[Bare Soil Moisture Inversion Model Based on Visible-Shortwave Infrared Reflectance].

Soil is the loose solum of land surface that can support plants. It consists of minerals, organics, atmosphere, moisture, microbes, et al. Among its complex compositions, soil moisture varies greatly. Therefore, the fast and accurate inversion of soil moisture by using remote sensing is very crucial. In order to reduce the influence of soil type on the retrieval of soil moisture, this paper proposed a normalized spectral slope and absorption index named NSSAI to estimate soil moisture. The modeling of the new index contains several key steps: Firstly, soil samples with different moisture level were artificially prepared, and soil reflectance spectra was consequently measured using spectroradiometer produced by ASD Company. Secondly, the moisture absorption spectral feature located at shortwave wavelengths and the spectral slope of visible wavelengths were calculated after analyzing the regular spectral feature change patterns of different soil at different moisture conditions. Then advantages of the two features at reducing soil types' effects was synthesized to build the NSSAI. Thirdly, a linear relationship between NSSAI and soil moisture was established. The result showed that NSSAI worked better (correlation coefficient is 0.93) than most of other traditional methods in soil moisture extraction. It can weaken the influences caused by soil types at different moisture levels and improve the bare soil moisture inversion accuracy.

[Modeling Soil Spectral Reflectance with Different Mass Moisture Content].

The spatio-temporal distribution and variation of soil moisture content have a significant impact on soil temperature, heat balance between land and atmosphere and atmospheric circulation. Hence, it is of great significance to monitor the soil moisture content dynamically at a large scale and to acquire its continuous change during a certain period of time. The object of this paper is to explore the relationship between the mass moisture content of soil and soil spectrum. This was accomplished by building a spectral simulation model of soil with different mass moisture content using hyperspectral remote sensing data. The spectra of soil samples of 8 sampling sites in Beijing were obtained using ASD Field Spectrometer. Their mass moisture contents were measured using oven drying method. Spectra of two soil samples under different mass moisture content were used to construct soil spectral simulation model, and the model was validated using spectra of the other six soil samples. The results show that the accuracy of the model is higher when the mass water content of soil is below field capacity. At last, we used the spectra of three sampling points on campus of Peking University to test the model, and the minimum value of root mean square error between simulated and measured spectral reflectance was 0.0058. Therefore the model is expected to perform well in simulating the spectrum reflectance of different types of soil when mass water content below field capacity.

[Monitoring of farmland drought based on LST-LAI spectral feature space].

Farmland drought has the characteristics of wide range and seriously affecting on agricultural production, so real-time dynamic monitored has been a challenging problem. By using MODIS land products, and constructing the spectral space of LST and LAI, the temperature LAI drought index (TLDI) was put forward and validated using ground-measured 0-10 cm averaged soil moisture of Ningxia farmland. The results show that the coefficient of determination (R2) of both them varies from 0.43 to 0.86. Compared to TVDI, the TLDI has higher accuracy for farmland moisture monitoring, and solves the saturation of NDVI during the late development phases of the crop. Furthermore, directly using MODIS land products LST and LAI and avoiding the complicated process of using the original MODIS data provide a new technical process to the regular operation of farmland drought monitoring.

[MTCARI: A kind of vegetation index monitoring vegetation leaf chlorophyll content based on hyperspectral remote sensing].

The chlorophyll content of plant has relative correlation with photosynthetic capacity and growth levels of plant. It affects the plant canopy spectra, so the authors can use hyperspectral remote sensing to monitor chlorophyll content. By analyzing existing mature vegetation index model, the present research pointed out that the TCARI model has deficiencies, and then tried to improve the model. Then using the PROSPECT+SAIL model to simulate the canopy spectral under different levels of chlorophyll content and leaf area index (LAI), the related constant factor has been calculated. The research finally got modified transformed chlorophyll absorption ratio index (MTCARI). And then this research used optimized soil background adjust index (OSAVI) to improve the model. Using the measured data for test and verification, the model has good reliability.