REMOTE SENSING AND GIS." Pakistan Journal of Science 75, no. 02 (2023): 373–97. http://dx.doi.org/10.57041/pjs.v75i02.875.

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The enlarged demand of water due to agriculture expansion, increasing industrial and domestic needs, compounded with climate change is causing scarcity of water globally and Pakistan is also facing same conditions. Due to limited availability of water, it is necessary to adopt global best practices being employed for sustainable agricultural water management. Remote sensing techniques are being intensively used around the globe with confidence to monitor the crops growth and crop water use. The aim of the research is to develop crop water consumption monitoring system at the outlet level to re

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Han, X., X. Li, H. J. Hendricks Franssen, H. Vereecken, and C. Montzka. "Spatial horizontal correlation characteristics in the land data assimilation of soil moisture." Hydrology and Earth System Sciences 16, no. 5 (2012): 1349–63. http://dx.doi.org/10.5194/hess-16-1349-2012.

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Abstract. Remote sensing images deliver important information about soil moisture, but often cover only part of an area, for example due to the presence of clouds or vegetation. This paper examines the potential of incorporating the spatial horizontal correlation characteristics of surface soil moisture observations in land data assimilation in order to obtain improved estimates of soil moisture at uncovered grid cells (i.e. grid cells without observations). Observing system simulation experiments were carried out to assimilate the synthetic surface soil moisture observations into the Communit

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Alonso-González, Esteban, Simon Gascoin, Sara Arioli, and Ghislain Picard. "Exploring the potential of thermal infrared remote sensing to improve a snowpack model through an observing system simulation experiment." Cryosphere 17, no. 8 (2023): 3329–42. http://dx.doi.org/10.5194/tc-17-3329-2023.

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Abstract. The assimilation of data from Earth observation satellites into numerical models is considered to be the path forward to estimate snow cover distribution in mountain catchments, providing accurate information on the mountainous snow water equivalent (SWE). The land surface temperature (LST) can be observed from space, but its potential to improve SWE simulations remains underexplored. This is likely due to the insufficient temporal or spatial resolution offered by the current thermal infrared (TIR) missions. However, three planned missions will provide global-scale TIR data at much h

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de Andrade, Bruno César Comini, Olavo Correa Pedrollo, Anderson Ruhoff, et al. "Artificial Neural Network Model of Soil Heat Flux over Multiple Land Covers in South America." Remote Sensing 13, no. 12 (2021): 2337. http://dx.doi.org/10.3390/rs13122337.

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Soil heat flux (G) is an important component for the closure of the surface energy balance (SEB) and the estimation of evapotranspiration (ET) by remote sensing algorithms. Over the last decades, efforts have been focused on parameterizing empirical models for G prediction, based on biophysical parameters estimated by remote sensing. However, due to the existing models’ empirical nature and the restricted conditions in which they were developed, using these models in large-scale applications may lead to significant errors. Thus, the objective of this study was to assess the ability of the arti

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Du, Baoyu, Kebiao Mao, Sayed M. Bateni, et al. "A Novel Fully Coupled Physical–Statistical–Deep Learning Method for Retrieving Near-Surface Air Temperature from Multisource Data." Remote Sensing 14, no. 22 (2022): 5812. http://dx.doi.org/10.3390/rs14225812.

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Retrieval of near-surface air temperature (NSAT) from remote sensing data is often ill-posed because of insufficient observational information. Many factors influence the NSAT, which can lead to the instability of the accuracy of traditional algorithms. To overcome this problem, in this study, a fully coupled framework was developed to robustly retrieve NSAT from thermal remote sensing data, integrating physical, statistical, and deep learning methods (PS-DL). Based on physical derivation, the optimal combinations of remote sensing bands were chosen for building the inversion equations to retr

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Kumar, Sujay V., David M. Mocko, Shugong Wang, Christa D. Peters-Lidard, and Jordan Borak. "Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental United States." Journal of Hydrometeorology 20, no. 7 (2019): 1359–77. http://dx.doi.org/10.1175/jhm-d-18-0237.1.

