Evaluating the Precision and Accuracy of Proximal Soil vis–NIR Sensors for Estimating Soil Organic Matter and Texture

Abstract

Measuring soil texture and soil organic matter (SOM) is essential given the way they affect the availability of crop nutrients and water during the growing season. Among the different proximal soil sensing (PSS) technologies, diffuse reflectance spectroscopy (DRS) has been deployed to conduct rapid soil measurements in situ. This technique is indirect and, therefore, requires site- and data-specific calibration. The quality of soil spectra is affected by the level of soil preparation and can be accessed through the repeatability (precision) and predictability (accuracy) of unbiased measurements and their combinations. The aim of this research was twofold: First, to develop a novel method to improve data processing, focusing on the reproducibility of individual soil reflectance spectral elements of the visible and near-infrared (vis–NIR) kind, obtained using a commercial portable soil profiling tool, and their direct link with a selected set of soil attributes. Second, to assess both the precision and accuracy of the vis–NIR hyperspectral soil reflectance measurements and their derivatives, while predicting the percentages of sand, clay and SOM content, in situ as well as in laboratory conditions. Nineteen locations in three agricultural fields were identified to represent an extensive range of soils, varying from sand to clay loam. All measurements were repeated three times and a ratio spread over error (RSE) was used as the main indicator of the ability of each spectral parameter to distinguish among field locations with different soil attributes. Both simple linear regression (SLR) and partial least squares regression (PLSR) models were used to define the predictability of % SOM, % sand, and % clay. The results indicated that when using a SLR, the standard error of prediction (SEP) for sand was about 10–12%, with no significant difference between in situ and ex situ measurements. The percentage of clay, on the other hand, had 3–4% SEP and 1–2% measurement precision (MP), indicating both the reproducibility of the spectra and the ability of a SLR to accurately predict clay. The SEP for SOM was only a quarter lower than the standard deviation of laboratory measurements, indicating that SLR is not an appropriate model for this soil property for the given set of soils. In addition, the MPs of around 2–4% indicated relatively strong spectra reproducibility, which indicated the need for more expanded models. This was apparent since the SEP of PLSR was always 2–3 times smaller than that of SLR. However, the relatively small number of test locations limited the ability to develop widely applicable calibration models. The most important finding in this study is that the majority of vis–NIR spectral measurements were sufficiently reproducible to be considered for distinguishing among diverse soil samples, while certain parts of the spectra indicate the capability to achieve this at α = 0.05. Therefore, the innovative methodology of evaluating both the precision and accuracy of DRS measurements will help future developers evaluate the robustness and applicability of any PSS instrument.

Evaluation of Two Portable Hyperspectral-Sensor-Based Instruments to Predict Key Soil Properties in Canadian Soils

“…When comparing with previous literature [ 14 , 45 , 58 , 59 , 60 , 61 , 62 , 63 ], the above listed mid-IR wavebands and their direct or indirect association pairs are to be: 5609, 5520, and 5945 nm with, H 2 O; 6818, 6990, and 6852 nm with calcium carbonate; 9398 nm with carbohydrate; 5609 and 5678 nm with organic material; 5520 and 6294 nm with clay material; 10,781 and 11,038 nm with silicon dioxide. Whereas, the vis wavebands near 445, 504, 533, 562, and 706 nm indirectly correlate with SOC due to color and the vis-NIR waveband near 1447 nm was found to help in predicting sand and the waveband near 1948 nm was found to help in predicting clay, where at 1420 nm and 1920 nm, both wavebands are known to be associated with water adsorption.…”

“…The next step of this research would be to assess the portable mid-IR and vis-NIR soil spectra obtained under field conditions and further eliminate the environmental and systematic factors [ 44 , 63 ] affecting both soil spectra in a similar way to [ 46 , 47 , 72 ]. In addition, a variety of calibration models (ranging from simple to complex), e.g., partial least squares regression (PLSR) [ 1 , 73 , 74 ], principal component regression (PCR) [ 7 , 75 ], artificial neural networks (ANN) [ 76 ], convolution neural networks (CNN) [ 77 , 78 , 79 ], support vector machines (SVM) [ 80 ], and deep learning [ 81 , 82 , 83 , 84 ], to name a few, could be studied.…”

Low-Cost Optical Sensors for Soil Composition Monitoring

“…In both cases, accuracies were below 100%, ranging from 70% to 90% with Vis and NIR data and 97% when X pXRF-ray information was added. Additional examples can be found identifying the % of sand and clay contents using VIS and NIR data in [ 40 , 41 ], or including pXRF data [ 42 ], but no information on accuracies is presented. The used metrics include R 2 , all of them below 0.88 [ 40 ], 0.90 [ 42 ], and 0.95 [ 41 ].…”

Spatial pattern consistency and repeatability of proximal soil sensor data for digital soil mapping

“…Most systems provide narrow‐band spectra across a range of wavelengths in the visible (VIS) and near infrared (NIR) part of the electromagnetic spectrum. Dhawale et al (2021) assessed the reproducibility of visible and near‐infrared reflectance data from topsoil profiles down to 20 cm (Veris P4000, Veris Technologies, KS, USA) and the predictability of soil sand, clay and organic matter contents based on these measurements. By assessing the random variations in the instrument's output while measuring similar soil within short measurement transects, they found that the spectra were sufficiently reproducible for detecting reliable textural differences between soil samples.…”