Detection and Classification of Non-Photosynthetic Vegetation from PRISMA Hyperspectral Data in Croplands

This study introduces a first assessment of the capabilities of PRISMA (PRecursore IperSpettrale della Missione Applicativa)—the new hyperspectral satellite sensor of the Italian Space Agency (ASI)—for Non-Photosynthetic Vegetation (NPV) monitoring, a topic which is becoming very relevant in the field of sustainable agriculture, being an indicator of crop residue (CR) presence in the field. Data-sets collected during the mission validation phase in croplands are used for mapping the NPV presence and for modelling the diagnostic absorption band of cellulose around 2.1 μm with an Exponential Gaussian Optimization approach, in the perspective of the prediction of the abundance of crop residues. Results proved that PRISMA data are suitable for these tasks, and call for further investigation to achieve quantitative estimates of specific biophysical variables, also in the framework of other hyperspectral missions.

[1]  D. Roberts,et al.  Green vegetation, nonphotosynthetic vegetation, and soils in AVIRIS data , 1993 .

[2]  José F. Moreno,et al.  Toward a Semiautomatic Machine Learning Retrieval of Biophysical Parameters , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Loredana Pompilio,et al.  Exponential Gaussian approach for spectral modelling: The EGO algorithm II. Band asymmetry , 2010 .

[4]  Jibo Yue,et al.  Estimating fractional cover of crop, crop residue, and soil in cropland using broadband remote sensing data and machine learning , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Ádám Kertész,et al.  Conservation Agriculture in Europe , 2014, International Soil and Water Conservation Research.

[6]  A. Skidmore,et al.  Applicability of the PROSPECT model for estimating protein and cellulose + lignin in fresh leaves , 2015 .

[7]  Jacob Shermeyer,et al.  Mapping Crop Residue and Tillage Intensity Using WorldView-3 Satellite Shortwave Infrared Residue Indices , 2018, Remote. Sens..

[8]  Katja Berger,et al.  Crop nitrogen monitoring: Recent progress and principal developments in the context of imaging spectroscopy missions. , 2020, Remote sensing of environment.

[9]  Loredana Pompilio,et al.  Exponential Gaussian approach for spectral modeling: The EGO algorithm I. Band saturation , 2009 .

[10]  Paul D. Gader,et al.  Comparison of Methods for Modeling Fractional Cover Using Simulated Satellite Hyperspectral Imager Spectra , 2019, Remote. Sens..

[11]  Ferdinand Bonn,et al.  Evaluation of soil erosion protective cover by crop residues using vegetation indices and spectral mixture analysis of multispectral and hyperspectral data , 2005 .

[12]  Xulin Guo,et al.  Remote sensing of terrestrial non-photosynthetic vegetation using hyperspectral, multispectral, SAR, and LiDAR data , 2016 .

[13]  David B. Lobell,et al.  Satellite mapping of tillage practices in the North Central US region from 2005 to 2016 , 2019, Remote Sensing of Environment.

[14]  F. Baret,et al.  Leaf optical properties with explicit description of its biochemical composition: Direct and inverse problems , 1996 .

[15]  Wolfram Mauser,et al.  Spaceborne Imaging Spectroscopy for Sustainable Agriculture: Contributions and Challenges , 2018, Surveys in Geophysics.

[16]  T. Painter,et al.  Reflectance quantities in optical remote sensing - definitions and case studies , 2006 .

[17]  Silvia Michelini,et al.  DIRECTORATE-GENERAL FOR AGRICULTURE AND RURAL DEVELOPMENT , 2007 .

[18]  Catherine Champagne,et al.  Spatial Variability Mapping of Crop Residue Using Hyperion (EO-1) Hyperspectral Data , 2015, Remote. Sens..

[19]  Bakhtiar Feizizadeh,et al.  Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue , 2019, Remote. Sens..

[20]  K. Beurs,et al.  Remote sensing of crop residue cover using multi-temporal Landsat imagery , 2012 .

[21]  C. Ananasso,et al.  THE PRISMA HYPERSPECTRAL MISSION , 2014 .