Cultivated Land Quality Evaluated Using the RNN Algorithm Based on Multisource Data

Abstract

Cultivated land quality (CLQ) is associated with national food security, benign economic development, social harmony, and stability. The scientific evaluation of CLQ provides the basis for achieving the “trinity” protection of cultivated land quantity, and quality, as well as ecology. However, the current research on CLQ evaluation has some limitations, mainly the poor consideration of evaluation indicators, time-consuming and labor-intensive data acquisition, and low precision of evaluation at the regional scale. Therefore, this study introduced multisource data to evaluate CLQ and proposed a new method for CLQ evaluation (natural grade evaluation, utilization grade evaluation, and economic grade evaluation), combining multisource data and the recurrent neural network (RNN) algorithm. Initially, optimal indicators were determined by correlation analysis and generalized linear regression coefficient methods based on factors related to CLQ acquired from multisource data. Then, CLQ evaluation models were constructed with the RNN algorithm on the basis of the aforementioned optimal indicators. Finally, the models were adopted to map CLQ. The present study was carried out in Guangzhou City, Guangdong Province, China. According to the results: (1) CLQ showed close relationship to pH, effective soil layer thickness (EST), chemical fertilizer application rate (CHFE), organic matter content (OMC), annual accumulated temperature (TEMA), 5–15 cm soil depth soil cation exchange capacity (CEC515), 0–5 cm soil depth soil cation exchange capacity (CEC05), 5–15 cm soil depth soil organic carbon content (SOC515), 0–5 cm soil depth soil organic carbon content (SOC05), field slope (FS), groundwater level (GWL), and terrain slope (TS). (2) All modeling accuracies (R2) were greater than 0.80 for the CLQ evaluation models constructed based on the RNN algorithm. The area and spatial distribution of each grade of CLQ evaluation were consistent with the actual situation. The best and the worst quality cultivated land occupied a small area, and the area without a gap with the actual CLQ was as high as 76%, indicating that the model results were reliable. The study shows the suitability of the method for evaluating CLQ at the regional scale, offering a scientific foundation for the rational utilization and management of cultivated land resources, as well as a reference for evaluating CLQ in the future.

Keywords

• cultivated land quality
• RNN algorithm
• multisource data
• Guangzhou city