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标题：Comparison of spectro-biophysical and yield parameters of cotton in irrigated lowlands of Amudaria River
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作者：Shavkat Kenjabaev ; Christopher Conrad ; Odilbek Eshchanov 等
期刊名称：InterCarto. InterGIS
印刷版ISSN：2414-9179
电子版ISSN：2414-9209
出版年度：2020
卷号：26
期号：3
DOI：10.35595/2414-9179-2020-3-26-294-308
语种：English
出版社：Laboratory of Complex Mapping, Faculty of Geography, MSU
摘要：This study aims defining the best predictors of biophysical parameters and yield with vegetation indices derived from Landsat 8 OLI surface reflectance data. The study was conducted in 2015 at five crop fields in Kulavat canal irrigation system in Khorezm province, Uzbekistan. The Environment for Visualizing Images (ENVI) ver. 4.5 and R programming software ver. 1.0.143 were used to process, calculate seven vegetation indices (VIs) and predict biophysical parameters and yield of cotton. The trend analysis show that in-situ measured biophysical parameters for the whole growth stage of cotton follows the 3rd order polinomial curve (R² = 0.96−0.99). The NDVI, SAVI, TVI and RVI had linear interrelationship between each other with strong positive correlation of R2>0.9 (highly significant with p-value=0). The VIs showed a logarithmic function relationship with crop height (crht), power function relationship with green biomass (gbm) and leaf area index (LAI), and linear function relationship with the fraction of photosyntetically active radiation below the plant canopy (FPAR) during the entire growing period of cotton. Among seven VIs tested in this study, the NDVI/SAVI and GCI explained 88 and 91 % of variation in crht, respectively. These three indices also well explained gbm variation (R2=0.86). The TVI was slightly better explained FPAR than NDVI and SAVI (all R2>0.87). The NDVI, SAVI and TCG explained 82 % of variation in LAI. Among all VIs, GCI, NDGI and RVI were found to be the best predictor of cotton yield during August, explaining 76-79% variability (p<0.001). Based on spectro-biophysical analysis, VIs derived from RS data on July and August (anthesis and peak growth stages of cotton) is more reliable to use for modeling cotton yield (seed and lint yields together). Therefore, field data collection is recommended to perform during these months taking into account in-field crop condition and remotely sensed data acquisition date. In addition, September 5-20 is the second important period (i.e., cotton pick-up) to conduct yield data collection for establishment of relationships between cotton yields with VIs (July-August).
关键词：remote sensing;GIS;spectro-biophysical parameters;yield;cotton