首页|Predicting soil nutrients with PRISMA hyperspectral data at the field scale:the Handan(south of Hebei Province)test cases
Predicting soil nutrients with PRISMA hyperspectral data at the field scale:the Handan(south of Hebei Province)test cases
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NETL
NSTL
万方数据
This research investigates the suitability of PRISMA and Sentinel-2 satellite imagery for retriev-ing topsoil properties such as Organic Matter(OM),Nitrogen(N),Phosphorus(P),Potassium(K),and pH in croplands using different Machine Learning(ML)algorithms and signal pre-treat-ments.Ninety-five soil samples were collected in Quzhou County,Northeast China.Satellite images captured soil reflectance data when bare soil was visible.For PRISMA data,a Linear Mixture Model(LMM)was used to separate soil and Photosynthetic Vegetation(PV)end-members,excluding Non-Photosynthetic Vegetation(NPV)using Band Depth values at the 2100 nm absorption peak of cellulose.Sentinel-2 bare soil reflectance spectra were obtained using thresholds based on NDVI and NBR2 indices.Results showed PRISMA data provided slightly better accuracy in retrieving topsoil nutrients than Sentinel-2.While no optimal pre-dictive algorithm was best,absorbance data proved more effective than reflectance.PRISMA results demonstrated potential for predicting soil nutrients in real scenarios.
NETL
NSTL
万方数据
