Journal of Geographical Sciences ›› 2016, Vol. 26 ›› Issue (1): 102-124.doi: 10.1007/s11442-016-1257-4
• Orginal Article • Previous Articles Next Articles
WERE Kennedy1,2(), Ram SINGH Bal3, Bjarne DICK Øystein1
Received:
2015-02-18
Accepted:
2015-06-12
Online:
2016-01-25
Published:
2016-01-25
About author:
Author: Kennedy Were, PhD, specialized in application of GIS and remote sensing techniques in environmental research. E-mail:
WERE Kennedy, Ram SINGH Bal, Bjarne DICK Øystein. Spatially distributed modelling and mapping of soil organic carbon and total nitrogen stocks in the Eastern Mau Forest Reserve, Kenya[J].Journal of Geographical Sciences, 2016, 26(1): 102-124.
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Table 1
Properties of the environmental predictors for spatial modelling"
Variables | Data format | Date | Source | Scale | Soil-forming factor |
---|---|---|---|---|---|
Target variables | |||||
1. SOC stocks | Points | 2012 | Field work | ||
2. TN stocks | Points | 2012 | Field work | ||
Predictor variables | |||||
1. SOC concentration | Raster | 2012 | Interpolated field data | 30 m | S |
2. TN concentration | Raster | 2012 | Interpolated field data | 30 m | S |
3. Magnesium | Raster | 2012 | Interpolated field data | 30 m | S |
4. Potassium | Raster | 2012 | Interpolated field data | 30 m | S |
5. Calcium | Raster | 2012 | Interpolated field data | 30 m | S |
6. Clay content | Raster | 2012 | Interpolated field data | 30 m | S |
7. Silt content | Raster | 2012 | Interpolated field data | 30 m | S |
8. Sand content | Raster | 2012 | Interpolated field data | 30 m | S |
9. pH | Raster | 2012 | Interpolated field data | 30 m | S |
10. Elevation | Raster | - | ASTER GDEM http://gdem.ersdac.jspacesystems.or.jp/ | 30 m | R |
11. Slope | Raster | - | ASTER GDEM | 30 m | R |
12. Aspect | Raster | - | ASTER GDEM | 30 m | R |
13. Curvature | Raster | - | ASTER GDEM | 30 m | R |
14. CTI | Raster | - | ASTER GDEM | 30 m | S |
15. Temperature | Raster | 1950-2000 | www.worldclim.org | 1 km | C |
16. Rainfall | Raster | 1950-2000 | www.worldclim.org | 1 km | C |
17. Surface reflectance & thermal emission | Raster | 30.05.2013 | Landsat 8 OLI (bands 2, 3, 4, 5, 6, 7, 10 & 11) http://earthexplorer.usgs.gov/ | 30 m | C, S |
18. NDVI | Raster | 30.05.2013 | Landsat 8 OLI (bands 4 & 5) | 30 m | O |
19. PC bands | Raster | 30.05.2013 | Landsat 8 OLI (bands 2, 3, 4, 5, 6 & 7) | 30 m | S |
20. Land cover | Raster | 17.01.2011 | Landsat 5 TM; Were et al. (2013) | 30 m | O |
Table 3
Pearson’s correlation coefficient between the predictors and target variables selected for spatial modelling"
Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1. SOC stock | 1.00 | ||||||||||||||
2. TN stock | 0.99 | 1.00 | |||||||||||||
3. TN content | 0.84 | 0.85 | 1.00 | ||||||||||||
4. SOC content | 0.85 | 0.84 | 0.99 | 1.00 | |||||||||||
5. Silt | -0.41 | -0.42 | -0.56 | -0.55 | 1.00 | ||||||||||
6. Magnesium | 0.35 | 0.35 | 0.44 | 0.44 | -0.36 | 1.00 | |||||||||
7. Clay | 0.28 | 0.29 | 0.40 | 0.39 | -0.61 | 0.08 | 1.00 | ||||||||
8. Temperature | -0.50 | -0.50 | -0.63 | -0.63 | 0.28 | -0.04 | -0.34 | 1.00 | |||||||
9. Rainfall | 0.44 | 0.45 | 0.56 | 0.55 | -0.23 | 0.25 | 0.10 | -0.61 | 1.00 | ||||||
