Downscaling Digital Soil Organic Carbon Map

Document Type : Research Article

Authors

• shahrokh fatehi ¹
• jahangard mohammadi ²
• Mohammad Hassan Salehi ¹
• aziz momeni ³
• Norair Toomanian ⁴
• Azam Jafari ⁵

¹ Shahr-e-Kord University

² isfahan university

³ soil and water research institute, Meshkin dasht

⁴ Agriculture and Natural Resource Research Center of Esfahan

⁵ Shahid Bahonar Kerman University

https://doi.org/10.22067/jsw.v30i4.45102

Abstract

Introduction: Spatial scale is a major concept in many sciences concerned with human activities and physical, chemical and biological processes occurring at the earth’s surface. Many environmental problems such as the impact of climate change on ecosystems, food, water and soil security requires not only an understanding of how processes operates at different scales and how they can be linked across scales but also gathering more information at finer spatial resolution. This paper presents results of different downscaling techniques taking soil organic matter data as one of the main and basic environmental piece of information in Mereksubcatchment (covered about 24000 ha) located in Kermanshah province. Techniques include direct model and point sampling under generalized linear model, regression tree and artificial neural networks. Model performances with respect to different indices were compared.
Materials and Methods: legacy soil data is used in this research, 320 observation points were randomly selected. Soil samples were collected from 0-30 cm of the soil surface layer in 2008 year. After preliminary data processing and point pattern analysis, spatial structure information of organic carbon determined using variography. Then, the support point data were converted to block support of 50 m by using block ordinary kriging. Covariates obtained from three resources including digital elevation model, TM Landsat imagery and legacy polygon maps. 23 relief parameters were derived from digital elevation model with 10m × 10m grid-cell resolution. Environmental information obtained from Landsat imagery included, clay index, normalized difference vegetation index, grain size index. The image data were re-sampled from its original spatial resolution of 30*30m to resolution of 10m*10m. Geomorphology, lithology and land use maps were also included in modelling process as categorical auxiliary variables. All auxiliary variables aggregated to 50*50 grid resolutions using mean filtering. In this study Direct and point sampling downscaling techniques were used under different statistical and data mining algorithms, including generalized linear models, regression trees and artificial neural networks. The direct approach was implemented here using generalized linear models, regression trees and artificial neural networks in following three steps, (i) creating the spatial resolution of 50m*50m averaged over 10m*10m grid resolution environmental variables within each coarse grid resolution, (ii) establishing relationships between these coarse grid resolutions of 50m*50m environmental variables and soil organic carbon using GLMs, regression tree and neural networks and (iii) using parameter values gained in step 2 in combination with the original 10m*10mgrid resolution environmental variables to produce predictions of soil organic carbon with10m*10m grid resolution. In point sampling approach, within each coarse resolution (50m*50m), a fixed number of fine grid resolution (10m*10m) were randomly selected to calibrate models at high resolution. In this study, 5 fine grid resolutions (20% fine grid cell within each coarse grid cell) randomlywere sampled at. Then, each selected point overlied on an underlying fine-resolution grid and recorded its environmental variables and averaged fine grid resolution (10m*10m) within their corresponding coarse grid resolution (50m*50m). To calibrate model parameters, these averaged environmental variables were used. The calibrated parameters applied to fine-resolution environmental data in order to predict soil organic carbon at spatial resolution of 10m*10m. The prediction accuracy of the resulting soil organic carbon maps was evaluated using a K-fold validation approach. For this purpose, the entire dataset was divided into calibration (n = 240) and validation (n = 80) datasets four times at random. Prediction of soil organic carbon using calibration datasets and their validation was conducted for each split, and the average validation indices are reported here. The obtained values of the observed and predicted SOC were interpreted by calculating Adjusted R2 and the root mean square error (RMSE).
Results and Discussion: Point pattern analysis showed the sampling design is, generally, representative relative to geographical space .A semi-variogram was used to drive the spatial structure information of soil organic carbon. We used an exponential model to map soil organic carbon using block kriging. Grid resolution block kriging map was 50m*50m. Since the distribution of organic carbon variable and covariates were normal or close to normal for run generalized linear models selected Gaussian families and identity link function. The validation results of this model in point sampling was slightly (Adjusted R2=0.57 and RMSE=0.22) better than the direct method (Adjusted R2 =0.47 and RMSE=0.26).The results of modelling using regression tree in point sampling approach (Adjusted R2 =0.57and RMSE=0.22) is very close to the direct method (Adjusted R2 =0.57 and RMSE=0.23).In implementation of neural networks, the combination of the number of neurons and learning rate for direct downscaling method were obtained 10 and 0.10, respectively and for point sampling downscaling method were, 20 and 0.1 The results of validation obtained from the implementation of this model in point sampling approach (Adjusted R2 =0.45 and RMSE=0.27) is very close to the direct method (Adjusted R2 =0.47 and RMSE=0.28).Validation results indicated that in both downscaling approaches, regression tree (Adjusted R2=0.57, root mean square root (RMSE) =0.22-0.23) has higher accuracy and efficiency better than generalized linear models (Adjusted R2=0.49-0.57, RMSE=0.22-0.26) and neural network (Adjusted R2=0.45-0.47, RMSE=0.27-0.28).
Conclusion: In general, the results showed that the efficiency and accuracy of the sampling point approach is slightly better than the direct approach. Validation results indicated that in both downscaling approaches, regression tree has higher accuracy and performed better than neural network and generalized linear models. However, it is required to perform more research on the different ways of downscaling digital soil maps in the future.

