A Method to Estimate Surface Soil Moisture and Map the Irrigated Cropland Area Using Sentinel-1 and Sentinel-2 Data

Massoud Tajrishy,Saman Rabiei,Ehsan Jalilvand

doi:10.3390/su132011355

Massoud Tajrishy, Saman Rabiei + Show 1 more

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https://doi.org/10.3390/su132011355

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Journal: Sustainability	Publication Date: Oct 14, 2021
Citations: 9	License type: CC BY 4.0

Affiliation: Sharif University of Technology

Abstract

Considering variations in surface soil moisture (SSM) is essential in improving crop yield and irrigation scheduling. Today, most remotely sensed soil moisture products have difficulties in resolving irrigation signals at the plot scale. This study aims to use Sentinel-1 radar backscatter and Sentinel-2 multispectral imagery to estimate SSM at high spatial (10 m) and temporal resolution (at least 5 days) over an agricultural domain. Three supervised machine learning algorithms, multilayer perceptron (MLP), a convolutional neural network (CNN), and linear regression models, were trained to estimate changes in SSM based on the variation in surface reflectance and backscatter over five different crops. Results showed that CNN is the best algorithm as it understands spatial relations and better represents two-dimensional images. Estimated values for SSM were in agreement with in-situ measurements regardless of the crop type, with RMSE=0.0292 (cm3/cm3) and R2=0.92 for the Sentinel-2 derived SSM and RMSE=0.0317 (cm3/cm3) and R2=0.84 for the Sentinel-1 soil moisture data. Moreover, a time series of estimated SSM based on Sentinel-1 (SSM-S1), Sentinel-2 (SSM-S2), and SSM derived from SMAP-Sentinel1 was compared. The developed SSM data showed a significantly higher mean SSM state over irrigated agriculture relative to the rainfed cropland area during the irrigation season. The multiple comparisons (fisher LSD) were tested and found that these two groups are different (pvalue=0.035 in 95% confidence interval). Therefore, by employing the maximum likelihood classification on the SSM data, we managed to map the irrigated agriculture. The overall accuracy of this unsupervised classification is 77%, with a kappa coefficient of 65%.

Highlights

Published: 14 October 2021Soil moisture content (SMC) is an essential factor in exchanging water, biogeochemical, and heat fluxes between the earth and its surrounding atmosphere [1–3]
The main objectives of the present study are as follows: (i) producing a soil moisture map based on Sentinel-2 images acquired over the five separate plantation zones; (ii) producing a soil moisture map based on Sentinel-1 images in the same study area; (iii) producing and comparing the generated soil map with ground measurements and soil moisture at active passive (SMAP)-Sentinel1 1 km soil moisture product; and (iv) mapping the irrigated agriculture using the derived surface soil moisture (SSM) data
−0.76).,previous previousstudies studiesshowed showed that the longer wavelengths are more sensitive to the variation if it increases that the longer wavelengths are more sensitive to the SSM variation if it increases more more than cm 3 [29]

Summary

Introduction

Published: 14 October 2021Soil moisture content (SMC) is an essential factor in exchanging water, biogeochemical, and heat fluxes between the earth and its surrounding atmosphere [1–3]. The generation of surface runoff and the rate of water infiltration in the soil are controlled by surface moisture [4]. The SMC variation is vital for precision farming applications since soil water storage influences the irrigation scheduling and fertilizer rate in low rainfall climates [5,6], especially for areas facing water scarcity [7–9]. Before the advent of remote sensing techniques, ground-based soil moisture sampling was the only solution to measure soil moisture and observe its changes. The soil moisture networks were established for measuring the SSM [10]. Many soil moisture networks exist to measure soil moisture at different depths, such as the TERENO [11], OzNet [12], COSMOS-UK [13], and ISMN, they are still not appropriate for monitoring SMC at the catchment or agricultural field scale due to SMC spatial and temporal variations [14]. It allows us to explore a larger area in short time intervals at a low cost, mainly due to recent advancements in sensors functionally [16]

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