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A proposal for Global Soil Mapping based on the participatory multiscale nested regression-kriging

A proposal for Global Soil Mapping based on the participatory multiscale nested regression-kriging

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  • 1. Global Soil MappingA proposal for a participatory multiscaleapproach to GSMTomislav HenglISRIC  World Soil Information, Wageningen University GlobalSoilMap.net presentation, 11 Feb 2011
  • 2. OutlineIntroduction This talk My backgrounds GlobalSoilMap.netMisconceptions about DSM/GSM Mapping eciency Soil geodata usability Soil prediction methodsA proposal for GSM Global Soil Mapping is not trivial Nested regression modeling The participatory approachMalawi show case Input data ResultsSummary points GlobalSoilMap.net presentation, 11 Feb 2011
  • 3. Topics My backgrounds; Some misconceptions about DSM/GSM; A proposal for GSM: A Global Multiscale Prediction Model The crowd-sourcing approach to soil data collection (Open Soil Proles, Soil covariates) Global task-oriented Land (Soil) Information System Report on the results ( Malawi show case). Get some feedback. GlobalSoilMap.net presentation, 11 Feb 2011
  • 4. Previous projects My expertise: spatio-temporal data analysis in FOSS (R), digital soil mapping, geomorphometry, geostatistics. . . I have worked with various type of data (climatic/meteorological, species occurrence records, geochemicals. . .); Recently published a repository of cca 100 global layers at resolution of 0.05 arcdegrees (5.6 km). Author of A Practical Guide to Geostatistical Mapping . Main organizer of the GEOSTAT summer school for PhD students (R+OSGeo). GlobalSoilMap.net presentation, 11 Feb 2011
  • 5. My dream is to build an Open multipurpose GLIS Soil properties (soil information system) - physical and chemical soil properties, nutrient capacity, water storage, acidity/salinity… Model library Live weather channel (meteorological forecasting) - anticipated temperature (min, max), rainfall, frost hazard, drought hazard, flood hazard… Fertilization Irrigation Plant monitoring channel (MODIS/ENVISAT) Pest treatment - current biomass production, biomass anomalies Best crop calendar (pest and diseases), plant health… Yield estimates Environmental risks Socio-economic data (site-specific) GLOBAL - administrative units, new laws and regulations, LAND INFORMATION market activity, closest offices, agro-dealers… SYSTEM Suggest the best land use practice Query site attributes Information Update with incorrect? ground truth data Spatial location (site) GlobalSoilMap.net presentation, 11 Feb 2011
  • 6. GlobalSoilMap.net An international initiative to make soil property maps (7+3) at six depths at 3 arcsecs (100 m). the lightmotive is to assemble, collate, and rescue as much of the worlds existing soil data ; Some 30 people directly involved (ISRIC is the main project coordinator). International compilation of soil data. The soil-equivalent of the OneGeology.org, GBIF, GlobCover and similar projects. See full specications at http://globalsoilmap.org/specifications GlobalSoilMap.net presentation, 11 Feb 2011
  • 7. World soils in numbers The total productive soil areas: about 104 million square km. GlobalSoilMap.net presentation, 11 Feb 2011
  • 8. World soils in numbers The total productive soil areas: about 104 million square km. k To map the world at 100 m (1:200 ), would cost about 5 billion EUR (0.5 EUR per ha) using traditional methods. GlobalSoilMap.net presentation, 11 Feb 2011
  • 9. World soils in numbers The total productive soil areas: about 104 million square km. k To map the world at 100 m (1:200 ), would cost about 5 billion EUR (0.5 EUR per ha) using traditional methods. We would require some 65M proles according to the strict rules of Avery (1987). GlobalSoilMap.net presentation, 11 Feb 2011
  • 10. World soils in numbers The total productive soil areas: about 104 million square km. k To map the world at 100 m (1:200 ), would cost about 5 billion EUR (0.5 EUR per ha) using traditional methods. We would require some 65M proles according to the strict rules of Avery (1987). World map at 0.008333333 arcdegrees (ca.1 km) resolution is an image of size 43,200 Ö21,600 pixels. GlobalSoilMap.net presentation, 11 Feb 2011
