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

Prashant K Srivastava,Dimitris Triantakonstantis,Rajendra Prasad,George P Petropoulos

doi:10.3390/ijgi10080507

Abstract

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.

Highlights

IntroductionPublisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
The results indicate that the Soil Moisture and Ocean Salinity (SMOS) soil moisture has proven its importance in hydrological sciences and can be used for the prediction of Soil Moisture Deficit (SMD)
The findings of this research show that satellite soil moisture, such as from SMOS, can be used for SMD estimation using sophisticated techniques, such as Random Forests (RF) and Genetic Algorithms coupled with Least Trimmed Squares (GALTS)

Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Soil moisture is of key importance in numerous fields from weather, climate, agricultural to hydrological sciences [1]. It is considered as an Essential Climatic Variable (ECV) in 2010 [2,3,4,5]. The estimation of soil moisture from the Earth’s surface is important for a successful management of soil water content and irrigation scheduling. Soil moisture can be assessed by utilizing the in-situ probes, an approach which is adequate with the local or point based applications

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