10. Elevation | 0.51 | 0.51 | 0.65 | 0.65 | -0.30 | 0.06 | 0.35 | -0.99 | 0.65 | 1.00 | |||||
11. Aspect | 0.22 | 0.23 | 0.18 | 0.17 | -0.08 | 0.01 | 0.02 | -0.16 | 0.16 | 0.16 | 1.00 | ||||
12. NDVI | 0.30 | 0.30 | 0.39 | 0.39 | -0.25 | 0.07 | 0.24 | -0.50 | 0.25 | 0.50 | 0.11 | 1.00 | |||
13. Land cover | -0.48 | -0.48 | -0.54 | -0.53 | 0.31 | 0.00 | -0.41 | 0.83 | -0.46 | -0.84 | -0.16 | -0.56 | 1.00 | ||
14. PC1 | -0.48 | -0.48 | -0.52 | -0.52 | 0.15 | -0.03 | -0.23 | 0.71 | -0.50 | -0.73 | -0.28 | -0.32 | 0.74 | 1.00 | |
15. Landsat 8 OLI band 11 | -0.58 | -0.58 | -0.65 | -0.65 | 0.35 | -0.09 | -0.37 | 0.81 | -0.56 | -0.84 | -0.29 | -0.63 | 0.89 | 0.82 | 1.00 |
Table 4
Parameter estimates of the MLR models"
Parameter | SOC stocks model | TN stocks model | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Estimate | SE | t value | Pr (>|t|) | VIF | Estimate | SE | t value | Pr (>|t|) | VIF | |
Intercept | 143.502 | 50.757 | 2.827 | 0.0053** | - | 16.741 | 5.131 | 3.263 | 0.0013** | - |
Silt | 0.443 | 0.202 | 2.191 | 0.0298* | 1.531 | - | - | - | - | - |
Band 11 | -0.003 | 0.001 | -2.360 | 0.0194* | 3.511 | -0.000 | 0.000 | -2.475 | 0.0143* | 3.489 |
Elevation | -0.022 | 0.009 | -2.503 | 0.0133* | 3.613 | -0.002 | 0.001 | -2.305 | 0.0223* | 3.558 |
TN | 178.200 | 12.269 | 14.524 | 0.0000*** | 2.471 | - | - | - | - | - |
SOC | - | - | - | - | - | 1.597 | 0.106 | 15.103 | 0.0000*** | 1.807 |
Adjusted R2 | 0.72 | 0.71 | ||||||||
RMSE | 13.07 | 1.33 | ||||||||
Moran’s I | 0.11 | 0.08 |
Table 5
Parameter estimates of the GWR models"
Parameter | SOC stocks model | TN stocks model | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Mean | SD | Min. | Max. | Range | Mean | SD | Min. | Max. | Range | |
Intercept | 129.525 | 31.412 | 50.031 | 199.881 | 149.851 | 14.790 | 4.771 | 8.037 | 26.423 | 18.387 |
Silt | 0.436 | 0.115 | 0.203 | 0.614 | 0.411 | - | - | - | - | - |
Band 11 | -0.003 | 0.001 | -0.004 | -0.001 | 0.004 | -0.000 | 0.000 | -0.000 | -0.000 | 0.000 |
Elevation | -0.021 | 0.007 | -0.041 | -0.004 | 0.037 | -0.002 | 0.001 | -0.005 | -0.001 | 0.004 |
TN | 177.230 | 23.072 | 142.790 | 238.558 | 95.768 | - | - | - | - | - |
SOC | - | - | - | - | - | 1.576 | 0.197 | 1.087 | 1.899 | 0.812 |
Global adjusted R2 | 0.73 | 0.72 | ||||||||
Global RMSE | 12.86 | 1.29 | ||||||||
Moran’s I | 0.06 | 0.02 |
Table 6
Parameters of the fitted variogram models for SOC and TN stocks, and the residuals of the respective GWR and MLR models"
Variable | Model | Nugget Mg ha-1 | Partial sill Mg ha-1 | Total sill Mg ha-1 | Range (m) | Nugget- to- sill ratio (%) | Spatial dependence |
---|---|---|---|---|---|---|---|
SOC stocks | Gaussian | 386 | 278 | 664 | 4845 | 58.1 | Moderate |
MLRsoc residuals | Gaussian | 143 | 39 | 182 | 4434 | 78.6 | Weak |
GWRsoc residuals | Gaussian | 141 | 26 | 167 | 5760 | 84.4 | Weak |
TN stocks | Exponential | 3.1 | 3.7 | 6.8 | 4493 | 45.6 | Moderate |
MLRtn residuals | Exponential | 1.4 | 0.5 | 1.9 | 4132 | 73.7 | Weak |
GWRtn residuals | Exponential | 1.4 | 0.2 | 1.6 | 2944 | 87.5 | Weak |
Table 8
Soil organic carbon and nitrogen stocks under different land cover types"
Land cover | Area | SOC stocks | TN stocks | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Min. | Max. | Mean | Total | Min. | Max. | Mean | Total | |||