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Abstract Accurate representation of vegetation states is required for the modeling of terrestrial water–energy–carbon exchanges and the characterization of the impacts of natural and anthropogenic vegetation changes on the land surface. This study presents a comprehensive evaluation of the impact of assimilating remote sensing–based leaf area index (LAI) retrievals over the continental United States in the Noah-MP land surface model, during a time period of 2000–17. The results demonstrate that the assimilation has a beneficial impact on the simulation of key water budget terms, such as soil m

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Wei, Shiqi, Tianfang Xu, Guo-Yue Niu, and Ruijie Zeng. "Estimating Irrigation Water Consumption Using Machine Learning and Remote Sensing Data in Kansas High Plains." Remote Sensing 14, no. 13 (2022): 3004. http://dx.doi.org/10.3390/rs14133004.

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Groundwater-based irrigation has dramatically expanded over the past decades. It has important implications for terrestrial water, energy fluxes, and food production, as well as local to regional climates. However, irrigation water use is hard to monitor at large scales due to various constraints, including the high cost of metering equipment installation and maintenance, privacy issues, and the presence of illegal or unregistered wells. This study estimates irrigation water amounts using machine learning to integrate in situ pumping records, remote sensing products, and climate data in the Ka

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Wang, Zhengdong, Peng Guo, Hong Wan, Fuyou Tian, and Linjiang Wang. "Integration of Microwave and Optical/Infrared Derived Datasets from Multi-Satellite Products for Drought Monitoring." Water 12, no. 5 (2020): 1504. http://dx.doi.org/10.3390/w12051504.

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Drought is among the most common natural disasters in North China. In order to monitor the drought of the typically arid areas in North China, this study proposes an innovative multi-source remote sensing drought index called the improved Temperature–Vegetation–Soil Moisture Dryness Index (iTVMDI), which is based on passive microwave remote sensing data from the FengYun (FY)3B-Microwave Radiation Imager (MWRI) and optical and infrared data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and takes the Shandong Province of China as the research area. The iTVMDI integrated the adv

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Ferguson, Craig R., and Eric F. Wood. "An Evaluation of Satellite Remote Sensing Data Products for Land Surface Hydrology: Atmospheric Infrared Sounder*." Journal of Hydrometeorology 11, no. 6 (2010): 1234–62. http://dx.doi.org/10.1175/2010jhm1217.1.

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Abstract The skill of instantaneous Atmospheric Infrared Sounder (AIRS) retrieved near-surface meteorology, including surface skin temperature (Ts), air temperature (Ta), specific humidity (q), and relative humidity (RH), as well as model-derived surface pressure (Psurf) and 10-m wind speed (w), is evaluated using collocated National Climatic Data Center (NCDC) in situ observations, offline data from the North American Land Data Assimilation System (NLDAS), and geostationary remote sensing (RS) data from the Spinning Enhanced Visible and Infrared Imager (SEVIRI). Such data are needed for RS-ba

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Saeid, Ahmed Ayad Alfaytouri. "Remote Sensing in Water Quality and Water Resources Management." International Journal for Research in Applied Sciences and Biotechnology 9, no. 1 (2022): 163–70. http://dx.doi.org/10.31033/ijrasb.9.1.19.

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The quality of water ascertains the ‘integrity’ of water for specific purposes. Tests and quality of examination of water can provide sufficient information about the waterway health. If tests are conducted over a span of time period, the water quality changes can be realized. There are several testing parameters like pH value, temperature, salinity, turbidity, phosphates and nitrates, which can help assess the water quality. Also, aquatic macro-invertebrates can give a proper water quality indication. Surface water contaminated can pose a high risk to the entire human population and it

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Jiang, Dejuan, and Kun Wang. "The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review." Water 11, no. 8 (2019): 1615. http://dx.doi.org/10.3390/w11081615.

