Performance of Automated Near‐Infrared Reflectance Spectrometry for Continuous in Situ Mapping of Soil Fertility at Field Scale

Abstract

Diffuse reflectance of soils in the near‐infrared reflectance (NIR) has been related to many chemical soil properties. Diffuse reflectance spectroscopy may become a part of proximal soil sensing and contribute to bridge the gap of knowledge imposed by the inability of conventional methods to resolve the spatial patterns of soil fertility at field scale. We used a mobile automated NIR spectrophotometer (1100–2300 nm) to map the topsoil of three fields located in an organic farm “on‐the‐go” at a speed of 3 to 6 km h−1 to a depth of 15 cm. Spectral data were related to results from conventional laboratory analysis of soil P, K, Mg, soil organic matter (SOM), N, and pH. Maps and semivariograms of the principal component scores computed from the spectral information showed consistent spatial patterns present at two different scales. The strength of correlation between field spectra and soil chemical parameters was location dependent, which will make it difficult to develop cross‐field calibration models. Stable, semiquantitative predictions were obtained for soil pH, N‐tot, SOM, K‐tot, and Mg‐tot (R2: 0.71, 0.69, 0.61, 0.55, 0.53) when training data from all fields where included. To improve relationships between soil spectra and soil attributes, dual‐wavelength indices (DWIs) were used. Even though DWIs showed better partial least squares regression model results compared with smoothed or differentiated spectra, they did not improve cross‐field model calibration.

Modeling Soil Processes: Review, Key Challenges, and New Perspectives

“…In addition to rapid and relatively inexpensive estimates of soil properties and processes, PSS can also rapidly provide information about the short‐scale spatial heterogeneity of soils, which is of particular use in modeling soils (Kruger et al, 2013). Proximal soil sensing can also play a gap‐filling role in increasing the level of spatial detail available from existing monitoring networks (Ochsner et al, 2013; Schirrmann et al, 2013), which will be important for soil process modeling that incorporates spatial processes.…”

Can citizen science assist digital soil mapping?

“…Traditional smallscale agriculture has always practiced this, but beginning in the 1980's technological innovations stimulated its development in large-scale commercial agriculture. Farm machinery with precision GPS and "onthe-go" sensors systematically traverses large fields and collects information on crop yields, surface spectral properties (e.g., Schirrmann et al, 2013) and near-surface geophysical properties such as electrical conductivity (e.g., Sudduth et al, 2005;Kweon, 2012). This highresolution information, on the order of 50 m 2 , can be used to differentiate management zones, which can be related by experts to soil types or continuous properties such as depth to root-limiting layers (Fridgen et al, 2004).…”

Digital Soil Mapping: Approaches to Integrate Sensing Techniques to the Prediction of Key Soil Properties

“…Sauer et al (2013) and Schirrmann et al (2013) present studies on producing fast and high resolution digital soil maps using geophysical sensor data, and both identify limitations when transferring results from plot scale to larger areas.…”

“…Schirmann et al (2013) used a mobile near infrared soil scanner (1100–2300 nm) to map the properties of the topsoil of three fields “on‐the‐go.” The spectral measurements were related to results from conventional laboratory analysis of soil P, K, Mg, soil organic matter, N, and pH. Maps and semivariograms of the principal component scores computed from the spectral information showed consistent spatial patterns, but the strength of the correlation between field spectra and soil chemical parameters was not consistent for the three fields.…”