(Ha) | (Mg ha-1) | (Tg) | (Mg ha-1) | (Tg) | ||||||
Forests | 32228.4 | 75.5 | 142.9 | 110.4 | 3.78 | 7.5 | 15.3 | 11.1 | 0.38 | |
Grasslands | 5509.4 | 66.7 | 129.8 | 103.5 | 0.57 | 6.7 | 12.6 | 10.4 | 0.06 | |
Croplands | 25828.1 | 62.9 | 126.9 | 95.2 | 2.46 | 6.5 | 12.2 | 9.6 | 0.25 | |
Total | 65565.9 | 6.81 | 0.69 |
1 | Amare T, Hergarten C, Hurni H et al., 2013. Prediction of soil organic carbon for Ethiopian highlands using soil spectroscopy. ISRN Soil Science, 720589 (11 pp), . |
2 |
Aynekulu E, Vågen T-G, Shepherd Ket al., 2011. A protocol for measurement and monitoring soil carbon stocks in agricultural landscapes. Version 1.1. World Agroforestry Centre, Nairobi.
doi: 10.1590/S0864-34662010000100008 |
3 |
Batjes N H, 2004. Soil carbon stocks and projected changes according to land use and management: A case study for Kenya.Soil Use and Management, 20: 350-356.
doi: 10.1111/j.1475-2743.2004.tb00380.x |
4 | Bewketa W, Stroosnijder L, 2003. Effects of agro-ecological land use succession on soil properties in Chemoga watershed, Blue Nile basin, Ethiopia.Geoderma, 111: 85-98. |
5 | Blake G R, 1965. Bulk density. In: Black C A (ed.). Methods of Soil Analysis, Part 1. Physical and Mineralogical Properties, Including Statistics of Measurement and Sampling, American Society of Agronomy, Inc., Madison, Wisconsin, USA. |
6 | Bremner J M, Mulvaney C S, 1982. Nitrogen - total. In: Page A L (ed.). Methods of Soil Analysis, Part 2. Chemical and Microbiological Properties. 2nd ed. American Society of Agronomy, Inc., Madison, Wisconsin, USA. |
7 |
Cambule A H, Rossiter D G, Stoorvogel J Jet al., 2014. Soil organic carbon stocks in the Limpopo National Park, Mozambique: Amount, spatial distribution and uncertainty.Geoderma, 213: 46-56.
doi: 10.1016/j.geoderma.2013.07.015 |
8 |
Chaplot V, Bouahom B, Valentin C, 2010. Soil organic carbon stocks in Laos: Spatial variations and controlling factors.Global Change Biology, 16: 1380-1393.
doi: 10.1111/j.1365-2486.2009.02013.x |
9 | Day P R, 1965. Particle fractionation and particle size analysis. In: Black C A (ed.). Methods of Soil Analysis, Part 1. Physical and Mineralogical Properties, Including Statistics of Measurement and Sampling. American Society of Agronomy, Inc., Madison, Wisconsin, USA. |
10 | Demessie A, Singh B R, Lal R, 2013. Soil carbon and nitrogen stocks under chronosequence of farm and traditional agro-forestry uses in Gambo district, southern Ethiopia.Nutr. Cycl. Agroecosys., 95: 365-375. |
11 | Doetterl S, Stevens A, van Oost Ket al., 2013. Spatially explicit regional scale prediction of soil organic carbon stocks in cropland using environmental variables and mixed model approaches.Geoderma, 204/205: 31-42. |
12 | Dorji T, Odeh I O A, Field D Jet al., 2014. Digital soil mapping of soil organic carbon stocks under different land use and land cover types in montane ecosystems, Eastern Himalayas.Forest Ecology and Management, 318: 91-102. |
13 |
Eclesia R P, Jobbagy E G, Jackson R Bet al., 2012. Shifts in soil organic carbon for plantation and pasture establishment in native forests and grasslands of South America.Global Change Biology, 18: 3237-3251.
