Comparisons between temporal statistical metrics, time series stacks and phenological features derived from NASA Harmonized Landsat Sentinel-2 data for crop type mapping

[1]  Zhengwei Yang,et al.  Towards automation of in-season crop type mapping using spatiotemporal crop information and remote sensing data , 2022, Agricultural Systems.

[2]  Bingfang Wu,et al.  Performance and the Optimal Integration of Sentinel-1/2 Time-Series Features for Crop Classification in Northern Mongolia , 2022, Remote. Sens..

[3]  Hankui K. Zhang,et al.  10 m crop type mapping using Sentinel-2 reflectance and 30 m cropland data layer product , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[4]  Dirk Pflugmacher,et al.  Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany , 2022, Remote Sensing of Environment.

[5]  Yaochen Qin,et al.  Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[6]  David M. Johnson,et al.  Pre- and within-season crop type classification trained with archival land cover information , 2021 .

[7]  D. Roy,et al.  A global analysis of the temporal availability of PlanetScope high spatial resolution multi-spectral imagery , 2021 .

[8]  M. Hansen,et al.  An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping , 2021 .

[9]  Jinwei Dong,et al.  The 10-m crop type maps in Northeast China during 2017–2019 , 2021, Scientific Data.

[10]  Danlu Chen,et al.  Phenology-based classification of crop species and rotation types using fused MODIS and Landsat data: The comparison of a random-forest-based model and a decision-rule-based model , 2021 .

[11]  Luo Liu,et al.  Mapping sugarcane plantation dynamics in Guangxi, China, by time series Sentinel-1, Sentinel-2 and Landsat images , 2020 .

[12]  Christopher Conrad,et al.  Crop Type Classification Using Fusion of Sentinel-1 and Sentinel-2 Data: Assessing the Impact of Feature Selection, Optical Data Availability, and Parcel Sizes on the Accuracies , 2020, Remote. Sens..

[13]  L. Di,et al.  Transfer Learning for Crop classification with Cropland Data Layer data (CDL) as training samples. , 2020, The Science of the total environment.

[14]  Mark A. Friedl,et al.  Continental-scale land surface phenology from harmonized Landsat 8 and Sentinel-2 imagery , 2020, Remote Sensing of Environment.

[15]  Luo Liu,et al.  Mapping cropping intensity in China using time series Landsat and Sentinel-2 images and Google Earth Engine , 2020 .

[16]  Yuan Gao,et al.  Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine , 2019, Remote. Sens..

[17]  David B. Lobell,et al.  Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques , 2019, Remote Sensing of Environment.

[18]  Gérard Dedieu,et al.  Near real-time agriculture monitoring at national scale at parcel resolution: Performance assessment of the Sen2-Agri automated system in various cropping systems around the world , 2019, Remote Sensing of Environment.

[19]  Patrick Hostert,et al.  Intra-annual reflectance composites from Sentinel-2 and Landsat for national-scale crop and land cover mapping , 2019, Remote Sensing of Environment.

[20]  C. Justice,et al.  The Harmonized Landsat and Sentinel-2 surface reflectance data set , 2018, Remote Sensing of Environment.

[21]  Miao Zhang,et al.  Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images , 2018, Remote. Sens..

[22]  David Johnson,et al.  Fusion of Moderate Resolution Earth Observations for Operational Crop Type Mapping , 2018, Remote. Sens..

[23]  Daniel Spengler,et al.  A Progressive Crop-Type Classification Using Multitemporal Remote Sensing Data and Phenological Information , 2018, PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science.

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

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

[26]  Hankui K. Zhang,et al.  Using the 500 m MODIS Land Cover Product to Derive a Consistent Continental Scale 30 m Landsat Land Cover Classification , 2017 .

[27]  Joanne C. White,et al.  Optical remotely sensed time series data for land cover classification: A review , 2016 .

[28]  Francisco Javier Gallego,et al.  Efficiency assessment of using satellite data for crop area estimation in Ukraine , 2014, Int. J. Appl. Earth Obs. Geoinformation.

[29]  Andrew E. Suyker,et al.  A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US , 2013, Remote. Sens..

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

[31]  Zhengwei Yang,et al.  Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program , 2011 .

[32]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[33]  L. Breiman Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[34]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[35]  D. Roy,et al.  Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD) , 2014 .

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

[37]  David M. Johnson A 2010 map estimate of annually tilled cropland within the conterminous , 2013 .