Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.

[1]  F. Ulaby,et al.  Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models , 1985, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Marco Frasca,et al.  Riemann manifolds from Hellinger distance , 2012, 2012 Tyrrhenian Workshop on Advances in Radar and Remote Sensing (TyWRRS).

[3]  Nicolas Baghdadi,et al.  Potential of SAR sensors TerraSAR-X, ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island , 2009 .

[4]  Russell G. Congalton,et al.  Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine , 2017, Remote. Sens..

[5]  Jitendra Behari,et al.  Microwave dielectric behavior of wet soils , 2005 .

[6]  Ramon F. Hanssen,et al.  Ground reference data for sugarcane biomass estimation in São Paulo state, Brazil , 2018, Scientific Data.

[7]  Steffen Fritz,et al.  Towards an Integrated Global Land Cover Monitoring and Mapping System , 2016, Remote. Sens..

[8]  Daniel Garbellini Duft,et al.  Analysis of socioeconomic and environmental sensitivity of sugarcane cultivation using a Geographic Information System , 2017 .

[9]  Jiaguo Qi,et al.  Monitoring Rice Agriculture across Myanmar Using Time Series Sentinel-1 Assisted by Landsat-8 and PALSAR-2 , 2017, Remote. Sens..

[10]  Waldo Kleynhans,et al.  Meta-optimization of the Extended Kalman Filter's parameters for improved feature extraction on hyper-temporal images , 2011, 2011 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Hamza Erol,et al.  A new supervised classification method for quantitative analysis of remotely-sensed multi-spectral data , 1998 .

[12]  B. Brisco,et al.  The application of C-band polarimetric SAR for agriculture: a review , 2004 .

[13]  Jean Paolo Gomes Minella,et al.  The expansion of Brazilian agriculture: Soil erosion scenarios , 2013, International Soil and Water Conservation Research.

[14]  Heather McNairn,et al.  Radar Remote Sensing of Agricultural Canopies: A Review , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Michael S. Warren,et al.  Leveraging Sentinel-1 time-series data for mapping agricultural land cover and land use in the tropics , 2017, 2017 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images (MultiTemp).

[16]  P. Defourny,et al.  Accuracy Assessment of a 300 m Global Land Cover Map : The GlobCover Experience , 2009 .

[17]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[18]  Eric Vaz,et al.  GlobeLand30 as an alternative fine-scale global land cover map: Challenges, possibilities, and implications for developing countries , 2016 .

[19]  Cristina Maria Bentz,et al.  Oil Slicks Detection From Polarimetric Data Using Stochastic Distances Between Complex Wishart Distributions , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  G. Huffman,et al.  Integrated Multi-satellitE Retrievals for GPM (IMERG) Technical Documentation , 2015 .

[21]  José Alexandre Melo Demattê,et al.  Multiple Geotechnological Tools Applied to Digital Mapping of Tropical Soils , 2015 .

[22]  Keith C. Clarke,et al.  Land use change and the carbon debt for sugarcane ethanol production in Brazil , 2018 .

[23]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[24]  Christian Thiel,et al.  Mapping CORINE Land Cover from Sentinel-1A SAR and SRTM Digital Elevation Model Data using Random Forests , 2015, Remote. Sens..

[25]  Steffen Fritz,et al.  Highlighting continued uncertainty in global land cover maps for the user community , 2011 .

[26]  Daniel Alves Aguiar,et al.  Studies on the Rapid Expansion of Sugarcane for Ethanol Production in São Paulo State (Brazil) Using Landsat Data , 2010, Remote. Sens..

[27]  Waldo Kleynhans,et al.  A search algorithm to meta-optimize the parameters for an extended KALMAN FILTER TO IMPROVE CLASSIFICATION ON HYPER-TEMPORAL IMAGES , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[28]  Ramon F. Hanssen,et al.  A HMM-based approach for historic and up-to-date land cover mapping through Landsat time-series in the state of Sao Paulo, Brazil , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[29]  R. Hanssen,et al.  Monitoring LULC dynamics in the Sao Paulo region through landsat and C-band SAR time series , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[30]  Mehrez Zribi,et al.  Analysis of Sentinel-1 Radiometric Stability and Quality for Land Surface Applications , 2016, Remote. Sens..

[31]  Mübeccel Demirekler,et al.  Quantitative Measure of Observability for Stochastic Systems , 2011 .

[32]  H. Eswaran,et al.  WORLD SOIL MAP , 2005 .

[33]  Sidnei J. S. Sant'Anna,et al.  Classification of Segments in PolSAR Imagery by Minimum Stochastic Distances Between Wishart Distributions , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[34]  D H Gadani,et al.  Dielectric mixing model for the estimation of complex permittivity of wet soils at C and X band microwave frequencies , 2015 .

[35]  Luis Alonso,et al.  A RADARSAT-2 Quad-Polarized Time Series for Monitoring Crop and Soil Conditions in Barrax, Spain , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Malcolm Davidson,et al.  C-Band SAR Data for Mapping Crops Dominated by Surface or Volume Scattering , 2014, IEEE Geoscience and Remote Sensing Letters.

[37]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .