Mingliang Jiang

Accurate and timely monitoring of soil water content (SWC) plays an essential supporting role in crop irrigation management and smart agricultural practice. Remote sensing technology provides rapid and convenient approaches to large-scale farmland SWC estimation or inversion. However, existing mechanistic modeling methods are limited by parameter localization and land surface heterogeneity, while machine learning data-driven methods lack quite good physical interpretability. To address deficiencies in single data-driven methods and single vegetation microwave scattering models, and explore the estimation effect of different feature variables in machine learning. This study proposes an innovative estimation model that integrates machine learning strategies and multi-source features, including spectral indices and features derived from the classic water cloud model (WCM). The novelty of the proposed model lies in balancing physical interpretability and data-driven accuracy, namely extracting relevant soil backscattering parameters through WCM, which are then used as input features in the machine learning. A combination of field-measured data and unmanned aerial vehicle (UAV) multi-source remote sensing data, including multispectral and synthetic aperture radar (SAR) data, was acquired synchronously at the jointing stage of winter wheat. Through extracting variables such as the SAR microwave backscattering coefficient and the optical vegetation index, and conducting feature screening and parameter sensitivity analysis, we optimized the input variable sets of models based on back-propagation (BP) neural network, support vector regression (SVR) and random forest (RF) algorithms. The study results showed that the established estimation model had excellent estimation performance, especially for the RF algorithm, with the highest R² of 0.855, lowest MAE of 0.0212 cm³/cm³, and lowest RMSE of 0.0253 cm³/cm³. It was also shown that different feature input schemes have obvious effects on SWC estimation performance. On the whole, RF had the best estimation accuracy, followed by SVR and then BPNN. This study demonstrated that models integrating multi-source remote sensing data and machine learning could enhance SWC estimation ability for winter wheat fields, and could also serve as a promising approach for SWC monitoring in winter wheat fields in the North China Plain.

Irrigation district plays an important role in China’s agricultural production. In recent years, China conducted many water-saving renovation construction projects of irrigation districts. However, the current implementation effect of irrigation district water-saving renovation has not been well-characterized. Comprehensive and systematic evaluation of the implementation effect of water-saving renovation in irrigation districts can provide scientific basis for further construction and management of irrigation districts. This study screened preliminary index system in four dimensions: preliminary work, completion of construction tasks, completion of planned investment, management and reform. Based on the statistical results of the questionnaire surveys and subsequently analyzed, fourteen key evaluation indicators were finally identified. Percentile system and expert evaluation method were then used to determine the assigned score of every indicator. Based on the evaluation criteria, the evaluation method of the implementation effect was formulated. Ten key medium-sized irrigation districts in southeastern China were taken as a research example in this study, with the implementation effect of water-saving renovation of 10 irrigation districts being comprehensively evaluated. The results show that these irrigation districts have a relatively high implementation effect of water-saving renovation. The data results from the scores of four dimensions and fourteen indicators show that the index system is reasonable and practicable, and the evaluation results are basically in line with actual situation. These findings have a good practical reference value for making decisions about how to instruct the modernization development of water-saving renovation of irrigation districts in China.