Prediction of the presence of topsoil nitrogen from spaceborne hyperspectral data

Conventional methods of soil nitrogen extraction are time consuming, expensive and tedious. Remote sensing and Geographical Information System technologies can be used for the rapid and efficient prediction of the presence of soil nitrogen. However, studies are limited by and large to fields of larger and homogeneous units. This research concentrates on the prediction of topsoil nitrogen from harvested, scattered and small-sized agricultural fields of India using hyperspectral data. Spaceborne hyperspectral Hyperion data are used for the prediction of the presence of nitrogen. Multivariate partial least square regression method was used to predict the presence of nitrogen from reflectance. Reflectance data were pretreated using moving average and Savitzky–Golay filters which resulted in moderate prediction of R2 0.65 and 0.63 for calibration and validation, respectively. It can be inferred that Hyperion data can be effectively used for the prediction of the presence of soil nitrogen with a moderate level of accuracy even in case of scattered fields and fields of sizes approximately equal to the spatial resolution of the satellite.

[1]  Tim R. McVicar,et al.  Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes , 2003, IEEE Trans. Geosci. Remote. Sens..

[2]  Lei Zhou,et al.  Estimation of vegetation Equivalent Water Thickness using hyperspectral data and partial least square regression , 2010, 2010 18th International Conference on Geoinformatics.

[3]  C. Hurburgh,et al.  Near-Infrared Reflectance Spectroscopy–Principal Components Regression Analyses of Soil Properties , 2001 .

[4]  A. Karnieli,et al.  Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over clayey soils in Israel , 2002 .

[5]  Eric R. Ziegel,et al.  Tsukuba Meeting: Largest Attendance Ever , 2004, Technometrics.

[6]  Quantitative inverse modeling of nitrogen content from hyperion data under stress of exhausted coal mining sites , 2009 .

[7]  Q. Ma.  Handbook of Turfgrass Management and Physiology , 2009 .

[8]  Zou Xiaobo,et al.  Diagnostics of nitrogen deficiency in mini-cucumber plant by near infrared reflectance spectroscopy , 2011 .

[9]  Harold M. van Es,et al.  Combined use of hyperspectral VNIR reflectance spectroscopy and kriging to predict soil variables spatially , 2011, Precision Agriculture.

[10]  R. V. Rossel,et al.  Visible, near infrared, mid infrared or combined diffuse reflectance spectroscopy for simultaneous assessment of various soil properties , 2006 .

[11]  A. Müller,et al.  Predictions of soil surface and topsoil organic carbon content through the use of laboratory and field spectroscopy in the Albany Thicket Biome of Eastern Cape Province of South Africa , 2011 .

[12]  M. P. Tuohy,et al.  In-field hyperspectral proximal sensing for estimating quality parameters of mixed pasture , 2011, Precision Agriculture.

[13]  R. V. Rossel,et al.  Soil organic carbon prediction by hyperspectral remote sensing and field vis-NIR spectroscopy: An Australian case study , 2008 .

[14]  Z. Niu,et al.  Prediction of soil properties using laboratory VIS–NIR spectroscopy and Hyperion imagery , 2013 .

[15]  William R DeTar,et al.  Detection of Soil Properties with Airborne Hyperspectral Measurements of Bare Fields , 2008 .

[16]  L. Hoffmann,et al.  Measuring soil organic carbon in croplands at regional scale using airborne imaging spectroscopy , 2010 .

[17]  Marvin H. Hall,et al.  Carbon and Nitrogen Analysis of Soil Fractions Using Near-Infrared Reflectance Spectroscopy , 1991 .

[18]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[19]  U. Schmidhalter,et al.  High resolution topsoil mapping using hyperspectral image and field data in multivariate regression modeling procedures , 2006 .

[20]  Andrew Rawson,et al.  The prediction of soil chemical and physical properties from mid-infrared spectroscopy and combined partial least-squares regression and neural networks (PLS-NN) analysis , 2009 .

[21]  Guofeng Wu,et al.  Comparison of multivariate methods for estimating soil total nitrogen with visible/near-infrared spectroscopy , 2012, Plant and Soil.

[22]  D. Gui,et al.  Studies on the reflectance spectral features of saline soil along the middle reaches of Tarim River: a case study in Xinjiang Autonomous Region, China , 2013, Environmental Earth Sciences.