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Image fusion is in its infancy in the application of Digital Soil Mapping, and the incorporation of the image pansharpened spectral indices into the soil prediction models has seldom been analyzed. This research performed image pansharpening of Landsat 8, WorldView-2, and Pleiades-1A in a smallholder village called Masuti in South India using three pansharpening techniques: Brovey, Gram-Schmit (GS), and Intensity-Hue-Saturation (IHS) methods. The research analyzed the relationships between multispectral (MS) and pansharpened (PAN) spectral indices and soil total nitrogen (TN), developed the soil TN prediction models using Random Forest methods, and explored the effects of different PAN spectral indices on soil TN prediction models. The results showed the spectral behavior of PAN spectral indices and MS spectral indices were similar. The results also demonstrated that soil TN models based on MS/PAN spectral indices have slightly higher model performance and more detailed characterization of TN spatial pattern compared with soil TN models based on MS spectral indices. Soil TN models based on the GS PAN and MS spectral indices attained slightly higher prediction accuracy compared with those based on other PAN and MS spectral indices. This research advocates the promotion of image pansharpening techniques in digital soil mapping and soil nutrient management research. [ABSTRACT FROM AUTHOR]

Mapping soil nutrients can help smallholder farmers identify soil nutrient status and implement site-specific soil management schemes. In the past, Digital Soil Mapping has seldom been utilized to guide soil nutrient management in smallholder farm settings in South India. The objective of this research was to analyze the spatial resolution effects of different remote sensing images on soil total nitrogen (TN) prediction models in two smallholder villages, Kothapally and Masuti in South India. Regression kriging (RK) was used to characterize the spatial pattern of TN in the topsoil (0–15 cm) by incorporating spectral indices with different spatial resolutions. The results suggested that soil moisture, vegetation, and soil crusts can contribute to the conservation of soil TN in both study areas. Soil prediction models with different spatial resolutions showed a similar spatial pattern of soil TN. The results also demonstrated that the effect of very fine spatial remote sensing spectral data inputs does not always lead to an increase of soil prediction model performance. A RapidEye-based (5 m) soil TN prediction model had lower prediction accuracy than a Landsat 8-based (30 m) soil TN prediction model in Masuti. WorldView-2/GeoEye-1/Pleiades-1A-based (2 m) soil TN prediction models had the highest prediction accuracy in both study areas. The spectral indices based on new bands of WorldView-2 such as coastal, yellow, red edge, and new near infrared bands had relatively strong correlations with soil TN. The utilization of Very High Spatial resolution images such as WorldView-2 in Digital Soil Mapping could improve soil model performance and spatial characterization. Remote sensing-based soil prediction models have high potential to be widely applied in smallholder farm settings. [ABSTRACT FROM AUTHOR]