Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images

Rice is a staple food in East Asia and Southeast Asia—an area that accounts for more than half of the world’s population, and 11% of its cultivated land. Studies on rice monitoring can provide direct or indirect information on food security, and water source management. Remote sensing has proven to be the most effective method for the large-scale monitoring of croplands, by using temporary and spectral information. The Google Earth Engine (GEE) is a cloud-based platform providing access to high-performance computing resources for processing extremely large geospatial datasets. In this study, by leveraging the computational power of GEE and a large pool of satellite and other geophysical data (e.g., forest and water extent maps, with high accuracy at 30 m), we generated the first up-to-date rice extent map with crop intensity, at 10 m resolution in the three provinces with the highest rice production in China (the Heilongjiang, Hunan and Guangxi provinces). Optical and synthetic aperture radar (SAR) data were monthly and metric composited to ensure a sufficient amount of up-to-date data without cloud interference. To remove the common confounding noise in the pixel-based classification results at medium to high resolution, we integrated the pixel-based classification (using a random forest classifier) result with the object-based segmentation (using a simple linear iterative clustering (SLIC) method). This integration resulted in the rice planted area data that most closely resembled official statistics. The overall accuracy was approximately 90%, which was validated by ground crop field points. The F scores reached 87.78% in the Heilongjiang Province for monocropped rice, 89.97% and 80.00% in the Hunan Province for monoand double-cropped rice, respectively, and 88.24% in the Guangxi Province for double-cropped rice.

[1]  Alexandre Bouvet,et al.  Monitoring of the Rice Cropping System in the Mekong Delta Using ENVISAT/ASAR Dual Polarization Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[2]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[3]  P. Swain,et al.  Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data , 1990 .

[4]  Ladislav Hluchý,et al.  A roadmap for a dedicated Earth Science Grid platform , 2010, Earth Sci. Informatics.

[5]  Hui Lin,et al.  Application of ENVISAT ASAR Data in Mapping Rice Crop Growth in Southern China , 2007, IEEE Geoscience and Remote Sensing Letters.

[6]  James C. Tilton,et al.  Image segmentation by region growing and spectral clustering with a natural convergence criterion , 1998, IGARSS '98. Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings. (Cat. No.98CH36174).

[7]  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..

[8]  Jos B. T. M. Roerdink,et al.  The Watershed Transform: Definitions, Algorithms and Parallelization Strategies , 2000, Fundam. Informaticae.

[9]  Bertrand Le Saux,et al.  Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks , 2016, ACCV.

[10]  D. Lobell,et al.  Landsat-based classification in the cloud: An opportunity for a paradigm shift in land cover monitoring , 2017 .

[11]  W. Wagner,et al.  European Rice Cropland Mapping with Sentinel-1 Data: The Mediterranean Region Case Study , 2017 .

[12]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Changsheng Li,et al.  Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images , 2006 .

[14]  Luca Gatti,et al.  Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project , 2014, Remote. Sens..

[15]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  C. Burnett,et al.  A multi-scale segmentation/object relationship modelling methodology for landscape analysis , 2003 .

[17]  M. Chakraborty,et al.  SAR signature investigation of rice crop using RADARSAT data , 2006 .

[18]  Jinwei Dong,et al.  Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images , 2015, Scientific Reports.

[19]  Changsheng Li,et al.  Observation of flooding and rice transplanting of paddy rice fields at the site to landscape scales in China using VEGETATION sensor data , 2002 .

[20]  Miao Zhang,et al.  Crop Mapping Using PROBA-V Time Series Data at the Yucheng and Hongxing Farm in China , 2016, Remote. Sens..

[21]  N. Ramankutty,et al.  Characterizing patterns of global land use: An analysis of global croplands data , 1998 .

[22]  P. Gong,et al.  Efficient corn and soybean mapping with temporal extendability: A multi-year experiment using Landsat imagery , 2014 .

[23]  Ovidiu Csillik,et al.  Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels , 2017, Remote. Sens..

[24]  Claudia Kuenzer,et al.  Remote sensing of rice crop areas , 2013 .

[25]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[26]  Hankui K. Zhang,et al.  Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data , 2013 .

[27]  Patricia Gober,et al.  Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery , 2011, Remote Sensing of Environment.

[28]  N. Ramankutty,et al.  Estimating historical changes in global land cover: Croplands from 1700 to 1992 , 1999 .

[29]  Ryutaro Tateishi,et al.  Production of the Japan 30-m Land Cover Map of 2013-2015 Using a Random Forests-Based Feature Optimization Approach , 2016, Remote. Sens..

[30]  David A. Seal,et al.  The Shuttle Radar Topography Mission , 2007 .

[31]  Su-Wei Huang,et al.  Mapping double-cropped irrigated rice fields in Taiwan using time-series Satellite Pour I'Observation de la Terre data , 2011 .

[32]  Jinwei Dong,et al.  Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine. , 2016, Remote sensing of environment.

[33]  Malcolm Davidson,et al.  GMES Sentinel-1 mission , 2012 .

[34]  Sushma Panigrahy,et al.  Evaluation of RADARSAT Standard Beam data for identification of potato and rice crops in India , 1999 .

