期刊
REMOTE SENSING
卷 9, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/rs9070708
关键词
unmanned aerial vehicle platforms; winter wheat biomass; hyperspectral image; crop height; partial least squares regression
类别
资金
- U.K. Science and Technology Facilities Council through PAFiC:Precision Agriculture for Family-farms in China [ST/N006801/1]
- Natural Science Foundation of China [61661136003, 41471285, 41471351, 41601346]
- National Key Research and Development Program [2016YFD0300602]
- Beijing Academy of Agriculture and Forestry Sciences [KJCX20170423]
- STFC [ST/N006801/1] Funding Source: UKRI
- Science and Technology Facilities Council [ST/N006801/1] Funding Source: researchfish
Correct estimation of above-ground biomass (AGB) is necessary for accurate crop growth monitoring and yield prediction. We estimated AGB based on images obtained with a snapshot hyperspectral sensor (UHD 185 firefly, Cubert GmbH, Ulm, Baden-Wurttemberg, Germany) mounted on an unmanned aerial vehicle (UAV). The UHD 185 images were used to calculate the crop height and hyperspectral reflectance of winter wheat canopies from hyperspectral and panchromatic images. We constructed several single-parameter models for AGB estimation based on spectral parameters, such as specific bands, spectral indices (e.g., Ratio Vegetation Index (RVI), NDVI, Greenness Index (GI) and Wide Dynamic Range VI (WDRVI)) and crop height and several models combined with spectral parameters and crop height. Comparison with experimental results indicated that incorporating crop height into the models improved the accuracy of AGB estimations (the average AGB is 6.45 t/ha). The estimation accuracy of single-parameter models was low (crop height only: R-2 = 0.50, RMSE = 1.62 t/ha, MAE = 1.24 t/ha; R-670 only: R-2 = 0.54, RMSE = 1.55 t/ha, MAE = 1.23 t/ha; NDVI only: R-2 = 0.37, RMSE = 1.81 t/ha, MAE = 1.47 t/ha; partial least squares regression R-2 = 0.53, RMSE = 1.69, MAE = 1.20), but accuracy increased when crop height and spectral parameters were combined (partial least squares regression modeling: R-2 = 0.78, RMSE = 1.08 t/ha, MAE = 0.83 t/ha; verification: R-2 = 0.74, RMSE = 1.20 t/ha, MAE = 0.96 t/ha). Our results suggest that crop height determined from the new UAV-based snapshot hyperspectral sensor can improve AGB estimation and is advantageous for mapping applications. This new method can be used to guide agricultural management.
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