Optimization of spectral pre‐processing for estimating soil condition on small farms. (20th December 2020)

• Record Type: Journal Article
• Authors: Singh, Kanika; Aitkenhead, Matt; Fidelis, Chris; Yinil, David; Sanderson, Todd; Snoeck, Didier; Field, Damien J
• Is Part Of: Soil use and management. Volume 38:Number 1(2022)
• Page Start: 150
• Page End: 163
• Publication Date: 2020-12-20
• Subjects: chemometrics -- soil conditions -- spectral software -- tropical soils; Soil management -- Periodicals; 631.4
• DOI: 10.1111/sum.12684
• Languages: English
• ISSNs: 0266-0032

Abstract

Abstract: The concepts of soil security (especially relating to soil condition) provide a useful framework in building spectral libraries. Spectral libraries can be used with the purpose of assessing soil condition by measuring soil organic carbon (SOC) or increasing productivity through soil nutrient management. A spectral library was generated by measuring SOC and nutrients (nitrogen, phosphorous and potassium) and spectral reflectance data over the visible to near‐infrared range (350–2, 500 nm) in soil samples collected from four production systems in Papua New Guinea (PNG). The spectral library was analysed using SpecOptim, a software tool developed at the James Hutton Institute to explore spectral pre‐processing and calibration options. From 192 model combinations of model, the best one was identified for each study area. Different combinations of data were also explored (e.g. by farm or all together). We believe that at the local‐scale, soil carbon and nitrogen variability can be captured; however, the spectrally inactive properties such as phosphorous and potassium need to have a higher variability and therefore pooling is required in order to predict properties chemometrically. The SpecOptim software is a useful tool where analysis of spectral data can be difficult to determine. Specifically, it helped improve the accuracy of predictions by 2% for C and N (except for East New Britain site) compared with previously used pre‐processing techniques and calibration models while automating identification of the optimal pre‐processing approach. We believe that we have developed research‐based evidence for using spectral libraries to fit with the soil priority areas of PNG.