Estimation of Winter Wheat Above-Ground Biomass Using Unmanned Aerial Vehicle-Based Snapshot Hyperspectral Sensor and Crop Height Improved Models
By Jibo Yue, Guijun Yang, Changchun Li, Zhenhai Li, Yanjie Wang, Haikuan Feng, Bo Xu. Remote Sensing, 2017
332 citations recorded by the source corpus.
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: R2 = 0.50, RMSE = 1.62 t/ha, MAE = 1.24 t/ha; R670 only: R2 = 0.54, RMSE = 1.55 t/ha, MAE = 1.23 t/ha; NDVI only: R2 = 0.37, RMSE = 1.81 t/ha, MAE = 1.47 t/ha; partial least squares regression R2 = 0.53, RMSE = 1.69, MAE = 1.20), but accuracy increased when crop height and spectral parameters were combined (partial least squares regression modeling: R2 = 0.78, RMSE = 1.08 t/ha, MAE = 0.83 t/ha; verification: R2 = 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.
Subfields: Ecology, Environmental Engineering, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/cdaeadef29e5aa8c88325386c53037a79aca5243
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Citations in the corpus
- Applications of Remote Sensing in Precision Agriculture: A Review (2020, methodology): “[262] used multiple spectral indices in conjunction with measured plant height to estimate biomass (R2 = 0.”
- Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture (2020, methodology): “[124] used PLSR and hyperspectral images for estimating different crop biophysical and biochemical variables (e.”