Fan L., Al-Yaari Amen, Frappart F., Peng J., Wen J. G., Xiao Q., Jin R., Li X. J., Liu X. Z., Wang M. J., Chen X. Z., Zhao L., Ma M. G., Wigneron J. P. (2022). Estimating high-resolution soil moisture over mountainous regions using remotely-sensed multispectral and topographic data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, p. 3637-3649. ISSN 1939-1404.
Titre du document
Estimating high-resolution soil moisture over mountainous regions using remotely-sensed multispectral and topographic data
Année de publication
2022
Auteurs
Fan L., Al-Yaari Amen, Frappart F., Peng J., Wen J. G., Xiao Q., Jin R., Li X. J., Liu X. Z., Wang M. J., Chen X. Z., Zhao L., Ma M. G., Wigneron J. P.
Source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022,
15, p. 3637-3649 ISSN 1939-1404
A surface soil moisture (SM) condition at high spatiotemportal resolutions is required by regional Earth system applications. Here, we mapped daily 1-km SM in the Babao River Basin in the northwest of China during the summers from 2013 to 2015 using a random forest (RF) method by merging SM information retrieved from in situ measurements, optical/thermal remote sensing, and topographical indices. Relative importance analysis was used to determine the optimal predictors for estimating high-resolution SM. A specific RF model (RFVI+sup) was constructed using the optimal predictors including remote sensing albedo, apparent thermal inertia (ATI), normalized difference vegetation index, normalized difference infrared index 5, soil adjusted vegetation index, and topographical indices (aspect and elevation). The RFVI+sup also accounted for missing observations of the thermal index (e.g., ATI) over the mountainous regions. In the comparison between the SM estimates using the new RFVI+sup model and other RF models, the spatial coverage of available estimates increased from 14% to 64% over the study region, the correlation coefficient values were improved to 0.75, the unbiased root-mean-squared difference values decreased to 0.032 m(3)/m(3). Thus, the proposed RF method provided accurate SM estimates with high spatiotemporal resolution over the mountainous regions, by merging multiresource datasets from in situ measurements, remotely-sensed, and topographical indices.
Plan de classement
Sciences du milieu [021]
;
Bioclimatologie [072]
;
Etudes, transformation, conservation du milieu naturel [082]
;
Télédétection [126]
Description Géographique
CHINE
Localisation
Fonds IRD [F B010085177]
Identifiant IRD
fdi:010085177
