Retrieval and Mapping of Soil Texture Based on Land Surface Diurnal Temperature Range Data from MODIS.

Numerous studies have investigated the direct retrieval of soil properties, including soil texture, using remotely sensed images. However, few have considered how soil properties influence dynamic changes in remote images or how soil processes affect the characteristics of the spectrum. This study investigated a new method for mapping regional soil texture based on the hypothesis that the rate of change of land surface temperature is related to soil texture, given the assumption of similar starting soil moisture conditions. The study area was a typical flat area in the Yangtze-Huai River Plain, East China. We used the widely available land surface temperature product of MODIS as the main data source. We analyzed the relationships between the content of different particle soil size fractions at the soil surface and land surface day temperature, night temperature and diurnal temperature range (DTR) during three selected time periods. These periods occurred after rainfalls and between the previous harvest and the subsequent autumn sowing in 2004, 2007 and 2008. Then, linear regression models were developed between the land surface DTR and sand (> 0.05 mm), clay (< 0.001 mm) and physical clay (< 0.01 mm) contents. The models for each day were used to estimate soil texture. The spatial distribution of soil texture from the studied area was mapped based on the model with the minimum RMSE. A validation dataset produced error estimates for the predicted maps of sand, clay and physical clay, expressed as RMSE of 10.69%, 4.57%, and 12.99%, respectively. The absolute error of the predictions is largely influenced by variations in land cover. Additionally, the maps produced by the models illustrate the natural spatial continuity of soil texture. This study demonstrates the potential for digitally mapping regional soil texture variations in flat areas using readily available MODIS data.

[Prediction of Cadmium Content in the Leaves of Navel Orange in Heavy Metal Contaminated Soil Using VIS-NIR Reflectance Spectroscopy].

Visual and Near-infrared (VIS-NIR) reflectance spectroscopy had been used widely in monitoring agricultural pollution in recent years, however, it was rarely applied in monitoring the contamination of heavy metal in orchards. In the present paper, Newhall navel orange (Citrus sinensis [L.] Osbeck cv. Newhall) were cultivated in the potted soil contaminated with cadmium (Cd) at different levels, and the spectral reflectance and Cd content in the leaves were measured simultaneously at different growing seasons, which then were used to establish the prediction model by partial least squares regression (PLSR) based on spectral reflectance and by linear regression based on spectral index. The results showed that Cd was more easily transferred to and cumulated in the new leaves, and this phenomenon was more obvious in heavily contaminated soils with Cd. Blue shift in red edge was found in the band of 700-730 nm in the new leaves, however, no such phenomenon was found in the old leaves. The coefficient of determination (R²) of linear regression model based on spectral index was nearly 0. 8, while the PLSR model had a better result in predicting Cd content in the new leaves than the linear regression with R²CV of approximately 0.9. Furthermore, the standard normal variate transformation(SNV) in spectral preprocessing can improve the precision significantly in PLSR model. These results suggest that the VIS-NIR method has a great potential in monitoring heavy metal pollution in the navel orange.

[Predicting soil salinity based on spectral symmetry under wet soil condition].

There has been a growing interest in using spectral reflectance as a rapid and inexpensive tool for soil salinity monitoring in recent years. However, since soil moisture often exerts a tremendous influence on soil reflectance, the monitoring accuracy under various moisture conditions cannot fully satisfy the requirements of agricultural practice. In the present paper, a linear model was built to relate the spectral symmetry in the band of 1 370 - 1 610 nm with the salt content and moisture content of the saline soil based on regularly measured data of reflectance, soil moisture and salt content of the surface of 5 soil columns during the simulated evaporation process in laboratory. The results showed that the model was good with r greater than 0.8. By inversing the model, soil salt content then was predicted after moisture content was determined. The results showed that the prediction accuracy was acceptable with a root mean square error (RMSE) of 2.059 g x kg(-1) and an r of 0.656. The results demonstrated the feasibility of using spectral symmetry to predict soil salt content under various moisture conditions.

Analyzing the Variation of Building Density Using High Spatial Resolution Satellite Images: the Example of Shanghai City.

Building density is an important issue in urban planning and land management. In the article, building coverage ratio (BCR) and floor area ratio (FAR) values extracted from high resolution satellite images were used to indicate buildings' stretching on the surface and growth along the third dimension within areas of interest in Shanghai City, P.R. China. The results show that the variation of FAR is higher than that of BCR in the inner circle, and that the newer commercial centers have higher FAR and lower BCR values, while the traditional commercial areas have higher FAR and BCR ratios. By comparing different residential areas, it was found that the historical "Shikumen" areas and the old residential areas built before 1980s have higher BCR and lower FAR, while the new residential areas have higher FAR and lower BCR, except for the villa areas. These results suggest that both older building areas and villa areas use land resources in an inefficient way, and therefore better planning and management of urban land are needed for those fast economic growing regions.