[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.

[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.