doi: 10.1111/j.1365-2486.2012.02761.x |
14 |
Elbasiouny H, Abowaly M, Abu_Alkheir Aet al., 2014. Spatial variation of soil carbon and nitrogen pools by using ordinary kriging method in an area of north Nile delta, Egypt.Catena, 113: 70-78.
doi: 10.1016/j.catena.2013.09.008 |
15 | Fotheringham A S, Brunsdon C, Charlton M E, 2002. Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. England: John Wiley & Sons Inc. |
16 |
Girmay G, Singh B R, 2012. Changes in soil organic carbon stocks and soil quality: Land use system effects in northern Ethiopia. Acta Agriculturae Scandinavica, Section B –,Soil & Plant Science, 62(6): 519-530.
doi: 10.1080/09064710.2012.663786 |
17 | [Accessed 2014, January 19]. |
18 |
Grimm R, Behrens T, Märker Met al., 2008. Soil organic carbon concentrations and stocks on Barro Colorado Island: Digital soil mapping using Random Forests analysis.Geoderma, 146: 102-113.
doi: 10.1016/j.geoderma.2008.05.008 |
19 |
Hengl T, Heuvelink G B M, Rossiter D G, 2007. About regression-kriging: From equations to case studies.Computers & Geosciences, 33: 1301-1315.
doi: 10.1016/j.cageo.2007.05.001 |
20 |
Hengl T, Heuvelink G B M, Stein A, 2004. A generic framework for spatial prediction of soil variables based on regression-kriging.Geoderma, 120: 75-93.
doi: 10.1016/j.geoderma.2003.08.018 |
21 | Hiemstra P, [Accessed 2013, December 15]. |
22 | IPCC, 2006. IPCC Guidelines for national greenhouse gas inventories, prepared by the national greenhouse gas inventories programme, Eggleston H S, Buendia L, Miwa K et al. (eds.). Published: IGES, Japan. |
23 |
Jaber S M, Al-Qinna M I, 2011. Soil organic carbon modelling and mapping in a semi-arid environment using thematic mapper data.Photogrammetric Engineering & Remote Sensing, 77(7): 709-719.
doi: 10.14358/PERS.77.7.709 |
24 | Jaetzold R, Schmidt H, Hornetz Bet al., 2010. Farm management handbook of Kenya, Vol. II. Natural conditions and farm management information. 2nd ed., Part B Central Kenya, Subpart B1a Southern Rift Valley Province. Ministry of Agriculture, Kenya and German Agency for Technical Cooperation (GTZ), Nairobi. |
25 |
Karunaratne S B, Bishop T F A, Baldock J Aet al., 2014. Catchment scale mapping of measureable soil organic carbon fractions.Geoderma, 219/220: 14-23.
doi: 10.1016/j.geoderma.2013.12.005 |
26 |
Kheir R B, Greve M H, BØcher P Ket al., 2010. Predictive mapping of soil organic carbon in wet cultivated lands using classification tree-based models: The case study of Denmark.Journal of Environmental Management, 91: 1150-1160.
doi: 10.1016/j.jenvman.2010.01.001 |
27 |
Kumar S, Lal R, 2011. Mapping the organic carbon stocks of surface soils using local spatial interpolator.Journal of Environmental Monitoring, 13: 3128-3135.
doi: 10.1039/c1em10520e pmid: 22009220 |
28 |
Kumar S, Lal R, Liu D, 2012. A geographically weighted regression kriging approach for mapping soil organic carbon stock.Geoderma, 189/190: 627-634.
doi: 10.1016/j.geoderma.2012.05.022 |
29 |
Kumar S, Lal R, Liu D, 2013. Estimating the spatial distribution of organic carbon density for the soils of Ohio, USA.Journal of Geographical Sciences, 23(2): 280-296.
doi: 10.1007/s11442-013-1010-1 |
30 |
Lacoste M, Minasny B, McBratney Aet al., 2014. High resolution 3D mapping of soil organic carbon in a heterogeneous agricultural landscape.Geoderma, 213: 296-311.
