首页|Researchers at Indian Institute of Remote Sensing Publish New Data on Machine Le arning [Machine Learning Modelling for Soil Moisture Retrieva l from Simulated NASA-ISRO SAR (NISAR) LBand Data]
Researchers at Indian Institute of Remote Sensing Publish New Data on Machine Le arning [Machine Learning Modelling for Soil Moisture Retrieva l from Simulated NASA-ISRO SAR (NISAR) LBand Data]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Researchers detail new data in artific ial intelligence. According to news reporting out of Dehradun, India, by NewsRx editors, research stated, “Soil moisture is a critical factor that supports plan t growth, improves crop yields, and reduces erosion. Therefore, obtaining accura te and timely information about soil moisture across large regions is crucial.” The news correspondents obtained a quote from the research from Indian Institute of Remote Sensing: “Remote sensing techniques, such as microwave remote sensing , have emerged as powerful tools for monitoring and mapping soil moisture. Synth etic aperture radar (SAR) is beneficial for estimating soil moisture at both glo bal and local levels. This study aimed to assess soil moisture and dielectric co nstant retrieval over agricultural land using machine learning (ML) algorithms a nd decomposition techniques. Three polarimetric decomposition models were used t o extract features from simulated NASA-ISRO SAR (NISAR) L-Band radar images. Mac hine learning techniques such as random forest regression, decision tree regress ion, stochastic gradient descent (SGD), XGBoost, K-nearest neighbors (KNN) regre ssion, neural network regression, and multilinear regression were used to retrie ve soil moisture from three different crop fields: wheat, soybean, and corn. The study found that the random forest regression technique produced the most preci se 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.”
Indian Institute of Remote SensingDehr adunIndiaAsiaCyborgsEmerging TechnologiesMachine LearningRemote Sens ing
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