Mapping Plastic-Mulched Farmland with C-Band Full Polarization SAR Remote Sensing Data

Plastic mulching is an important technology in agricultural production both in China and the rest of the world. In spite of its benefit of increasing crop yields, the booming expansion of the plastic mulching area has been changing the landscape patterns and affecting the environment. Accurate and effective mapping of Plastic-Mulched Farmland (PMF) can provide useful information for leveraging its advantages and disadvantages. However, mapping the PMF with remote sensing is still challenging owing to its varying spectral characteristics with the crop growth and geographic spatial division. In this paper, we investigated the potential of Radarsat-2 data for mapping PMF. We obtained the backscattering intensity of different polarizations and multiple polarimetric decomposition descriptors. These remotely-sensed information was used as input features for Random Forest (RF) and Support Vector Machine (SVM) classifiers. The results indicated that the features from Radarsat-2 data have great potential for mapping PMF. The overall accuracies of PMF mapping with Radarsat-2 data were close to 75%. Although the classification accuracy with the back-scattering intensity information alone was relatively lower owing to the inherent speckle noise in SAR data, it has been improved significantly by introducing the polarimetric decomposition descriptors. The accuracy was nearly 75%. In addition, the features derived from the Entropy/Anisotropy/Alpha (H/A/Alpha) polarimetric decomposition, such as Alpha, entropy, and so on, made a greater contribution to PMF mapping than the Freeman decomposition, Krogager decomposition and the Yamaguchi4 decomposition. The performances of different classifiers were also compared. In this study, the RF classifier performed better than the SVM classifier. However, it is expected that the classification accuracy of PMF with SAR remote sensing data can be improved by combining SAR remote sensing data with optical remote sensing data.

[1]  Jia Liu,et al.  Selecting Appropriate Spatial Scale for Mapping Plastic-Mulched Farmland with Satellite Remote Sensing Imagery , 2017, Remote. Sens..

[2]  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.

[3]  Steven E. Franklin,et al.  A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery , 2012 .

[4]  Dilek Koc-San,et al.  Evaluation of different classification techniques for the detection of glass and plastic greenhouses from WorldView-2 satellite imagery , 2013 .

[5]  Liping Di,et al.  A Decision-Tree Classifier for Extracting Transparent Plastic-Mulched Landcover from Landsat-5 TM Images , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[6]  Abderrahim Nemmaoui,et al.  Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain) , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[7]  C. Arcidiacono,et al.  Accuracy of crop-shelter thematic maps: A case study of maps obtained by spectral and textural classification of high-resolution satellite images , 2012 .

[8]  Fei Deng,et al.  Integration of orthoimagery and lidar data for object-based urban thematic mapping using random forests , 2013 .

[9]  David Paull,et al.  Using phase-spaces to characterize land surface phenology in a seasonally snow-covered landscape , 2015 .

[10]  Hongsheng Zhang,et al.  Improving the impervious surface estimation with combined use of optical and SAR remote sensing images , 2014 .

[11]  Stefano Pignatti,et al.  Wheat lodging monitoring using polarimetric index from RADARSAT-2 data , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[12]  D. V. Ivonin,et al.  Interpreting sea surface slicks on the basis of the normalized radar cross‐section model using RADARSAT‐2 copolarization dual‐channel SAR images , 2016 .

[13]  Hamid Reza Moradi,et al.  COMPARISON OF DIFFERENT CLASSIFICATION ALGORITHMS IN SATELLITE IMAGERY TO PRODUCE LAND USE MAPS (CASE STUDY: NOOR CITY) , 2014 .

[14]  S. Subramaniam,et al.  Automated Water Spread Mapping Using ResourceSat-1 AWiFS Data for Water Bodies Information System , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Yu Zhou,et al.  The Performance of Airborne C-Band PolInSAR Data on Forest Growth Stage Types Classification , 2017, Remote. Sens..

[16]  Barbara Koch,et al.  Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation , 2012, Int. J. Appl. Earth Obs. Geoinformation.

[17]  Abderrahim Nemmaoui,et al.  Object-Based Greenhouse Mapping Using Very High Resolution Satellite Data and Landsat 8 Time Series , 2016, Remote. Sens..

[18]  Jiali Shang,et al.  Application of polarization signature to land cover scattering mechanism analysis and classification using multi-temporal C-band polarimetric RADARSAT-2 imagery. , 2017 .

[19]  Alexis J. Comber,et al.  Random forest classification of salt marsh vegetation habitats using quad-polarimetric airborne SAR, elevation and optical RS data , 2014 .

[20]  Liu Qin,et al.  Review of agricultural plastic mulching and its residual pollution and prevention measures in China. , 2014 .

[21]  Mahesh Pal,et al.  Support vector machine‐based feature selection for land cover classification: a case study with DAIS hyperspectral data , 2006 .

[22]  Søren Nørvang Madsen,et al.  Feature-motivated Sinclair matrix (sphere/diplane/helix) decomposition and its application to target sorting for land feature classification , 1997, Optics & Photonics.

[23]  He Li,et al.  Monitoring Plastic-Mulched Farmland by Landsat-8 OLI Imagery Using Spectral and Textural Features , 2016, Remote. Sens..

[24]  Feng Fan.  Pollution of Mulching Plastic Film and Controlling Measures , 2015 .

