Abstract
Visible, near, and shortwave infrared (VIS-NIR-SWIR) reflectance spectroscopy, a cost-effective and rapid means of characterizing soils, was used to predict soil sample properties for four vineyards (central and north-western Spain). Sieved and air-dried samples were measured using a portable spectroradiometer (350–2500 nm) and compared for pistol grip (PG) versus contact probe (CP) setups. Raw data processed using standard normal variate (SVN) and detrending transformation (DT) were grouped into four subsets (VIS: 350–700 nm; NIR: 701–1000 nm; SWIR: 1001–2500 nm; and full range: 350–2500 nm) in order to identify the most suitable range for determining soil characteristics. The performance of partial least squares regression (PLSR) models in predicting soil properties from reflectance spectra was evaluated by cross-validation. The four spectral subsets and transformed reflectances for each setup were used as PLSR predictor variables. The best performing PLSR models were obtained for pH, electrical conductivity, and phosphorous (R2 values above 0.92), while models for sand, nitrogen, and potassium showed moderately good performances (R2 values between 0.69 and 0.77). The SWIR subset and SVN + DT processing yielded the best PLSR models for both the PG and CP setups. VIS-NIR-SWIR reflectance spectroscopy shows promise as a technique for characterizing vineyard soils for precision viticulture purposes. Further studies will be carried out to corroborate our findings.
Highlights
Knowledge of soil properties and mapping is regarded as key to decision making in precision viticulture, mainly because of a growing interest in more environmentally friendly and sustainable practices [1]
The objectives of this study were (1) to compare spectral signatures of soils as measured in two setups, using a pistol grip (PG) and fibre optic cable, with light provided by an external illuminator lamp, and using a contact probe (CP), with light provided by an internal halogen bulb; and (2) to assess the ability of linear regression models to calculate soil properties from preprocessed and non-preprocessed spectral data
The partial least squares regression (PLSR) predictions explained about 90% of the variance in the laboratory analyses of pH, electrical conductivity (Ec), and P, with root mean square error (RMSE) values of 0.340–0.329, 0.01–0.01 dS m−1, and 6.530–5.770 mg kg−1, respectively, for the PG and CP setups
Summary
Knowledge of soil properties and mapping is regarded as key to decision making in precision viticulture, mainly because of a growing interest in more environmentally friendly and sustainable practices [1]. Modern viticulture requires the evaluation of a wide range of soil properties in a timely and cost-effective way. Conventional methods for laboratory analysis of soils are expensive, timeconsuming, and non-environmentally friendly (they require the use of chemical reagents), and need a whole range of sophisticated protocols and equipment [3]. Soil assessment using visible (VIS), near infrared (NIR), and shortwave infrared (SWIR) spectroscopy, it cannot replace laboratory chemical analysis, is fast, cost-effective, environmentally friendly, non-destructive, reproducible, and repeatable analytical technique [4]. Spectroscopic applications to the soil include NIR, VIS-NIR, and mid-infrared (MIR) analyses comprising Fourier transform infrared (FTIR), FTIR-attenuated total reflection (FTIR-ATR), and Raman spectroscopy [3]
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