Retrieving crop albedo based on radar sentinel-1 and random forest approach
Amazirh, Abdelhakim ; Bouras, El Houssaine LU
; Olivera-Guerra, Luis Enrique
; Er-Raki, Salah
and Chehbouni, Abdelghani
(2021)
In Remote Sensing
13(16).
- Abstract
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... (More)
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.
(Less)
- author
- Amazirh, Abdelhakim
; Bouras, El Houssaine
LU
; Olivera-Guerra, Luis Enrique
; Er-Raki, Salah
and Chehbouni, Abdelghani
- publishing date
- 2021-08-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Crop vegetation, Landsat, Random forest, Sentinel-1, Surface albedo
- in
- Remote Sensing
- volume
- 13
- issue
- 16
- article number
- 3181
- publisher
- MDPI AG
- external identifiers
-
- scopus:85112521950
- ISSN
- 2072-4292
- DOI
- 10.3390/rs13163181
- language
- English
- LU publication?
- no
- additional info
- Funding Information: Funding: This research was carried out within the Center for Remote Sensing Applications, at the Mohammed VI Polytechnic University Morocco. This work was funded by OCP (AS No 71) and by the European Commission Horizon 2020 Programme for Research and Innovation (H2020) in the context of the Marie Sklodowska-Curie Research and Innovation Staff Exchange (RISE) action (REC project, grant agreement no: 645642), followed by ACCWA project, grant agreement no. 823965). Publisher Copyright: © 2021 by the authors.
- id
- fb2f925f-cf92-44bb-a02f-d19ec5f9c5e5
- date added to LUP
- 2023-01-04 09:47:14
- date last changed
- 2023-01-20 17:01:40
@article{fb2f925f-cf92-44bb-a02f-d19ec5f9c5e5,
abstract = {{<p>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.</p>}},
author = {{Amazirh, Abdelhakim and Bouras, El Houssaine and Olivera-Guerra, Luis Enrique and Er-Raki, Salah and Chehbouni, Abdelghani}},
issn = {{2072-4292}},
keywords = {{Crop vegetation; Landsat; Random forest; Sentinel-1; Surface albedo}},
language = {{eng}},
month = {{08}},
number = {{16}},
publisher = {{MDPI AG}},
series = {{Remote Sensing}},
title = {{Retrieving crop albedo based on radar sentinel-1 and random forest approach}},
url = {{http://dx.doi.org/10.3390/rs13163181}},
doi = {{10.3390/rs13163181}},
volume = {{13}},
year = {{2021}},
}