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A hydrological model is a useful tool to study the effects of human activities and climate change on hydrology. Accordingly, the performance of hydrological modeling is vitally significant for hydrologic predictions. In watersheds with intense human activities, there are difficulties and uncertainties in model calibration and simulation. Alternative approaches, such as machine learning techniques and coupled models, can be used for streamflow predictions. However, these models also suffer from their respective limitations, especially when data are unavailable. Satellite-based remote sensing ma

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Li, Wenzhao, Hesham El-Askary, Rejoice Thomas, et al. "An Assessment of the Hydrological Trends Using Synergistic Approaches of Remote Sensing and Model Evaluations over Global Arid and Semi-Arid Regions." Remote Sensing 12, no. 23 (2020): 3973. http://dx.doi.org/10.3390/rs12233973.

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Drylands cover about 40% of the world’s land area and support two billion people, most of them living in developing countries that are at risk due to land degradation. Over the last few decades, there has been warming, with an escalation of drought and rapid population growth. This will further intensify the risk of desertification, which will seriously affect the local ecological environment, food security and people’s lives. The goal of this research is to analyze the hydrological and land cover characteristics and variability over global arid and semi-arid regions over the last decade (2010

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Marshall, M., K. Tu, C. Funk, et al. "Improving operational land surface model canopy evapotranspiration in Africa using a direct remote sensing approach." Hydrology and Earth System Sciences 17, no. 3 (2013): 1079–91. http://dx.doi.org/10.5194/hess-17-1079-2013.

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Abstract. Climate change is expected to have the greatest impact on the world's economically poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled evapotranspiration (ET), a key input in continental-scale hydrologic models. In this study, a remote sensing model of transpiration (the primary component of ET), driven by a time seri

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Pan, Ming, and Eric F. Wood. "A Multiscale Ensemble Filtering System for Hydrologic Data Assimilation. Part II: Application to Land Surface Modeling with Satellite Rainfall Forcing." Journal of Hydrometeorology 10, no. 6 (2009): 1493–506. http://dx.doi.org/10.1175/2009jhm1155.1.

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Abstract Part I of this series of studies developed procedures to implement the multiscale filtering algorithm for land surface hydrology and performed assimilation experiments with rainfall ensembles from a climate model. However, a most important application of the multiscale technique is to assimilate satellite-based remote sensing observations into a land surface model—and this has not been realized. This paper focuses on enabling the multiscale assimilation system to use remotely sensed precipitation data. The major challenge is the generation of a rainfall ensemble given one satellite ra

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van Dijk, A. I. J. M., and L. J. Renzullo. "Water resource monitoring systems and the role of satellite observations." Hydrology and Earth System Sciences Discussions 7, no. 4 (2010): 6305–49. http://dx.doi.org/10.5194/hessd-7-6305-2010.

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Abstract. Spatial water resource monitoring systems (SWRMS) can provide valuable information in support of water management, but current operational systems are few and provide only a subset of the information required. Necessary innovations include the explicit description of water redistribution and water use from river and groundwater systems, achieving greater spatial detail (particularly in key features such as irrigated areas and wetlands), and improving accuracy as assessed against hydrometric observations, as well as assimilating those observations. The Australian water resources asses

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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 (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 sate

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Wang, Jun, Heping Li, and Haiyuan Lu. "An estimation of the evapotranspiration of typical steppe areas using Landsat images and the METRIC model." Journal of Water and Climate Change 13, no. 2 (2021): 926–42. http://dx.doi.org/10.2166/wcc.2021.432.

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Abstract Remote sensing excels in estimating regional evapotranspiration (ET). However, most remote sensing energy balance models require researchers to subjectively extract the characteristic parameters of the dry and wet limits of the underlying surfaces. The regional ET accuracy is affected by wrong determined ideal pixels. This study used Landsat images and the METRIC model to evaluate the effects of different dry and wet pixel combinations on the ET in the typical steppe areas. The ET spatiotemporal changes of the different land cover types were discussed. The results show that the surfac

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