Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery

• Published in: Precision agriculture Vol. 20; no. 3; pp. 611 - 629
• Main Authors: Zheng, Hengbiao, Cheng, Tao, Zhou, Meng, Li, Dong, Yao, Xia, Tian, Yongchao, Cao, Weixing, Zhu, Yan
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.06.2019; Springer Nature B.V
• Subjects: aboveground biomass; Agriculture; altitude; Atmospheric Sciences; Biomass; Biomedical and Life Sciences; cameras; canopy; Chemistry and Earth Sciences; Computer Science; Crop management; Crop yield; Crops; Cultivars; Forest biomass; forests; Growing season; heading; Image acquisition; Life Sciences; Model accuracy; Nitrogen; Parameters; Physics; Plant breeding; plant density; Regression analysis; Regression models; Remote Sensing/Photogrammetry; Rice; Root-mean-square errors; Seasons; soil; Soil Science & Conservation; Spectra; Spectral analysis; Statistics for Engineering; Texture; Unmanned aerial vehicles; Vegetation; Vegetation index; yield forecasting; Texture index; Vegetation index; UAV multispectral imagery; Aboveground biomass; Rice

Crop aboveground biomass (AGB) is one of the most important indicators in crop breeding and crop management, and can be used for crop yield prediction. A number of vegetation indices (VIs) have been proposed to estimate crop biomass, but they perform poorly at high biomass levels and are easily affe...