doi: 10.1016/j.geoderma.2013.07.002 |
31 |
Lal R, 2004. Soil carbon sequestration to mitigate climate change.Geoderma, 123: 1-22.
doi: 10.1016/j.geoderma.2004.01.032 |
32 |
Lamsal S, Grunwald S, Bruland G Let al., 2006. Regional hybrid geospatial modeling of soil nitrate-nitrogen in the Santa Fe River watershed.Geoderma, 135: 233-247.
doi: 10.1016/j.geoderma.2005.12.009 |
33 | Lemenih M, Karltun E, Olsson M, 2005. Assessing soil chemical and physical property responses to deforestation and subsequent cultivation in smallholders farming system in Ethiopia.Agriculture, Ecosystems and Environment, 105: 373-386. |
34 | Lesch S M, Corwin D L, 2008. Prediction of spatial soil property information from ancillary sensor data using ordinary linear regression: Model derivations, residual assumptions and model validation tests.Geoderma, 148: 130-140. |
35 |
Li D, Shao M, 2014. Soil organic carbon and influencing factors in different landscapes in an arid region of north-western China.Catena, 116: 95-104.
doi: 10.1016/j.catena.2013.12.014 |
36 |
Li M, Zhang X, Pang Get al., 2013b. The estimation of soil organic carbon distribution and storage in a small catchment area of the Loess Plateau.Catena, 101: 11-16.
doi: 10.1016/j.catena.2012.09.012 |
37 |
Li Q, Yue T, Wang Cet al., 2013a. Spatially distributed modeling of soil organic matter across China: An application of artificial neural network approach.Catena, 104: 210-218.
doi: 10.1016/j.catena.2012.11.012 |
38 | Li Y, 2010. Can the spatial prediction of soil organic matter contents at various sampling scales be improved by using regression kriging with auxiliary information?Geoderma, 159: 63-75. |
39 | Liu Z, Shao M, Wang Y, 2011. Effect of environmental factors on regional soil organic carbon stocks across the Loess Plateau region, China.Agriculture, Ecosystems and Environment, 142: 184-194. |
40 |
Malone B P, McBratney A B, Minasny Bet al., 2009. Mapping continuous depth functions of soil carbon storage and available water capacity.Geoderma, 154: 138-152.
doi: 10.1016/j.geoderma.2009.10.007 |
41 | Marchetti A, Piccini C, Francaviglia Ret al., 2012. Spatial distribution of soil organic matter using geostatistics: A key indicator to assess soil degradation status in central Italy.Pedosphere, 22(2): 230-242. |
42 | Martin M P, Orton T G et al., 2014. Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale. Geoderma, . |
43 |
Martin M P, Wattenbach M, Smith Pet al., 2011. Spatial distribution of soil organic carbon stocks in France.Biogeosciences, 8: 1053-1065.
doi: 10.5194/bg-8-1053-2011 |
44 | McBratney A B, Santos M L M, Minasny B, 2003. On digital soil mapping.Geoderma, 117: 3-52. |
45 | McCall G J H, 1967. Geology of the Nakuru-Thomson’s falls-Lake Hannington area: Degree sheet No. 35, S.W. Quarter and 43 N.W. Quarter, Report No. 78. Government Printer, Nairobi. |
46 |
McKenzie N J, Ryan P J, 1999. Spatial prediction of soil properties using environmental correlation.Geoderma, 89: 67-94.
doi: 10.1016/S0016-7061(98)00137-2 |
47 | Meersmans J, de Ridder F, Canters Fet al., 2008. A multiple regression approach to assess the spatial distribution of Soil Organic Carbon (SOC) at the regional scale (Flanders, Belgium).Geoderma, 143: 1-13. |
48 |
Mehrjardi R T, Minasny B, Sarmadian Fet al., 2014. Digital mapping of soil salinity in Ardakan region, central Iran.Geoderma, 213: 115-128.
doi: 10.1016/j.geoderma.2013.07.020 |
49 |
Mishra U, Lal R, Liu Det al., 2010. Predicting the spatial variation of the soil organic carbon pool at a regional scale.Soil Science Society of America Journal, 74: 906-914.
doi: 10.2136/sssaj2009.0158 |
50 |
Mishra U, Riley W J, 2012. Alaskan soil carbon stocks: Spatial variability and dependence on environmental factors.Biogeosciences, 9: 3637-3645.
