Spatial-temporal modeling of soil moisture using optical and thermal remote sensing data and machine learning algorithms

Document Type : Research Paper

Authors

1 Department of Irrigation and Drainage, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran

2 Centre Eau Terre Environnement, Institut National de la Recherche Scientifique (INRS), Quebec, Canada

Abstract

 
Spatiotemporal estimation and monitoring of soil moisture based on remote sensing observations (optical and thermal) is challenging due to its physical nature in high vegetation conditions, necessitating improving and increasing the accuracy of soil moisture estimation in these areas. Therefore, this research aimed to develop a new approach to estimating surface soil moisture in agricultural fields with dense vegetation using machine learning algorithms by incorporating optical and thermal remote sensing data and soil physical properties. For this objective, 16 Landsat-8 satellite images and more than 430 control locations were used during the sugarcane crop’s growth period in 2018-2019 at the Hakim Farabi Sugarcane Agro-Industrial company in the Khuzestan province of Iran. A set of 10 scenarios of various unique combinations of the available input variables were developed and then evaluated by five machine learning algorithms, including multiple linear regression (MLR), decision tree-based algorithms (CART and M5P), and ensemble learning-based algorithms (gradient-boosted regression trees (GBRT) and random forest regression (RFR)). According to the results, the highest correlation between input variables and surface soil moisture was observed in Soil Wetness Index (SWI) and Normalized Soil Moisture Index (NSMI) with R values of 0.79 and 0.69, respectively. Also, the highest accuracy of machine learning algorithms based on R2, RMSE, and MAE results was obtained in GBRT (0.99, 0.011, and 0.006) and RFR (0.99, 0.014, and 0.007), respectively. In general, the findings of this research show the importance of using variables based on Landsat-8 remote sensing data in combination with ensemble learning algorithms that can be independent of any ground measurements.

Keywords

Main Subjects

EXTENDED ABSTRACT

Introduction:

Spatiotemporal estimation and monitoring of soil moisture based on remote sensing observations are essential for managing water resources, improving agricultural land productivity, increasing water use efficiency, and assessing crop drought conditions. In this regard, methods based on optical and thermal remote sensing data have successfully estimated surface soil moisture at different scales. However, the physical nature of these data has limited and challenged their application in dense vegetation conditions, necessitating improving and increasing the estimation accuracy in these areas.

 

Objective:

This research aims to develop a new approach to estimating surface soil moisture in agricultural fields with dense vegetation conditions, such as sugarcane fields, using machine learning algorithms by incorporating optical and thermal remote sensing data and soil physical properties.

 

Materials and methods:

This study used 16 Landsat-8 images during the sugarcane crop’s growth period in 2018-2019 at Hakim Farabi Sugarcane Agro-Industrial company in the Khuzestan province of Iran. Soil moisture measurements were collected simultaneously as the satellite passed through the study area at more than 430 control locations during the period. A set of 10 scenarios of various unique combinations of the available input variables were developed. Five popular machine learning algorithms evaluated the scenarios, including multiple linear regression (MLR), decision tree-based algorithms (Classification and Regression Trees (CART) and M5-pruned (M5P)), and ensemble learning-based algorithms (gradient-boosted regression trees (GBRT) and random forest regression (RFR)).

 

Results and discussion:

According to the results, the highest correlation between input variables and surface soil moisture was observed in Soil Wetness Index (SWI) and Normalized Soil Moisture Index (NSMI) with R values of 0.79 and 0.69, respectively. While the NIR band with an R-value of 0.56 showed the lowest correlation. The obtained results showed the high ability of machine learning algorithms to estimate surface soil moisture in the area. The highest accuracy of machine learning algorithms based on R2, RMSE, and MAE results was obtained in GBRT (0.99, 0.011, and 0.006) in scenario 9, RFR (0.99, 0.014, and 0.007) in scenario 9, M5P (0.90, 0.054, and 0.042) in scenario 9, CART (0.87, 0.058, and 0.046) and MLR (0.70, 0.07, and 0.056) in scenario 6, respectively. The importance of incorporating soil physical properties, especially clay percentage, with remote sensing data was observed only in the MLR algorithm. While in CART, M5P, GBRT, and RFR algorithms, the use of soil physical properties in combination with optical bands and different vegetation, humidity, and temperature indices did not lead to proper surface soil moisture predictions.

 

Conclusion:

In general, the findings of this research show the importance of using variables based on Landsat-8 remote sensing data (NTR, NSMI, NDVI, SWIR2, NIR, LST, and SWI) in combination with ensemble learning algorithms (RFR and GBRT) that can be independent of any ground measurements. The proposed method provides valuable results for estimating and monitoring surface soil moisture in high-vegetation areas.

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Iranian Journal of Soil and Water Research
Volume 54, Issue 4
July 2023
Pages 637-653

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