Estimating Soil Properties and Nutrients by Visible and Infrared Diffuse Reflectance Spectroscopy to Characterize Vineyards

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.

Determination of Mehlich 3 Extractable Elements with Visible and Near Infrared Spectroscopy in a Mountainous Agricultural Land, the Caucasus Mountains

“…Such differences in the model performances were found in previous studies. Similar to our results, it was reported that calibration, validation, and prediction processes can be complicated and dependent on soil types and its physiochemical properties, the soil management history, chemical characteristics, and the analytical method, along with the sample size and distribution, as well as the prediction approaches [4,7,13,14,[27][28][29]54,61]. Based on 2250 soil samples that were collected in a 5-year period from four soil groups varying in clay mineralogy (Alfisols, Inceptisols, Entisols, and Vertisols) and a series of robust regression analyses, Golia and Diakoloukas [61] concluded that several factors (pH, electric conductivity, SOC, soil texture, exchangeable cations, and metal oxides along with coexisting metals) affect the total concentrations of Fe and Cd in soils.…”

“…In accordance with recent studies, K is one of the complex elements to estimate with VIS-NIR spectra regardless of the extraction procedure. For instance, good model was obtained by Rodríguez-Pérez et al [7], whereas Bilgili et al [13] found a poor model (R 2 = 0.25-0.32) for the K content. Chang et al [27] obtained a more accurate model (R 2 = 0.64) for K extracted in M3 compared to K extracted in NH4OAc (R 2 = 0.55).…”

“…The number of studies examining the potential of applications of reflectance spectroscopy in agriculture is steadily increasing all over the world [4]. Many of these investigations have evaluated individual wavelength ranges separately, while others have combined several ranges of wavelength to estimate soil properties and quality indices [1,[5][6][7]. Among these techniques, laboratory-based visible and near infrared spectroscopy (VIS-NIR or NIR) has been reported as an alternative to traditional soil analytical methods, which are often time consuming and costly, providing limited information on the soil spatial variability [4,[8][9][10].…”

“…Many studies have focused on the potential of VIS-NIR spectroscopy to predict a wide range of soil physical, chemical, and biological properties in different climatic regions of the world [4,16,17]. Along with basic soil properties, different forms of nutrients and heavy metals in soils have been predicted with VIS-NIR spectra in varying scales [6,7,18,19]. Compared to basic soil properties (pH, texture, soil organic carbon (SOC), exchangeable cations, electrical conductivity (EC), available N and P, microbial biomass, and respiration), fewer studies have focused on the prediction of Mehlich 3 (M3) extractable chemicals, e.g., microelements and metals using VIS-NIR reflectance spectra.…”

Rapid Determination of β-Glucan Content of Hulled and Naked Oats Using near Infrared Spectroscopy Combined with Chemometrics

“…Similar results were also observed in wavelength range 1900–2500 nm with R 2 c and R 2 p of 0.460 and 0.336 for hulled and 0.382 and 0.274 for naked oats, respectively, whereas in the spectral range from 700 to 1300 nm, the R 2 c and R 2 p for naked oats were 0.677 and 0.620, and the highest values for R 2 c (0.789) and R 2 p (0.735) and the minimum values for RMSEC (0.177) and RMSEP (0.199) were observed for the hulled oat samples. Previously, it was mentioned that the calibration models with R 2 c between 0.66 and 0.81 present satisfactory predictions whereas prediction of chemical properties with R 2 c < 0.5 are reported as being unreliable [ 32 , 33 , 34 ]. A study by Seefeldt et al also reported the best correlation of β -glucan of barley with the wavelengths in these regions [ 18 ].…”