  • 11. World soils in numbers The total productive soil areas: about 104 million square km. k To map the world at 100 m (1:200 ), would cost about 5 billion EUR (0.5 EUR per ha) using traditional methods. We would require some 65M proles according to the strict rules of Avery (1987). World map at 0.008333333 arcdegrees (ca.1 km) resolution is an image of size 43,200 Ö21,600 pixels. 27 billion pixels needed to represent the whole world in 100 m (productive soil areas). GlobalSoilMap.net presentation, 11 Feb 2011
  • 12. GSM in comparison with other similar projects 4.0 GLWD EcoRegions HWSDv1 5.6 km MOD12C1 MOD13C2 CHLO/SST 3.5 FRA Resolution (m) in log-scale WorldClim GPWv3 3.0 DMSP-OLSv4 GlobCov2 OneGeology? 2.5 SRTM GADM GlobalSoilMap? 2.0 1990 1995 2000 2005 2010 2015 2020 Year GlobalSoilMap.net presentation, 11 Feb 2011
  • 13. Misconceptions #1Mapping eciency can be expressed as cost in $ per area.To map world soils at 100 m using per unit costs of $2/km2 would cost ca.$300 million1 . 1 Pedro Sanchez; the NY GlobalSoiMap.net meeting (17th Feb 2009). GlobalSoilMap.net presentation, 11 Feb 2011
  • 14. Survey costs and mapping scale q Minimum survey costs in EUR / ha (log−scale) 3 q 2 q 1 q 0 −1 q 9.5 10.0 10.5 11.0 11.5 12.0 12.5 Scale number (log−scale) GlobalSoilMap.net presentation, 11 Feb 2011
  • 15. Mapping accuracy and survey costsThe cost of a soil survey is a function of mapping scale, roughly: log(X) = b0 + b1 · log(SN) (1)We can t a linear model to the empirical table data frome.g.Legros (2006; p.75), and hence we get: X = exp (19.0825 − 1.6232 · log(SN)) (2)where X is the minimum cost/ha in Euros (based on estimates in2002). To map 1 ha of soil at 1:100,000 scale, for example, oneneeds (at least) 1.5 Euros. GlobalSoilMap.net presentation, 11 Feb 2011
  • 16. The GSM calculus The total productive soil areas: about 104 million square km. GlobalSoilMap.net presentation, 11 Feb 2011
  • 17. The GSM calculus The total productive soil areas: about 104 million square km. k To map the world soils at 100 m (1:200 ), would cost about 5 billion EUR (0.5 EUR per ha) using traditional methods. According to Pedro Sanchez, soils could be mapped for $0.20 USD per ha ( $300 million USD). GlobalSoilMap.net presentation, 11 Feb 2011
  • 18. The GSM calculus The total productive soil areas: about 104 million square km. k To map the world soils at 100 m (1:200 ), would cost about 5 billion EUR (0.5 EUR per ha) using traditional methods. According to Pedro Sanchez, soils could be mapped for $0.20 USD per ha ( $300 million USD). We would require some 65M proles according to the strict rules of Avery (1987). GlobalSoilMap.net presentation, 11 Feb 2011
  • 19. The GSM calculus The total productive soil areas: about 104 million square km. k To map the world soils at 100 m (1:200 ), would cost about 5 billion EUR (0.5 EUR per ha) using traditional methods. According to Pedro Sanchez, soils could be mapped for $0.20 USD per ha ( $300 million USD). We would require some 65M proles according to the strict rules of Avery (1987). World map at 0.008333333 arcdegrees (ca.1 km) resolution is an image of size 43,200 Ö21,600 pixels. GlobalSoilMap.net presentation, 11 Feb 2011
  • 20. The GSM calculus The total productive soil areas: about 104 million square km. k To map the world soils at 100 m (1:200 ), would cost about 5 billion EUR (0.5 EUR per ha) using traditional methods. According to Pedro Sanchez, soils could be mapped for $0.20 USD per ha ( $300 million USD). We would require some 65M proles according to the strict rules of Avery (1987). World map at 0.008333333 arcdegrees (ca.1 km) resolution is an image of size 43,200 Ö21,600 pixels. We would need immense storage capacities  one image of the world at a 100 m resolution contains 27 billion pixels (productive soil areas only!). GlobalSoilMap.net presentation, 11 Feb 2011
  • 21. Mapping eciencyThe costs-per-area measure is not really informative (it is easy tospend money).We propose instead a measure called mapping eciency, denedas the amount of money needed to map an area of standard sizeand explain each one percent of variation in the target variable: X θ= [EUR · km−2 · %−1 ] (3) A · RMSE rwhere X is the total costs of a survey, A is the size of area inkm −2 , and RMSE r is the amount of variation explained by thespatial prediction model. GlobalSoilMap.net presentation, 11 Feb 2011
  • 22. Prediction accuracy and survey costs GlobalSoilMap.net presentation, 11 Feb 2011
