Machine Learning Modelling for Soil Moisture Retrieval from Simulated NASA-ISRO SAR (NISAR) L-Band Data

• Published in: Remote sensing (Basel, Switzerland) Switzerland), 2024-09, Vol.16 (18), p.3539
• Main Authors: Dinesh, Dev, Kumar, Shashi, Saran, Sameer
• Format: Article
• Language: English
• Subjects: Accuracy; Agricultural land; Algorithms; Analysis; Cereal crops; Crop fields; Crop yield; Data analysis; Data mining; Datasets; Decision trees; Decomposition; Dielectric constant; Dielectric properties; Electric properties; Environmental monitoring; Information management; Learning algorithms; Machine learning; Neural networks; NISAR; Plant growth; polarimetric decomposition; Radar; Radar imaging; Radar systems; Radiation; Radiometers; random forest; Regression; Regression analysis; Remote monitoring; Remote sensing; Retrieval; Sensors; Soil analysis; soil dielectric constant; Soil improvement; Soil mapping; Soil moisture; Soil water; Soybeans; Stochasticity; Synthetic aperture radar; Vegetation; Vegetation effects; Wheat
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16183539

Soil moisture is a critical factor that supports plant growth, improves crop yields, and reduces erosion. Therefore, obtaining accurate and timely information about soil moisture across large regions is crucial. Remote sensing techniques, such as microwave remote sensing, have emerged as powerful tools for monitoring and mapping soil moisture. Synthetic aperture radar (SAR) is beneficial for estimating soil moisture at both global and local levels. This study aimed to assess soil moisture and dielectric constant retrieval over agricultural land using machine learning (ML) algorithms and decomposition techniques. Three polarimetric decomposition models were used to extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Machine learning techniques such as random forest regression, decision tree regression, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regression, neural network regression, and multilinear regression were used to retrieve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most precise soil moisture estimations for soybean fields, with an R2 of 0.89 and RMSE of 0.050 without considering vegetation effects and an R2 of 0.92 and RMSE of 0.042 considering vegetation effects. The results for real dielectric constant retrieval for the soybean field were an R2 of 0.89 and RMSE of 6.79 without considering vegetation effects and an R2 of 0.89 and RMSE of 6.78 with considering vegetation effects. These findings suggest that machine learning algorithms and decomposition techniques, along with a semi-empirical technique like Water Cloud Model (WCM), can be effective tools for estimating soil moisture and dielectric constant values precisely. The methodology applied in the current research contributes essential insights that could benefit upcoming missions, such as the Radar Observing System for Europe in L-band (ROSE-L) and the collaborative NASA-ISRO SAR (NISAR) mission, for future data analysis in soil moisture applications.