棉花生长初期灌溉信息遥感提取与校正

为提高生长初期低覆盖度作物长势的遥感监测精度,需要消除灌溉引起的土壤水分背景变化对归一化差值植被指数(NDVI)的影响。为了实现棉花生长初期灌溉信息提取与校正,提高棉花作物长势监测与产量预判精度,本文以美国加利福尼亚州San Joaquin Valley的2个棉花地块为研究区,选取棉花生长初期灌溉过程中的遥感影像,构建两种灌溉信息提取方法(分阶段阈值法和灌溉线提取法),确定最优灌溉像元提取方法;比较分析灌溉与未灌溉情况下棉花的NDVI与归一化差值水分指数(NDWI)以及土壤调节植被指数的关系,提取含有灌溉信息的像元,并对NDVI进行校正,消除灌溉对NDVI的影响。研究结果表明:在棉花生长初期,灌溉与未灌溉像元NDVI变化率达12%,差异较显著;灌溉与否的棉花NDVI与NDWI间均存在极显著的线性关系,决定系数在0.80以上;利用灌溉线方法提取灌溉信息与分阶段阈值相比精度更高,精度达88%以上;校正后线性回归模型精度达0.95,灌溉校正效果明显,灌溉与未灌溉像元的NDVI差异减小至2%。本研究通过对含有灌溉信息像元NDVI值的校正,去除灌溉对NDVI造成的影响,反映了真实的植被信息,可实现对作物生长初期长势的准确遥感监测,为遥感定量监测提供便利。

Using remote sensing to extract and correct irrigation data during early cotton growth stage

Vegetation index is affected by background soil, especially in the early stage of crop growth. When vegetation cover is low, the effect of background soil is very obvious. In order to improve the precision of remote sensing (RS) monitoring on crop growth in the early growth stage, it is necessary to eliminate the effect of background soil moisture due to irrigation on normalized difference vegetation index (NDVI). Agricultural irrigation districts have failed to develop an effective method to eliminate difference in NDVI change, which has in turn hindered efforts to limit the effect of irrigation on NDVI. Thus, in order to increase the accuracy of RS monitoring of crop growth at early stage, this study explored the effects of difference in soil moisture information between irrigated and non-irrigated cotton field on NDVI. Two cotton plots in San Joaquin Valley in California (US) were selected as the research area. Day of Year (DOY) 174 was determined as the critical phase at early growth stage of cotton for the extract of irrigation data through band reflectance, NDVI analysis of cotton field for 2002. Based on RS images, NDVI, normalized difference water index (NDWI), soil adjusted vegetation index (SAVI) and modified soil adjusted vegetation index (MSAVI) of irrigated and non-irrigated pixels were calculated. Also the relationships between NDWI and different vegetation indexes (VIs) were analyzed, and the two methodsthe standard deviation of the NDWI method (STD_WI) and irrigation line extraction method (based on relationship between NDVI and NDWI of irrigation and non-irrigation pixels, IR_L)] were used to extract the irrigation data. Then the accuracies of different methods were compared to determine the optimum extraction method of irrigation information. The IR_L method was next used to extract irrigation data and correct the NDVI of irrigation pixels in the early stage of cotton to improve monitoring accuracy of cotton growth. The results showed that difference in NDVI between irrigation and non-irrigation pixels was as high as 12% in the early growth of cotton. There was an extremely significant linear correlation between NDVI and NDWI of both irrigation and non-irrigation pixels, with coefficients of determination greater than 0.80. Compared with STD_WI method, IR_L method had a higher accuracy and with a precision greater than 88%. Through IR_L model correction, the accuracy of irrigation linear regression model was as high as 0.95. With this, correction effect of irrigation was obvious and the difference in NDVI between irrigated and non-irrigated pixels dropped to 2%. Thus in this study, NDVI with irrigation data was corrected, the effect of irrigation on NDVI eliminated while the effect of background soil moisture reduced. Finally, the study reflected the true vegetation data, obtained accurate remote sensing monitoring of cotton growth at the early growth stage and provided convenient monitoring method of crop growth via remote sensing. Moreover, it promoted accurate irrigation towards saving water resources.