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Exploring the potential role of environmental and multi-source satellite data in crop yield prediction across Northeast China

产量(工程) 环境科学 植被(病理学) 卫星 作物产量 归一化差异植被指数 线性回归 生长季节 预测建模 粮食安全 回归分析 农业 遥感 大气科学 统计 气候变化 数学 地理 农学 生态学 工程类 地质学 病理 航空航天工程 生物 考古 冶金 材料科学 医学
作者
Zhenwang Li,Lei Ding,Donghui Xu
出处
期刊:Science of The Total Environment [Elsevier]
卷期号:815: 152880-152880 被引量:17
标识
DOI:10.1016/j.scitotenv.2021.152880
摘要

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.
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