Integration of spatial attractions between and within pixels for sub-pixel mapping

As a promising technique to enhance the spatial reso- lution of remote sensing imagery, sub-pixel mapping is processed based on the spatial dependence theory with the assumption that the land cover is spatially dependent both within pixels and be- tween them. The spatial attraction is used as a tool to describe the dependence. First, the spatial attractions between pixels, sub- pixel/pixel spatial attraction model （SPSAM）, are described by the modified SPSAM （MSPSAM） that estimates the attractions accord- ing to the distribution of sub-pixels within neighboring pixels. Then a mixed spatial attraction model （MSAM） for sub-pixel mapping is proposed that integrates the spatial attractions both within pix- els and between them. According to the expression of the MSAM maximumising the spatial attraction, the genetic algorithm is em- ployed to search the optimum solution and generate the sub-pixel mapping results. Experiments show that compared with SPSAM, MSPSAM and pixel swapping algorithm modified by initialization from SPSAM （MPS）, MSAM can provide higher accuracy and more rational sub-pixel mapping results.

Reconstruction of a Global 9km,8-Day SMAP Surface Soil Moisture Dataset during 2015-2020 by Spatiotemporal Fusion

Soil moisture,a crucial property for Earth surface research,has been focused widely in various studies.The Soil Moisture Active Passive(SMAP)global products at 36 km and 9 km(called P36 and AP9 in this research)have been published from April 2015.However,the 9 km AP9 product was retrieved from the active radar and L-band passive radiometer and the active radar failed in July 2015.In this research,the virtual image pair-based spatiotemporal fusion model was coupled with a spatial weighting scheme(VIPSTF-SW)to simulate the 9 km AP9 data after failure of the active radar.The method makes full use of all the historical AP9 and P36 data available between April and July 2015.As a result,8-day composited 9 km SMAP data at the global scale were produced from 2015 to 2020,by downscaling the corresponding 8-day composited P36 data.The available AP9 data and in situ reference data were used to validate the predicted 9 km data.Generally,the predicted 9 km SMAP data can provide more spatial details than P36 and are more accurate than the existing EP9 product.The VIPSTF-SW-predicted 9 km SMAP data are an accurate substitute for AP9 and will be made freely available to support research and applications in hydrology,climatology,ecology,and many other fields at the global scale.