Soil Moisture Mapping with Moisture-Related Indices, OPTRAM, and an Integrated Random Forest-OPTRAM Algorithm from Landsat 8 Images
نویسندگان
چکیده
Remote sensing tools have been extensively used for large-scale soil moisture (SM) mapping in recent years, using Landsat satellite images. Rainfall, clay percentage, and the standardized precipitation index play key roles determining content of crop fields. The objective this study was to (i) calculate determine effectiveness moisture-related indices predicting surface SM, (ii) predict SM from images Optical Trapezoid Model (OPTRAM), (iii) evaluate if OPTRAM predictions can be improved by incorporating weather station, soil, data with a random forest algorithm. ENVI® platform create maps, Google Earth Engine (GEE) prepare maps. results showed very weak relationship between where r2 slopes were ?0.10 ?0.20, respectively. when compared situ moisture, regression values ?0.2. Surface then predicted values, rainfall, (SPI), percent high goodness fit (r2 = 0.69) low root mean square error (RMSE 0.053 m3 m?3).
منابع مشابه
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14153801