Measuring cotton water status using water-related vegetation indices at leaf and canopy levels |
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| Authors: | QiuXiang YI AnMing BAO Yi LUO Jin ZHAO |
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| Affiliation: | Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China |
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| Abstract: | Drought is one of the major environmental threats in the world. In recent years, the damage from droughts to the environment and economies of some countries has been extensive, and drought monitoring has caused widespread concerns. Remote sensing has a proven ability to provide spatial and temporal measurements of surface properties, and it offers an opportunity for the quantitative assessment of drought indicators such as the vegetation water content at different levels. In this study, sites of cotton field in Shihezi, Xinjiang, Northwest China were sampled. Four classical water content parameters, namely the leaf equivalent water thickness (EWT leaf ), the fuel moisture content (FMC), the canopy equivalent water thickness (EWT canopy ) and vegetation water content (VWC) were evaluated against seven widely-used water-related vegetation indices, namely the NDII (normalized difference infrared index), NDWI 2130 (normalized difference water index), NDVI (normalized difference vegetation index), MSI (moisture stress index), SRWI (simple ratio water index), NDWI 1240 (normalized difference water index) and WI (water index), respectively. The results proved that the relationships between the water-related vegetation indices and EWT leaf were much better than that with FMC, and the relationships between vegetation indices and EWT canopy were better than that with VWC. Furthermore, comparing the significance of all seven water-related vegetation in- dices, WI and NDII proved to be the best candidates for EWT detecting at leaf and canopy levels, with R 2 of 0.262 and 0.306 for EWT leaf-WI and EWT canopy -NDII linear models, respectively. Besides, the prediction power of linear regression technique (LR) and artificial neural network (ANN) were compared using calibration and validation dataset, respectively. The results indicated that the performance of ANN as a predictive tool for water status measuring was as good as LR. The study should further our understanding of the relationships between water-related vegetation indices and water parameters. |
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| Keywords: | artificial neural network cotton linear regression vegetation indices water parameters |
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