Soil Moisture and Irrigation Mapping in A Semi-Arid Region, Based on the Synergetic Use of Sentinel-1 and Sentinel-2 Data

Safa Bousbih,Zohra Lili-Chabaane,Mohammad El Hajj,Qi Gao,Nicolas Baghdadi,Pascal Fanise,Mehrez Zribi

doi:10.3390/rs10121953

Abstract

This paper presents a technique for the mapping of soil moisture and irrigation, at the scale of agricultural fields, based on the synergistic interpretation of multi-temporal optical and Synthetic Aperture Radar (SAR) data (Sentinel-2 and Sentinel-1). The Kairouan plain, a semi-arid region in central Tunisia (North Africa), was selected as a test area for this study. Firstly, an algorithm for the direct inversion of the Water Cloud Model (WCM) was developed for the spatialization of the soil water content between 2015 and 2017. The soil moisture retrieved from these observations was first validated using ground measurements, recorded over 20 reference fields of cereal crops. A second method, based on the use of neural networks, was also used to confirm the initial validation. The results reported here show that the soil moisture products retrieved from remotely sensed data are accurate, with a Root Mean Square Error (RMSE) of less than 5% between the two moisture products. In addition, the analysis of soil moisture and Normalized Difference Vegetation Index (NDVI) products over cultivated fields, as a function of time, led to the classification of irrigated and rainfed areas on the Kairouan plain, and to the production of irrigation maps at the scale of individual fields. This classification is based on a decision tree approach, using a combination of various statistical indices of soil moisture and NDVI time series. The resulting irrigation maps were validated using reference fields within the study site. The best results were obtained with classifications based on soil moisture indices only, with an accuracy of 77%.

Highlights

With the world population being projected to reach 9.1 billion by 2050, agricultural production will need to increase substantially, to meet the surging demand for food [1,2]
The aim of the present study is to propose an accurate technique for the mapping of soil water content, through the inversion of Sentinel-1 (S-1) and Sentinel-2 (S-2) data, combined with the use of the Water Cloud Model
These need to be observed at a high spatial resolution, in order to facilitate the detection of irrigation events in individual fields

Summary

Introduction

With the world population being projected to reach 9.1 billion by 2050, agricultural production will need to increase substantially, to meet the surging demand for food [1,2]. In this context, increased production must be accompanied by strategies for improved irrigation efficiency and the development of more sustainable agricultural techniques. It has been shown that the irrigation process requires a high level of precision, in order to optimize water input and crop response, while minimizing its potentially adverse environmental impacts [6]. An accurate knowledge of spatio-temporal variations in the soil’s water content is crucial, when it comes to assessing the optimal volume of water to be delivered to the crops, at specific times throughout the year

Objectives

Methods

Findings

Discussion

Conclusion