A Quantification Methodology of Temperature-Vegetation Drought Index Based on Thefarmland Microclimate Method
|School||Liaoning Technical University|
|Course||Photogrammetry and Remote Sensing|
|Keywords||Drought Remote Sensing Vegetation Index Farmland Microclimate Process Model Radiation Transmission Model|
Using remote sensing technology to achieve greater scope farmland drought monitoring has gradually become advanced methods in terms of agricultural resources environmental monitoring and efficient utilization of water. Because of remote sensing technology hit qualitative analysis levels of farmland drought, but not quantitative levels, there is a certain gap between remote sensing drought monitoring and agricultural applications. In order to solve above problem, this paper simulated soil moisture, vegetation coverage and canopy temperature in different growth stages using farmland microclimate process model, and obtained the quantified model based on the feature space. Then, this model is applied to the feature space which is derived from remote sensing data to get space distribution of soil moisture. The main research contents are as follows:(1)Canopy structure parameters including leaf Area Ratio distribution, LAI etc., spectral data and meteorological data of three representative types of winter wheat in three classical growth stages are analyzed using field observation data of continuous growth stages(4– 6 month). The data are provided by XIAOTANG Mountain national precision agriculture demonstration base in 2010. Through above analysis, input parameters are provided for coupling farmland microclimate process model (CUPID) and radiation transmission model (SAILH).(2)Data of environment satellite is processed by radiation correction, atmosphere correction and temperature inversion to get NDVI and TS in the area. The NDVI/TS feature space, dry/wet edge position and slope in the area are computed by TVDI index method.(3)Coupling of CUPID model and SAILH model is achieved through two key parameters which are soil moisture and LAI. LAI and soil moisture are separately changed by LAI value in simulative growth stage and selecting rational gradient. The two key parameters are input SAILH model and COUPID model (other parameters are the field observation data or default values), canopy temperature and vegetation index will be output. Aiming at three classical growth stages, the corresponding database is established in order to quantificational analysis.(4)Using simulation results, dry/wet edges position and the feature space slope, the quantitative model which is made of NDVI/TS characteristic space and soil moisture is built. On this basis, the quantitative model is applied to NDVI/TS feature space which is calculated utilizing remote sensing data, so as to realize ration of remote sensing drought index.