Land parcel-based digital soil mapping of soil nutrient properties in an alluvial-diluvia plain agricultural area in China

Abstract The ability to accurately and precisely perform soil nutrient mapping over large areas is essential in the decision-making processes for precision agriculture. However, existing grid-based or non-grid-based digital soil mapping (DSM) can lead to the problem of mixed units of input information, which causes the mapping results to be unsuitable for direct use in guiding the implementation of precision agriculture. Instead, the goal of this study was to achieve DSM based on land parcels, which are the basic units of agricultural management and have practical geographical significance for precision mapping in agricultural areas. This study established a convolutional neural network-based automatic extraction model to extract land parcels from high resolution remote sensing images. Thirty environmental covariates were chosen and calibrated at land parcels to establish the relationships between soils and landscapes. Four prediction algorithms, namely, ordinary kriging, cokriging, random forest and artificial neural network, were combined with the land-parcel-based DSM framework to develop and evaluate their effectiveness in predicting four topsoil nutrient properties in an alluvial-diluvia plain agricultural region located in Ningxia province, China. The results of comparisons show that, overall, the land-parcel-based RF model achieved the best prediction accuracy; its relative improvement (RMSE%) values over the competing models were 1.27, 4.23, 3.19 and 9.01 for soil organic matter, soil total nitrogen, available phosphorus and available potassium, respectively. In addition, land-parcel-based mapping can improve algorithmic efficiency by approximately 4 times by effectively reducing the mapping units for complex agricultural areas compared with the grid-based mapping results when using the same algorithm, and it also achieves a better performance at the detail level. Overall, the land-parcel-based DSM approach achieved good results in plain agricultural areas, but the model still needs improvement for land-parcel-based DSM in mountainous and hilly agricultural areas, and a challenge remains in selecting the most appropriate environmental covariates.

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