  • 23. Information production eciency informationAn additional measure of mapping eciency is theproduction eciency, i.e.the amount of money spent to producea given quantity of soil information: X Υ= [EUR · B−1 ] (4) gzipwhere gzip is the amount of data (in Bytes) left after compression: gzip = fc · (fE · M ) · cZ [B] (5)where fc is the loss-less data compression factor, fE is theextrapolation adjustment factor, cZ is the variable coding size, andM is the total number of pixels. GlobalSoilMap.net presentation, 11 Feb 2011
  • 24. Map information contentVariable coding can be set by deriving the (global) eectiveprecision of a soil property map: RMSE ∆z = ; Z = {Z(s), ∀s ∈ A} (6) 2Following the Nyquist frequency concept from signal processing,there is no justication in saving the predictions with betterprecision than half the average accuracy. GlobalSoilMap.net presentation, 11 Feb 2011
  • 25. Map information contentEective information content (bytes remaining after compression)in a soil map for a given map extent is basically a function of threefactors: Support size (point or block). Size of a map in terms of number of pixels, determined, in fact, by the eective pixel size (which is in fact determined by sampling intensity). Eective precision (Eq.6) estimated using validation points. GlobalSoilMap.net presentation, 11 Feb 2011
  • 26. Conclusions Mapping eciency (cost / area / percent of variance explained) is an objective criteria to compare spatial prediction methods. $ / area is incomplete (anyone can spend money to produce maps  the question is how good are the maps?). GlobalSoilMap.net presentation, 11 Feb 2011
  • 27. Conclusions Mapping eciency (cost / area / percent of variance explained) is an objective criteria to compare spatial prediction methods. $ / area is incomplete (anyone can spend money to produce maps  the question is how good are the maps?). Maps are not what they seem  always assess and visualize the accuracy of your maps. GlobalSoilMap.net presentation, 11 Feb 2011
  • 28. Conclusions Mapping eciency (cost / area / percent of variance explained) is an objective criteria to compare spatial prediction methods. $ / area is incomplete (anyone can spend money to produce maps  the question is how good are the maps?). Maps are not what they seem  always assess and visualize the accuracy of your maps. Soil mapping is an iterative process, in each iteration we explain a bit more of variability. GlobalSoilMap.net presentation, 11 Feb 2011
  • 29. Conclusions Mapping eciency (cost / area / percent of variance explained) is an objective criteria to compare spatial prediction methods. $ / area is incomplete (anyone can spend money to produce maps  the question is how good are the maps?). Maps are not what they seem  always assess and visualize the accuracy of your maps. Soil mapping is an iterative process, in each iteration we explain a bit more of variability. We might not ever be able to explain 100% variability in the target soil variable. GlobalSoilMap.net presentation, 11 Feb 2011
  • 30. Misconceptions #2 Each node will produce soil property maps for theirarea of interest, which can then be stitched together2 These maps will become the most used soil information in the World. 2 This is not species on GlobalSoilMap.net, but there is a general agreement. GlobalSoilMap.net presentation, 11 Feb 2011
  • 31. A hierarchical approach to GSM Country nodes continental nodes (major players) Global coverage. Each country node is responsible for producing maps for their territory. The nodes havea complete freedom to select applicable spatial prediction methods (delivery tempo, data sharing policy etc.). As long as the technical specications are satised (10 properties, 6 depths, upper lower condence limits, 100 m), the maps will be put on GlobalSoilMap.net. Inputs and methods to be used for GSM are secondary. GlobalSoilMap.net presentation, 11 Feb 2011
  • 32. Lessons from geodata usability Geodata usability is a function of: (1) adequacy, (2) consistency, (3) completeness, (4) accuracy of the metadata, (5) data interoperability, (6) accessibility and data sharing capacity, (7) attribute and thematic accuracy. GlobalSoilMap.net presentation, 11 Feb 2011
  • 33. Lessons from geodata usability Geodata usability is a function of: (1) adequacy, (2) consistency, (3) completeness, (4) accuracy of the metadata, (5) data interoperability, (6) accessibility and data sharing capacity, (7) attribute and thematic accuracy. Each of these aspects can be optimized. GlobalSoilMap.net presentation, 11 Feb 2011
