Determination of Spatial Distribution Patterns of Clay and Plant Available Potassium Contents in Surface Soils at the Farm Scale using High Resolution Gamma Ray Spectrometry
74
Citation
60
Reference
10
Related Paper
Citation Trend
Keywords:
Soil texture
Soil test
Summary We investigated the utility of three interpolation techniques that ignored descriptive `soft' information and one that used it for mapping topsoil texture classes: re‐coding of soil map units within Geographical Information Systems (GIS), Thiessen polygons, and classifcation of probability vectors estimated by ordinary indicator kriging and simple indicator kriging with local prior means. The results were compared with texture maps based on a classifcation of kriged maps of particle size distribution. The methods were applied to two distinct regions, which represent large areas in rain‐fed rice ecosystems and irrigated rice ecosystems. The `hard' databases for both areas contained soil information needed for mapping at regional scales (1:100 000‐1:150 000). These data were complemented with 'soft' information derived from farmers and soil experts (Northeast Thailand) and soil maps (Nueva Ecija, Philippines). Interpolated maps agreed with the texture map based on interpolation of particle size distribution, and ®eld estimates of soil texture proved to be valuable surrogates for laboratory measurements of soil texture classes. The interpolation of categorical data such as soil texture classes allows for upgrading and increasing the resolution of maps in sparsely sampled regions by using simple field measurements. Validation using independent test sets showed that indicator kriging with local prior means performed best in the rain‐fed lands, whereas soft information did not improve the predictions in Nueva Ecija. Local knowledge in a formalized form was valuable in Northeast Thailand and the interpolated soil texture map for this area had an accuracy and resolution to support agronomic decisions at the village scale. The poor quality of the soil map and the fact that the gradually changing variability in young alluvial soils can be modelled with fewer data explained the lower accuracy of simple indicator kriging with local prior means in Nueva Ecija. Thiessen polygons performed well in the undulating rain‐fed lands but were not as reliable as indicator kriging in the gradually changing irrigated lands.
Soil texture
Digital Soil Mapping
Interpolation
Topsoil
Geostatistics
Texture (cosmology)
Cite
Citations (48)
Mapping soil texture in a river basin is critically important for eco-hydrological studies and water resource management at the watershed scale. However, due to the scarcity of in situ observation of soil texture, it is very difficult to map the soil texture in high resolution using traditional methods. Here, we used an integrated method based on fuzzy logic theory and data fusion to map the soil texture in the Heihe River basin in an arid region of Northwest China, by combining in situ soil texture measurement data, environmental factors, a previous soil texture map, and other thematic maps. Considering the different landscape characteristics over the whole Heihe River basin, different mapping schemes have been used to extract the soil texture in the upstream, middle, and downstream areas of the Heihe River basin, respectively. The validation results indicate that the soil texture map achieved an accuracy of 69% for test data from the midstream area of the Heihe River basin, which represents a much higher accuracy than that of another existing soil map in the Heihe River basin. In addition, compared with the time-consuming and expensive traditional soil mapping method, this new method could ensure greater efficiency and a better representation of the explicitly spatial distribution of soil texture and can, therefore, satisfy the requirements of regional modeling.
Soil texture
Thematic map
Midstream
Cite
Citations (22)
Soil texture
Digital Soil Mapping
Texture (cosmology)
Cite
Citations (11)
High-quality soil maps are urgently needed by diverse stakeholders, but errors in existing soil maps are often unknown, particularly in countries with limited soil surveys. To address this issue, we used field soil data to assess the accuracy of seven spatial soil databases (Digital Soil Map of the World, Namibian Soil and Terrain Digital Database, Soil and Terrain Database for Southern Africa, Harmonized World Soil Database, SoilGrids1km, SoilGrids250m, and World Inventory of Soil Property Estimates) using topsoil texture as an example soil property and Namibia as a case study area. In addition, we visually compared topsoil texture maps derived from these databases. We found that the maps showed the correct topsoil texture in only 13% to 42% of all test sites, with substantial confusion occurring among all texture categories, not just those in close proximity in the soil texture triangle. Visual comparisons of the maps moreover showed that the maps differ greatly with respect to the number, types, and spatial distribution of texture classes. The topsoil texture information provided by the maps is thus sufficiently inaccurate that it would result in significant errors in a number of applications, including irrigation system design and predictions of potential forage and crop productivity, water runoff, and soil erosion. Clearly, the use of these existing maps for policy- and decision-making is highly questionable and there is a critical need for better on-site estimates and soil map predictions. We propose that mobile apps, citizen science, and crowdsourcing can help meet this need.
Topsoil
Digital Soil Mapping
Soil texture
Agricultural soil science
Soil series
Soil survey
Cite
Citations (12)
Spatial information about physical soil properties is in great demand, being basic input data in numerous applications. Soil texture can be characterized by different approaches, such as particle size distribution, plasticity index or soil texture classification. In accordance with the increasing demands for spatial soil texture information, our aim was to compile a topsoil texture class map for Hungary with an appropriate spatial resolution, using the United States Department of Agriculture soil texture classes. The 'Classification and Regression Trees' method was applied because it is widely used in Digital Soil Mapping, and has numerous advantages. Primary soil data were provided by the Hungarian Soil Information and Monitoring System. A digital elevation model and its derived components, geological and land cover map, and appropriate remotely sensed products together with the soil map featuring overall physical properties provided by the Digital Kreybig Soil Information System were used as auxiliary environmental co-variables. The resulting map can be used as direct input data in meteorological and hydrological modelling as well as in spatial planning.
Topsoil
Soil texture
Digital Soil Mapping
Texture (cosmology)
Land Cover
Cite
Citations (27)
Soil texture
Silt
Topsoil
Texture (cosmology)
Digital Soil Mapping
Cite
Citations (36)