Spatially explicit precipitation data is often responsible for the prediction accuracy of hydrological and ecological models. Several statistical downscaling approaches have been developed to map precipitation at a high spatial resolution, which are mainly based on the valid conjugations between satellite-driven precipitation data and geospatial predictors. Performance of the existing approaches should be first evaluated before applying them to larger spatial extents with a complex terrain across different climate zones. In this paper, we investigate the statistical downscaling algorithms to derive the high spatial resolution maps of precipitation over continental China using satellite datasets, including the Normalized Distribution Vegetation Index (NDVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), the Global Digital Elevation Model (GDEM) from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and the rainfall product from the Tropical Rainfall Monitoring Mission (TRMM). We compare three statistical techniques (multiple linear regression, exponential regression, and Random Forest regression trees) for modeling precipitation to better understand how the selected model types affect the prediction accuracy. Then, those models are implemented to downscale the original TRMM product (3B43; 0.25° resolution) onto the finer grids (1 × 1 km 2 ) of precipitation. Finally we validate the downscaled annual precipitation (a wet year 2001 and a dry year 2010) against the ground rainfall observations from 596 rain gauge stations over continental China. The result indicates that the downscaling algorithm based on the Random Forest regression outperforms, when compared to the linear regression and the exponential regression. It also shows that the addition of the residual terms does not significantly improve the accuracy of results for the RF model. The analysis of the variable importance reveals the NDVI related predictors, latitude, and longitude, elevation are key elements for statistical downscaling, and their weights vary across different climate zones. In particular, the NDVI, which is generally considered as a powerful geospatial predictor for precipitation, correlates weakly with precipitation in humid regions.

Near infrared spectroscopy combined with chemometrics and pattern recognition has become a primary focus in the discriminant analysis of agricultural products. To date, most studies have focused on using a single classifier to discriminate the origins, varieties and grades of products. Others have focused on using multiple classifier fusion by weighted voting. Due to their attributes of continuity and internal similarity, discriminant models sometimes present poor performance. In this study, we achieved better performance by applying multiple classifier fusion models, including support vector machine (SVM), discriminant partial least squares (DPLS) and principal component and Fisher criterion (PPF). PPF showed continuity and similarity among different parts of tobacco leaves [i.e. upper (B), cutter (C) and lug (X)]. The similarities between each class and the others were quantified to values, and the sum of the similarity values of each class was defined as its similarity. SVM-DPLS-PPF fusion by voting and similarity constraint for decision resulted in better performance, with the correct discriminant rate improved on average by 14.1%, 8.2%, 17.3% and 4.6%compared with those achieved using SVM, DPLS, PPF and SVM-DPLS-PPF fusion by weighted voting for decision, respectively; in addition, the incorrect discriminant rate between B and X was reduced to zero. Therefore, we demonstrated the feasibility of using SVM-DPLS-PPF fusion by voting and similarity constraint for decision to discriminate between different parts of tobacco leaves.This technique could provide a new method for tobacco quality management, computer-aided grading and intelligent acquisition. It also provides a new discriminant method for analysing the attributes of continuity and similarity of agricultural products using near infrared spectroscopy .

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