Shi Hongzhao, Liu Zhiying, Li Siqi, Jin Ming, Tang Zijun, Sun Tao, Liu Xiaochi, Li Zhijun, Zhang Fucang, Xiang Youzhen
Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Xianyang 712100, China.
Institute of Water-Saving Agriculture in Arid Areas of China, Northwest A&F University, Xianyang 712100, China.
Plants (Basel). 2024 Aug 29;13(17):2417. doi: 10.3390/plants13172417.
By integrating the thermal characteristics from thermal-infrared remote sensing with the physiological and structural information of vegetation revealed by multispectral remote sensing, a more comprehensive assessment of the crop soil-moisture-status response can be achieved. In this study, multispectral and thermal-infrared remote-sensing data, along with soil-moisture-content (SMC) samples (020 cm, 2040 cm, and 4060 cm soil layers), were collected during the flowering stage of soybean. Data sources included vegetation indices, texture features, texture indices, and thermal-infrared vegetation indices. Spectral parameters with a significant correlation level ( < 0.01) were selected and input into the model as single- and fuse-input variables. Three machine learning methods, eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Genetic Algorithm-optimized Backpropagation Neural Network (GA-BP), were utilized to construct prediction models for soybean SMC based on the fusion of UAV multispectral and thermal-infrared remote-sensing information. The results indicated that among the single-input variables, the vegetation indices (VIs) derived from multispectral sensors had the optimal accuracy for monitoring SMC in different soil layers under soybean cultivation. The prediction accuracy was the lowest when using single-texture information, while the combination of texture feature values into new texture indices significantly improved the performance of estimating SMC. The fusion of vegetation indices (VIs), texture indices (TIs), and thermal-infrared vegetation indices (TVIs) provided a better prediction of soybean SMC. The optimal prediction model for SMC in different soil layers under soybean cultivation was constructed based on the input combination of VIs + TIs + TVIs, and XGBoost was identified as the preferred method for soybean SMC monitoring and modeling, with its R = 0.780, RMSE = 0.437%, and MRE = 1.667% in predicting 020 cm SMC. In summary, the fusion of UAV multispectral and thermal-infrared remote-sensing information has good application value in predicting SMC in different soil layers under soybean cultivation. This study can provide technical support for precise management of soybean soil moisture status using the UAV platform.
通过将热红外遥感的热特性与多光谱遥感揭示的植被生理和结构信息相结合，可以对作物土壤水分状况响应进行更全面的评估。在本研究中，在大豆开花期收集了多光谱和热红外遥感数据，以及土壤含水量（SMC）样本（020厘米、2040厘米和4060厘米土层）。数据来源包括植被指数、纹理特征、纹理指数和热红外植被指数。选择具有显著相关水平（<0.01）的光谱参数作为单输入和融合输入变量输入模型。利用极限梯度提升（XGBoost）、随机森林（RF）和遗传算法优化的反向传播神经网络（GA-BP）三种机器学习方法，基于无人机多光谱和热红外遥感信息的融合构建大豆SMC预测模型。结果表明，在单输入变量中，多光谱传感器获取的植被指数（VIs）在监测大豆种植不同土层的SMC时具有最佳精度。使用单纹理信息时预测精度最低，而将纹理特征值组合成新的纹理指数显著提高了SMC估算性能。植被指数（VIs）、纹理指数（TIs）和热红外植被指数（TVIs）的融合对大豆SMC有更好的预测效果。基于VIs+TIs+TVIs的输入组合构建了大豆种植不同土层SMC的最优预测模型，XGBoost被确定为大豆SMC监测和建模的首选方法，其预测020厘米SMC时的R=0.780，RMSE=0.437%，MRE=1.667%。综上所述，无人机多光谱和热红外遥感信息的融合在预测大豆种植不同土层的SMC方面具有良好的应用价值。本研究可为利用无人机平台精确管理大豆土壤水分状况提供技术支持。
