Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions

Amazirh, Abdelhakim; Er-Raki, Salah; Ojha, Nitu; Bouras, Elhoussaine; Rivalland, Vincent; Merlin, Olivier; Chehbouni, A.

doi:10.1016/j.agwat.2021.107290

Cited by 15 publications

(3 citation statements)

References 82 publications

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“…However, uncertainties related to LAI estimation from Sentinel-2 data necessitate further examination, and could be improved using machine learning algorithms [79] or by combining Sentinel-1 and 2 data [80,81], which could provide a continuous estimate of LAI during the growth cycle regardless of weather conditions. In addition, in order to further improve crop yield estimation at high spatial resolution, it might be useful to assimilate other environmental variables derived from satellite observations; for example, soil moisture from Sentinel-1 data [82], and land surface temperature [83], which may improve the quantification of water stress. Since this study only encompasses field data collected during one growing season, the current model parameterizations and validation do not represent the anticipated range of environmental variability.…”

Section: Discussionmentioning

confidence: 99%

Wheat Yield Estimation at High Spatial Resolution through the Assimilation of Sentinel-2 Data into a Crop Growth Model

Bouras,

Olsson,

Thapa

et al. 2023

Remote Sensing

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Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m × 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R2) and a root mean square error (RMSE) of 0.80 and 0.65 m2/m2, respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production.

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Section: Discussionmentioning

confidence: 99%

Wheat Yield Estimation at High Spatial Resolution through the Assimilation of Sentinel-2 Data into a Crop Growth Model

Bouras,

Olsson,

Thapa

et al. 2023

Remote Sensing

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“…With the development of thermal infrared remote sensing technology, Land Surface Temperature (LST) can be derived from satellite information or direct measurements. LST variations related to heat sources can be easily measured and mapped using thermal infrared (TIR) radiation sensors through the radiative transfer equation [3][4][5][6] . As the transport channel of high temperature heat fluid, fault structure is also one of the symbols of geothermal resources exploration, and there is a close relationship between it and the spatial distribution of geothermal resources.…”

Section: Introductionmentioning

confidence: 99%

Spatial relationship between land surface temperature (LST) and main faults in Nimu, Tibet

Bian,

Guo,

Chen

et al. 2024

Fifth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2023)

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“…It is thus often necessary to calibrate the model input parameters using external data. Such a calibration strategy can be implemented at the field scale using in-situ measurements (Kharrou et al, 2021;Saadi et al, 2015;Paredes et al, 2014;Er-Raki et al, 2007;Zhang et al, 2013), or over extended areas using remotely sensed soil moisture or ET data (Amazirh et al, 2022;Ouaadi et al, 2021;Er-Raki et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

A Sensitivity Analysis of a Fao-56 Dual Crop Coefficient-Based Model Under Various Field Conditions

Laluet¹,

Olivera-Guerra²,

Rivalland³

et al. 2022

SSRN Journal

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Assimilation of SMAP disaggregated soil moisture and Landsat land surface temperature to improve FAO-56 estimates of ET in semi-arid regions

Cited by 15 publications

References 82 publications

Wheat Yield Estimation at High Spatial Resolution through the Assimilation of Sentinel-2 Data into a Crop Growth Model

Wheat Yield Estimation at High Spatial Resolution through the Assimilation of Sentinel-2 Data into a Crop Growth Model

Spatial relationship between land surface temperature (LST) and main faults in Nimu, Tibet

A Sensitivity Analysis of a Fao-56 Dual Crop Coefficient-Based Model Under Various Field Conditions

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