期刊
REMOTE SENSING
卷 11, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/rs11070890
关键词
unmanned aerial vehicle (UAV); above ground biomass (AGB); triangulated irregular network (TIN); growing degree days (GDD)
类别
资金
- National 863 Project of China [2013AA102401]
- National Natural Science Foundation of China [41771381]
- Fundamental Research Funds for the Central Universities [2042017kf0236]
Accurate estimation of above ground biomass (AGB) is very important for crop growth monitoring. The objective of this study was to estimate rice biomass by utilizing structural and meteorological features with widely used spectral features. Structural features were derived from the triangulated irregular network (TIN), which was directly built from structure from motion (SfM) point clouds. Growing degree days (GDD) was used as the meteorological feature. Three models were used to estimate rice AGB, including the simple linear regression (SLR) model, simple exponential regression (SER) model, and machine learning model (random forest). Compared to models that do not use structural and meteorological features (NDRE, R-2 = 0.64, RMSE = 286.79 g/m(2), MAE = 236.49 g/m(2)), models that include such features obtained better estimation accuracy (NDRE*Hcv/GDD, R-2 = 0.86, RMSE = 178.37 g/m(2), MAE = 127.34 g/m(2)). This study suggests that the estimation accuracy of rice biomass can benefit from the utilization of structural and meteorological features.
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