National Engineering Laboratory for Efficient Utilization of Soil and Fertilizer Resources, College of Resources and Environment, Shandong Agricultural University, Taian 271018, China.
Shandong Huibangbohai Agricultural Development Co., Ltd., Dongying 257091, China.
Sensors (Basel). 2019 Mar 27;19(7):1485. doi: 10.3390/s19071485.
Chlorophyll is the most important component of crop photosynthesis, and the reviving stage is an important period during the rapid growth of winter wheat. Therefore, rapid and precise monitoring of chlorophyll content in winter wheat during the reviving stage is of great significance. The satellite-UAV-ground integrated inversion method is an innovative solution. In this study, the core region of the Yellow River Delta (YRD) is used as a study area. Ground measurements data, UAV multispectral and Sentinel-2A multispectral imagery are used as data sources. First, representative plots in the Hekou District were selected as the core test area, and 140 ground sampling points were selected. Based on the measured SPAD values and UAV multispectral images, UAV-based SPAD inversion models were constructed, and the most accurate model was selected. Second, by comparing satellite and UAV imagery, a reflectance correction for satellite imagery was performed. Finally, based on the UAV-based inversion model and satellite imagery after reflectance correction, the inversion results for SPAD values in multi-scale were obtained. The results showed that green, red, red-edge and near-infrared bands were significantly correlated with SPAD values. The modeling precisions of the best inversion model are R² = 0.926, Root Mean Squared Error (RMSE) = 0.63 and Mean Absolute Error (MAE) = 0.92, and the verification precisions are R² = 0.934, RMSE = 0.78 and MAE = 0.87. The Sentinel-2A imagery after the reflectance correction has a pronounced inversion effect; the SPAD values in the study area were concentrated between 40 and 60, showing an increasing trend from the eastern coast to the southwest and west, with obvious spatial differences. This study synthesizes the advantages of satellite, UAV and ground methods, and the proposed satellite-UAV-ground integrated inversion method has important implications for real-time, rapid and precision SPAD values collected on multiple scales.
叶绿素是作物光合作用的最重要组成部分，返青期是冬小麦快速生长的重要时期。因此，快速、准确地监测冬小麦返青期的叶绿素含量具有重要意义。卫星-无人机-地面综合反演方法是一种创新的解决方案。本研究以黄河三角洲（YRD）核心区为研究区，以地面测量数据、无人机多光谱和 Sentinel-2A 多光谱图像为数据源。首先，选择河口区作为核心试验区，选取 140 个地面采样点。基于测量的 SPAD 值和无人机多光谱图像，构建了基于无人机的 SPAD 反演模型，并选择了最准确的模型。其次，通过比较卫星和无人机图像，对卫星图像进行了反射率校正。最后，基于基于无人机的反演模型和经过反射率校正的卫星图像，获得了多尺度 SPAD 值的反演结果。结果表明，绿光、红光、红边和近红外波段与 SPAD 值显著相关。最佳反演模型的建模精度为 R²=0.926、均方根误差（RMSE）=0.63 和平均绝对误差（MAE）=0.92，验证精度为 R²=0.934、RMSE=0.78 和 MAE=0.87。经过反射率校正的 Sentinel-2A 图像具有明显的反演效果；研究区的 SPAD 值集中在 40 到 60 之间，从东海岸向西南和西部逐渐增加，具有明显的空间差异。本研究综合了卫星、无人机和地面方法的优势，提出的卫星-无人机-地面综合反演方法对多尺度实时、快速、精确采集 SPAD 值具有重要意义。
