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

Saman Rabiei, Ehsan Jalilvand and Massoud Tajrishy
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Saman Rabiei: Department of Civil and Environment Engineering, Sharif University of Technology, Tehran 11155-8639, Iran
Ehsan Jalilvand: Department of Civil and Environment Engineering, Sharif University of Technology, Tehran 11155-8639, Iran
Massoud Tajrishy: Department of Civil and Environment Engineering, Sharif University of Technology, Tehran 11155-8639, Iran

Sustainability, 2021, vol. 13, issue 20, 1-17

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 R M S E = 0.0292 ( cm 3 / cm 3 ) and R 2 = 0.92 for the Sentinel-2 derived SSM and R M S E = 0.0317 ( cm 3 / cm 3 ) and R 2 = 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 ( p v a l u e = 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%.

Keywords: soil moisture; Sentinel-1; Sentinel-2; irrigation mapping; change detection; supervised learning; machine learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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