Developing the Swiss mid-infrared soil spectral library for local estimation and monitoring

Abstract

Abstract. Information on soils' composition and physical, chemical and biological properties is paramount to elucidate agroecosystem functioning in space and over time. For this purpose, we developed a national Swiss soil spectral library (SSL; n=4374) in the mid-infrared (mid-IR), calibrating 16 properties from legacy measurements on soils from the Swiss Biodiversity Monitoring program (BDM; n=3778; 1094 sites) and the Swiss long-term Soil Monitoring Network (NABO; n=596; 71 sites). General models were trained with the interpretable rule-based learner CUBIST, testing combinations of {5,10,20,50, and 100} ensembles of rules (committees) and {2, 5, 7, and 9} nearest neighbors used for local averaging with repeated 10-fold cross-validation grouped by location. To evaluate the information in spectra to facilitate long-term soil monitoring at a plot level, we conducted 71 model transfers for the NABO sites to induce locally relevant information from the SSL, using the data-driven sample selection method RS-LOCAL. In total, 10 soil properties were estimated with discrimination capacity suitable for screening (R2≥0.72; ratio of performance to interquartile distance (RPIQ) ≥ 2.0), out of which total carbon (C), organic C (OC), total nitrogen (N), pH and clay showed accuracy eligible for accurate diagnostics (R2>0.8; RPIQ ≥ 3.0). CUBIST and the spectra estimated total C accurately with the root mean square error (RMSE) = 8.4 g kg−1 and the RPIQ = 4.3, while the measured range was 1–583 g kg−1 and OC with RMSE = 9.3 g kg−1 and RPIQ = 3.4 (measured range 0–583 g kg−1). Compared to the general statistical learning approach, the local transfer approach – using two respective training samples – on average reduced the RMSE of total C per site fourfold. We found that the selected SSL subsets were highly dissimilar compared to validation samples, in terms of both their spectral input space and the measured values. This suggests that data-driven selection with RS-LOCAL leverages chemical diversity in composition rather than similarity. Our results suggest that mid-IR soil estimates were sufficiently accurate to support many soil applications that require a large volume of input data, such as precision agriculture, soil C accounting and monitoring and digital soil mapping. This SSL can be updated continuously, for example, with samples from deeper profiles and organic soils, so that the measurement of key soil properties becomes even more accurate and efficient in the near future.