Soil geography and land evaluation

Soil geographic databases

• Soil Geographic Databases (at ISRIC-World Soil Information)

  An attempt to catalogue all freely-downloadable primary soils information usable in a GIS. Some of the linked sites also have interpreted information.

• Cornell Soil & Crop Sciences seminar “State of the art in global soil resource mapping” (PDF, 16-Feb-2023)

• Journal paper in SOIL (Copernicus publications): “How well does digital soil mapping represent soil geography? An investigation from the USA”

  “We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by Predictive Soil Mapping (PSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales, and compare them to spatial patterns known from detailed field surveys (gNATSGO and gSSURGO).”

  • Case Studies of four areas in the USA as ISRIC Report 2021/03

  • R code to apply the methods anywhere in the lower 48 States of the USA.

• Accessing WoSIS using R

  Access ISRIC’s WoSIS Soil Profile Database, using R Markdown scripts. WoSIS is explained in this paper (DOI: https://doi.org/10.5194/essd-9-1-2017)

• Working with ISRIC’s SoilGrids in Google Earth Engine (GEE)

  • Tutorial (PDF)

  This is an exercise and assignment on “Big Data” developed for Cornell course PLSCS/NTRES 6200: Spatial Modelling and Analysis. The first part introduces Google Earth Engine (GEE) with a simple remote sensing example. The second part shows how to process ISRIC’s SoilGrids in GEE. The example is a Principal Components Analysis (PCA) of the six depth slices for a single property.

• Using SoilGrids and Google Earth Engine with R to identify “homogeneous” soil zones

  • R Markdown source

  Use this script is to identify relatively homogeneous areas with similar soil properties, based on ISRIC’s SoilGrids. These can be used as a pre-map for field work, or for stratified sampling. The homogeneous zones are identified by standardized Principal Components Analysis (PCA) of the stack of SoilGrids soil properties, accessed via Google Earth Engine (GEE). The analysis is also performed in GEE.

  To run this script you must first set up R to access Google Earth Engine, as explained in Using Google Earth Engine with R

  • compiled PDF

  • compiled HTML

• Processing the Harmonized World Soil Database (Version 2.0) in R

  • Tutorial (PDF)

  • Dataset (SQLite, zipped)

  The HWSD v2 is a harmonized map and linked attribute table of the World’s soils, at a nominal scale of 1:1M. It is intended for global or regional modelling: “Its main objective is to serve as a basis for prospective studies on agro-ecological zoning, food security and climate change”. This semi-expert approach, based on legacy maps and expert judgement, has been somewhat superseded by ISRIC’s SoilGrids 250m resolution raster layers. Version 2 is improved, with seven soil layers to 2 m depth and with a consistent soil classification system (WRB 2020) to name map units and their components.

• Processing the Harmonized World Soil Database (Version 1.2) in R

  • Tutorial (PDF)

  • Dataset (SQLite, zipped)

  This is the previous version of the HWSD; the material is kept here in case you have legacy work with this version, otherwise use the HWSD 2 (see just above).

• Video: ISRIC-World Soil Information: providing consistent information on the world’s soils (Cornell seminar, 25-Jan-2020)

  My introduction to ISRIC-World Soil Information

• Presentation: Soil functions, global diversity, and distribution – An introduction in the context of global cropping systems

• Presentation: Three thoughts on soil class maps: (1) Evaluating classification accuracy (2) Taxonomic vs. geographic soil classes (3) Soil geoforms & phenoforms (Cornell seminar, 22-March-2018)

  Topic (1) based on this paper: 10.1016/j.geoderma.2017.01.012

  Topic (3) based on this paper: 10.1016/j.geoderma.2017.11.002

• Video: Can citizen science be used to support digital soil mapping? (Cornell seminar, 18-Mar-2016)

  Based on this paper: 10.1016/j.geoderma.2015.05.006

• Video: GSIF course: Digital soil resource inventories stutus and prospects (ISRIC Spring School, 3-Aug-2015)

  Somewhat outdated but interesting history. Based on this book chapter: 10.1007/978-981-10-0415-5_22

• A spreadsheet (XLS) to compute with map scales

  Representative Fraction; Maximum Location Accuracy; Minimum Legible Area; Maximum/Minimum number of field observations; Grid resolution

  • same, as a PDF, pre-computed scales 1:2 500 – 1:1’000 000

Land suitability evaluation

• Rossiter, DG (2003) Biophysical models in land evaluation. Article 1.5.27 in Theme 1.5 Land Use and Land Cover, Verheye, W.H. (ed.), in Encyclopedia of Life Support Systems (EOLSS), developed under the auspices of the UNESCO. Oxford: EOLSS Publishers.