[35]  N. Lam-Dao.  Rice crop monitoring using new generation Synthetic Aperture Radar (SAR) imagery , 2009 .

[36]  U. Benz,et al.  Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information , 2004 .

[37]  Hugo Carrão,et al.  Combining per-pixel and object-based classifications for mapping land cover over large areas , 2014 .

[38]  Roberto Cipolla,et al.  Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding , 2015, BMVC.

[39]  Cristina Milesi,et al.  Collaborative Supercomputing for Global Change Science , 2011 .

[40]  Arno Schäpe,et al.  Multiresolution Segmentation : an optimization approach for high quality multi-scale image segmentation , 2000 .

[41]  T. Sakamoto,et al.  Global warming, rice production, and water use in China: Developing a probabilistic assessment , 2008 .

[42]  Masaharu Fujita,et al.  Monitoring of rice crop growth from space using the ERS-1 C-band SAR , 1995, IEEE Trans. Geosci. Remote. Sens..

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

[44]  Clement Atzberger,et al.  Single- and Multi-Date Crop Identification Using PROBA-V 100 and 300 m S1 Products on Zlatia Test Site, Bulgaria , 2015, Remote. Sens..

[45]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[46]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[47]  Fang Fuping.  Characteristics of Rice Production in South China and Policy Proposals for Its Steady Development , 2013 .

[48]  J. Maclean,et al.  Rice Almanac: source book for the most important economic activity on earth. , 2002 .

[49]  Zhenyu Jin,et al.  Empirical comparison of noise reduction techniques for NDVI time-series based on a new measure , 2014 .

[50]  Alexandre Bouvet,et al.  Understanding the temporal behavior of crops using Sentinel-1 and Sentinel-2-like data for agricultural applications , 2017 .

[51]  Prasad S. Thenkabail,et al.  Fast Segmentation and Classification of Very High Resolution Remote Sensing Data Using SLIC Superpixels , 2017 .

[52]  Brian P. Salmon,et al.  Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers , 2016, Remote. Sens..

[53]  Changsheng Li,et al.  Mapping paddy rice agriculture in southern China using multi-temporal MODIS images , 2005 .

[54]  C. Justice,et al.  A merged surface reflectance product from the Landsat and Sentinel-2 Missions , 2013 .

[55]  A. Huete,et al.  A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .

[56]  Michael Dixon,et al.  Google Earth Engine: Planetary-scale geospatial analysis for everyone , 2017 .

[57]  P. Gong,et al.  A phenology-based approach to map crop types in the San Joaquin Valley, California , 2011 .

[58]  Yongqiang Yu,et al.  Projective analysis of staple food crop productivity in adaptation to future climate change in China , 2017, International Journal of Biometeorology.

[59]  Michael J. Wilson,et al.  Implementation on Landsat Data of a Simple Cloud-Mask Algorithm Developed for MODIS Land Bands , 2011, IEEE Geoscience and Remote Sensing Letters.

[60]  David P. Roy,et al.  A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring , 2017, Remote. Sens..

[61]  B. Gao.  NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[62]  Zhiming Feng,et al.  Changes in rice cropping systems in the Poyang Lake Region, China during 2004–2010 , 2012, Journal of Geographical Sciences.

[63]  Jie Tian,et al.  Random Forest Classification of Wetland Landcovers from Multi-Sensor Data in the Arid Region of Xinjiang, China , 2016, Remote. Sens..

[64]  Emmanuelle Gouillart,et al.  scikit-image: image processing in Python , 2014, PeerJ.

[65]  Gérard Dedieu,et al.  Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas , 2016 .

[66]  J. Pekel,et al.  High-resolution mapping of global surface water and its long-term changes , 2016, Nature.

[67]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[68]  Åke Grönlund,et al.  Agriculture Market Information Services (AMIS) in the Least Developed Countries (LDCs): Nature, Scopes, and Challenges , 2010, EGOV.

[69]  C. Justice,et al.  High-Resolution Global Maps of 21st-Century Forest Cover Change , 2013, Science.

[70]  Steve Frolking,et al.  New district-level maps of rice cropping in India: a foundation for scientific input into policy assessment , 2006 .

[71]  G. Khush.  What it will take to Feed 5.0 Billion Rice consumers in 2030 , 2005, Plant Molecular Biology.

[72]  Claudia Kuenzer,et al.  Mapping Paddy Rice in China in 2002, 2005, 2010 and 2014 with MODIS Time Series , 2016, Remote. Sens..

[73]  Prasad S. Thenkabail,et al.  Mapping rice areas of South Asia using MODIS multitemporal data , 2011 .

[74]  Jinwei Dong,et al.  Evolution of regional to global paddy rice mapping methods: A review , 2016 .

[75]  Nandin-Erdene Tsendbazar,et al.  Global land cover mapping: current status and future trends , 2014 .

[76]  Jay Gao,et al.  Use of normalized difference built-up index in automatically mapping urban areas from TM imagery , 2003 .

[77]  Emily Elert,et al.  Rice by the numbers: A good grain , 2014, Nature.

[78]  Raymond H. Chan,et al.  Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization , 2005, IEEE Transactions on Image Processing.