Capability of Spaceborne Hyperspectral EnMAP Mission for Mapping Fractional Cover for Soil Erosion Modeling

Soil erosion can be linked to relative fractional cover of photosynthetic-active vegetation (PV), non-photosynthetic-active vegetation (NPV) and bare soil (BS), which can be integrated into erosion models as the cover-management C-factor. This study investigates the capability of EnMAP imagery to map fractional cover in a region near San Jose, Costa Rica, characterized by spatially extensive coffee plantations and grazing in a mountainous terrain. Simulated EnMAP imagery is based on airborne hyperspectral HyMap data. Fractional cover estimates are derived in an automated fashion by extracting image endmembers to be used with a Multiple End-member Spectral Mixture Analysis approach. The C-factor is calculated based on the fractional cover estimates determined independently for EnMAP and HyMap. Results demonstrate that with EnMAP imagery it is possible to extract quality endmember classes with important spectral features related to PV, NPV and soil, and be able to estimate relative cover fractions. This spectral information is critical to separate BS and NPV which greatly can impact the C-factor derivation. From a regional perspective, we can use EnMAP to provide good fractional cover estimates that can be integrated into soil erosion modeling.

[1]  Keith D. Shepherd,et al.  Soil Spectroscopy: An Alternative to Wet Chemistry for Soil Monitoring , 2015 .

[2]  Oinam Bakimchandra.  Integrated fuzzy-GIS approach for assessing regional soil erosion risks , 2011 .

[3]  Gregory S. Okin,et al.  Comparison of methods for estimation of absolute vegetation and soil fractional cover using MODIS normalized BRDF-adjusted reflectance data , 2013 .

[4]  V. L. Mulder,et al.  The use of remote sensing in soil and terrain mapping — A review , 2011 .

[5]  Peter Strauss,et al.  Modelling of event-based soil erosion in Costa Rica, Nicaragua and Mexico : evaluation of the EUROSEM model. , 2001 .

[6]  Gregory S. Okin,et al.  Relative spectral mixture analysis — A multitemporal index of total vegetation cover , 2005 .

[7]  Martin Bachmann,et al.  Including Quality Measures in an Automated Processing Chain for Airborne Hyperspectral Data , 2007 .

[8]  A. French,et al.  Effect of image spatial and spectral characteristics on mapping semi-arid rangeland vegetation using multiple endmember spectral mixture analysis (MESMA) , 2013 .

[9]  J. C. Price.  How unique are spectral signatures , 1994 .

[10]  David E. Knapp,et al.  Ecosystem Structure throughout the Brazilian Amazon from Landsat Observations and Automated Spectral Unmixing , 2005 .

[11]  G. Asner,et al.  Spectral unmixing of vegetation, soil and dry carbon cover in arid regions: Comparing multispectral and hyperspectral observations , 2002 .

[12]  H. D. Scott,et al.  Applications of fuzzy logic to the prediction of soil erosion in a large watershed , 1998 .

[13]  D. Lobell,et al.  A Biogeophysical Approach for Automated SWIR Unmixing of Soils and Vegetation , 2000 .

[14]  J. Poesen,et al.  The European Soil Erosion Model (EUROSEM): A dynamic approach for predicting sediment transport from fields and small catchments. , 1998 .

[15]  Patrick Hostert,et al.  The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation , 2015, Remote. Sens..

[16]  A. Vrieling.  Satellite remote sensing for water erosion assessment: A review , 2006 .

[17]  Thomas Panagopoulos,et al.  Seasonality of Soil Erosion Under Mediterranean Conditions at the Alqueva Dam Watershed , 2014, Environmental Management.

[18]  Donald H. Gray,et al.  Biotechnical and Soil Bioengineering Slope Stabilization: A Practical Guide for Erosion Control , 1996 .

[19]  Martial Bernoux,et al.  Soil erosion in the humid tropics: a systematic quantitative review. , 2015 .

[20]  Fred A. Kruse,et al.  Comparison of airborne hyperspectral data and EO-1 Hyperion for mineral mapping , 2003, IEEE Trans. Geosci. Remote. Sens..

[21]  Lawrence Ong,et al.  The Earth Observing One (EO-1) Satellite Mission: Over a Decade in Space , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[22]  Martin Bachmann,et al.  Spatial Sub-Sampling Using Local Endmembers for Adapting OSP and SSEE for Large-Scale Hyperspectral Surveys , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[23]  M. Niemiec,et al.  Satellite remote sensing for water erosion assessment , 2009 .

[24]  P. Reinartz,et al.  A program for direct georeferencing of airborne and spaceborne line scanner images , 2002 .

[25]  W. H. Wischmeier,et al.  A universal soil-loss equation to guide conservation farm planning. , 1960 .

[26]  Margaret E. Gardner,et al.  Mapping Chaparral in the Santa Monica Mountains Using Multiple Endmember Spectral Mixture Models , 1998 .

[27]  Karl Segl,et al.  Assessment of sediment connectivity from vegetation cover and topography using remotely sensed data in a dryland catchment in the Spanish Pyrenees , 2014, Journal of Soils and Sediments.