• Lecture: Methods and models in land evaluation

• Cornell course: SCAS 494: Land Suitability Evaluation

Soil survey

• Rossiter, D. G. (2021). Soil mapping today: Computer-generated predictive soil maps – their role in soil survey and land evaluation. Agriculture for Development 44: 26-31

• Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. DOI: 10.5194/soil-7-217-2021

• Liu, F., Rossiter, D. G., Zhang, G.-L., & Li, D.-C. (2020). A soil colour map of China. Geoderma, 379, 114556. DOI: 10.1016/j.geoderma.2020.114556

• Rasaei, Z., Rossiter, D. G., & Farshad, A. (2020). Rescue and renewal of legacy soil resource inventories in Iran as an input to digital soil mapping. Geoderma Regional, e00262. DOI: 10.1016/j.geodrs.2020.e00262

• Rossiter, D. G., Zeng, R., & Zhang, G.-L. (2017). Accounting for taxonomic distance in accuracy assessment of soil class predictions. Geoderma, 292, 118–127. DOI: 10.1016/j.geoderma.2017.01.012

  • R functions developed in this paper

• Rossiter, D. G., & Bouma, J. (2018). A new look at soil phenoforms - Definition, identification, mapping. Geoderma, 314, 113–121. DOI:10.1016/j.geoderma.2017.11.002

• Forbes, T. R., Rossiter, D. G., & Van Wambeke, A R. (1982). Guidelines for evaluating the adequacy of soil resources inventories, SMSS Technical Monograph No. 4. Soil Management Support Service, USDA/Soil Conservation Service, Washington, DC. 50pp.

• Lecture Notes & Reference: Methodology for Soil Resource Inventories (2000)

  Somewhat outdated but perhaps a useful introduction to traditional soil survey concepts. If you can find it, Dent, D., & Young, A. (1981). Soil survey and land evaluation. George Allen & Unwin, ISBN 978-0-04-631013-4 is a more formal introduction to this topic.

• Metodologías para el Levantamiento del Recurso Suelo

  El anterior, traducido y adaptado al Español por Ronald Vargas Rojas, Universidad Mayor de San Simón, Bolivia (2004)

• Lecture Notes: Soil Geographic Databases (1999)

• Bases de Datos Geográficos de Suelos y el Uso de Programas para su Construcción

  El anterior, traducido y adaptado al Español por Ronald Vargas Rojas, Universidad Mayor de San Simón, Bolivia y FAO-Proyecto SWALIM (2006)

• Atkilt Girma; Rossiter, D. G.; Siderius, W; & Henneman, R. (2001). Soils of the Lake Naivasha area, Kenya: Summary of investigations by the Soil Science Division, ITC , Technical Report ITC Soil Science Division, Enschede, NL, July 2001

• Atkilt Girma & Rossiter, D. G. (2001). Soil investigations on the Sulmac farm, Naivasha, Kenya by the Soil Science Division, ITC, Technical Report ITC Soil Science Division, Enschede, NL, July 2001

• Husnjak, S., Rossiter, D.G., Hengl, T., & Miloš, B. (2004) Soil inventory and soil classification in Croatia: historical review, current activities, future directions

Datasets

• Cambule, A. H. (Armindo); Rossiter, D. G. (David) (2013). Limpopo National Park (Mozambique) Soil Organic Carbon study. University of Twente. Dataset. doi: 10.4121/uuid:6cb98f84-f0de-47d4-8a2c-d6aaeaf5db08

  Data source for DOI: 10.1016/j.geoderma.2013.07.015

• Liu, Feng; Rossiter, D. G.; Zhang, Gan-Lin, Li, De-Cheng. (2020). Predicted colours of Chinese soils. doi:10.11666/00072.ver1.db

  Maps produced as described in DOI:10.1016/j.geoderma.2020.114556

Computer programs

• Automated Land Evaluation System (ALES)

  A computer program that allows land evaluators to build expert systems to evaluate land according to the method presented in the Food and Agriculture Organization’s “Framework for Land Evaluation” (1976) and subsequent Guidelines. Last updated 1996, runs under Microsoft MS-DOS v2.3 or higher, and under MS-DOS emulators.

• Newhall Simulation Model (NSM)

  Simulates subdivisions of USDA Soil Taxonomy soil moisture & temperature regimes for well-drained soils, from a time-sequence of monthly climate data. These pages also have links to more modern methods.

• Riha, Susan J., Rossiter, DG, and Simoens, Patrick (1994). GAPS: General-purpose Atmosphere-Plant-Soil Simulator. Version 3.0 User’s Manual. Ithaca, NY: Cornell University Department of Soil, Crop & Atmospheric Sciences. 195pp.

  This program has since become a Windows program (sorry I can’t find a link) but this manual explains the principles of the simulation models.

Last modified 16-August-2024