Integrating Multidimensional Feature Indices and Phenological Windows for Mapping Cropping Patterns in Complex Agricultural Landscape Regions

Integrating Multidimensional Feature Indices and Phenological Windows for Mapping Cropping Patterns in Complex Agricultural Landscape Regions

Acquiring a comprehensive understanding of cropping patterns and their spatiotemporal distribution is crucial for sustainable agricultural development and ecological environment protection. However, the similarity of crop spectra and the diversity of ecosystem types hinder the accurate mapping of cr...

Full description

Bibliographic Details
Main Authors:	Haichao Yang, Danyang Wang, Jingda Xin, Hao Qian, Cheng Li, Yunqi Wang, Yayi Tan, Jingyu Dai, Haiyan Zhao, Zhaofu Li
Format:	Article
Language:	English
Published:	IEEE 2024-01-01
Series:	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:	Cropping pattern feature indices Google earth engine phenological window Sentinel-1/2
Online Access:	https://ieeexplore.ieee.org/document/10475427/

Similar Items

Development of a New Phenology Algorithm for Fine Mapping of Cropping Intensity in Complex Planting Areas Using Sentinel-2 and Google Earth Engine
by: Yan Guo, et al.
Published: (2021-09-01)

Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine
by: Li Pan, et al.
Published: (2021-10-01)

Identification of Rubber Plantations in Southwestern China Based on Multi-Source Remote Sensing Data and Phenology Windows
by: Guokun Chen, et al.
Published: (2023-02-01)

Investigating Sentinel-1 and Sentinel-2 Data Efficiency in Studying the Temporal Behavior of Wheat Phenological Stages Using Google Earth Engine
by: Hajar Saad El Imanni, et al.
Published: (2022-10-01)

High-Resolution Mapping of Paddy Rice Extent and Growth Stages across Peninsular Malaysia Using a Fusion of Sentinel-1 and 2 Time Series Data in Google Earth Engine
by: Fatchurrachman, et al.
Published: (2022-04-01)

MONITORING THE CROPS PHENOLOGY USING TIME SERIES SENTINEL 2 IMAGES
by: Iuliana Gabriela Breaban, et al.
Published: (2020-06-01)

Mapping the Northern Limit of Double Cropping Using a Phenology-Based Algorithm and Google Earth Engine
by: Yan Guo, et al.
Published: (2022-02-01)

Multi-year mapping of cropping systems in regions with smallholder farms from Sentinel-2 images in Google Earth engine
by: Hongwei Qi, et al.
Published: (2024-12-01)

Crop Area Mapping in Southern and Central Malawi With Google Earth Engine
by: Seth Peterson, et al.
Published: (2021-07-01)

National-Scale Cropland Mapping Based on Phenological Metrics, Environmental Covariates, and Machine Learning on Google Earth Engine
by: Abdelaziz Htitiou, et al.
Published: (2021-10-01)

Phenology-based delineation of irrigated and rain-fed paddy fields with Sentinel-2 imagery in Google Earth Engine
by: Daniel Marc G. dela Torre, et al.
Published: (2021-10-01)

Cropping intensity map of China with 10 m spatial resolution from analyses of time-series Landsat-7/8 and Sentinel-2 images
by: Luo Liu, et al.
Published: (2023-11-01)

Improved Estimates of Arctic Land Surface Phenology Using Sentinel-2 Time Series
by: Adrià Descals, et al.
Published: (2020-11-01)

Monitoring Cropland Phenology on Google Earth Engine Using Gaussian Process Regression
by: Matías Salinero-Delgado, et al.
Published: (2021-12-01)

Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine
by: Chong LUO, et al.
Published: (2021-07-01)

A Google Earth Engine Algorithm to Map Phenological Metrics in Mountain Areas Worldwide with Landsat Collection and Sentinel-2
by: Tommaso Orusa, et al.
Published: (2023-02-01)

Smallholder Crop Type Mapping and Rotation Monitoring in Mountainous Areas with Sentinel-1/2 Imagery
by: Tingting Ren, et al.
Published: (2022-01-01)