doi: 10.5194/bgd-9-5695-2012 |
51 | Montgomery D C, Peck E A, Vining G G, 2006. Introduction to Linear Regression Analysis. John Wiley & Sons, Inc., New Jersey. |
52 |
Mora-Vallejo A, Claessens L, Stoorvogel Jet al., 2008. Small-scale digital soil mapping in southeastern Kenya.Catena, 76: 44-53.
doi: 10.1016/j.catena.2008.09.008 |
53 |
Murty D, Kirschbaum M F, McMurtrie R Eet al., 2002. Does conversion of forest to agricultural land change soil carbon and nitrogen? A review of the literature.Global Change Biology, 8: 105-123.
doi: 10.1046/j.1354-1013.2001.00459.x |
54 |
Nelson D W, Sommers L E, 1982. Total carbon, organic carbon and organic matter. In: Page A L (ed.) Methods of Soil Analysis, Part 2, Chemical and Microbiological Properties. 2nd ed. American society of agronomy, Inc., Madison, Wisconsin, USA.
doi: 10.2136/sssabookser5.3.c34 |
55 |
Obade V P, Lal R, 2013. Assessing land cover and soil quality by remote sensing and geographical information systems (GIS).Catena, 104: 77-92.
doi: 10.1016/j.catena.2012.10.014 |
56 | Okalebo J R, Gathna K W, Woomer P L, 2002. Laboratory methods for soil and plant analysis: A working manual. 2nd ed. Tropical Soil Biology and Fertility Programme, Nairobi. |
57 |
Overmars K P, Verburg P H, 2005. Analysis of land use drivers at the watershed and household level: Linking two paradigms at the Philippine forest fringe.International Journal of Geographical Information Science, 19(2): 125-152.
doi: 10.1080/13658810410001713380 |
58 |
Pachomphon K, Dlamini P, Chaplot V, 2010. Estimating carbon stocks at regional level using soil information and easily accessible auxiliary variables.Geoderma, 155: 372-380.
doi: 10.1016/j.geoderma.2009.12.020 |
59 | Pebesma E, Bivand R S, Rowlingson B et al., 2013. Classes and methods for spatial data in R. Available: [Accessed 2013, December 15]. |
60 | R Core Team, . |
61 |
Razakamanarivo R H, Grinand C, Razafindrakoto M Aet al., 2011. Mapping organic carbon stocks in eucalyptus plantations of the central highlands of Madagascar: A multiple regression approach.Geoderma, 162: 335-346.
doi: 10.1016/j.geoderma.2011.03.006 |
62 |
Scull P, Franklin J, Chadwick O Aet al., 2003. Predictive soil mapping: A review. Progress in Physical Geography, 27(2): 171-197.
doi: 10.1191/0309133303pp366ra |
63 |
Selige T, Böhner J, Schmidhalter U, 2006. High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures.Geoderma, 136: 235-244.
doi: 10.1016/j.geoderma.2006.03.050 |
64 |
Smith P, 2004. Soils as carbon sinks: The global context.Soil Use and Management, 20: 212-218.
doi: 10.1111/j.1475-2743.2004.tb00361.x |
65 |
Smith P, 2008. Land use change and soil organic carbon dynamics.Nutr. Cycl. Agroecosyst., 81: 169-178.
doi: 10.1007/s10705-007-9138-y |
66 |
Sumfleth K, Duttmann R, 2008. Prediction of soil property distribution in paddy soil landscapes using terrain data and satellite information as indicators.Ecological Indicators, 485-501.
doi: 10.1016/j.ecolind.2007.05.005 |
67 | Szymanowski M, Kryza M, 2012. Local regression models for spatial interpolation of urban heat island: An example from Wrocław, SW Poland.Theor. Appl. Climatol., 108: 53-71. |
68 | Tamooh F, van den Meersche K, Meysman Fet al., 2012. Distribution and origin of suspended matter and organic carbon pools in the Tana River basin, Kenya.Biogeosciences, 9: 2905-2920. |
69 |
Tesfahunegn G B, Tamene L, Vlek P L G, 2011. Catchment scale spatial variability of soil properties and implications on site-specific soil management in northern Ethiopia.Soil & Tillage Research, 117: 124-139.