  • 34. Lessons from geodata usability Geodata usability is a function of: (1) adequacy, (2) consistency, (3) completeness, (4) accuracy of the metadata, (5) data interoperability, (6) accessibility and data sharing capacity, (7) attribute and thematic accuracy. Each of these aspects can be optimized. In reality, we can only increase each of the listed factors up to a certain level, then due to objective reasons, we reach the best possible performance given the available funds and methods. Any other improvement would require additional funds (or radical improvement of the data/operation models). GlobalSoilMap.net presentation, 11 Feb 2011
  • 35. Soil proles from various projects (65k points) GlobalSoilMap.net presentation, 11 Feb 2011
  • 36. Conclusions A hierarchical (isolation) approach to global soil mapping (stitching of country maps) would probably lead to products that are inconsistent, incomplete and irreproducible. GlobalSoilMap.net presentation, 11 Feb 2011
  • 37. Conclusions A hierarchical (isolation) approach to global soil mapping (stitching of country maps) would probably lead to products that are inconsistent, incomplete and irreproducible. Considering the current state of legacy data, any GSM will need to be largely based on extrapolation and downscaling. GlobalSoilMap.net presentation, 11 Feb 2011
  • 38. Conclusions A hierarchical (isolation) approach to global soil mapping (stitching of country maps) would probably lead to products that are inconsistent, incomplete and irreproducible. Considering the current state of legacy data, any GSM will need to be largely based on extrapolation and downscaling. The Global Soil Mapping initiative should be about building live repositories (Open Soil Proles, Soil Covariates) and tools (Global Soil Information Facility). GlobalSoilMap.net presentation, 11 Feb 2011
  • 39. Conclusions A hierarchical (isolation) approach to global soil mapping (stitching of country maps) would probably lead to products that are inconsistent, incomplete and irreproducible. Considering the current state of legacy data, any GSM will need to be largely based on extrapolation and downscaling. The Global Soil Mapping initiative should be about building live repositories (Open Soil Proles, Soil Covariates) and tools (Global Soil Information Facility). k $300 To map the world soils at 100 m (1:200 ), would cost ca. million USD. To update such map would cost (again!) $300 million USD. GlobalSoilMap.net presentation, 11 Feb 2011
  • 40. Conclusions A hierarchical (isolation) approach to global soil mapping (stitching of country maps) would probably lead to products that are inconsistent, incomplete and irreproducible. Considering the current state of legacy data, any GSM will need to be largely based on extrapolation and downscaling. The Global Soil Mapping initiative should be about building live repositories (Open Soil Proles, Soil Covariates) and tools (Global Soil Information Facility). k To map the world soils at 100 m (1:200 ), would cost ca. $300 million USD. To update such map would cost (again!) $300 million USD. The future of digital soil mapping lays in task-oriented Soil Information Systems (idea by Gerard Heuvelink). GlobalSoilMap.net presentation, 11 Feb 2011
  • 41. Misconceptions #3 There are many possible DSM techniques that are equally suitable for GSM.Each node should use which ever technique they nd applicable. GlobalSoilMap.net presentation, 11 Feb 2011
  • 42. GSM techniques Data rich areas Data poor areas Know extrapolation Profile data and polygon maps ledge trans fer Profile data only Polygon maps only No soil data available Purely Knowledge- Hybrid Extrapolation geostatistical driven methods methods methods methodsFigure: Groups of techniques suitable for global soil mapping; afterMinasny and McBratney (2010). GlobalSoilMap.net presentation, 11 Feb 2011
  • 43. Conclusions Most of the DSM techniques are in fact somehow connected (weighted averaging per polygon is in fact type of regression, SOLIM is type of multiple linear regression), hence, there are not as many techniques. GlobalSoilMap.net presentation, 11 Feb 2011
  • 44. Conclusions Most of the DSM techniques are in fact somehow connected (weighted averaging per polygon is in fact type of regression, SOLIM is type of multiple linear regression), hence, there are not as many techniques. For the consistency and completeness of nal outputs it is probably better to build one global model for each soil property (or even one multivariate model). GlobalSoilMap.net presentation, 11 Feb 2011
