TY - JOUR
T1 - Combining shape and crop models to detect soybean growth stages
AU - Lou, Zihang
AU - Wang, Fumin
AU - Peng, Dailiang
AU - Zhang, Xiaoyang
AU - Xu, Junfeng
AU - Zhu, Xiaolin
AU - Wang, Yan
AU - Shi, Zhou
AU - Yu, Le
AU - Liu, Guohua
AU - Xie, Qiaoyun
AU - Dou, Changyong
N1 - Funding Information:
The data used in this work were derived from the USDA NASS ( https://www.nass.usda.gov/ ) and VIIRS products ( https://e4ftl01.cr.usgs.gov/VIIRS/ ). This work was partially supported by the National Key Research and Development Program of China (2019YFE0115200), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28050100), and the National Natural Science Foundation of China (42071329). The authors also would like to thank the anonymous reviewers for their thoughtful comments and efforts towards improving our manuscript.
Funding Information:
The data used in this work were derived from the USDA NASS (https://www.nass.usda.gov/) and VIIRS products (https://e4ftl01.cr.usgs.gov/VIIRS/). This work was partially supported by the National Key Research and Development Program of China (2019YFE0115200), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28050100), and the National Natural Science Foundation of China (42071329). The authors also would like to thank the anonymous reviewers for their thoughtful comments and efforts towards improving our manuscript.
Publisher Copyright:
© 2023
PY - 2023/12/1
Y1 - 2023/12/1
N2 - Accurately monitoring soybean growth stages (SGSs) is crucial for successful crop management and the development of agricultural information systems. This study focused on 18 states that accounted for over 95% of the soybean area in the United States from 2013 to 2020. We proposed an SMFs-APTT method that integrates crop data layers (CDLs), time-series of VIIRS data, and meteorological data. It combines a shape-model function in separate meteorological stages (SMFs) with a crop model that relies on an accumulated photothermal time (APTT). This approach provides both full-season and within-season monitoring of four soybean growth stages (SGSs); i.e., emergence, blooming, pod-setting, and leaf-dropping. Based on the results obtained from the SMFs-APTT method, a long short-term memory (LSTM) model was employed to predict SGSs early. The dates of the detected and predicted SGSs were compared with National Agricultural Statistics Service (NASS) Crop Progress and Condition Report (CPR) statistical data for analysis and verification. Our results show the following. (1) For the full-season extraction of SGSs, the average root mean square error (RMSE) derived using the SMFs–APTT method was 0.86–3.06 days and that obtained using the SMFs method was 1.56–3.26 days. (2) For the within-season monitoring of SGSs, the SMFs–APTT method was also able to accurately track the growth stages as early as ∼30 days after they reach 50% completion with an average RMSE of 2.1 days. (3) For the early prediction of SGSs, the LSTM model that was trained based on the SMFs–APTT results achieved an RMSE of ∼4.3 days at the state scale and could approximately predict SGSs ∼30 days in advance. Our findings suggest that the SMFs–APTT method provides accurate and reliable extraction of SGSs for full-season and within-season monitoring, which is of benefit to crop modeling and management. Furthermore, the LSTM model successfully forecast SGSs, indicating its potential for making early predictions.
AB - Accurately monitoring soybean growth stages (SGSs) is crucial for successful crop management and the development of agricultural information systems. This study focused on 18 states that accounted for over 95% of the soybean area in the United States from 2013 to 2020. We proposed an SMFs-APTT method that integrates crop data layers (CDLs), time-series of VIIRS data, and meteorological data. It combines a shape-model function in separate meteorological stages (SMFs) with a crop model that relies on an accumulated photothermal time (APTT). This approach provides both full-season and within-season monitoring of four soybean growth stages (SGSs); i.e., emergence, blooming, pod-setting, and leaf-dropping. Based on the results obtained from the SMFs-APTT method, a long short-term memory (LSTM) model was employed to predict SGSs early. The dates of the detected and predicted SGSs were compared with National Agricultural Statistics Service (NASS) Crop Progress and Condition Report (CPR) statistical data for analysis and verification. Our results show the following. (1) For the full-season extraction of SGSs, the average root mean square error (RMSE) derived using the SMFs–APTT method was 0.86–3.06 days and that obtained using the SMFs method was 1.56–3.26 days. (2) For the within-season monitoring of SGSs, the SMFs–APTT method was also able to accurately track the growth stages as early as ∼30 days after they reach 50% completion with an average RMSE of 2.1 days. (3) For the early prediction of SGSs, the LSTM model that was trained based on the SMFs–APTT results achieved an RMSE of ∼4.3 days at the state scale and could approximately predict SGSs ∼30 days in advance. Our findings suggest that the SMFs–APTT method provides accurate and reliable extraction of SGSs for full-season and within-season monitoring, which is of benefit to crop modeling and management. Furthermore, the LSTM model successfully forecast SGSs, indicating its potential for making early predictions.
KW - Crop models
KW - Early prediction
KW - Full-season extraction
KW - Soybean growth stages
KW - Within-season monitoring
UR - http://www.scopus.com/inward/record.url?scp=85171731313&partnerID=8YFLogxK
U2 - 10.1016/j.rse.2023.113827
DO - 10.1016/j.rse.2023.113827
M3 - Article
AN - SCOPUS:85171731313
SN - 0034-4257
VL - 298
JO - Remote Sensing of Environment
JF - Remote Sensing of Environment
M1 - 113827
ER -