Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings

Yiming Xu; Scot E Smith; Sabine Grunwald; Amr Abd-Elrahman; Suhas P Wani

doi:10.1016/j.jenvman.2017.06.017

Evaluating the effect of remote sensing image spatial resolution on soil exchangeable potassium prediction models in smallholder farm settings

J Environ Manage. 2017 Sep 15:200:423-433. doi: 10.1016/j.jenvman.2017.06.017. Epub 2017 Jun 12.

Authors

Yiming Xu¹, Scot E Smith², Sabine Grunwald³, Amr Abd-Elrahman⁴, Suhas P Wani⁵

Affiliations

¹ School of Natural Resource and Environment, University of Florida, 103 Black Hall, PO Box 116455, Gainesville, FL, 32611, USA; School of Forest Resources and Conservation - Geomatics Program, University of Florida, 301 Reed Lab, PO Box 110565, Gainesville, FL, 32611-0565, USA; Department of Environmental Science and Engineering, Beijing Technology and Business University, Beijing, 100048, China. Electronic address: xuyimi@ufl.edu.
² School of Natural Resource and Environment, University of Florida, 103 Black Hall, PO Box 116455, Gainesville, FL, 32611, USA; School of Forest Resources and Conservation - Geomatics Program, University of Florida, 301 Reed Lab, PO Box 110565, Gainesville, FL, 32611-0565, USA. Electronic address: sesmith@ufl.edu.
³ School of Natural Resource and Environment, University of Florida, 103 Black Hall, PO Box 116455, Gainesville, FL, 32611, USA; Pedometrics, Landscape Analysis and GIS Laboratory, Soil and Water Science Department, University of Florida, 2181 McCarty Hall, PO Box 110290, Gainesville, FL, 32611, USA. Electronic address: sabgru@ufl.edu.
⁴ School of Forest Resources and Conservation - Geomatics Program, University of Florida, 301 Reed Lab, PO Box 110565, Gainesville, FL, 32611-0565, USA; Gulf Coast REC/School of Forest Resources and Conservation - Geomatics Program, University of Florida, 1200 N. Park Road, Plant City, FL, 33563, USA. Electronic address: aamr@ufl.edu.
⁵ International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, 502324, Hyderabad, India. Electronic address: s.wani@cgiar.org.

PMID: 28614763
DOI: 10.1016/j.jenvman.2017.06.017

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 (K_ex) in the topsoil (0-15 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 K_ex 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.

Keywords: Digital soil mapping; Remote sensing; Smallholder farm settings; Soil exchangeable potassium; Very fine resolution image.

MeSH terms

Bayes Theorem
Farms*
Humans
India
Potassium*
Remote Sensing Technology*
Soil

Substances

Soil
Potassium