  • 45. Conclusions Most of the DSM techniques are in fact somehow connected (weighted averaging per polygon is in fact type of regression, SOLIM is type of multiple linear regression), hence, there are not as many techniques. For the consistency and completeness of nal outputs it is probably better to build one global model for each soil property (or even one multivariate model). Selection of covariates and prediction techniques needs to be clearly driven by objective accuracy assessment. GlobalSoilMap.net presentation, 11 Feb 2011
  • 46. Other global mapping projects SRTM (DEM)  100 m near-to-global coverage. MODIS products  a variety of RS-based products (vegetation indices, LAI, land cover maps etc) at resolutions 250 m, 500 m, 1 km and 5.6 km. GlobCov  ESAs ENVISAT global consistent land cover map (300 m). WorldClim  maps of bioclimatic variables interpolated using dense point data (1 km). ... there are many more examples (see also: publicly available data sets).All these are based on using unied methodology. GlobalSoilMap.net presentation, 11 Feb 2011
  • 47. Diculties There is probably not enough point data in the world to make soil property maps at so ne resolution (maps will be largely based on extrapolation and downscaling). GlobalSoilMap.net presentation, 11 Feb 2011
  • 48. Diculties There is probably not enough point data in the world to make soil property maps at so ne resolution (maps will be largely based on extrapolation and downscaling). The most serious problem of GSM is the discrepancy between the countries considering the amount of (eld) data. GlobalSoilMap.net presentation, 11 Feb 2011
  • 49. Diculties There is probably not enough point data in the world to make soil property maps at so ne resolution (maps will be largely based on extrapolation and downscaling). The most serious problem of GSM is the discrepancy between the countries considering the amount of (eld) data. Soils are NOT vegetation  it is much more dicult to map distribution of soils accurately (RS is helpful, but only up to a certain degree). GlobalSoilMap.net presentation, 11 Feb 2011
  • 50. Diculties There is probably not enough point data in the world to make soil property maps at so ne resolution (maps will be largely based on extrapolation and downscaling). The most serious problem of GSM is the discrepancy between the countries considering the amount of (eld) data. Soils are NOT vegetation  it is much more dicult to map distribution of soils accurately (RS is helpful, but only up to a certain degree). The nal global soil property maps might be of poor accuracy in >50% of the world. GlobalSoilMap.net presentation, 11 Feb 2011
  • 51. Question: Can we do GSM @ 100 m with such limited data? GlobalSoilMap.net presentation, 11 Feb 2011
  • 52. Opportunities getting the legacy data There is an enormous potential of together (there must be thousands and thousands of soil proles unused). GlobalSoilMap.net presentation, 11 Feb 2011
  • 53. Opportunities getting the legacy data There is an enormous potential of together (there must be thousands and thousands of soil proles unused). There is an impressive enthusiasm about this project (many national soil survey agencies see this as an opportunity to get funding). GlobalSoilMap.net presentation, 11 Feb 2011
  • 54. Opportunities getting the legacy data There is an enormous potential of together (there must be thousands and thousands of soil proles unused). There is an impressive enthusiasm about this project (many national soil survey agencies see this as an opportunity to get funding). World (scientists, policy makers, crediting organizations, private sector, ... farmers) need soil information! GlobalSoilMap.net presentation, 11 Feb 2011
  • 55. The proposal We propose that, for the purpose of achieving thehighest geodata usability, the project should promote use of a single (participatory) global multiscalenested regression-kriging model (5 km, 1 km, 250 m and 100 m resolution)and then engage local DSM teams to contribute soil ground truth data, polygon maps and predictionsthat can be integrated into one information system. GlobalSoilMap.net presentation, 11 Feb 2011
