Exploring PlanetScope Satellite Capabilities for Soil Salinity Estimation and Mapping in Arid Regions Oases

نویسندگان

چکیده

One reason for soil degradation is salinization in inland dryland, which poses a substantial threat to arable land productivity. Remote-sensing technology provides rapid and accurate assessment salinity monitoring, but there lack of high-resolution remote-sensing spatial estimations. The PlanetScope satellite array high-precision mapping surface monitoring through its 3-m resolution near-daily revisiting frequency. This study’s use the new attempt estimate drylands. We hypothesized that field observations, data, spectral indices derived from data using partial least-squares regression (PLSR) method would produce reasonably regional maps based on 84 ground-truth various parameters, like band reflectance, published indices. results showed newly constructed red-edge yellow indices, we were able develop several inversion models maps. Different algorithms, including Boruta feature preference, Random Forest algorithm (RF), Extreme Gradient Boosting (XGBoost), applied variable selection. (YRNDSI YRNDVI) had best Pearson correlations 0.78 −0.78. also found proportions bands accounted large proportion essential strategies three with preference at 80%, RF XGBoost 60%, indicating these two contributed more estimation results. PLSR model different XGBoost-PLSR coefficient determination (R2), root mean square error (RMSE), ratio performance deviation (RPD) values 0.832, 12.050, 2.442, respectively. These suggest has potential significantly advance research by providing wealth fine-scale information.

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ژورنال

عنوان ژورنال: Remote Sensing

سال: 2023

ISSN: ['2315-4632', '2315-4675']

DOI: https://doi.org/10.3390/rs15041066