理化复合参数和神经网络结合的冬小麦长势遥感监测

准确、及时地监测区域作物长势状况对农业规划和政策的制定与调整具有重要的意义。遥感技术作为一种收集大面积作物长势信息的有效手段,正日益受到关注。为提高冬小麦长势遥感监测的准确性和全面性,该研究基于田间实测的冬小麦拔节期地上鲜生物量（aboveground fresh biomass,AFB）、叶面积指数（leaf area index,LAI）、叶片叶绿素相对含量（soil and plant analyzer development,SPAD）和叶片氮含量（leaf nitrogen content,LNC）4种生长相关理化参数,利用熵值法获取各参数权重构建冬小麦理化复合参数（physico-chemical composite parameter,PCCP）。利用显著性检验和籽粒产量数据分析复合参数在量化冬小麦长势方面的性能。然后,以Sentinel-2A作为数据源,分析不同遥感指数与LAI、SPAD、AFB、LNC和PCCP的相关性。选取相关性较高的遥感指数作为反向传播（back propagation,BP）人工神经网络（artificial neural networks,ANN）的输入,建立冬小麦长势遥感监测模型,对PCCP进行估计。评价模型精度并用于监测研究区冬小麦长势分布特征。赋权结果表明,作物物理参数的权重大于生化参数,其中LAI的权重最大,为0.387,AFB和SPAD次之,LNC的权重最小,为0.105;PCCP性能评估结果表明,与单一理化参数相比,PCCP值能更好地揭示作物长势状况的差异,其与最终籽粒产量的相关性更好, 决定系数提高0.035～0.468,均方根误差减少46.2～520.0 kg/hm²;在遥感监测过程中,PCCP比单一理化参数有更好的应用潜力,BP-ANN长势遥感监测模型模拟PCCP精度较高,在测试集中决定系数为0.830,均方根误差为0.080;研究区冬小麦总体长势稳定且分布集中,呈现"中部差,南北好"的空间分布特征。因此,构建作物理化复合参数用于量化作物长势是提高长势监测可靠性和准确性的一种有效方式,可为冬小麦田间管理提供科学依据,服务于发展智慧农业和建设农业强国的战略需求。

Remote sensing monitoring of winter wheat growth using physico-chemical composite parameter and neural network

Accurate and timely regional crop growth monitoring will be of great benefit to the establishment and adjustment of agricultural planning and policies. Remote sensing technology as an effective measure for collecting crop growth information across large areas has been receiving increasing attention nowadays. To enhance the accuracy and comprehensiveness of remote sensing monitoring winter wheat growth, a physico-chemical composite parameter (PCCP) was constructed using field measurements of aboveground fresh biomass (AFB), leaf area index (LAI), soil and plant analyzer development (SPAD) and leaf nitrogen content (LNC) of winter wheat at jointing stage. This construction was achieved through the utilization of the entropy weight method (EWM). Based on individual and community characteristics, winter wheat growth at jointing stage was divided into 3 levels in the study area, which were poor (Ⅰ), medium (Ⅱ) and well (Ⅲ). On this basis, the differences of each parameter under different growth levels were evaluated by Kruskal-Wallis test. To further validate the reliability of the composite parameter, linear regression models between grain yield of winter wheat and each parameter were constructed. Then, Sentinel-2A was used as the data source to analyze the correlation between different remote sensing indexes and LAI、SPAD、AFB、LNC、PCCP of winter wheat at jointing stage. Remote sensing indexes with high correlation were selected as inputs of back propagation (BP) artificial neural networks (ANN) to estimate PCCP. The 10-fold cross-validation method was used to obtain the optimal parameters of the BP-ANN model. The best model was selected to simulate values of the PCCP and to map the regional winter wheat growth conditions pixel by pixel at jointing stage. The weighting results showed that the weight of crop physical parameters was greater than biochemical parameters, among which LAI had the largest weight (0.387), followed by AFB and SPAD, and LNC had the least weight (0.105). The performance evaluation results of PCCP showed that the difference of PCCP under different growth levels was the most significant. The correlation between PCCP value and grain yield was closer than that between grain yield and LAI, SPAD, AFB, or LNC alone. The coefficient of determination was increased by 0.035 to 0.468, and the root-mean-square error is reduced by 46.2 kg/hm² to 520.0 kg/hm². During remote sensing monitoring, the correlation among the PCCP constructed by LAI, SPAD, AFB, and LNC and remote sensing indexes were all improved to different degrees compared with the single parameter. The accuracy of PCCP simulation by BP-ANN remote sensing monitoring model was high, which the coefficient of determination and the root mean square error were 0.830 and 0.080 in the test set, respectively. The overall growth of winter wheat at jointing stage in the study area was stable and concentrated, showing the spatial distribution characteristics of "the middle bad and the north-south well". Therefore, the construction of PCCP is an effective way to improve the reliability and accuracy of growth remote sensing monitoring, which can provide scientific basis for field management of winter wheat and serve the strategic needs of developing intelligent agriculture and building an agricultural power in China.