  • 56. Global Multiscale Nested RKPredictions are based on a nested RK model: z(sB ) = m0 (sB−k ) + e1 (sB−k |sB−[k+1] ) + . . . + ek (sB−2 |sB−1 ) + ε(sB ) (7)where z(sB ) is the value of the target variable estimated at groundscale (B), B−1 , . . . ,B−k are the higher order components,ek (sB−k |sB−(k+1) ) is the residual variation from scale sB−(k+1) to ahigher resolution scale sB−k , and ε is spatially auto-correlatedresidual soil variation (dealt with ordinary kriging). GlobalSoilMap.net presentation, 11 Feb 2011
  • 57. Some drawbacks GM-NRK makes all other DSM eorts in the World redundant(!); GM-NRK ignores all other sub-100 m resolution data and mapping eorts; It could also delay delivery of soil property maps because the mapping activities would be more dicult to organize internationally; GlobalSoilMap.net presentation, 11 Feb 2011
  • 58. The best combined spatial predictor participatoryTo avoid these diculties, we propose using aapproach to GSM  a combination of GM-NRK and localprediction models. Assuming that at local and global scalesindependent inputs/models are used to generate predictions, thebest combined predictor can be obtained by using: 1 1 zGM−NRK (s0 ) · ˆ RMSE r (GM−NRK) + zLM (s0 ) ˆ · RMSE r (LM) zBCSP (s0 ) = ˆ (8) 2 1 RMSE r (Mj) j=1where RMSE r is the prediction error estimated usingcross-validation (Eq.3). GlobalSoilMap.net presentation, 11 Feb 2011
  • 59. The proposed system Multiscale prediction Spatial aggregation model 5.6 km ISRIC 1 km downscaling GlobalSoilMap.net 250 m continental nodes automated validation Regional mapping 100 m new submission organization 1x1 degree tiles FTP service (clearing house) (7 properties, 6 depths) PostGIS Raster DB GeoTiff (3 arcsec) soil property maps Data portal WMS KML GeoTIff (visualization: web browser) (visualization: Google Earth) (analysis: GIS) GlobalSoilMap.net presentation, 11 Feb 2011
  • 60. GM-NRK in action: Malawi showcase 2740 soil observations, from which some 8001000 contain complete analytical and descriptive data. GlobalSoilMap.net presentation, 11 Feb 2011
  • 61. GM-NRK in action: Malawi showcase 2740 soil observations, from which some 8001000 contain complete analytical and descriptive data. 1:800k polygon soil map. GlobalSoilMap.net presentation, 11 Feb 2011
  • 62. GM-NRK in action: Malawi showcase 2740 soil observations, from which some 8001000 contain complete analytical and descriptive data. 1:800k polygon soil map. Some 30-40 gridded layers at various resolutions (covariates). GlobalSoilMap.net presentation, 11 Feb 2011
  • 63. Data sets available for Malawi (a) (b) (c) 48.8 32.7 16.6 0.5 10° 11° 12° 13° 14° 15° 16° 38000 32667 27333 22000 17° 33° 34° 35° GlobalSoilMap.net presentation, 11 Feb 2011
  • 64. Gridded maps for Malawi Parent General land Erosion Land Climate Biomes material use deposition management Rainfall map of the world 5.6 km MODIS-based long term Land Surface Temperature (day/night) Elevation 1 km Geologic Provinces of Africa Soil polygon map (FAO classes) ENVISAT Land Cover map (GlobCov) MODIS (MCD12Q1) land cover dynamics 250 m MODIS (MCD13Q1) Enhanced Vegetation Index (EVI) and medium infrared band (MIR) TWI, TRI, Slope, Surface roughness, 100 m Insolation Landsat ETM thermal band GlobalSoilMap.net presentation, 11 Feb 2011
  • 65. Loading the data# library(GSIF)# This library is still not available, hence just load the functions:> source("http://globalsoilmap.org/data/functions.R")# load the input data:> source("http://globalsoilmap.org/data/malawi.RData")> ls()# mw_soil.utm --- soil polygon map at 1:800k scale;# malawi.utm --- ca 2000 soil profiles for the whole Malawi;# malawi.poly.utm --- country borders (lines);This will load all point, polygon data and and R functions requiredto run this exercise. The input gridded data can be obtained from:> download.file("http://globalsoilmap.org/data/malawi_grids.zip",+ destfile=paste(getwd(), "malawi_grids.zip", sep="/"))# 313 MB GlobalSoilMap.net presentation, 11 Feb 2011