doi: 10.1016/j.still.2011.09.005 |
70 | UNEP, 2009[Accessed 2013, August 28]. |
71 |
Vågen T G, Winowiecki L A, 2013a. Mapping of soil organic carbon stocks for spatially explicit assessments of climate change mitigation potential. Environmental Research Letters 8, 015011 (9pp). doi: 10.1088/1748- 9326/8/1/015011.
doi: 10.1088/1748-9326/8/1/015011 |
72 |
Vågen T G, Winowiecki L A, Abegaz Aet al., 2013b. Landsat-based approaches for mapping of land degradation prevalence and soil functional properties in Ethiopia.Remote Sensing of Environment, 134: 266-275.
doi: 10.1016/j.rse.2013.03.006 |
73 |
Vasques G M, Grunwald S, Comerford N Bet al., 2010a. Regional modelling of soil carbon at multiple depths within a subtropical watershed.Geoderma, 156: 326-336.
doi: 10.1016/j.geoderma.2010.03.002 |
74 |
Vasques G M, Grunwald S, Sickman J Oet al., 2010b. Up-scaling of dynamic soil organic carbon pools in a north-central Florida watershed.Soil Science Society of America Journal, 74. doi: 10.2136/sssaj2009.0242.
doi: 10.2136/sssaj2009.0242 |
75 | Wang K, Zhang C, Li W, 2013. Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and co-kriging.Applied Geography, 42: 73-85. |
76 |
Were K O, Dick Ø B, Singh B R, 2013. Remotely sensing the spatial and temporal land cover changes in Eastern Mau forest reserve and Lake Nakuru drainage basin, Kenya.Applied Geography, 41: 75-86.
doi: 10.1016/j.apgeog.2013.03.017 |
77 | Were K O, Singh B R, Dick Ø B, 2015. Effects of land cover changes on soil organic carbon and nitrogen stocks in the Eastern Mau Forest Reserve, Kenya. In: Lal R, Singh B R, Mwaseba D L et al., (eds.). Sustainable Intensification to Advance Food Security and Enhance Climate Resilience in Africa. Springer International Publishing, Switzerland, 113-133. |
78 |
Wiesmeier M, Spörlein P, Geuß Uet al., 2012. Soil organic carbon stocks in southeast Germany (Bavaria) as affected by land use, soil type and sampling depth.Global Change Biology, 18: 2233-2245.
doi: 10.1111/j.1365-2486.2012.02699.x |
79 | Winowiecki L, Vågen T G, Huising J, . |
80 |
Wu C, Wu J, Luo Yet al., 2009. Spatial prediction of soil organic matter content using co-kriging with remotely sensed data.Soil Science Society of America Journal, 73: 1202-1208.
doi: 10.2136/sssaj2008.0045 |
81 |
Yang R, Su Y Z, Wang Met al., 2014. Spatial pattern of soil organic carbon in desert grasslands of the diluvial-alluvial plains of northern Qilian Mountains.Journal of Arid Land, 6(2): 136-144.
doi: 10.1007/s40333-013-0200-0 |
82 |
Yang Y, Fang J, Tang Yet al., 2008. Storage, patterns and controls of soil organic carbon in the Tibetan grasslands.Global Change Biology, 14: 1592-1599.
doi: 10.1111/j.1365-2486.2008.01591.x |
83 |
Zaehle S, Ciais P, Friend A Det al., 2011. Carbon benefits of anthropogenic reactive nitrogen offset by nitrous oxide emissions.Nature Geoscience, 4: 601-605.
doi: 10.1038/ngeo1207 |
84 |
Zhang C, Tang Y, Xu Xet al., 2011. Towards spatial geochemical modelling: Use of geographically weighted regression for mapping soil organic carbon contents in Ireland.Applied Geochemistry, 26: 1239-1248.
doi: 10.1016/j.apgeochem.2011.04.014 |
85 |
Zhang S, Huang Y, Shen Cet al., 2012. Spatial prediction of soil organic matter using terrain indices and categorical variables as auxiliary information.Geoderma, 171/172: 35-43.
doi: 10.1016/j.geoderma.2011.07.012 |
86 |
Zhang Z, Yu C, Shi Xet al., 2010. Application of categorical information in the spatial prediction of soil organic carbon in the red soil area of China.Soil Science and Plant Nutrition, 56: 307-318.
doi: 10.1111/j.1747-0765.2010.00457.x pmid: 2786487 |
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