Soil moisture plays an important role in the water cycle within the surface ecosystem and it is the basic condition for the growth and development of plants. Currently, the spatial resolution of most soil moisture data from remote sensing ranges from ten to several tens of kilometres whilst those observed<i> in situ </i>and simulated for watershed hydrology, ecology, agriculture, weather and drought research are generally less than 1 kilometre. Therefore, the existing coarse resolution remotely sensed soil moisture data needs to be down-scaled. In this paper, a universal soil moisture downscaling model through stepwise regression with moving window suitable for large areas and multi temporal has been established. Datasets comprise land surface, brightness temperature, precipitation, soil and topographic parameters from high resolution data, and active/passive microwave remotely sensed soil moisture data from Essential Climate Variables (ECV_SM) with 25 km spatial resolution were used. With this model, a total of 288 soil moisture maps of 1 km resolution from the first ten-day of January 2003 to the last tenth-day of December 2010 were derived. The<i> in situ </i>observations were used to validate the down-scaled ECV_SM for different land cover and land use types and seasons. In addition, various errors comparative analysis was also carried out for the down-scaled ECV_SM and original one. In general, the down-scaled soil moisture for different land cover and land use types is consistent with the<i> in situ </i>observations. The accuracy is relatively high in autumn and winter. The validation results show that downscaled soil moisture can be improved not only on spatial resolution, but also on estimation accuracy.
Image matching is a very important technique in image processing. It has been widely used for object recognition and
tracking, image retrieval, three-dimensional vision, change detection, aircraft position estimation, and multi-image
registration. Based on the requirements of matching algorithm for craft navigation, such as speed, accuracy and
adaptability, a fast key point image matching method is investigated and developed. The main research tasks includes:
(1) Developing an improved celerity key point detection approach using self-adapting threshold of Features from
Accelerated Segment Test (FAST). A method of calculating self-adapting threshold was introduced for images with
different contrast. Hessian matrix was adopted to eliminate insecure edge points in order to obtain key points with higher
stability. This approach in detecting key points has characteristics of small amount of computation, high positioning
accuracy and strong anti-noise ability; (2) PCA-SIFT is utilized to describe key point. 128 dimensional vector are formed
based on the SIFT method for the key points extracted. A low dimensional feature space was established by eigenvectors
of all the key points, and each eigenvector was projected onto the feature space to form a low dimensional eigenvector.
These key points were re-described by dimension-reduced eigenvectors. After reducing the dimension by the PCA, the
descriptor was reduced to 20 dimensions from the original 128. This method can reduce dimensions of searching
approximately near neighbors thereby increasing overall speed; (3) Distance ratio between the nearest neighbour and
second nearest neighbour searching is regarded as the measurement criterion for initial matching points from which the
original point pairs matched are obtained. Based on the analysis of the common methods (e.g. RANSAC (random sample
consensus) and Hough transform cluster) used for elimination false matching point pairs, a heuristic local geometric
restriction strategy is adopted to discard false matched point pairs further; and (4) Affine transformation model is
introduced to correct coordinate difference between real-time image and reference image. This resulted in the matching
of the two images. SPOT5 Remote sensing images captured at different date and airborne images captured with different
flight attitude were used to test the performance of the method from matching accuracy, operation time and ability to
overcome rotation. Results show the effectiveness of the approach.