干旱区生态环境知识资源中心(Arid): Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach

Arid

DOI	10.3390/rs13163181
	Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach
	Amazirh, Abdelhakim; Bouras, El Houssaine; Olivera-Guerra, Luis Enrique; Er-Raki, Salah; Chehbouni, Abdelghani
通讯作者	Amazirh, A (corresponding author), Univ Mohammed VI Polytechn UM6P, Ctr Remote Sensing Applicat CRSA, Benguerir 43150, Morocco.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2021
卷号	13 期号:16
英文摘要	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.
英文关键词	surface albedo random forest Sentinel-1 crop vegetation Landsat
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000689961400001
WOS关键词	LAND-SURFACE ALBEDO ; IN-SITU MEASUREMENTS ; LEAF-AREA INDEX ; SOIL-MOISTURE ; WATER-CONTENT ; REFLECTANCE ; EVAPOTRANSPIRATION ; TEMPERATURE ; SATELLITE ; WHEAT
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/364473
作者单位	[Amazirh, Abdelhakim; Er-Raki, Salah; Chehbouni, Abdelghani] Univ Mohammed VI Polytechn UM6P, Ctr Remote Sensing Applicat CRSA, Benguerir 43150, Morocco; [Bouras, El Houssaine; Er-Raki, Salah] Cadi Ayyad Univ, Fac Sci & Techn, ProcEDE, Marrakech 40000, Morocco; [Bouras, El Houssaine; Olivera-Guerra, Luis Enrique; Chehbouni, Abdelghani] Univ Toulouse, Ctr Etudes Spati BIOsphere CESBIO, CNES CNRS INRA IRD UPS, F-31013 Toulouse, France
推荐引用方式 GB/T 7714	Amazirh, Abdelhakim,Bouras, El Houssaine,Olivera-Guerra, Luis Enrique,et al. Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach[J],2021,13(16).
APA	Amazirh, Abdelhakim,Bouras, El Houssaine,Olivera-Guerra, Luis Enrique,Er-Raki, Salah,&Chehbouni, Abdelghani.(2021).Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach.REMOTE SENSING,13(16).
MLA	Amazirh, Abdelhakim,et al."Retrieving Crop Albedo Based on Radar Sentinel-1 and Random Forest Approach".REMOTE SENSING 13.16(2021).