Keywords

• Downscaling
• Direct approach
• Point sampling approach
• Soil organic carbon

20.1001.1.20084757.1395.30.4.14.0

References

1- Araújo M. B., Thuiller W., Williams P. H., and Reginster I. 2005. Downscaling European species atlas distributions to a finer resolution: implications for conservation planning. Global Ecology and Biogeography, 14 (1): 17– 30.

2- Barbosa A.M., Real R., Olivero J., and Vargas J.M. 2003. Otter (Lutra lutra) distribution modeling at two resolution scales suited to conservation planning in the Iberian Peninsula. Biological Conservation, 114: 377– 387.

3- Bergmeir C., and Benitez Jose M. 2012. Artificial neural networks in R Using the Stuttgart Neural Network Simulator: RSNNS. Journal of Statistical Software, 46:1- 26.

4- Bivan R. S., Pebesma E. J., and Rubi V. G. 2008. Applied Spatial Data Analysis with R. UseR! Springer.

5- Bloschl G. 2005. Statistical upscaling and downscaling in hydrology. In: Anderson M.G., and Mc Donnell J.J., editors, Encyclopaedia of hydrological sciences. John Wiley & Sons, Chichester, West Sussex, England.

6- Breiman L., Friedman J.H., Olshen R.A., and Stone C.J. 1984. Classification and regression. Tress. Wadsworth, Belmont, CA.

7- Bui E., Henderson B., and Viergever K. 2009. Using knowledge discovery with data mining from the Australian Soil Resource Information System database to inform soil carbonnmapping in Australia. Global Biogeochem. Cycles 23.

8- Fatehi Sh. 2008. Semi-detailed soil survey of Merek plain in Kharkeh river basin. Soil and Water Research Institute. (In Persian with English abstract)

9- Garson G.D. 1991. Interpreting neural network connection weights. Artificial Intelligence Expert, 6(4):46- 51.

10- Grunwald S. 2009. Multi-criteria characterization of recent digital soil mapping and modelling approaches. Geoderma, 152: 195– 207.

11- Hastie T., Tibshirani R., and Friedman J. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Second Edition).

12- Jafari A., Finke P.A., de Wauw J.V., Ayoubi S., and Khademi H. 2012. Spatial prediction of USDA- great soil groups in the arid Zarand region, Iran: comparing logistic regression approaches to predict diagnostic horizons and soil types. Europian Journal Soil Science, 63: 284– 298.

13- Jafari A., Ayoubi Sh., and Khademi H. 2012. Application of regression models for prediction of soil classes in some regions of central Iran (Zarand district, Kerman Province). Journal of Water and Soil, 25(6): 1353-1364. (In Persian with English abstract).

14- Kerry R., Goovaerts P., Rawlins B.G., and Marchant B.P. 2012. Disaggregation of legacy soil data using area to point kriging for mapping soil organic carbon at the regional scale. Geoderma, 170: 347– 358.

15- Kuhn M. 2008. Building Predictive Models in using the caret Package. Journal of Statistical Software, 28(5): 1: 26.

16- Lloyd P., and Palmer A.R. 1998. Abiotic factors as predictors of distribution in southern African Bulbuls. Auk, 115: 404– 411.

17- Luoto M., and Hjort J. 2005. Evaluation of current statistical approaches for predictive geomorphological mapping. Geomorph, 67: 299- 315.

18- Luoto M., and Hjort J. 2008. Downscaling of coarse-grained geomorphological Data. Earth Surface Processes and Landforms, 33: 75– 89.

