Identification of soybean in Argentina using Sentinel-2 composite images  

作　　者：Linsheng Huang Yue Chen Yuhao Pan Zihang Lou Shijun Zheng Xiaoyang Zhang Le Yu Shengwei Liu Dailiang Peng 

机构地区：[1]National Engineering Research Center for Agro-Ecological Big Data Analysis&Application,Anhui University,Hefei 230601,China [2]Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China [3]Research Area of Ecology and Biodiversity,School of Biological Sciences,The University of Hong Kong,Pokfulam,Hong Kong,China [4]College of Environmental and Resource Sciences,Zhejiang University,Hangzhou 310058,China [5]Department of Earth System Science,Ministry of Education Key Laboratory for Earth System Modeling,Institute for Global Change Studies,Tsinghua University,Beijing 100084,China [6]Geospatial Sciences Center of Excellence,Department of Geography&Geospatial Sciences,South Dakota State University,Brookings,SD 57007,USA [7]Ministry of Education Ecological Field Station for East Asian Migratory Birds,Beijing 100084,China [8]Tsinghua University(Department of Earth System Science)-Xi’an Institute of Surveying and Mapping Joint Research Center for Next-Generation Smart Mapping,Beijing 100084,China [9]School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454000,Henan,China

出　　处：《International Journal of Agricultural and Biological Engineering》2024年第5期266-274,共9页国际农业与生物工程学报（英文）

基　　金：funded by the Science and Disruptive Technology Program, AIRCAS(Grant No. 2024-AIRCAS-SDPT- 15);the National Natural Science Foundation of China (Grant No. 42471372).

摘　　要：Soybean is one of the most important oil crops, and Argentina is the third-largest soybean producer in the world, accounting for 17% of the global soybean yield. Timely and accurate information on soybean spatial distribution is critical for ensuring global food security. Sentinel-2 multispectral data and machine learning classification models are used to investigate the potential of soybean identification in the early stage of the growing season in Argentina, with the help of Google Earth Engine (GEE). The earliest time window and optimal feature set for soybean identification are explored. Results are as follows: 1) the random forest (RF) classification model demonstrated the highest level of classification accuracy compared to the backpropagation neural network (BPNN), support vector machine (SVM), and naive Bayes (NB) models;2) Soybean can be accurately identified as early as the end of February (filling stage), which is approximately one month before harvest;3) The optimal feature-subset can reduce the amount of input data by 80% while maintaining high classification accuracy. The overall accuracy (OA) of the RF classification model is 85.87%, and the relative error between the estimated soybean planting area and the agricultural statistics is 3.45%. This study provided a high-precision method for early-season identification of soybeans over large scales. The results can provide a data support for early futures trading and agricultural insurance, as well as a reference for policy-making to ensure global soybean food security.

关 键 词：SOYBEAN machine learning time window feature selection Sentinel-2 Google Earth Engine 

分 类 号：S565.1[农业科学—作物学]