.Our study established a machine learning (ML) model that could predict the apple yield based on various satellite multisensor data, such as climatological, SAR backscatter, terrain distribution, and soil factors, grouped as 26 subcriteria. A total of 986 apple orchards database were collected from 2018 to 2021 in Kashmir Valley, India covering an area of 277953.7 ha farmland. The novelty of our research is the integration of Google Earth Engine cloud and ML models, namely random forest, support vector machine, extreme gradient boosting, K-nearest neighbors, and Cubist along with the geographic information system/remote sensing technology to create an accurate and comprehensive apple yield prediction model in the precision agriculture realm for highlands. The multicollinearity testing indicated that the tolerance and VIF values of all the conditioning factors were <0.1 and <6.85, respectively, indicating no multicollinearity problems among the apple yield suitability factors. Among the tested ML models, the Cubist model performed best, with R2 of 0.83, root-mean-squared error of 0.56 t / ha, and mean absolute error of 0.2 t / ha. The results showed a low mean fruit yield during 2018 of 12.36 ton / ha, whereas maximum fruit yield was reflected in 2021 of 14.05 ton / ha. The heat map revealed the highest normalized differential vegetation index along with vertical-vertical/ vertical-horizontal polarization backscatter, detected during the pre-event of severe snowfall compared to on- and postevent of snowfall for the respective years. Untimely snowing and infestation due to fungi and bacterial diseases regularly reduce fruit yield in the study area. Our study successfully used of high-resolution optical-SAR data combined with ML models as a promising tool for monitoring the yield variability over the highland areas.
Remote sensing technology provides a spatial-temporal database to track the dynamics of water bodies. Global climatic change has a substantial impact on the dynamic activities of glaciers and glacial lakes and in turn affects the surrounding ecosystem. Thus, monitoring and maintaining an updated database about glacial activity is essential for disaster preparedness. In this study, spatiotemporal analysis of glacial lakes of the Western Himalayas in Chamoli and Pithoragarh District of Uttarakhand during 1994, 2000, 2010, and 2020 were done. These glacial lakes showed significant spatiotemporal changes and an increase in their aerial extent, nearly doubled from 1994 to 2020. GL_A had a sudden tremendous growth in the interim period may tend to many disasters like GLOF (glacial lake outburst floods). The study area holds many tourist pilgrimage attractions, and it needs sufficient monitoring of several factors over the entire fragile region for the construction of any infrastructures, disaster mitigation, and urban development processes.
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