19- Malone B.P., Mc Bratney A.B., Minasny B., and Laslett G.M. 2009. Mapping continuous depth functions of soil carbon storage and available water capacity. Geoderma 154, 138– 152.

20- Malone B.P., McBratney A.B., Minasny B., and Wheeler I. 2012. General method for downscaling earth resource information. Computers & Geosciences, 41: 119– 125.

21- Malone B.P., McBratney A.B., and Minasny B. 2013. Spatial Scaling for Digital Soil Mapping. Soil Science Society American Journal, 77: 890– 902.

22- Mc Bratney A.B. 1998. Some considerations on methods for spatially aggregating and disaggregating soil information. Nutrient Cycling in Agroecosystems, 50: 51– 62.

23- Mc Pherson J.M., Jetz W., and Rogers D.J. 2006. Using coarse-grained occurrence data to predict species distributions at finer spatial resolutions–possibilities and limitations. Ecological Modeling, 192: 499– 522.

24- Miklos M., Short M.G., Mc Bratney A.B., and Minasny B. 2010. Mapping and comparing the distribution of soil carbon under cropping and grazing management practices in Narrabri, north-west New South Wales. Australian Journal Soil Research, 48: 248– 257.

25- Minasny B., McBratney A.B., Mendonça-Santos M.L., Odeh I.O.A., and Guyon B. 2006. Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley. Australian Journal Soil Research, 44: 233– 244.

26- Minasny B., McBratney A. B., Malone B. P., and Wheeler I. 2013. Digital Mapping of Soil Carbon. Pp.1–47. In D. L. Sparks (Ed.), Advances in Agronomy. Elsevier Inc.

27- Nabiollahi K., Haidari A., and Taghizadeh Mehrjerdi R. 2014. Digital Mapping of Soil Texture Using Regression Tree and Artificial NeuralNetwork in Bijar, Kurdistan. Journal of Water and Soil, 28(5): 1025-1036. (in Persian with English abstract)

28- Rouse J. W., Hass R. H.J., Schell A., and Deering D. W. 1973. Monitoring vegetation systems in the Great Plains with ERTS. Third ERTS Symposium, NASA SP-351, Vol. 1, Washington, DC. PP. 309- 317.

29- Sanchez P.A., Ahamed S., Carre F., Hartemink A.E., Hempel J., and Huising J. 2009. Digital soil map of the world. Science, 325: 680– 681.

30- Taghizadeh-Mehrjardi R., Minasny B., Sarmadian F., and Malone P.B. 2013. Digital mapping of soil salinity in Ardakan region, central Iran. Geoderma, 213: 15- 28.

31- Taghizadeh Mehrjerdi R., Amirian Chekan A., and Sarmadian F. 2014. 3D Digital Mapping of Soil Cation Exchange Capacity in Dorud, Lorestan Province. Journal of Water and Soil, 28(5): 998- 1010. (In Persian with English abstract).

32- Taylor J.A., Jacob F., Galleguillos M., Prevot L., Guix N., and Lagacherie P. 2013. The utility of remotely-sensed vegetative and terrain covariates at different spatial resolutions in modelling soil and watertable depth (for digital soil mapping). Geoderma, 193: 83– 93.

33- Van Deventer A.P., Ward A.D., Gowda P.H., and Lyon J.G. 1997. Using Thematic Mapper Data to Identify Contrasting Soil Plains and Tillage Practices. Photogrammetric Engineering and Remote Sensing, 63: 87- 93.

34- Wilby R.L., and Wigley T.M.L. 1997. Downscaling general circulation model output: a review of methods and limitations. Progress in Physical Geography, 21: 530– 548.

35- Wu J., Jones K.B., Li H., and Locks O.L. 2006. Scaling and Uncertainty Analysis in Ecology. Springer. Printed in the Netherlands.

36- Xiao J., Shen Y., Tateishi R., and Bayaer W. 2006. Development of topsoil grain size index for monitoring desertification in arid land using remote sensing. International Journal of Remote Sensing, 27: 2411– 2422.

37- Zhao Y-C., and Shi X-Z. 2010. Spatial Prediction and Uncertainty Assessment of Soil Organic Carbon in Hebei Province, China. pp. 227– 240. In: Boettinger J.L., Howell D.W., Moore A.C., Hartemink A.E., Kienast-Brown S. (Eds.), Digital Soil Mapping. Bridging Research, Environmental Application, and Operation, Springer, Heidelberg.

38- Zink J. A. 1989. Physiography and soils. Lecture notes for K6 course. Soils Division, (ITC), Enschede, the Netherlands.