Mapping Cropland Soil Nutrients Contents Based on Multi-Spectral Remote Sensing and Machine Learning
نویسندگان
چکیده
Nitrogen (N) and phosphorus (P) are primary indicators of soil nutrients in agriculture. Accurate management these is essential for ensuring food security. High-resolution, multi-spectral remote sensing images can provide crucial information mapping at the field scale. This study compares capabilities ZH-1 Sentinel-2 satellite data, along with different spectral indices, (total N Olsen-P) using two machine learning algorithms, random forest (RF) XGBoost (XGB). Two agricultural fields Suihua City were selected as areas this investigation. The results showed that data performed best computing total content RF model (R2 = 0.74, RMSE 0.10 g/kg). However, Olsen-P content, better 0.75, 9.79 mg/kg) than model. demonstrates both perform well terms accurately contents learning. Due to its higher spatial resolution, provides more detailed on nutrient during inversion exhibits comparable accuracy.
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ژورنال
عنوان ژورنال: Agriculture
سال: 2023
ISSN: ['2077-0472']
DOI: https://doi.org/10.3390/agriculture13081592