Mapping high resolution National Soil Information Grids of China

Soil spatial information has traditionally been presented as polygon maps at coarse scales. Solving global and local issues, including food security, water regulation, land degradation, and climate change requires higher quality, more consistent and detailed soil information. Accurate prediction of soil variation over large and complex areas with limited samples remains a challenge, which is especially significant for China due to its vast land area which contains the most diverse soil landscapes in the world. Here, we integrated predictive soil mapping paradigm with adaptive depth function fitting, state-of-the-art ensemble machine learning and high-resolution soil-forming environment characterization in a highperformance parallel computing environment to generate 90-m resolution national gridded maps of nine soil properties(pH, organic carbon, nitrogen, phosphorus, potassium, cation exchange capacity, bulk density, coarse fragments, and thickness) at multiple depths across China. This was based on approximately5000 representative soil profiles collected in a recent national soil survey and a suite of detailed covariates to characterize soil-forming environments. The predictive accuracy ranged from very good to moderate(Model Efficiency Coefficients from 0.71 to 0.36) at 0–5 cm. The predictive accuracy for most soil properties declined with depth. Compared with previous soil maps, we achieved significantly more detailed and accurate predictions which could well represent soil variations across the territory and are a significant contribution to the GlobalSoilMap.net project. The relative importance of soil-forming factors in the predictions varied by specific soil property and depth, suggesting the complexity and non-stationarity of comprehensive multi-factor interactions in the process of soil development.

Regional Soil Mapping Using Multi-Grade Representative Sampling and a Fuzzy Membership-Based Mapping Approach

High-resolution and detailed regional soil spatial distribution information is increasingly needed for ecological modeling and land resource management. For areas with no point data, regional soil mapping includes two steps： soil sampling and soil mapping. Because sampling over a large area is costly, efficient sampling strategies are required. A multi-grade representative sampling strategy, which designs a small number of representative samples with different representative grades to depict soil spatial variations at different scales, could be a potentially efficient sampling strategy for regional soil mapping. Additionally, a suitable soil mapping approach is needed to map regional soil variations based on a small number of samples. In this study, the multi-grade representative sampling strategy was applied and a fuzzy membership-weighted soil mapping approach was developed to map soil sand percentage and soil organic carbon （SOC） at 0-20 and 20-40 cm depths in a study area of 5 900 km2 in Anhui Province of China. First, geographical sub-areas were delineated using a parent lithology data layer. Next, fuzzy c-means clustering was applied to two climate and four terrain variables in each stratum. The clustering results （environmental cluster chains） were used to locate representative samples. Evaluations based on an independent validation sample set showed that the addition of samples with lower representativeness generally led to a decrease of root mean square error （RMSE）. The declining rates of RMSE with the addition of samples slowed down for 20-40 cm depth, but fluctuated for 0-20 cm depth. The predicted SOC maps based on the representative samples exhibited higher accuracy, especially for soil depth 20-40 cm, as compared to those based on legacy soil data. Multi-grade representative sampling could be an effective sampling strategy at a regional scale. This sampling strategy, combined with the fuzzy membership-based mapping approach, could be an optional effective framework for regional soil property mapping. A more detailed and accurate soft parent material map and the addition of environmental variables representing human activities would improve mapping accuracy.

Multidimensional and quantitative interlinking approach for Linked Geospatial Data

Linked Data is known as one of the best solutions for multisource and heterogeneous web data integration and discovery in this era of Big Data.However,data interlinking,which is the most valuable contribution of Linked Data,remains incomplete and inaccurate.This study proposes a multidimensional and quantitative interlinking approach for Linked Data in the geospatial domain.According to the characteristics and roles of geospatial data in data discovery,eight elementary data characteristics are adopted as data interlinking types.These elementary characteristics are further combined to form compound and overall data interlinking types.Each data interlinking type possesses one specific predicate to indicate the actual relationship of Linked Data and uses data similarity to represent the correlation degree quantitatively.Therefore,geospatial data interlinking can be expressed by a directed edge associated with a relation predicate and a similarity value.The approach transforms existing simple and qualitative geospatial data interlinking into complete and quantitative interlinking and promotes the establishment of high-quality and trusted Linked Geospatial Data.The approach is applied to build data intra-links in the Chinese National Earth System Scientific Data Sharing Network(NSTI-GEO)and data-links in NSTI-GEO with the Chinese Meteorological Data Network and National Population and Health Scientific Data Sharing Platform.

Evapotranspiration partitioning using an optimality-based ecohydrological model in a semiarid shrubland

Partitioning of evapotranspiration(ET)into biological component transpiration(T)and non-biological component evaporation(E)is crucial in understanding the impact of environmental change on ecosystems and water resources.However,direct measurement of transpiration is still challenging.In this paper,an optimality-based ecohydrological model named Vegetation Optimality Model(VOM)is applied for ET partitioning.The results show that VOM model can reasonably simulate ET and ET components in a semiarid shrubland.Overall,the ratio of transpiration to evapotranspiration is 49%for the whole period.Evaporation and plant transpiration mainly occur in monsoon following the precipitation events.Evaporation responds immediately to precipitation events,while transpiration shows a lagged response of several days to those events.Different years demonstrate different patterns of T/ET ratio dynamic in monsoon.Some of the years show a low T/ET ratio at the beginning of monsoon and slowly increased T/ET ratio.Other years show a high level of T/ET ratio for the whole monsoon.We find out that spring precipitation,especially the size of the precipitation,has a significant influence on the T/ET ratio in monsoon.

An approach to extracting surface supply relationships between glaciers and lakes on the Tibetan Plateau

Surface supply relationships between glaciers and lakes are needed to analyze and understand hydrological processes at regional and global scales.However,these supply relationships still cannot be extracted efficiently by existing methods.This paper proposes an automatic and efficient approach to extracting surface supply relationships between glaciers and lakes based on meltwater flow paths.The approach includes two stages:(1)identifying direct connections between objects(i.e.glaciers and lakes)based on flow direction derived from digital terrain analysis on a gridded digital elevation model(DEM)and(2)deriving all(or user-specified)kinds of surface supply relationships based on graph search.All computation-intensive steps in this approach have been parallelized;and all steps in the proposed approach have been integrated as an automatic program.Results for the Tibetan Plateau show that given outline data for glaciers and lakes and the Shuttle Radar Topography Mission DEM,the proposed approach can automatically derive diverse surface supply relationships under userspecified restrictions on the attributes of the supply route.The parallelization in the approach effectively improves the computing efficiency.The proposed approach could also be applied to developing a detailed fundamental dataset of supply relationships between glaciers and lakes for other region or period.