State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China.
Sci Total Environ. 2022 Apr 1;815:152880. doi: 10.1016/j.scitotenv.2021.152880. Epub 2022 Jan 6.
Developing an accurate crop yield predicting system at a large scale is of paramount importance for agricultural resource management and global food security. Earth observation provides a unique source of information to monitor crops from a diversity of spectral ranges. However, the integrated use of these data and their values in crop yield prediction is still understudied. Here we proposed the combination of environmental data (climate, soil, geography, and topography) with multiple satellite data (optical-based vegetation indices, solar-induced fluorescence (SIF), land surface temperature (LST), and microwave vegetation optical depth (VOD)) into the framework to estimate crop yield for maize, rice, and soybean in northeast China, and their unique value and relative influence on yield prediction was assessed. Two linear regression methods, three machine learning (ML) methods, and one ML ensemble model were adopted to build yield prediction models. Results showed that the individual ML methods outperformed the linear regression methods, the ML ensemble model further improved the single ML models. Moreover, models with more inputs achieved better performance, the combination of satellite data with environmental data, which explained 72%, 69%, and 57% of maize, rice, and soybean yield variability, respectively, demonstrated higher yield prediction performance than individual inputs. While satellite data contributed to crop yield prediction mainly at the early-peak of the growing season, climate data offered extra information mainly at the peak-late season. We also found that the combined use of EVI, LST and SIF has improved the model accuracy compared to the benchmark EVI model. However, the optical-based vegetation indices shared similar information and did not provide much extra information beyond EVI. The within-season yield forecasting showed that crop yields can be satisfactorily forecasted at two to three months prior to harvest. Geography, topography, VOD, EVI, soil hydraulic and nutrient parameters are more important for crop yield prediction.
开发精确的作物产量预测系统对于农业资源管理和全球粮食安全至关重要。地球观测为监测作物提供了独特的信息来源，可以从多种光谱范围进行监测。然而，这些数据的综合利用及其在作物产量预测中的价值仍在研究之中。在这里，我们提出将环境数据（气候、土壤、地理和地形）与多种卫星数据（基于光学的植被指数、太阳诱导荧光（SIF）、地表温度（LST）和微波植被光学深度（VOD））结合到框架中，以估算中国东北地区玉米、水稻和大豆的作物产量，并评估其对产量预测的独特价值和相对影响。采用了两种线性回归方法、三种机器学习（ML）方法和一种 ML 集成模型来构建产量预测模型。结果表明，单独的 ML 方法优于线性回归方法，ML 集成模型进一步提高了单一 ML 模型的性能。此外，具有更多输入的模型表现更好，卫星数据与环境数据的结合分别解释了 72%、69%和 57%的玉米、水稻和大豆产量变化，表现出更高的产量预测性能。虽然卫星数据主要在生长季节早期峰值时对作物产量预测有贡献，但气候数据主要在峰值后期提供额外信息。我们还发现，与基准 EVI 模型相比，组合使用 EVI、LST 和 SIF 可提高模型精度。然而，基于光学的植被指数共享相似的信息，并且除了 EVI 之外没有提供太多额外信息。在季节内产量预测中，发现可以在收获前两到三个月对作物产量进行满意的预测。地理、地形、VOD、EVI、土壤水力和养分参数对作物产量预测更为重要。
