In coastal China, there is an urgent need to increase land for agriculture. One solution is land reclamation from coastal tidelands, but soil salinization poses a problem. Thus, there is need to map saline areas and identify appropriate management strategies. One approach is the use of digital soil mapping. At the first stage, auxiliary data such as remotely sensed multispectral imagery can be used to identify areas of low agricultural productivity due to salinity. Similarly, proximal sensing instruments can provide data on the distribution of soil salinity. In this study, we first used multispectral QuickBird imagery (Bands 1–4) to provide information about crop growth and then EM38 data to indicate relative salt content using measurements of apparent soil electrical conductivity (ECa) in the horizontal (ECh) and vertical (ECv) modes of operation. Second, we used a fuzzy k‐means (FKM) algorithm to identify three salinity management zones using the normalized difference vegetation index (NDVI), ECh and ECv/ECh. The three identified classes were statistically different in terms of auxiliary and topsoil properties (e.g. soil organic matter) and more importantly in terms of the distribution of soil salinity (ECe) with depth. The resultant three classes were mapped to demonstrate that remote and proximally sensed auxiliary data can be used as surrogates for identifying soil salinity management zones.
The World Meteorological Organization stipulates a minimum of 30 years of historical data is needed to obtain meaningful results in climatological research. However, large numbers of studies have explored downscaling approaches based on the TRMM Multi-Satellite Precipitation Analysis (TMPA) data, which span only from 1998 to the present, to obtain the precipitation estimates (~1-km resolution). The main aim of the present study was to develop a new method for obtaining long-term (>30 years) precipitation estimates at~1-km resolution and to apply that method to a region with complex topography, the Tibetan Plateau. First, PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record) data were used for downscaling. Considering the characteristics of the PERSIANN-CDR data, a new downscaling-calibration procedure utilizing a combination of a spatial data mining downscaling algorithm (Cubist) and a geographical ratio analysis calibration method was proposed. We found that (1) both the original PERSIANN-CDR data (Bias~40.79%) and the downscaled results before calibration (Bias~26.78%) overestimated the precipitation compared with ground observations; (2) the final downscaled results based on the PERSIANN-CDR data after calibration were close to the ground observations (Bias~5%); (3) compared to the results interpolated based on the PERSIANN-CDR data (E <−1.0), both the downscaling procedure and calibration procedure contributed significantly to the accuracy of the final downscaled results (E~0.83). These findings suggest that the proposed downscaling-calibration procedure has great potential as an approach for retrieving long-term precipitation estimates (~1-km resolution) over the Tibetan Plateau.
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