一种基于植被指数-地表温度特征空间的蒸散指数

利用遥感获取的植被指数和地表温度信息,进行地表能量和水分平衡过程研究是目前陆表过程研究的前沿。根据地表能量和水分平衡原理,对地表温度(Ts)、植被指数(VI),地表蒸散发(ET)之间的空间关系进行了分析,并基于假设条件,构建了温度植被蒸散指数(TVETI)。为验证TVETI表征地表蒸散的能力,利用环境卫星数据和SEBAL模型,对SEBAL模型中各能量通量构建的蒸散指数与TVETI进行线性回归分析,从2012年4~9月6个不同日期的确定性系数分别为:0.838、0.935、0.912、0.921、0.926、0.825, TVETI能很好的表征地表蒸散能力。通过对SEBAL模型估算的ET和TVETI估算的ET进行交叉验证发现,两者大小一致性显著, TVETI可以实现区域尺度地表蒸散发的快速估算。

An evapotranspiration index from Ts-VI feature space

Evapotranspiration(ET) in arid and semi-arid regions is a key factor for regional energy balance,hydrological cycle and water utilization. Remote sensing technology can provide land surface parameters as inputs for the land surface energy balance model and has been successfully applied to estimate surface evapotranspiration(ET) at different regions. Since 1990s, a series of ET models are build on the use of net solar radiation flux (Rn), soil heat flux(G), sensible heat flux(H) estimated from remote sensing data of visible, near infrared, thermal infrared spectral band. These models can be divided into two categories, the single-layer model of Penman-Monteith(P-M)and the "residue approach" that H is the core inversion parameter. Although great progress has been made on the improvement of the ET models, there are several related problems that have not yet been solved properly. A typical problem is that most of high-precision ET models cannot be used operationally for lack of ground-based measurements. In this paper, an ET model only need land surface temperature(LST), normalized difference vegetation index(NDVI) is introduced. In the model, the space relation of land surface temperature, vegetable index and ET were analyzed according to the principle of energy and water balance, based on assumptions that the study area cover a full range of vegetation cover and soil wetness conditions, and H is in proportional to the temperature difference between atmospheric before receiving solar radiation and land surface after receiving solar radiation, the temperature vegetable evapotranspiration index(TVETI) was built by using the dry and wet edges in the Ts-VI trapezoid space. As a typical arid area, Karamay area, Xinjiang, China was selected as the study area and HJ-1 satellite data was selected as the remote sensing data, ET on six different dates from April to September were estimated using combined TVETI and Rn. Then, ET estimated from SEBAL model was used to validate the TVETI. Regression analysis of TVETI and ET index obtained from SEBAL model showed that dates from April to September, certainty coefficient are respectively:0.838, 0.935, 0.912, 0.921, 0.926, 0.825. TVETI is effectively in characterizing the surface evapotranspiration condition. Moreover, cross validation were carry out between ET estimated by using TVETI and SEBAL model, regression analysis showed that the certainty coefficient are respectively:0.712, 0.831, 0.828, 0.884, 0.877, 0.690. TVETI can be used to estimate ET operationally in the study area and provide a reasonable estimation accuracy with only satellite derived parameters. However, for lack of ground experimental data, ET estimated from SEBAL model was selected as the validation data, leading to errors in the validation part. The proposed TVETI depends on assumption that H is in proportional to the temperature difference between atmospheric before receiving solar radiation and land surface, calculative process of H is more complicated in actual calculation. On the other hand, the dry and wet edges in the Ts-VI trapezoid space is built on the condition that the study area cover a full range of vegetation cover and soil wetness conditions, this cannot be easily realized in arid areas. In practical, the dry and wet edges are obtained from the fitting result of the LST and NDVI, equation of the dry and wet edges are short of rigid physical significance that causing uncertainty in the building of TVETI. To reduce the uncertainty in the proposed TVETI, further work needs to be carried out to verify the relevant parameters and more validation work needs to be performed for different remote sensing data in different regions.