[28]  Suresh Kumar,et al.  Hyperspectral Satellite Data in Mapping Salt-Affected Soils Using Linear Spectral Unmixing Analysis , 2012, Journal of the Indian Society of Remote Sensing.

[29]  Susan L. Ustin,et al.  Evaluation of the potential of Hyperion for fire danger assessment by comparison to the Airborne Visible/Infrared Imaging Spectrometer , 2003, IEEE Trans. Geosci. Remote. Sens..

[30]  Stefan Dech,et al.  Increasing and evaluating the reliability of multiple endmember spectral mixture analysis (MESMA) , 2009 .

[31]  J. Hill,et al.  Using Imaging Spectroscopy to study soil properties , 2009 .

[32]  Cindy Ong,et al.  Using airborne hyperspectral data to characterize the surface pH and mineralogy of pyrite mine tailings , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[33]  R. Lal,et al.  Soil erosion and the global carbon budget. , 2003, Environment international.

[34]  Thomas Esch,et al.  Urban Footprint Processor—Fully Automated Processing Chain Generating Settlement Masks From Global Data of the TanDEM-X Mission , 2013, IEEE Geoscience and Remote Sensing Letters.

[35]  Jean Poesen,et al.  Impact of plant roots on the resistance of soils to erosion by water: a review , 2005 .

[36]  José F. Moreno,et al.  rown and green LAI mapping through spectral indices , 2014 .

[37]  Timo Stuffler,et al.  EnMAP A Hyperspectral Sensor for Environmental Mapping and Analysis , 2006, 2006 IEEE International Symposium on Geoscience and Remote Sensing.

[38]  Luis Guanter,et al.  Simulation of Optical Remote-Sensing Scenes With Application to the EnMAP Hyperspectral Mission , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Margaret A. Goldman,et al.  Wetland Restoration and Creation for Nitrogen Removal: Challenges to Developing a Watershed-Scale Approach in the Chesapeake Bay Coastal Plain , 2015 .

[40]  A. J. Stern,et al.  Remote sensing of crop residue cover and soil tillage intensity , 2006 .

[41]  Daniel C. Yoder,et al.  Universal Soil Loss Equation and Revised Universal Soil Loss Equation , 2011 .

[42]  Simon J. Hook,et al.  Synergies Between VSWIR and TIR Data for the Urban Environment: An Evaluation of the Potential for the Hyperspectral Infrared Imager (HyspIRI) , 2012 .

[43]  Saro Lee,et al.  Soil erosion assessment and its verification using the Universal Soil Loss Equation and Geographic Information System: a case study at Boun, Korea , 2004 .

[44]  Derek M. Rogge,et al.  Mapping of NiCu–PGE ore hosting ultramafic rocks using airborne and simulated EnMAP hyperspectral imagery, Nunavik, Canada , 2014 .

[45]  Yaolin Liu,et al.  Application of Hyperion data to land degradation mapping in the Hengshan region of China , 2010 .

[46]  D. Roberts,et al.  Practical limits on hyperspectral vegetation discrimination in arid and semiarid environments , 2001 .

[47]  Stefan Kaiser,et al.  Simulation of Spatial Sensor Characteristics in the Context of the EnMAP Hyperspectral Mission , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[48]  C. Elvidge.  Visible and near infrared reflectance characteristics of dry plant materials , 1990 .

[49]  Kenji Omasa,et al.  Estimation of vegetation parameter for modeling soil erosion using linear Spectral Mixture Analysis of Landsat ETM data , 2007 .

[50]  Ibrahim A. Mirsal.  Soil Pollution: Origin, Monitoring & Remediation , 2004 .

[51]  W. H. Wischmeier,et al.  Predicting rainfall erosion losses : a guide to conservation planning , 1978 .

[52]  Hermann Kaufmann,et al.  A Comparison of Feature-Based MLR and PLS Regression Techniques for the Prediction of Three Soil Constituents in a Degraded South African Ecosystem , 2012 .

[53]  Daniel Schläpfer,et al.  Operational BRDF Effects Correction for Wide-Field-of-View Optical Scanners (BREFCOR) , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[54]  Naftaly Goldshleger,et al.  Monitoring of agricultural soil degradation by remote-sensing methods: a review , 2013 .

[55]  D. Roberts,et al.  Using Imaging Spectroscopy to Study Ecosystem Processes and Properties , 2004 .

[56]  李幼升,et al.  Ph , 1989 .

[57]  Shuichi Rokugawa,et al.  Current status of Hyperspectral Imager Suite (HISUI) , 2013, 2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS.

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

[59]  Luis Guanter,et al.  EeteS—The EnMAP End-to-End Simulation Tool , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[60]  S. Linden,et al.  Extending the vegetation–impervious–soil model using simulated EnMAP data and machine learning , 2015 .

[61]  T. Malthus,et al.  Assessing the effects of site heterogeneity and soil properties when unmixing photosynthetic vegetation, non-photosynthetic vegetation and bare soil fractions from Landsat and MODIS data , 2015 .