NDLI: Random Forests with Bagging and Genetic Algorithms Coupled with Least Trimmed Squares Regression for Soil Moisture Deficit Using SMOS Satellite Soil Moisture

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Random Forests with Bagging and Genetic Algorithms Coupled with Least Trimmed Squares Regression for Soil Moisture Deficit Using SMOS Satellite Soil Moisture

Content Provider	MDPI
Author	Srivastava, Prashant Petropoulos, George Prasad, Rajendra Triantakonstantis, Dimitris
Copyright Year	2021
Description	Soil Moisture Deficit (SMD) is a key indicator of soil water content changes and is valuable to a variety of applications, such as weather and climate, natural disasters, agricultural water management, etc. Soil Moisture and Ocean Salinity (SMOS) is a dedicated mission focused on soil moisture retrieval and can be utilized for SMD estimation. In this study, the use of soil moisture derived from SMOS has been provided for the estimation of SMD at a catchment scale. Several approaches for the estimation of SMD are implemented herein, using algorithms such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS) regression. The results show that for SMD estimation, the RF algorithm performed best as compared to the GALTS, with Root Mean Square Errors (RMSEs) of 0.021 and 0.024, respectively. All in all, our study findings can provide important assistance towards developing the accuracy and applicability of remote sensing-based products for operational use.
Starting Page	507
e-ISSN	22209964
DOI	10.3390/ijgi10080507
Journal	ISPRS International Journal of Geo-Information
Issue Number	8
Volume Number	10
Language	English
Publisher	MDPI
Publisher Date	2021-07-27
Access Restriction	Open
Subject Keyword	ISPRS International Journal of Geo-Information Isprs International Journal of Geo-information Remote Sensing Smos Soil Moisture Deficit Rainfall-runoff Model Random Forest Genetic Algorithm
Content Type	Text
Resource Type	Article

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