Soil map

About: Soil map is a research topic. Over the lifetime, 5310 publications have been published within this topic receiving 149217 citations. The topic is also known as: Soil map.

World Reference Base for Soil Resources

Abstract: In 1998, the International Union of Soil Sciences (IUSS) officially adopted the world reference base for soil resources (WRB) as the Union's system for soil correlation. The structure, concepts, and definitions of the WRB are strongly influenced by the FAO-UNESCO legend of the soil map of the world (1-2). At the time of itsinception, the WRB proposed 30 "Soil Reference Groups" accommodating more than 200 ("second level") soil units. WRB (3-5) was endorsed by the IUSS in 1998 and provides an opportunity to create and refine a common and global language for soil classification. WRB aims to serve as a framework through which ongoing soil classification throughout the world can be harmonized. The ultimate objective is to reach international agreement on the major soil groups to be recognized at a global scale as well as on the criteria and methodology to be applied for defining and separating them. Such an agreement is needed to facilitate the exchange of information and experience, to provide a common scientific language, to strengthen the applications of soil science, and to enhance the communication with other disciplines and make the major soil names into household names

Total carbon and nitrogen in the soils of the world

Abstract: Summary The soil is important in sequestering atmospheric CO2 and in emitting trace gases (e.g. CO2, CH4 and N2O) that are radiatively active and enhance the ‘greenhouse’ effect. Land use changes and predicted global warming, through their effects on net primary productivity, the plant community and soil conditions, may have important effects on the size of the organic matter pool in the soil and directly affect the atmospheric concentration of these trace gases. A discrepancy of approximately 350 × 1015 g (or Pg) of C in two recent estimates of soil carbon reserves worldwide is evaluated using the geo-referenced database developed for the World Inventory of Soil Emission Potentials (WISE) project. This database holds 4353 soil profiles distributed globally which are considered to represent the soil units shown on a 1/2° latitude by 1/2° longitude version of the corrected and digitized 1:5 M FAO–UNESCO Soil Map of the World. Total soil carbon pools for the entire land area of the world, excluding carbon held in the litter layer and charcoal, amounts to 2157–2293 Pg of C in the upper 100 cm. Soil organic carbon is estimated to be 684–724 Pg of C in the upper 30 cm, 1462–1548 Pg of C in the upper 100 cm, and 2376–2456 Pg of C in the upper 200 cm. Although deforestation, changes in land use and predicted climate change can alter the amount of organic carbon held in the superficial soil layers rapidly, this is less so for the soil carbonate carbon. An estimated 695–748 Pg of carbonate-C is held in the upper 100 cm of the world's soils. Mean C: N ratios of soil organic matter range from 9.9 for arid Yermosols to 25.8 for Histosols. Global amounts of soil nitrogen are estimated to be 133–140 Pg of N for the upper 100 cm. Possible changes in soil organic carbon and nitrogen dynamics caused by increased concentrations of atmospheric CO2 and the predicted associated rise in temperature are discussed.

The Soil Moisture Active Passive (SMAP) Mission

Abstract: The Soil Moisture Active Passive (SMAP) mission is one of the first Earth observation satellites being developed by NASA in response to the National Research Council's Decadal Survey SMAP will make global measurements of the soil moisture present at the Earth's land surface and will distinguish frozen from thawed land surfaces Direct observations of soil moisture and freeze/thaw state from space will allow significantly improved estimates of water, energy, and carbon transfers between the land and the atmosphere The accuracy of numerical models of the atmosphere used in weather prediction and climate projections are critically dependent on the correct characterization of these transfers Soil moisture measurements are also directly applicable to flood assessment and drought monitoring SMAP observations can help monitor these natural hazards, resulting in potentially great economic and social benefits SMAP observations of soil moisture and freeze/thaw timing will also reduce a major uncertainty in quantifying the global carbon balance by helping to resolve an apparent missing carbon sink on land over the boreal latitudes The SMAP mission concept will utilize L-band radar and radiometer instruments sharing a rotating 6-m mesh reflector antenna to provide high-resolution and high-accuracy global maps of soil moisture and freeze/thaw state every two to three days In addition, the SMAP project will use these observations with advanced modeling and data assimilation to provide deeper root-zone soil moisture and net ecosystem exchange of carbon SMAP is scheduled for launch in the 2014-2015 time frame

SoilGrids250m: Global gridded soil information based on machine learning

Abstract: This paper describes the technical development and accuracy assessment of the most recent and improved version of the SoilGrids system at 250m resolution (June 2016 update). SoilGrids provides global predictions for standard numeric soil properties (organic carbon, bulk density, Cation Exchange Capacity (CEC), pH, soil texture fractions and coarse fragments) at seven standard depths (0, 5, 15, 30, 60, 100 and 200 cm), in addition to predictions of depth to bedrock and distribution of soil classes based on the World Reference Base (WRB) and USDA classification systems (ca. 280 raster layers in total). Predictions were based on ca. 150,000 soil profiles used for training and a stack of 158 remote sensing-based soil covariates (primarily derived from MODIS land products, SRTM DEM derivatives, climatic images and global landform and lithology maps), which were used to fit an ensemble of machine learning methods-random forest and gradient boosting and/or multinomial logistic regression-as implemented in the R packages ranger, xgboost, nnet and caret. The results of 10-fold cross-validation show that the ensemble models explain between 56% (coarse fragments) and 83% (pH) of variation with an overall average of 61%. Improvements in the relative accuracy considering the amount of variation explained, in comparison to the previous version of SoilGrids at 1 km spatial resolution, range from 60 to 230%. Improvements can be attributed to: (1) the use of machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate layers and (3) to insertion of additional soil profiles. Further development of SoilGrids could include refinement of methods to incorporate input uncertainties and derivation of posterior probability distributions (per pixel), and further automation of spatial modeling so that soil maps can be generated for potentially hundreds of soil variables. Another area of future research is the development of methods for multiscale merging of SoilGrids predictions with local and/or national gridded soil products (e.g. up to 50 m spatial resolution) so that increasingly more accurate, complete and consistent global soil information can be produced. SoilGrids are available under the Open Data Base License.