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Statistical diagnostics for sensing spatial residue cover

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Abstract

Timely information on within field soil quality spatial variability is pertinent for sustainable agroecosystem management. Although, plant residues constitute a critical input influencing soil quality dynamic, robust baseline residue maps to inform agricultural policy are nonexistent. Remote sensing-based indices can be used to generate maps that can provide timely information on within-field spatial distribution of residue cover. However, heavily used indices such as the Cellulose Absorption Index (CAI), Lignin Cellulose Absorption (LCA) index, or the Shortwave Infrared Normalized Difference Residue Index (SINDRI) rely on data scanned within the 2000 to 2500 nm spectral wavelength window, thus are impractical for mapping because most freely accessible spaceborne sensors collect significant data within the 450 to 1750 nm wavelength range. Here, insitu line transect residue cover measurements were integrated with spectral reflectance data scanned by Cropscan handheld multispectral radiometer (MSR) and satellite sensors, to identify alternative indices within the 450 to 1750 nm wavelength range suitable for monitoring residue cover. The green band reflectance had the highest correlation with spatial variability in residue cover, followed closely by the blue band. Analyzed data shows that Normalized Difference Vegetation Indexwide band (NDVIw), preeminently mapped corn (Zea mays) residue cover, with an R2 of 0.95 between residue mapped by satellite vis a vis in situ field measurements. The line transect residue measurements from field plots sampled at Aurora (48%) and Badger (57%) fell within the range estimated by satellite (30 to 60%), but this was not true for the Lennox site (72%).

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All data generated and analysed in this study are included in this article, and appendix.

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Authors and Affiliations

  1. BioResource and Agricultural Engineering Department, California Polytechnic State University, 1 Grand Ave, San Luis Obispo, CA, USA

    Vincent de Paul Obade

  2. Department of Geomatic Engineering and Geospatial Information Systems, Jomo Kenyatta University of Agriculture and Technology, Juja, Kenya

    Charles Onyango Gaya

  3. Department of Environmental Science and Education, Kenyatta University, Nairobi, Kenya

    Paul Thomas Obade

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Appendix

Appendix

See Figs. 9 and 10 and Tables 16, 17, 18, 19, 20, and 21.

Table 16 Principal Components (PC) importance for Aurora from year 2009 data
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Table 17 Principal Components (PC) importance for Badger from year 2009 data
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Table 18 Principal components (PC) importance for Aurora site, year 2009
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Table 19 Principal components (PC) importance for Badger site, year 2009
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Table 20 Principal components (PC) importance for Aurora site, year 2010
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Table 21 Principal components (PC) importance for Badger site, year 2010
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Fig. 9
figure 9

Percent residue cover versus indices correlation graphs for Aurora (A), and Badger (B), from year 2009 data. The x-axis are respective principal components (PC)

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Fig. 10
figure 10

Percent residue cover verses indices correlation graphs for Aurora (A), and Badger (B) site, year 2010. The x-axis are respective principal components (PC)

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de Paul Obade, V., Gaya, C.O. & Obade, P.T. Statistical diagnostics for sensing spatial residue cover. Precision Agric 24, 1932–1964 (2023). https://doi.org/10.1007/s11119-023-10024-w

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