Spatial variability

Random forest

Scale (ratio)

Ecology

Biology

Statistics

Support vector machine

Cartography

Spatial ecology

Computer science

Soil nutrients

Geology

Soil water

Mathematics

Geography

Hyperspectral imaging

Kriging

Environmental science

Soil science

Remote sensing

Stepwise regression

Mean squared error

Nutrient

Machine learning

Soil test

Sensors

Rapid acquisition of the spatial distribution of soil nutrients holds great implications for farmland soil productivity safety, food security and agricultural management. To this end, we collected 1297 soil samples and measured the content of soil total nitrogen (TN), soil available phosphorus (AP) and soil available potassium (AK) in Zengcheng, north of the Pearl River Delta, China. Hyperspectral remote sensing images (115 bands) of the Chinese Environmental 1A satellite were used as auxiliary variables and dimensionality reduction was performed using Pearson correlation analysis and principal component analysis. The TN, AP and AK of soil were predicted in the study area based on auxiliary variables after dimensionality reduction, along with stepwise linear regression (SLR), support vector machine (SVM), random forest (RF) and back-propagation neural network (BPNN) models; 324 independent points were used to verify the predictive performance. The BPNN model, which demonstrated the best predictive accuracy among all methods, combined ordinary kriging (OK) with mapping the spatial variations of soil nutrients. Results show that the BPNN model with double hidden layers had better predictive accuracy for soil TN (root mean square error (RMSE) = 0.409 mg kg−1, R2 = 44.24%), soil AP (RMSE = 40.808 mg kg−1, R2 = 42.91%) and soil AK (RMSE = 67.464 mg kg−1, R2 = 48.53%) compared with the SLR, SVM and RF models. The back propagation neural network-ordinary kriging (BPNNOK) model showed the best predictive results of soil TN (RMSE = 0.292 mg kg−1, R2 = 68.51%), soil AP (RMSE = 29.62 mg kg−1, R2 = 69.30%) and soil AK (RMSE = 49.67 mg kg−1 and R2 = 70.55%), indicating the best fitting ability between hyperspectral remote sensing bands and soil nutrients. According to the spatial mapping results of the BPNNOK model, concentrations of soil TN (north-central), soil AP (central and southwest) and soil AK (central and southeast) were respectively higher in the study area. The most important bands (464–517 nm) for soil TN (b10, b14, b20 and b21), soil AP (b3, b19 and b22) and soil AK (b4, b11, b12 and b25) exhibited the best response and sensitivity according to the SLR, SVM, RF and BPNN models. It was concluded that the application of hyperspectral images (visible-near-infrared data) with BPNNOK model was found to be an efficient method for mapping and monitoring soil nutrients at the regional scale.

Predicting Spatial Variations in Soil Nutrients with Hyperspectral Remote Sensing at Regional Scale