Garlic and Winter Wheat Identification Based on Active and Passive Satellite Imagery and the Google Earth Engine in Northern China

[1]  C. Woodcock,et al.  Making better use of accuracy data in land change studies: Estimating accuracy and area and quantifying uncertainty using stratified estimation , 2013 .

[2]  David B. Lobell,et al.  Smallholder maize area and yield mapping at national scales with Google Earth Engine , 2019, Remote Sensing of Environment.

[3]  Wang Futang,et al.  Monitoring winter wheat growth in North China by combining a crop model and remote sensing data , 2008 .

[4]  Jiali Shang,et al.  Agricultural Monitoring in Northeastern Ontario, Canada, Using Multi-Temporal Polarimetric RADARSAT-2 Data , 2014, Remote. Sens..

[5]  Mingze Li,et al.  Forest type identification by random forest classification combined with SPOT and multitemporal SAR data , 2017, Journal of Forestry Research.

[6]  P. Atkinson,et al.  Remote sensing of mangrove forest phenology and its environmental drivers , 2018 .

[7]  A. Huete,et al.  A 30-m landsat-derived cropland extent product of Australia and China using random forest machine learning algorithm on Google Earth Engine cloud computing platform , 2018, ISPRS Journal of Photogrammetry and Remote Sensing.

[8]  J. Benediktsson,et al.  Nomination-favoured opinion pool for optical-SAR-synergistic rice mapping in face of weakened flooding signals , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[9]  Hsuan-Ming Feng,et al.  An novel random forests and its application to the classification of mangroves remote sensing image , 2015, Multimedia Tools and Applications.

[10]  Zheng Niu,et al.  Time Series of Landsat Imagery Shows Vegetation Recovery in Two Fragile Karst Watersheds in Southwest China from 1988 to 2016 , 2019, Remote. Sens..

[11]  R. Congalton,et al.  Automated cropland mapping of continental Africa using Google Earth Engine cloud computing , 2017 .

[12]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[13]  Mitchell D. Goldberg,et al.  Deriving Water Fraction and Flood Maps From MODIS Images Using a Decision Tree Approach , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[14]  Joachim Denzler,et al.  Deep learning and process understanding for data-driven Earth system science , 2019, Nature.

[15]  Jian Wang,et al.  Mapping Winter Crops in China with Multi-Source Satellite Imagery and Phenology-Based Algorithm , 2019, Remote. Sens..

[16]  Andrej Ceglar,et al.  Detecting flowering phenology in oil seed rape parcels with Sentinel-1 and -2 time series , 2020, Remote sensing of environment.

[17]  Fang Chen,et al.  Extraction of Glacial Lake Outlines in Tibet Plateau Using Landsat 8 Imagery and Google Earth Engine , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[18]  Mario Chica-Olmo,et al.  An assessment of the effectiveness of a random forest classifier for land-cover classification , 2012 .

[19]  Dehai Zhu,et al.  Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model , 2015 .

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

[21]  Weimin Ju,et al.  Improved modeling of gross primary productivity (GPP) by better representation of plant phenological indicators from remote sensing using a process model , 2018 .

[22]  Zhe Zhu,et al.  Object-based cloud and cloud shadow detection in Landsat imagery , 2012 .

[23]  Hongsheng Zhang,et al.  Exploring the optimal integration levels between SAR and optical data for better urban land cover mapping in the Pearl River Delta , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[24]  Bruno Basso,et al.  Multi-temporal RADARSAT-2 polarimetric SAR for maize mapping supported by segmentations from high-resolution optical image , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[25]  Damien Sulla-Menashe,et al.  Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index , 2012 .

[26]  Andrew E. Suyker,et al.  A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data , 2010 .

[27]  J. Kumhálová,et al.  Winter oilseed rape and winter wheat growth prediction using remote sensing methods. , 2016 .

[28]  Paul D. Wagner,et al.  Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions , 2018, Int. J. Appl. Earth Obs. Geoinformation.

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

[30]  Jiulin Sun,et al.  An Improved Multi-temporal and Multi-feature Tea Plantation Identification Method Using Sentinel-2 Imagery , 2019, Sensors.

[31]  D. Roy,et al.  The availability of cloud-free Landsat ETM+ data over the conterminous United States and globally , 2008 .

[32]  Jungho Im,et al.  ISPRS Journal of Photogrammetry and Remote Sensing , 2022 .

[33]  P. Convey,et al.  Detecting and mapping vegetation distribution on the Antarctic Peninsula from remote sensing data , 2011, Polar Biology.

