Monitoring the Cotton Growth Conditions Based on LAI from Remote Sensing and Expert Knowledge

Abstract

To manage the cotton field effectively, the quantitative classification method for cotton growth conditions would be researched based on LAI from remote sensing inversion in the field scale. The experiment was carried out in 2006-2007 in Xinjiang, China, and the eighteen cotton fields were selected as the standard observation station, the 255 quadrats data about LAI were obtained, and three vegetation indexes, including NDVI, PVI, and EVI, were used to estimate LAI by the correlativity between remote sensing and field measurement. The ideal LAI were proposed by the cotton cultivation experts when the cotton filed would realize the high yield, LAI from 2.8 to 3.2 was applicable at the first flowering stage in the research region. All of R2 on LAI estimation from three vegetation indexes got through the extremely significance test but the two models from PVI and EVI had the more excellent estimation ability when LAI was more than 3.0, and the overall estimation level of EVI is highest, RMSE and REPE is 0.67 and 0.12 respectively, and so LAI was retrieved by EVI in the paper. Through LAI inversed by the remote sensing data, the cotton growth conditions was quantitatively translated into three types of small population, classic population, and big population with LAI expert knowledge. The method classifying the cotton growth conditions was demonstrated in the research region in 2006 and 2007. The research results would contribute to the precision management on the cotton filed.