[25]  Irena Hajnsek,et al.  Potential of Estimating Soil Moisture Under Vegetation Cover by Means of PolSAR , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Jon Atli Benediktsson,et al.  Sensitivity of Support Vector Machines to Random Feature Selection in Classification of Hyperspectral Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Christoph Hütt,et al.  Rice monitoring with multi-temporal and dual-polarimetric TerraSAR-X data , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[28]  M. Pal,et al.  Random forests for land cover classification , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[29]  Claudia Arcidiacono,et al.  A model to manage crop-shelter spatial development by multi-temporal coverage analysis and spatial indicators , 2010 .

[30]  Manuel A. Aguilar,et al.  Relationship between atmospheric corrections and training-site strategy with respect to accuracy of greenhouse detection process from very high resolution imagery , 2010 .

[31]  Jin Chen,et al.  Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index , 2017 .

[32]  Jian Yang,et al.  Decomposition of the Kennaugh Matrix Based on a New Norm , 2014, IEEE Geoscience and Remote Sensing Letters.

[33]  Monique Bernier,et al.  Land Cover Classification in SubArctic Regions Using Fully Polarimetric RADARSAT-2 Data , 2016, Remote. Sens..

[34]  Zhongxin Chen,et al.  Mapping Plastic-Mulched Farmland with Multi-Temporal Landsat-8 Data , 2017, Remote. Sens..

[35]  Di Liu,et al.  A Support Vector Machine-Based Particle Filter Method for Improved Flooding Classification , 2015, IEEE Geoscience and Remote Sensing Letters.

[36]  Liping Di,et al.  Threshold model for detecting transparent plastic-mulched landcover using moderate-resolution imaging spectroradiometer time series data: a case study in southern Xinjiang, China , 2015 .

[37]  Heather McNairn,et al.  Multiyear Crop Monitoring Using Polarimetric RADARSAT-2 Data , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Fernando Vicente-Guijalba,et al.  Polarimetric Response of Rice Fields at C-Band: Analysis and Phenology Retrieval , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[39]  Biswajeet Pradhan,et al.  A refined classification approach by integrating Landsat Operational Land Imager (OLI) and RADARSAT-2 imagery for land-use and land-cover mapping in a tropical area , 2016 .

[40]  Eyal Ben-Dor,et al.  Remote sensing as a tool for monitoring plasticulture in agricultural landscapes , 2007 .

[41]  F. Agüera,et al.  Automatic greenhouse delineation from QuickBird and Ikonos satellite images , 2009 .

[42]  William Perrie,et al.  A Hurricane Morphology and Sea Surface Wind Vector Estimation Model Based on C-Band Cross-Polarization SAR Imagery , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[43]  Wang Wen-guang.  Ship detection based on classification of polarimetric SAR images , 2007 .

[44]  Manuel A. Aguilar,et al.  Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery , 2014, Remote. Sens..

[45]  Linlin Xu,et al.  A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery , 2014 .

[46]  Wang Ke,et al.  Object-based classification approach for greenhouse mapping using Landsat-8 imagery , 2016 .

[47]  Xiao-Dong Hu,et al.  Multiscale Water Body Extraction in Urban Environments From Satellite Images , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[48]  Maj-Liz Nordberg,et al.  Monitoring Change in Mountainous Dry-heath Vegetation at a Regional ScaleUsing Multitemporal Landsat TM Data , 2003, Ambio.

[49]  Russell G. Congalton,et al.  Assessing the accuracy of remotely sensed data : principles and practices , 1998 .

[50]  Maurizio Migliaccio,et al.  Dual-Polarimetric C- and X-Band SAR Data for Coastline Extraction , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[51]  Jia Zhikuan.  Effects of mulching with different kinds of plastic film on growth and water use efficiency of winter wheat in Weibei Highland , 2010 .

[52]  Hiroyoshi Yamada,et al.  Four-component scattering model for polarimetric SAR image decomposition , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[53]  Marko Mäkynen,et al.  Thin Ice Detection in the Barents and Kara Seas With AMSR-E and SSMIS Radiometer Data , 2015, IEEE Trans. Geosci. Remote. Sens..

[54]  Gabriele Nolè,et al.  Agricultural plastic waste spatial estimation by Landsat 8 satellite images , 2017, Comput. Electron. Agric..

[55]  F. Agüera,et al.  Detecting greenhouse changes from QuickBird imagery on the Mediterranean coast , 2006 .

[56]  Thomas Jagdhuber,et al.  Classification and Monitoring of Reed Belts Using Dual-Polarimetric TerraSAR-X Time Series , 2016, Remote. Sens..

[57]  Dorota Michalska-Hejduk,et al.  Application of multisensoral remote sensing data in the mapping of alkaline fens Natura 2000 habitat , 2016 .

[58]  Manuel A. Aguilar,et al.  Object-Based Greenhouse Horticultural Crop Identification from Multi-Temporal Satellite Imagery: A Case Study in Almeria, Spain , 2015, Remote. Sens..

[59]  P. Atkinson,et al.  Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture , 2012 .

[60]  Xia Li,et al.  A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data , 2012 .

[61]  S. M. Jong,et al.  Optimizing spatial image support for quantitative mapping of natural vegetation , 2009 .

[62]  Xing Zhao,et al.  Optimizing Subspace SVM Ensemble for Hyperspectral Imagery Classification , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.