Xu, Y and Smith, S E and Grunwald, S and Abd-Elrahman, A and Wani, S P (2017) Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings. Journal of Environmental Management, 200. pp. 423-433. ISSN 03014797
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Abstract
Major end users of Digital Soil Mapping (DSM) such as policy makers and agricultural extension workers are faced with choosing the appropriate remote sensing data. The objective of this research is to analyze the spatial resolution effects of different remote sensing images on soil prediction models in two smallholder farms in Southern India called Kothapally (Telangana State), and Masuti (Karnataka State), and provide empirical guidelines to choose the appropriate remote sensing images in DSM. Bayesian kriging (BK) was utilized to characterize the spatial pattern of exchangeable potassium (Kex) in the topsoil (0e15 cm) at different spatial resolutions by incorporating spectral indices from Landsat 8 (30 m), RapidEye (5 m), and WorldView-2/GeoEye-1/Pleiades-1A images (2 m). Some spectral indices such as band reflectances, band ratios, Crust Index and Atmospherically Resistant Vegetation Index from multiple images showed relatively strong correlations with soil Kex in two study areas. The research also suggested that fine spatial resolution WorldView-2/GeoEye-1/Pleiades-1A-based and RapidEye-based soil prediction models would not necessarily have higher prediction performance than coarse spatial resolution Landsat 8-based soil prediction models. The end users of DSM in smallholder farm settings need select the appropriate spectral indices and consider different factors such as the spatial resolution, band width, spectral resolution, temporal frequency, cost, and processing time of different remote sensing images. Overall, remote sensing-based Digital Soil Mapping has potential to be promoted to smallholder farm settings all over the world and help smallholder farmers implement sustainable and field-specific soil nutrient management scheme.
| Item Type: | Article |
|---|---|
| Divisions: | Research Program : Asia |
| CRP: | UNSPECIFIED |
| Uncontrolled Keywords: | Remote sensing, DSM, Very fine resolution image, Soil exchangeable potassium, Smallholder farm settings, Digital Soil Mapping |
| Subjects: | Others > Remote Sensing Others > Soil Others > Smallholder Agriculture Others > Digital Agriculture |
| Depositing User: | Mr Ramesh K |
| Date Deposited: | 01 Aug 2017 08:05 |
| Last Modified: | 01 Aug 2017 08:05 |
| URI: | http://oar.icrisat.org/id/eprint/10119 |
| Official URL: | http://dx.doi.org/10.1016/j.jenvman.2017.06.017 |
| Projects: | UNSPECIFIED |
| Funders: | UNSPECIFIED |
| Acknowledgement: | Funding for this project was provided by the grant award No. 1201943 “Development of a Geospatial Soil-Crop Inference Engine for Smallholder Farmers” EAGER National Science Foundation. The soil analysis was performed in the soil laboratory at the International Crops Research Institute for the Semi-Arid Tropics (ICRISAT) in Patancheru/Hyderabad, Telangana State, India. We thank Christopher M. Clingensmith at University of Florida, and other ICRISAT staff members and villagers of Kothapally for support with field sampling. We also thank Yiming Xu's PhD committee members Dr. Thomas K. Frazer and Dr. Vimala D. Nair for their guidance and commitment. A matching assistantship for Yiming Xuwas provided by School of Natural Resources and Environment, University of Florida, and China Scholarship Council. |
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