%0 Journal Article
%9 ACL : Articles dans des revues avec comité de lecture répertoriées par l'AERES
%A Amazirh, A.
%A Bouras, E.
%A Olivera-Guerra, L. E.
%A Er-Raki, S.
%A Chehbouni, Abdelghani
%T Retrieving crop albedo based on radar Sentinel-1 and random forest approach
%D 2021
%L fdi:010082761
%G ENG
%J Remote Sensing
%K surface albedo ; random forest ; Sentinel-1 ; crop vegetation ; Landsat
%K MAROC ; ZONE SEMIARIDE
%M ISI:000689961400001
%N 16
%P 3181 [17 ]
%R 10.3390/rs13163181
%U https://www.documentation.ird.fr/hor/fdi:010082761
%> https://horizon.documentation.ird.fr/exl-doc/pleins_textes/2021-10/010082761.pdf
%V 13
%W Horizon (IRD)
%X Monitoring agricultural crops is of paramount importance for preserving water resources and increasing water efficiency over semi-arid areas. This can be achieved by modelling the water resources all along the growing season through the coupled water-surface energy balance. Surface albedo is a key land surface variable to constrain the surface radiation budget and hence the coupled water-surface energy balance. In order to capture the hydric status changes over the growing season, optical remote sensing becomes impractical due to cloud cover in some periods, especially over irrigated winter crops in semi-arid regions. To fill the gap, this paper aims to generate cloudless surface albedo product from Sentinel-1 data that offers a source of high spatio-temporal resolution images. This can help to better capture the vegetation development along the growth season through the surface radiation budget. Random Forest (RF) algorithm was implemented using Sentinel-1 backscatters as input. The approach was tested over an irrigated semi-arid zone in Morocco, which is known by its heterogeneity in term of soil conditions and crop types. The obtained results are evaluated against Landsat-derived albedo with quasi-concurrent Landsat/Sentinel-1 overpasses (up to one day offset), while a further validation was investigated using in situ field scale albedo data. The best model-hyperparameters selection was dependent on two validation approaches (K-fold cross-validation 'k = 10', and holdout). The more robust and accurate model parameters are those that represent the best statistical metrics (root mean square error 'RMSE', bias and correlation coefficient 'R'). Coefficient values ranging from 0.70 to 0.79 and a RMSE value between 0.0002 and 0.00048 were obtained comparing Landsat and predicted albedo by RF method. The relative error ratio equals 4.5, which is acceptable to predict surface albedo.
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