农业工程学报Issue(7)：163-172,10.DOI:10.3969/j.issn.1002-6819.2014.07.019
基于温度植被干旱指数的江苏淮北地区农业旱情监测
Agricultural drought monitoring in north Jiangsu by using temperature vegetation dryness index
摘要
Abstract
This paper focuses on developing an agricultural droughty monitoring method in north Jiangsu province based on the measurements from the Moderate Resolution Imaging Spectroradiometer (MODIS). In order to build soil moisture estimation model, we collected gravimetric water content of soil at experimental sites in 2011, measured the soil moisture of the sites in 2012, and downloaded the 8-day MODIS reflectance and land surface temperature data from January to May in 2011 and 2012 in this study region. The gravimetric water content of soil included soil moisture at 10 cm depth and at 20 cm depth. The used MODIS data have some noise from atmospheric effects, and some data can not be acquired because of cloud. Therefore, a Savitzky-Golay (S-G) filter method was selected to remove NDVI and LST noise, and generate lost NDVI and LST. Then, the Temperature-Vegetation Dryness Index (TVDI) was calculated from the re-created NDVI and LST data. A correlation analysis between TVDI and soil moisture at 10 cm and 20 cm depth were conducted. The results showed that TVDI was more correlative with soil moisture at 10 cm depth compared to at 20 cm depth, and that soil moisture at 10 cm depth was highly correlative with soil moisture at 20 cm depth. Based on the TVDI and soil moisture data at 10 cm depth, an empirical model for soil moisture estimation was built and validated. In addition, an empirical model was also built to describe the relationship between soil moisture at 10 cm and 20 cm depth. Finally, the two models was utilized to estimate soil moisture at 20 cm depth in the area from MODIS data, and the estimated soil moisture was used to monitor field droughty status with a criterion about wheat field draughty evaluation. The results show that S-G filter method removes the MODIS data noise, and can be used to generate the lost data. The correlation analysis between soil moisture and TVDI shows that TVDI has higher correlation with soil moisture at 10 cm depth, and a linear model can be used to best-fit the relationship between TVDI and the soil moisture at 10 cm depth. The correlation analysis between soil moisture at 10 cm depth and at 20 cm depth shows that soil moisture at 20 cm depth has higher correlation with soil moisture at 10 cm depth, and a linear model can be used to best-fit the relationship between soil moisture at 10 cm depth and at 20 cm depth. The validation experiments show that the model obtains a high accuracy of soil moisture estimation with an r2 of 0.575 and a RMSE of 2.59 %. Using this model, soil moisture maps at 10 cm depth were obtained. The linear model describing the relationship between soil moisture at 10 cm and 20 cm depth was used to obtain soil moisture maps at 20 cm depth. Wheat field draught maps in north Jiangsu Province were obtained by the criterion about wheat field draughty evaluation. Validation experiments showed that the experiments showed the droughty monitoring method was promising in monitoring the droughty, which appeared in north Jiangsu province.关键词
干旱/监测/遥感/土壤湿度/中分辨率成像光谱仪/温度植被干旱指数Key words
drought/monitoring/remote sensing/soil moisture/moderate-resolution imaging spectroradiometer (MODIS)/temperature vegetation drought index (TVDI)分类
天文与地球科学引用本文复制引用
鲍艳松,严婧,闵锦忠,王冬梅,李紫甜,李鑫川..基于温度植被干旱指数的江苏淮北地区农业旱情监测[J].农业工程学报,2014,(7):163-172,10.基金项目
国家重点基础研究发展计划（973计划）资助项目（2013CB430101）；中国博士后科学基金资助项目（20090461131，201003596）；江苏高校优势学科建设工程资助项目（PAPD）资助 （）