  • 66. geology for CLYPPT. At 250 m resolution, the models are again more significant: the predictors explain 18.7%RegressionsoilanalysisPHIHO5,elevation, EVI maps and soil types for PHIHO5, andMODISelevation, of variability for ORCDRC, 21.1% for Infrared band and type map for ORCDRC, and 26.8% for CLYPPT. The best predictors are: again Medium EVI and soil maps for CLYPPT. At finest resolution, we use a smallest subset of predictors (DEM derivatives and Landsat thermal infrared band). Consequently, the R-squares are somewhat lower: 5.5% for ORCDRC; 12.1% for PHIHO5 and 9.3% for CLYPPT. The overall best predictors are elevations, landsat TIR and Topographic Wetness Index (Table 12.2). Table 12.2 Summary results of regression analysis for three selected soil variables at various scales (case study Malawi). Best predictors Best predictors Best predictors Best predictors Variable name OSP code N and R-square and R-square and R-square and R-square (5 km) (1 km) (250 m) (100 m) rainfall, MODIS MIR, soil elevation, landsat Soil organic temperature of elevation ORCDRC 785 types TIR, TRI carbon warmest month (R2 =0.213) (R2 =0.187) (R2 =0.055) (R2 =0.315) precipitation, LAI, MODIS EVI, soil elevation, TWI, TWI pH PHIH5O 793 daily LST types TRI (R2 =0.213) (R2 =0.464) (R2 =0.211) (R2 =0.121) soil mapping units, elevation, MODIS elevation, TWI, geological units Clay content CLYPPT 756 daily LST EVI devmean (R2 =0.127) (R2 =0.148) (R2 =0.268) (R2 =0.093) It is clear from the results shown in Fig. 12.5 that at each scale different predictors play different role. These results also confirm that some soil properties, such as clay content, can be better explained using fine-scale predictors (SRTM DEM derivatives), others such as organic carbon are controlled by global (coarse) predictors GlobalSoilMap.net presentation, 11 Feb 2011
  • 67. Organic carbon (values in log-scale) 5 km 1 km 250 m 3.200 2.533 1.867 1.200 0 100 km GlobalSoilMap.net presentation, 11 Feb 2011
  • 68. pH visualized in GE (1 degree block) GlobalSoilMap.net presentation, 11 Feb 2011
  • 69. Conclusions GSM at 100 m is doable (even without 6M proles!). GlobalSoilMap.net presentation, 11 Feb 2011
  • 70. Conclusions GSM at 100 m is doable (even without 6M proles!). The multiscale approach allows us to extrapolate in large area (even to areas where we have no soil data!). GlobalSoilMap.net presentation, 11 Feb 2011
  • 71. Conclusions GSM at 100 m is doable (even without 6M proles!). The multiscale approach allows us to extrapolate in large area (even to areas where we have no soil data!). Selection of covariates and prediction techniques needs to be clearly driven by objective accuracy assessment. GlobalSoilMap.net presentation, 11 Feb 2011
  • 72. Conclusions GSM at 100 m is doable (even without 6M proles!). The multiscale approach allows us to extrapolate in large area (even to areas where we have no soil data!). Selection of covariates and prediction techniques needs to be clearly driven by objective accuracy assessment. The point data is the key to GSM  we need to motivate governmental agencies and private persons to contribute to OSP. GlobalSoilMap.net presentation, 11 Feb 2011
  • 73. Conclusions GSM at 100 m is doable (even without 6M proles!). The multiscale approach allows us to extrapolate in large area (even to areas where we have no soil data!). Selection of covariates and prediction techniques needs to be clearly driven by objective accuracy assessment. The point data is the key to GSM  we need to motivate governmental agencies and private persons to contribute to OSP. We need to start developing and testing tools  if you have the inputs and the tools to generate outputs, they can be re-generated as many times as you wish. GlobalSoilMap.net presentation, 11 Feb 2011
  • 74. GSM products (revisited) SoilGrids.org  covariates at 5 km, 1 km (250 m). SoilProles.org  Open Soil Proles (once we reach 1M points we should be able to produce soil property maps with reasonable accuracy). R/Python package  automated analysis of point and gridded data. GSIF  Global Information Facilities for soil data. GlobalSoilMap.net presentation, 11 Feb 2011
  • 75. Next steps Re-implement the method using a `clean data set (USA data). GlobalSoilMap.net presentation, 11 Feb 2011
  • 76. Next steps Re-implement the method using a `clean data set (USA data). Finalize the blue-paper (technical specs and methods for GSM). GlobalSoilMap.net presentation, 11 Feb 2011
  • 77. Next steps Re-implement the method using a `clean data set (USA data). Finalize the blue-paper (technical specs and methods for GSM). Package a showcase that anyone can use. GlobalSoilMap.net presentation, 11 Feb 2011
  • 78. Next steps Re-implement the method using a `clean data set (USA data). Finalize the blue-paper (technical specs and methods for GSM). Package a showcase that anyone can use. Set-up web-services (ISRIC servers) and start publishing the data (launch OSP, worldmaps). GlobalSoilMap.net presentation, 11 Feb 2011