Tropical Texture Determination by Proximal Sensing Using a Regional Spectral Library and Its Relationship with Soil Classification

Abstract

Abstract:The search for sustainable land use has increased in Brazil due to the important role that agriculture plays in the country. Soil detailed classification is related with texture attribute. How can one discriminate the same soil class with different textures using proximal soil sensing, as to reach surveys, land use planning and increase crop productivity? This study aims to evaluate soil texture using a regional spectral library and its usefulness on classification. We collected 3750 soil samples covering 3 million ha within strong soil class variations in São Paulo State. The spectral analyses of soil samples from topsoil and subsoil were measured in laboratory (400-2500 nm). The potential of a regional soil spectral library was evaluated on the discrimination of soil texture. We considered two types of soil texture systems, one related with soil classification and another with soil managements. The soil line technique was used to assess differentiation between soil textural groups. Soil spectra were summarized by principal component analysis (PCA) to select relevant information on the spectra. Partial least squares regression (PLSR) was used to predict texture. Spectral curves indicated different shapes according to soil texture and discriminated particle size classes from clayey to sandy soils. In the visible region, differences were small because of the organic matter, while the short wave infrared (SWIR) region showed more differences; thus, soil texture variation could be differentiated by quartz. Angulation differences are on a spectral curve from NIR to SWIR. The statistical models predicted clay and sand levels with R 2 = 0.93 and 0.96, respectively. Indeed, we achieved a difference of 1.2% between laboratory and spectroscopy measurement for clay. The spectral information was useful to classify Ferralsols with different texture classification. In addition, the spectra differentiated Lixisols from Ferralsols and Arenosols. This work can help the development of computer programs that allow soil texture classification and subsequent digital soil mapping at detailed scales. In addition, it complies with requirements for sustainable land use and soil management.

Estimating soil texture from vis–NIR spectra

“…In contrast, the models fitted with the RF algorithm were improved markedly after converting to z ‐scores. This resulted in the best prediction of the silt fraction, which is traditionally poorly predicted by vis–NIR (Viscarra Rossel et al ., ; Viscarra Rossel & Webster, ) and is not always reported in soil textural studies (Lacerda et al ., ). The RF models might therefore help to overcome traditional limitations in predicting soil silt contents.…”

“…The best predictive models for each fraction compared favourably with other predictions of soil texture based on IR spectra; for example, for predicting clay content, R 2 of 0.56 (Ben‐Dor & Banin, ), 0.67 (Viscarra Rossel et al ., ), 0.88 (Viscarra Rossel & Behrens, ) to up to 0.93 (Lacerda et al ., ); for predicting sand content, R 2 of 0.75 (Viscarra Rossel et al ., ) or 0.96 (Lacerda et al ., ); for predicting silt content, R 2 of 0.52 (Viscarra Rossel et al ., ). The good prediction performance was further emphasized by the large RPDs; the best models had RPDs well above the cut‐off value of 2.0 used to distinguish unreliable from reliable models (Chang et al ., ), and well above the values of 2.35, 1.63 and 2.06 reported for clay, silt and sand content in Australia (Viscarra Rossel & Webster, ) or 3.17 for clay content in Brazil (Lacerda et al ., ). Similarly, the RMSEs of the best performing models were around 4–5% for clay and silt, indicating excellent prediction accuracy (Stenberg et al ., ; Viscarra Rossel & Behrens, ).…”

“…Because of their sensitivity to the vis, near infrared (NIR) and mid infrared (MIR) portions of the electromagnetic spectrum, both organic and inorganic soil components can be qualitatively and quantitatively assessed using vis-NIR-MIR (or subsets thereof) spectroscopic analysis. Spectroscopy is routinely used to estimate numerous soil properties, including soil organic carbon contents (Viscarra Rossel et al, 2006;Stenberg et al, 2010;Stevens et al, 2013) and its components such as charcoal or particulate organic carbon (Hobley et al, 2016;Hobley et al, 2017), mineralogical properties (Viscarra Rossel et al, 2009;Viscarra Rossel, 2011) and textural composition (Ben-Dor & Banin, 1995;Viscarra Rossel et al, 2009;Lacerda et al, 2016).…”

“…Furthermore, the cumulative sum of these three independently modelled fractions might not be 100%, which is required for soil classification. Consequently, one fraction is sometimes calculated as the difference from 100% of the sum of the other two (Lacerda et al, 2016;Nussbaum et al, 2018). This has drawbacks for error estimation and robustness of estimates, which may affect soil classification.…”

Particles Size Estimation of Polydopamine Based Polymeric Nanoparticles Using Near-Infrared Spectroscopy Combined with Linear Regression Method

“…Importantly, the drug substrate design and a precise investigation of the physiochemical properties including particle size are primordial for understanding of the possibility of DDS leakage by enhanced Plasmon Resonance effect (EPR) [12] [13], besides it capacity of drug loading in case of chemotherapy or photothermal or photodynamic effect in case of other physical therapeutic modalities [10]. The determination of particle characteristics using conventional electronic microscope of laser diffractometer with high accuracy and reproducibility was always challenging inversion problem [14], besides the nonavailability of such apparatus in every lab. NIRS instead is largely available apparatus and noninvasive quantitative and qualitative analysis method.…”