[34]  Peter Reinartz,et al.  Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas , 2018, Remote. Sens..

[35]  Matthew F. McCabe,et al.  A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning , 2018 .

[36]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[37]  Ling Tong,et al.  Multitemporal radar backscattering measurement of wheat fields using multifrequency (L, S, C, and X) and full‐polarization , 2013 .

[38]  Massimo Menenti,et al.  Comparing Thresholding with Machine Learning Classifiers for Mapping Complex Water , 2019, Remote. Sens..

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

[40]  Yu Wang,et al.  Mapping winter wheat using phenological feature of peak before winter on the North China Plain based on time-series MODIS data , 2017 .

[41]  Chongcheng Chen,et al.  Winter wheat mapping combining variations before and after estimated heading dates , 2017 .

[42]  R. Lunetta,et al.  Land-cover change detection using multi-temporal MODIS NDVI data , 2006 .

[43]  Gunter Menz,et al.  Multi-temporal wheat disease detection by multi-spectral remote sensing , 2007, Precision Agriculture.

[44]  José Moreno,et al.  Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI) , 2019, Sensors.

[45]  Jianxi Huang,et al.  Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches , 2020, Remote. Sens..

[46]  Shaowen Wang,et al.  A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach , 2018, Remote Sensing of Environment.

[47]  Rui Liu,et al.  Remote Sensing of Wetland Flooding at a Sub-Pixel Scale Based on Random Forests and Spatial Attraction Models , 2019, Remote. Sens..

[48]  Lei Shi,et al.  Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine , 2018, Remote. Sens..

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

[50]  Pramod K. Varshney,et al.  Decision tree regression for soft classification of remote sensing data , 2005 .

[51]  Gang Chen,et al.  Multiscale object-based drought monitoring and comparison in rainfed and irrigated agriculture from Landsat 8 OLI imagery , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[52]  Quazi K. Hassan,et al.  Spatio-Temporal Patterns of Land Use/Land Cover Change in the Heterogeneous Coastal Region of Bangladesh between 1990 and 2017 , 2019, Remote. Sens..

[53]  Xiang Li,et al.  Dynamic Monitoring of the Largest Freshwater Lake in China Using a New Water Index Derived from High Spatiotemporal Resolution Sentinel-1A Data , 2017, Remote. Sens..

[54]  Roshanak Darvishzadeh,et al.  Discriminating transplanted and direct seeded rice using Sentinel-1 intensity data , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[55]  Lei Wang,et al.  Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the CERES-Wheat model , 2017 .

[56]  Jinwei Dong,et al.  High resolution paddy rice maps in cloud-prone Bangladesh and Northeast India using Sentinel-1 data , 2019, Scientific Data.

[57]  Mirco Boschetti,et al.  Understanding wheat lodging using multi-temporal Sentinel-1 and Sentinel-2 data , 2020, Remote Sensing of Environment.

[58]  Li Wang,et al.  Mapping Early, Middle and Late Rice Extent Using Sentinel-1A and Landsat-8 Data in the Poyang Lake Plain, China , 2018, Sensors.

[59]  Brian Brisco,et al.  Wetland classification in Newfoundland and Labrador using multi-source SAR and optical data integration , 2017 .

[60]  Xinyu Li,et al.  Mapping wetland using the object-based stacked generalization method based on multi-temporal optical and SAR data , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[61]  Yang Song,et al.  Mapping Winter Wheat Planting Area and Monitoring Its Phenology Using Sentinel-1 Backscatter Time Series , 2019, Remote. Sens..

[62]  Y. S. Rao,et al.  Dual polarimetric radar vegetation index for crop growth monitoring using sentinel-1 SAR data , 2020 .

[63]  Serge Rambal,et al.  Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements , 2013 .

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

[65]  Jean-Pierre Wigneron,et al.  Comprehensive study of the biophysical parameters of agricultural crops based on assessing Landsat 8 OLI and Landsat 7 ETM+ vegetation indices , 2016 .

[66]  Jian Wang,et al.  A new algorithm for the estimation of leaf unfolding date using MODIS data over China’s terrestrial ecosystems , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[67]  S. Erasmi,et al.  Sentinel-1 time series data for monitoring the phenology of winter wheat , 2020 .

[68]  Aigong Xu,et al.  Winter wheat mapping using temporal signatures of MODIS vegetation index data , 2012 .

[69]  Dehai Zhu,et al.  Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids , 2019, Remote. Sens..