Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/20149
Title: Estimation of Winter Wheat Biomass and Yield by Combining the AquaCrop Model and Field Hyperspectral Data
Contributor(s): Jin, Xiuliang (author); Kumar, Lalit  (author)orcid ; Li, Zhenhai (author); Xu, Xingang (author); Yang, Guijun (author); Wang, Jihua (author)
Publication Date: 2016
Open Access: Yes
DOI: 10.3390/rs8120972Open Access Link
Handle Link: https://hdl.handle.net/1959.11/20149
Abstract: Knowledge of spatial and temporal variations in crop growth is important for crop management and stable crop production for the food security of a country. A combination of crop growth models and remote sensing data is a useful method for monitoring crop growth status and estimating crop yield. The objective of this study was to use spectral-based biomass values generated from spectral indices to calibrate the AquaCrop model using the particle swarm optimization (PSO) algorithm to improve biomass and yield estimations. Spectral reflectance and concurrent biomass and yield were measured at the Xiaotangshan experimental site in Beijing, China, during four winter wheat-growing seasons. The results showed that all of the measured spectral indices were correlated with biomass to varying degrees. The normalized difference matter index (NDMI) was the best spectral index for estimating biomass, with the coefficient of determination (R²), root mean square error (RMSE), and relative RMSE (RRMSE) values of 0.77, 1.80 ton/ha, and 25.75%, respectively. The data assimilation method (R² = 0.83, RMSE = 1.65 ton/ha, and RRMSE = 23.60%) achieved the most accurate biomass estimations compared with the spectral index method. The estimated yield was in good agreement with the measured yield (R² = 0.82, RMSE = 0.55 ton/ha, and RRMSE = 8.77%). This study offers a new method for agricultural resource management through consistent assessments of winter wheat biomass and yield based on the AquaCrop model and remote sensing data.
Publication Type: Journal Article
Source of Publication: Remote Sensing, 8(12), p. 1-15
Publisher: MDPIAG
Place of Publication: Switzerland
ISSN: 2072-4292
Field of Research (FOR): 090903 Geospatial Information Systems
070302 Agronomy
090905 Photogrammetry and Remote Sensing
Socio-Economic Outcome Codes: 960904 Farmland, Arable Cropland and Permanent Cropland Land Management
970107 Expanding Knowledge in the Agricultural and Veterinary Sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
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