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

• Published in: The Science of the total environment 2022-04, Vol.815, p.152880-152880, Article 152880
• Main Authors: Li, Zhenwang, Ding, Lei, Xu, Dawei
• Format: Article
• Language: English
• Subjects: Agriculture; Climate; Crop yield; Crops, Agricultural; Environmental data; Machine learning; Multi-source satellite data; Seasons; Yield prediction; Zea mays
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2021.152880

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. [Display omitted] •Stacked ensemble model outperformed single machine learning models.•Environmental and satellite data are complementary and useful to estimate crop yield.•SIF, LST and VOD provided extra information beyond EVI for crop yield prediction.•Crop yield can be satisfactorily forecasted at two to three months before harvest.•Geography, DEM, VOD, EVI, soil hydraulic and nutrient properties are import