A large-scale, long time-series (1984‒2020) of soybean mapping with phenological features: Heilongjiang Province as a test case
出版年份 2021 全文链接
标题
A large-scale, long time-series (1984‒2020) of soybean mapping with phenological features: Heilongjiang Province as a test case
作者
关键词
-
出版物
INTERNATIONAL JOURNAL OF REMOTE SENSING
Volume 42, Issue 19, Pages 7332-7356
出版商
Informa UK Limited
发表日期
2021-08-22
DOI
10.1080/01431161.2021.1957177
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- The 10-m crop type maps in Northeast China during 2017–2019
- (2021) Nanshan You et al. Scientific Data
- Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm
- (2020) Yidi Xu et al. Science China-Earth Sciences
- Mapping croplands of Europe, Middle East, Russia, and Central Asia using Landsat, Random Forest, and Google Earth Engine
- (2020) Aparna R. Phalke et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Remote sensing phenological monitoring framework to characterize corn and soybean physiological growing stages
- (2020) Chunyuan Diao REMOTE SENSING OF ENVIRONMENT
- Mapping twenty years of corn and soybean across the US Midwest using the Landsat archive
- (2020) Sherrie Wang et al. Scientific Data
- Crop type mapping without field-level labels: Random forest transfer and unsupervised clustering techniques
- (2019) Sherrie Wang et al. REMOTE SENSING OF ENVIRONMENT
- Assessing the pasturelands and livestock dynamics in Brazil, from 1985 to 2017: A novel approach based on high spatial resolution imagery and Google Earth Engine cloud computing
- (2019) Leandro Parente et al. REMOTE SENSING OF ENVIRONMENT
- Robust Landsat-based crop time series modelling
- (2018) D.P. Roy et al. REMOTE SENSING OF ENVIRONMENT
- High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform
- (2018) Xiaoping Liu et al. REMOTE SENSING OF ENVIRONMENT
- Estimating Sub-Pixel Soybean Fraction from Time-Series MODIS Data Using an Optimized Geographically Weighted Regression Model
- (2018) Qiong Hu et al. Remote Sensing
- 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
- (2018) Pierre Defourny et al. REMOTE SENSING OF ENVIRONMENT
- Automated cropland mapping of continental Africa using Google Earth Engine cloud computing
- (2017) Jun Xiong et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Google Earth Engine: Planetary-scale geospatial analysis for everyone
- (2017) Noel Gorelick et al. REMOTE SENSING OF ENVIRONMENT
- Characterizing spatiotemporal dynamics in phenology of urban ecosystems based on Landsat data
- (2017) Xuecao Li et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Automated mapping of soybean and corn using phenology
- (2016) Liheng Zhong et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine
- (2016) Jinwei Dong et al. REMOTE SENSING OF ENVIRONMENT
- Using a global reference sample set and a cropland map for area estimation in China
- (2016) Le Yu et al. Science China-Earth Sciences
- A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data
- (2015) Sofia Siachalou et al. Remote Sensing
- A New Automatic Stratification Method for U.S. Agricultural Area Sampling Frame Construction Based on the Cropland Data Layer
- (2014) Claire Boryan et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Landsat-8: Science and product vision for terrestrial global change research
- (2014) D.P. Roy et al. REMOTE SENSING OF ENVIRONMENT
- Improving 30 m global land-cover map FROM-GLC with time series MODIS and auxiliary data sets: a segmentation-based approach
- (2013) Le Yu et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Improved Land Cover Mapping using Random Forests Combined with Landsat Thematic Mapper Imagery and Ancillary Geographic Data
- (2013) Xiaodong Na et al. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING
- Detecting interannual variation in deciduous broadleaf forest phenology using Landsat TM/ETM+ data
- (2013) Eli K. Melaas et al. REMOTE SENSING OF ENVIRONMENT
- High-Resolution Global Maps of 21st-Century Forest Cover Change
- (2013) M. C. Hansen et al. SCIENCE
- A phenology-based approach to map crop types in the San Joaquin Valley, California
- (2011) Liheng Zhong et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Comparison of different vegetation indices for the remote assessment of green leaf area index of crops
- (2011) Andrés Viña et al. REMOTE SENSING OF ENVIRONMENT
- Monitoring US agriculture: the US Department of Agriculture, National Agricultural Statistics Service, Cropland Data Layer Program
- (2011) Claire Boryan et al. Geocarto International
- Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product
- (2010) K. Chandrasekar et al. INTERNATIONAL JOURNAL OF REMOTE SENSING
- Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000
- (2008) Chad Monfreda et al. GLOBAL BIOGEOCHEMICAL CYCLES
- Evaluation of Random Forest and Adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery
- (2008) Jonathan Cheung-Wai Chan et al. REMOTE SENSING OF ENVIRONMENT
Add your recorded webinar
Do you already have a recorded webinar? Grow your audience and get more views by easily listing your recording on Peeref.
Upload NowBecome a Peeref-certified reviewer
The Peeref Institute provides free reviewer training that teaches the core competencies of the academic peer review process.
Get Started