Estimation of soil nitrogen in agricultural regions by VNIR reflectance spectroscopy
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Estimation of soil nitrogen in agricultural regions by VNIR reflectance spectroscopy Amol D. Vibhute1 · Karbhari V. Kale2 · Sandeep V. Gaikwad2 · Rajesh K. Dhumal2 Received: 6 May 2020 / Accepted: 10 August 2020 © Springer Nature Switzerland AG 2020
Abstract In the present paper, the novel hyperspectral model was developed for the estimation of Soil Nitrogen (SN) in agricultural lands using Partial Least Squares Regression (PLSR) method. In this regard, an effort has been made on predicting and analyzing SN from several agricultural lands of Phulambri Tehsil of Aurangabad district of Maharashtra, India. The spectra of seventy four (74) agricultural soil samples were acquired between 350–2500 nm by Analytical Spectral Device Field Spec-4 Spectroradiometer under controlled laboratory conditions. The preprocessing was done on acquired spectra by First-derivative Transformation (FDT) and Savitzky–Golay (SG) method for getting suitable information. The PLSR approach was derived from correlation analysis between reflectance spectra and SN features. The resulted coefficient of determination (R2) values was 0.68 and 0.94 before and after pre-treatment with root mean square error of prediction (RMSEP) 4.34 and 1.56, respectively. The identified sensitive wavelength bands of nitrogen content were 480 nm, 511 nm, 653 nm, 997 nm, 1472 nm, 1795 nm, 2210 nm and 2296 nm. In the conclusion, the model is reliable for prediction of SN from agricultural areas. The present research will be useful for decision making in agricultural management. Keywords Soil nitrogen · Partial least squares regression · First-derivative transformation · Reflectance spectroscopy · Agricultural soil
1 Introduction Soil properties play a crucial role in efficient farming having various aspects. However, efficient farming practices are possible when soils have sufficient amount of vital macro- and micronutrients. Soil nitrogen is one of the important macronutrient which helps to the growth of crops [1, 2] and healthy practices in agricultural soil. Moreover, the nutrient absorption process of crops/plants is fulfilled by the SNs [1]. Therefore, the precise extraction and estimation of SNs are imperative for precision farming, crop growth and yield estimation, evaluations of soil health, etc. Nevertheless, soils may vary according to spatio-temporal dynamics as compared to water and air. The extractions of soil physicochemical nutrients
by conventional methods are risky task due to harmful chemicals. Furthermore, these conventional methods are complicated, laborious, time consuming and expensive. They do not offer the spatio-temporal information of soil nutrients [3–5]. Recently, Remotely Sensed Hyperspectral Reflectance Spectra (RSHRS) has provided valuable source in soil sciences to replace the customary ways of soil study for overcoming several limitations. The RSHRS is generally known as visible-near-infrared (VNIR) reflectance spectroscopy. The RSHRS is more precise, non-wasting, rapid and inexpensive than conventional methods. In addition, the s
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