To some extent, the estimations of regional evapotranspiration using remotely sensed data could help researchers and
decision makers to analyze regional crop water stress information, to estimate drought extent and water supply situation,
and to make decisions on crop water management, especially for irrigation farming systems. In this paper, the Surface
Energy Balance Algorithm for Land (SEBAL) is applied to retrieve land surface evapotranspiration in Guanzhong Plain
and Weibei Tablelands by using Landsat TM data. After the preprocessing of the remotely sensed data and weather
observation data, some algorithms of inversing the parameters under the SEBAL are developed locally. By using energy
balance equation, latent heat flux during satellite overpass and the integrated daily fluxes in Guanzhong Plain and
Weibei Tablelands are estimated. On the basis of the study area, some locally retrieved land surface parameters are used
as inputs for the SEBAL. Spatial distributions of surface albedo, land surface temperature, as well as various energy
fluxes are drawn. After parameter improvement, statistical analysis indicates that the operating results in Guanzhong
Plain and Weibei Tablelands are simple and reasonable, also shows good application prospect of SEBAL.
A parabola model is employed to fit high voltage power lines by using the remotely identified coordinates of the spacers
and tower corners of power lines. The shortest distance between land surface height at a pixel and heights of power lines
is defined as the warning index for indicating dangerousness of power lines. Three approaches on visualizing the
warning index map are developed and implemented in the Airborne Multi-angle Power Line Inspection System. The
visualization results show that the sliced color warning approach costs a relative low computing time, and can highlight
the dangerous warning sites, the fused color warning approach integrates land surface properties and the warning levels,
and the pseudo-color warning approach costs a relative high computing time and can be applied to visually interpret the
dangerousness by gradual color change.
The directional normalized difference vegetation index (NDVI) was calculated by using field measured spectral data of the winter wheat canopies at the crop different growth and development stages. In the principal plane, the hotspot and directional NDVI were analyzed. The relationships between NDVI and leaf area index (LAI) were developed, and the effects of the directionality on vegetation index based applications were discussed as well. Considering the reflectance changes near the hotspot for the two winter wheat cultivars which have a horizontal leaf type and an erectile leaf type, respectively, we find spectral reflectances of the red and near-infrared bands at the hotspot are slightly higher than those near the hotspot. However, NDVI at the hotspot is significantly lower than that near the hotspot. The regression analysis results between NDVI and LAI show that there are significant exponent correlations between directional NDVI and LAI for the two cultivars. However, the coefficients of the exponent equations and the correlation coefficients are varied with the viewing zenith angles, and the correlation coefficients for the two wheat canopy types are also varied at a given viewing angle. These results indicate the directionality of NDVI should be considered for NDVI based agricultural applications.
With remote sensing techniques, we could relate remote sensing measurements to the biochemical characteristics of the Earth surfaces in a reliable and operational way. It plays an important role in the estimate of the biochemical contents. The spectroscopic estimation of vegetation biochemical concentration was welcoming a new dawn with the developments of high spectral remote sensing technologies. Leafs of wheat at different grow period were used to measure reflectance spectra and biochemical components concentration. Reflectance spectra of leaf were measured by ASD field spectrometer in the spectra range of 350nm ~ 1650nm. Two kinds of statistical methods were used to inversion the biochemical concentrations: Stepwise regression analysis and partial least-squares regression, which were applied to established models of biochemical components concentrations (chlorophyll and water) with reflectance spectra of wheat's leaf at different grow period. The inversion results of two methods are: For chlorophyll, the correlation coefficient is 0.894, 0.898, and the relative standard deviation is 13.8%, 13.6% respectively; For water, the correlation coefficient is 0.983, 0.999, and the relative standard deviation is 2.3%, 0.3% respectively. Stepwise regression analysis method and partial least-squares regression method may inversion the chlorophyll and water of wheat leaf at different grow periods.
The study is focused on the methods for determining the warm edge and cold edge of vegetation temperature condition index (VTCI) drought monitoring approach by using the multi-years' composited LST and NDVI products. The results show the warm edge can be determined by using multi-years' maximum value composited LST and NDVI products at a specific period, While, the cold edge is determined by a maximum-minimum value composited approach. The ground-measured precipitation data in Guanzhong Plain, Shaanxi Province, PR China are employed to validate the edge determining methods and VTCI approach. The correlation coefficients between VTCI and cumulative precipitation at 1 or 2 periods of ten days' interval are the highest. These results indicate that the edge determining methods are suitable for VTCI approach, and VTCI is a near-real time drought monitoring approach.
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