| Estimation and validation of above-ground biomass of cotton during main growth period using Unmanned Aerial Vehicle (UAV) |
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| DOI：10.7606/j.issn.1000-7601.2019.05.09 |
| Key Words: cotton UAV remote sensing above-ground biomass vegetation index |
| Author Name | Affiliation | | DENG Jiang | Xinjiang Key Laboratory of Soil and Plant Ecological Processes, College of Grassland and Environmental Sciences, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China | | GU Hai-bin | Xinjiang Key Laboratory of Soil and Plant Ecological Processes, College of Grassland and Environmental Sciences, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China | | WANG Ze | Xinjiang Key Laboratory of Soil and Plant Ecological Processes, College of Grassland and Environmental Sciences, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China | | SHENG Jian-dong | Xinjiang Key Laboratory of Soil and Plant Ecological Processes, College of Grassland and Environmental Sciences, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China | | MA Yu-cheng | Xinjiang Key Laboratory of Soil and Plant Ecological Processes, College of Grassland and Environmental Sciences, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China | | XIN Hui-nan | Xinjiang Key Laboratory of Soil and Plant Ecological Processes, College of Grassland and Environmental Sciences, Xinjiang Agricultural University, Urumqi, Xinjiang 830052, China |
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| Abstract: |
| Using near-infrared image data collected by Unmanned Aerial Vehicle (UAV) during the main growth period of cotton, four different vegetation indices were extracted to construct optimal estimation model with above-ground biomass (AGB). The results showed that, with growth of cotton, Normalized Difference Vegetation Index (NDVI), Wide Dynamic Range Vegetation Index (WDRVI), Ratio Vegetation Index (RVI), and Difference Vegetation Index (DVI) all increased firstly and then stayed constant. However, AGB of cotton varied significantly among all growth periods. AGB at seedling period was best fitted by binary linear model between NDVI and DVI (R2=0.84, RMSE=0.13 kg·m-2), while AGB at bud period was best fitted by binary linear model between WDRVI and DVI (R2=0.87, RMSE=0.52 kg·m-2). At blooming period, AGB was best predicted by nonlinear model of RVI (R2=0.79, RMSE=0.95 kg·m-2). At boll period, AGB was best estimated by binary linear model between WDRVI and RVI (R2=0.86, RMSE=0.96 kg·m-2). |
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