Mapping the Complex Crop Rotation Systems in Southern China Considering Cropping Intensity, Crop Diversity, and Their Seasonal Dynamics
by: Yuan Liu, et al.
Published: (2022-01-01)

The Classification Method Study of Crops Remote Sensing with Deep Learning, Machine Learning, and Google Earth Engine
by: Jinxi Yao, et al.
Published: (2022-06-01)

Phenology-pigment based automated peanut mapping using sentinel-2 images
by: Bingwen Qiu, et al.
Published: (2021-11-01)

Multi-temporal phenological indices derived from time series Sentinel-1 images to country-wide crop classification
by: Edyta Woźniak, et al.
Published: (2022-03-01)

Examining rice distribution and cropping intensity in a mixed single- and double-cropping region in South China using all available Sentinel 1/2 images
by: Yingli He, et al.
Published: (2021-09-01)

Mapping the Natural Distribution of Bamboo and Related Carbon Stocks in the Tropics Using Google Earth Engine, Phenological Behavior, Landsat 8, and Sentinel-2
by: Manjunatha Venkatappa, et al.
Published: (2020-09-01)

Crop Mapping Using the Historical Crop Data Layer and Deep Neural Networks: A Case Study in Jilin Province, China
by: Deyang Jiang, et al.
Published: (2022-08-01)

Remote sensing of evapotranspiration for irrigated crops at Yuma, Arizona, USA
by: Andrew N. French, et al.
Published: (2023-12-01)

Mapping Paddy Rice in Rice–Wetland Coexistence Zone by Integrating Sentinel-1 and Sentinel-2 Data
by: Duan Huang, et al.
Published: (2024-02-01)

Remote Sensing Crop Recognition by Coupling Phenological Features and Off-Center Bayesian Deep Learning
by: Yongchuang Wu, et al.
Published: (2023-01-01)

Evaluation of Accuracy Enhancement in European-Wide Crop Type Mapping by Combining Optical and Microwave Time Series
by: Babak Ghassemi, et al.
Published: (2022-08-01)

Using Time Series Sentinel-1 Images for Object-Oriented Crop Classification in Google Earth Engine
by: Chong Luo, et al.
Published: (2021-02-01)

Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data
by: Yuchuan Luo, et al.
Published: (2022-04-01)

Towards High-Resolution Land-Cover Classification of Greenland: A Case Study Covering Kobbefjord, Disko and Zackenberg
by: Daniel Alexander Rudd, et al.
Published: (2021-09-01)

Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region
by: Hajar Saad El Imanni, et al.
Published: (2022-11-01)

Early-season and refined mapping of winter wheat based on phenology algorithms - a case of Shandong, China
by: Xiuyu Liu, et al.
Published: (2023-07-01)

Delineation of Orchard, Vineyard, and Olive Trees Based on Phenology Metrics Derived from Time Series of Sentinel-2
by: Mukhtar Adamu Abubakar, et al.
Published: (2023-05-01)

Mapping bamboo forest and expansion intensity in China by coupling vegetation phenology and C-band SAR with Sentinel-1 and Sentinel-2 images
by: Saibo Li, et al.
Published: (2023-07-01)

Optimal Grid-Based Filtering for Crop Phenology Estimation with Sentinel-1 SAR Data
by: Lucio Mascolo, et al.
Published: (2021-10-01)

Improving digital mapping of soil organic matter in cropland by incorporating crop rotation
by: Yuan Liu, et al.
Published: (2023-10-01)

Mapping winter rapeseed in South China using Sentinel-2 data based on a novel separability index
by: Jian-bin TAO, et al.
Published: (2023-06-01)

Mangrove Forest Cover and Phenology with Landsat Dense Time Series in Central Queensland, Australia
by: Debbie A. Chamberlain, et al.
Published: (2021-08-01)

An Approach to High-Resolution Rice Paddy Mapping Using Time-Series Sentinel-1 SAR Data in the Mun River Basin, Thailand
by: He Li, et al.
Published: (2020-12-01)