[Assessment of the impacts of soil erosion on water environment based on the integration of soil erosion process and landscape pattern].

The integration of the effects of landscape pattern to the assessment of the impacts of soil erosion on eco-environmental is of practical significance in methodological prospect, being able to provide an approach for identifying water body's sediment source area, assessing the potential risks of sediment export of on-site soil erosion to the target water body, and evaluating the capacity of regional landscape pattern in preventing soil loss. In this paper, the RUSLE model was applied to simulate the on-site soil erosion rate. With the consideration of the soil retention potential of vegetation cover and topography, a quantitative assessment was conducted on the impacts of soil erosion in the water source region of the middle route for South-to-North Water Transfer Project on rivers and reservoirs by delineating landscape pattern at point (or cell) scale and sub-watershed level. At point (or grid cell) scale, the index of soil erosion impact intensity (I) was developed as an indicator of the potential risk of sediment export to the water bodies. At sub-watershed level, the landscape leakiness index (LI) was employed to indicate the sediment retention capacity of a given landscape pattern. The results revealed that integrating the information of landscape pattern and the indices of soil erosion process could spatially effectively reflect the impact intensity of in situ soil erosion on water bodies. The LI was significantly exponentially correlated to the mean sediment retention capacity of landscape and the mean vegetation coverage of watershed, and the sediment yield at sub-watershed scale was significantly correlated to the LI in an exponential regression. It could be concluded that the approach of delineating landscape pattern based on soil erosion process and the integration of the information of landscape pattern with its soil retention potential could provide a new approach for the risk evaluation of soil erosion.

[Effect of spectral resolution on black soil organic matter content predicting model based on laboratory reflectance].

Laboratory reflectance of Black soil samples was re-sampled with different spectral resolution, and the correlation between soil organic matter (OM) and reflectance, spectral variables was analyzed to study the effect of spectral resolution on black soil OM predicting model. The results are as follows: the spectral response range of black soil OM is between 445 and 1 380 nm, high OM content shades the spectral effect of other soil properties. The precision of black soil OM predicting models increases and decreases with spectral resolution, and the maximum accuracy is at 50 nm, which is wider than hyperspectral resolution, and narrower than the bandwidth of multispectral sensors; with the derivative of logarithmic reflectance reciprocal as input variables, the optimal black soil organic matter predicting model shows high accuracy, with R2 = 0.799 and RMSE = 0.439; the results can provide the academic and technical support for soil organic matter remote sensing reversing and quick instrument developing.

[Accuracy improvement of spectral classification of crop using microwave backscatter data].

In the present study, VV polarization microwave backscatter data used for improving accuracies of spectral classification of crop is investigated. Classification accuracy using different classifiers based on the fusion data of HJ satellite multi-spectral and Envisat ASAR VV backscatter data are compared. The results indicate that fusion data can take full advantage of spectral information of HJ multi-spectral data and the structure sensitivity feature of ASAR VV polarization data. The fusion data enlarges the spectral difference among different classifications and improves crop classification accuracy. The classification accuracy using fusion data can be increased by 5 percent compared to the single HJ data. Furthermore, ASAR VV polarization data is sensitive to non-agrarian area of planted field, and VV polarization data joined classification can effectively distinguish the field border. VV polarization data associating with multi-spectral data used in crop classification enlarges the application of satellite data and has the potential of spread in the domain of agriculture.

[Review of crop residue fractional cover monitoring with remote sensing].

Crop residue, as an important element of agro-ecosystem, can influence the flow of nutrients, carbon, water, and energy in agro-ecosystem. As a crucial indicator of distribution of crop residue, crop residue fractional cover is a key parameter of agro-ecosystem carbon cycle process model. Since remote sensing can easily obtain quantities of data, many researches were carried out on monitoring crop residue fractional cover with remote sensing. The present paper summarizes crop residue fractional cover estimation methods and latest progress in remote sensing, and these methods are classified into five categories according to the differences in methodologies and data sources. The principle of every method is described and compared. The advantages and shortages are also discussed and analyzed. Eventually, this paper points out some methods that should be improved, and presents the prospects of crop residue fractional cover estimation in the future.

[A review of classification methods of remote sensing imagery].

Remote sensing data classification is an important way of information extraction and a hot research topic of remote sensing technique. Classification method of remote sensing data is an important issue, and effective selection of appropriate classifier is especially significant for improving classification accuracy. Along with the development of remote sensing technique, traditional parametric classifier is difficult to meet accuracy requirement, leading to the rapid development of intelligent algorithm based non-parametric classifiers. Recently, combined classifiers become a new hot topic for its ability of utilizing complement information of single classifier. In the present paper, characters and advantages of different classifiers as well as the research prospect are analyzed. The paper provides a scientific reference for the development of remote sensing data classification technique.

[Validation of HJ-1 HSI red edge parameters based on spectral reflectance of Hyperion].

To validate the HJ-1A HSI red edge indices, spectral reflectance data from EO-1 Hyperion of close date were used to simulate the band reflectance of HJ-1A HSI. Four red edge indices (red vale position, red edge position, red edge slop and red edge swing) were extracted from both simulated and actual HJ-1A HSI band reflectance. Comparisons of the 4 red edge indices between simulated and actual HJ-1A HSI were made to validate the red edge indices product of HJ-1A HSI. The average correlation coefficient of red edge reflectance between actual and simulated HJ-1A HSI is 0.946 and its standard deviation is 0.011, thus a high consistency could be found. The correlation coefficients of red edge indices between simulated and actual HJ-1A HSI were 0.414, 0.543, 0.808 and 0.802 for red edge position, red vale position, the red edge swing and red edge slop respectively. An obvious regular varying trend was found for these 4 red edge indices along different vegetation cover fraction. The standard deviations of differential images between real and simulated HJ-1A HSI red edge indices are 5.75, 1.86, 5.7 e(-4) and 0.024. The result showed that the red edge indices from HJ-1A HSI is consistent with that from simulated indices from Hyperion; the vegetation variation could be effectively reflected in HJ-1A HSI red edge indices.

[Overview on monitoring crop biomass with remote sensing].

Crop biomass is one of the key indicators not only in crop condition monitoring, but also in crop production estimation. With the development of 3S technology, it is feasible to estimate crop biomass at large scales with remote sensing. In the present paper, the researches on crop biomass estimation models with remote sensing were reviewed, and the methods of crop biomass estimation were classified into six categories according to their differences in data source and methodology. The advantages and deficiencies of each method were discussed and analyzed. Finally, the paper presents the prospect of the methods of crop biomass estimation.

[Study on spectral reflectance characteristics of hemp canopies].

Hemp (Cannabis sativa L.) is a special economic crop and widely used in many field. It is significative for the government to master the information about planting acreage and spatial distribution of hemp for hemp industrial policy decision in China. Remote sensing offers a potential way of monitoring large area for the cultivation of hemp. However, very little study on the spectral properties of hemp is available in the scientific literature. In the present study, the spectral reflectance characteristics of hemp canopy were systematically analyzed based on the spectral data acquired with ASD FieldSpec portable spectrometer. The wavebands and its spectral resolution for discriminating hemp from other plants were identified using difference analysis. The major differences in canopy reflectance of hemp and other plants were observed near 530, 552, 734, 992, 1 213, 1 580 and 2 199 nm, and the maximal difference is near 734 nm. The spectral resolution should be 30 nm or less in visible and near infrared regions, and 50 nm or less in middle infrared regions.