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

abstract

  • Major end users of Digital Soil Mapping (DSM) such as policy makers and agricultural extension workersare faced with choosing the appropriate remote sensing data. The objective of this research is to analyzethe spatial resolution effects of different remote sensing images on soil prediction models in twosmallholder 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. Bayesiankriging (BK) was utilized to characterize the spatial pattern of exchangeable potassium (Kex) in thetopsoil (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 asband reflectances, band ratios, Crust Index and Atmospherically Resistant Vegetation Index from multipleimages showed relatively strong correlations with soil Kex in two study areas. The research alsosuggested that fine spatial resolution WorldView-2/GeoEye-1/Pleiades-1A-based and RapidEye-basedsoil prediction models would not necessarily have higher prediction performance than coarse spatialresolution Landsat 8-based soil prediction models. The end users of DSM in smallholder farm settingsneed 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 remotesensing images. Overall, remote sensing-based Digital Soil Mapping has potential to be promoted tosmallholder farm settings all over the world and help smallholder farmers implement sustainable andfield-specific soil nutrient management scheme

publication date

  • 2017
  • 2017