WFV (Wide Field of View) cameras on-board Gaofen-1 satellite (gaofen means high resolution) provide unparalleled global observations with both high spatial and high temporal resolutions. However, the accuracy of the radiometric calibration remains unknown. Using an improved cross calibration method, the WFV cameras were re-calibrated with well-calibrated Landsat-8 OLI (Operational Land Imager) data as reference. An objective method was proposed to guarantee the homogeneity and sufficient dynamic coverage for calibration sites and reference sensors. The USGS spectral library was used to match the most appropriate hyperspectral data, based on which the spectral band differences between WFV and OLI were adjusted. The TOA (top-of-atmosphere) reflectance of the cross-calibrated WFV agreed very well with that of OLI, with the mean differences between the two sensors less than 5% for most of the reflectance ranges of the four spectral bands, after accounting for the spectral band difference between the two sensors. Given the calibration error of 3% for Landsat-8 OLI TOA reflectance, the uncertainty of the newly-calibrated WFV should be within 8%. The newly generated calibration coefficients established confidence when using Gaofen-1 WFV observations for their further quantitative applications, and the proposed simple cross calibration method here could be easily extended to other operational or planned satellite missions.

An increase in vegetation greenness can improve ecosystem productivity, but also reduce the water supply, creating the potential for conflicting water demands between ecosystems and humans. This problem has been well-assessed and is most evident in dry environments. However, in humid regions, the potential effects of vegetation greenness on water yields under drought conditions are not well understood. To address this gap, we focused on the Poyang Lake watershed in the humid region of southern China. Based on the Standardized Precipitation Evapotranspiration Index and a satellite-derived leaf area index dataset during the growing seasons of 1984 to 2013, several typical dry growing seasons were selected as the study conditions. An existing Water Supply Stress Index model was modified to investigate how the changes in vegetation greenness affected water yield and to explore potentially conflicting water demands between ecosystems and humans under drought conditions. Our results showed that an increase of 20–80% in vegetation greenness generally resulted in a reduction of 3–27% in water yield under drought conditions. Large reductions in water yield mainly were observed in forested areas due to large increases in forest greenness. Moreover, increased vegetation greenness caused a 2 to 3 times greater reduction in water yield during continuing and intensifying droughts than during a short moderate drought period. Thus, in this study, during continuing and intensifying droughts, increased vegetation greenness can cause or aggravate water conflicts in sub-watersheds with high forest cover and high human water demands. Therefore, given the increasing frequency of extreme climatic events, afforestation with a targeted approach should be implemented as it would provide the most benefits. In addition, selective harvesting in forested areas with high density could be an effective strategy to maintain water supply in humid regions.

To optimize the efficiency of the geospatial service in the flood response decision making system, a Parallel Agent-as-a-Service (P-AaaS) method is proposed and implemented in the cloud. The prototype system and comparisons demonstrate the advantages of our approach over existing methods. The P-AaaS method includes both parallel architecture and a mechanism for adjusting the computational resources—the parallel geocomputing mechanism of the P-AaaS method used to execute a geospatial service and the execution algorithm of the P-AaaS based geospatial service chain, respectively. The P-AaaS based method has the following merits: (1) it inherits the advantages of the AaaS-based method (i.e., avoiding transfer of large volumes of remote sensing data or raster terrain data, agent migration, and intelligent conversion into services to improve domain expert collaboration); (2) it optimizes the low performance and the concurrent geoprocessing capability of the AaaS-based method, which is critical for special applications (e.g., highly concurrent applications and emergency response applications); and (3) it adjusts the computing resources dynamically according to the number and the performance requirements of concurrent requests, which allows the geospatial service chain to support a large number of concurrent requests by scaling up the cloud-based clusters in use and optimizes computing resources and costs by reducing the number of virtual machines (VMs) when the number of requests decreases.

Epipolar rectification is of great importance for 3D modeling by using UAV (Unmanned Aerial Vehicle) images; however, the existing methods seldom consider the perspective distortion relative to surface planes. Therefore, an algorithm for the rectification of oblique images is proposed and implemented in detail. The basic principle is to minimize the rectified images’ perspective distortion relative to the reference planes. First, this minimization problem is formulated as a cost function that is constructed by the tangent value of angle deformation; second, it provides a great deal of flexibility on using different reference planes, such as roofs and the façades of buildings, to generate rectified images. Furthermore, a reasonable scale is acquired according to the dihedral angle between the rectified image plane and the original image plane. The low-quality regions of oblique images are cropped out according to the distortion size. Experimental results revealed that the proposed rectification method can result in improved matching precision (Semi-global dense matching). The matching precision is increased by about 30% for roofs and increased by just 1% for façades, while the façades are not parallel to the baseline. In another designed experiment, the selected façades are parallel to the baseline, the matching precision has a great improvement for façades, by an average of 22%. This fully proves our proposed algorithm that elimination of perspective distortion on rectified images can significantly improve the accuracy of dense matching.