ABSTRACT
Soil moisture content (SMC) is one of the most important indicators influencing the exchange of energy and water among vegetation, soil, and the atmosphere. Accurate detection of soil moisture content is beneficial to improving the precision of crop yield evaluating and field management measures. In this paper, a novel method ADI (Angle Dryness Index) based on NIR-RED spectral feature space used for calculating SMC was proposed, which improved the accuracy of calculating SMC with red and near infrared band reflectance. It was found that an intermediate parameter θ in NIR-RED feature space was significantly related to SMC, and independent of vegetation coverage according to the linear decomposition of mixed pixel and the empirical correlation between SMC and red/NIR band reflectance which were achieved by previous researches. Then, ADI was proposed with the feature discovered in the paper. The mathematical expression on SMC is nonlinear, and the newton iterative method is applied to ADI for calculation SMC. Then, the newly proposed method was validated with two kinds of remote sensing imagery data (Thematic Mapper (TM) and moderate resolution imaging spectrometer (MODIS)) and the synchronous observed data in the field. Validation results revealed that the ADI- derived SMC was highly accordant with the in-situ results with high correlation (R2=0.74 with TM and R2=0.64 with MODIS data). We also calculated MPDI (Modified Perpendicular Drought Index) developed by Ghulam, which is also proposed with the red and near infrared reflectance. The result showed that the accuracy of MPDI was lower than that of ADI. The most likely reason was that ADI was insensitive to fv, but the calculation errors of fv would reduce the accuracy of SMC estimation. MODIS had a low spatial resolution, thus there may be more than two end members in a mixed pixel. In this case, the linear decomposition of mixed pixel was not applicable and the errors would finally be enlarged. ADI achieved good results in monitoring SMC in vegetated area because it was less influenced by vegetation coverage than other similar approaches. ADI only requires the satellite image data including the red and near infrared band which are available from most of the optical sensors. Therefore, it is an effective and promising method for monitoring SMC in vegetated area, and would be widely used in agriculture, meteorology, and hydrology.
ABSTRACT
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.
ABSTRACT
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.
ABSTRACT
Land surface temperature (Ts) is influenced by soil background and vegetation growing conditions, and the combination of Ts and vegetation indices (Vis) can indicate the status of surface soil moisture content (SMC). In this study, Advanced Temperature Vegetation Dryness Index (ATVDI) used for monitoring SMC was proposed on the basis of the simulation results with agricultural climate model CUPID. Previous studies have concluded that Normalized Difference Vegetation Index (NDVI) easily reaches the saturation point, andLeaf Area Index (LAI) was then used instead of NDVI to estimate soil moisture content in the paper. With LAI-Ts scatter diagram established by the simulation results of CUPID model; how Ts varied with LAI and SMC was found. In the case of the identical soil background, the logarithmic relations between Ts and LAI were more accurate than the linear relations included in Temperature Vegetation Dryness Index (TVDI), based on which ATVDI was then developed. LAI-Ts scatter diagram with satellite imagery were necessary for determining the expression of the upper and lower logarithmic curves while ATVDI was used for monitoring SMC. Ts derived from satellite imagery were then transformed to the Ts-value which has the same SMC and the minimum LAI in study area with look-up table. The measured SMC from the field sites in Weihe Plain, Shanxi Province, China, and the products of LAI and Ts (MOD15A2 and MOD11A2, respectively) produced by the image derived from Moderate Resolution Imaging Spectrometer (MODIS) were collected to validate the new method proposed in this study. The validation results shown that ATVDI (R² = 0.62) was accurate enough to monitor SMC, and it achieved better result than TVDI. Moreover, ATVDI-derived result were Ts values with some physical meanings, which made it comparative in different periods. Therefore, ATVDI is a promising method for monitoring SMC in different time-spatial scales in agricultural fields.
