Applying deep-learning enhanced fusion methods for improved NDVI reconstruction and long-term vegetation cover study: A case of the Danjiang River Basin
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Title
Applying deep-learning enhanced fusion methods for improved NDVI reconstruction and long-term vegetation cover study: A case of the Danjiang River Basin
Authors
Keywords
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Journal
Ecological Indicators
Volume 155, Issue -, Pages 111088
Publisher
Elsevier BV
Online
2023-10-16
DOI
10.1016/j.ecolind.2023.111088
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Note: Only part of the references are listed.- Spatiotemporal evolution and driving mechanisms of vegetation in the Yellow River Basin, China during 2000–2020
- (2022) Zuguang Ren et al. ECOLOGICAL INDICATORS
- Quantitative Evaluation of Grassland SOS Estimation Accuracy Based on Different MODIS-Landsat Spatio-Temporal Fusion Datasets
- (2022) Yungang Cao et al. Remote Sensing
- Spatio-Temporal Changes of Vegetation Cover and Its Influencing Factors in Northeast China from 2000 to 2021
- (2022) Maolin Li et al. Remote Sensing
- Application of remote sensing for assessment of change in vegetation cover and the subsequent impact on climatic variables
- (2021) Rahul Mishra et al. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
- Quantitative spatial analysis of vegetation dynamics and potential driving factors in a typical alpine region on the northeastern Tibetan Plateau using the Google Earth Engine
- (2021) Chenli Liu et al. CATENA
- Climate Change and Ecological Projects Jointly Promote Vegetation Restoration in Three-River Source Region of China
- (2021) Xiaohui He et al. Chinese Geographical Science
- Spatiotemporal variation and influencing factors of vegetation dynamics based on Geodetector: A case study of the northwestern Yunnan Plateau, China
- (2021) Hong Huo et al. ECOLOGICAL INDICATORS
- Generating High Resolution LAI Based on a Modified FSDAF Model
- (2020) Huan Zhai et al. Remote Sensing
- Spatio-temporal fusion for remote sensing data: an overview and new benchmark
- (2020) Jun Li et al. Science China-Information Sciences
- Effects of Climate Change on Land Cover Change and Vegetation Dynamics in Xinjiang, China
- (2020) Haochen Yu et al. International Journal of Environmental Research and Public Health
- Examining Fractional Vegetation Cover Dynamics in Response to Climate from 1982 to 2015 in the Amur River Basin for SDG 13
- (2020) Ran Yang et al. Sustainability
- Applying Geodetector to disentangle the contributions of natural and anthropogenic factors to NDVI variations in the middle reaches of the Heihe River Basin
- (2020) Lijun Zhu et al. ECOLOGICAL INDICATORS
- Sensitivity of six typical spatiotemporal fusion methods to different influential factors: A comparative study for a normalized difference vegetation index time series reconstruction
- (2020) Junxiong Zhou et al. REMOTE SENSING OF ENVIRONMENT
- Relationship between net primary production and climate change in different vegetation zones based on EEMD detrending – A case study of Northwest China
- (2020) Huiyu Liu et al. ECOLOGICAL INDICATORS
- Coupled Convolutional Neural Network With Adaptive Response Function Learning for Unsupervised Hyperspectral Super Resolution
- (2020) Ke Zheng et al. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
- Spatial associations between NDVI and environmental factors in the Heihe River Basin
- (2019) Lihua Yuan et al. Journal of Geographical Sciences
- Vegetation response to climatic variation and human activities on the Ordos Plateau from 2000 to 2016
- (2019) Qimin Ma et al. Environmental Earth Sciences
- Temporal changes in vegetation around a shale gas development area in a subtropical karst region in southwestern China
- (2019) Yu Guo et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Spatiotemporal Satellite Image Fusion Using Deep Convolutional Neural Networks
- (2018) Huihui Song et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Comparison of partial least square regression, support vector machine, and deep-learning techniques for estimating soil salinity from hyperspectral data
- (2018) Wenzhi Zeng et al. Journal of Applied Remote Sensing
- Spatio-temporal fusion for daily Sentinel-2 images
- (2018) Qunming Wang et al. REMOTE SENSING OF ENVIRONMENT
- Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions
- (2018) et al. Remote Sensing
- NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015
- (2018) Hongshuai Chu et al. SCIENCE OF THE TOTAL ENVIRONMENT
- Multi-source remotely sensed data fusion for improving land cover classification
- (2017) Bin Chen et al. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING
- Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery
- (2017) Feng Gao et al. REMOTE SENSING OF ENVIRONMENT
- Analysis of the Driving Forces in Vegetation Variation in the Grain for Green Program Region, China
- (2017) Hao Wang et al. Sustainability
- Comprehensive study of the biophysical parameters of agricultural crops based on assessing Landsat 8 OLI and Landsat 7 ETM+ vegetation indices
- (2016) Nima Ahmadian et al. GIScience & Remote Sensing
- Development and evaluation of oxaliplatin and irinotecan co-loaded liposomes for enhanced colorectal cancer therapy
- (2016) Bo Zhang et al. JOURNAL OF CONTROLLED RELEASE
- A flexible spatiotemporal method for fusing satellite images with different resolutions
- (2016) Xiaolin Zhu et al. REMOTE SENSING OF ENVIRONMENT
- A logistic-based method for rice monitoring from multitemporal MODIS-Landsat fusion data
- (2016) Nguyen-Thanh Son et al. European Journal of Remote Sensing
- An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data
- (2015) Ruyin Cao et al. AGRICULTURAL AND FOREST METEOROLOGY
- Impacts of climate change and human activities on vegetation cover in hilly southern China
- (2015) Jing Wang et al. ECOLOGICAL ENGINEERING
- Spatio-temporal analysis of vegetation variation in the Yellow River Basin
- (2015) Weiguo Jiang et al. ECOLOGICAL INDICATORS
- Detection and attribution of vegetation greening trend in China over the last 30 years
- (2015) Shilong Piao et al. GLOBAL CHANGE BIOLOGY
- A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion
- (2015) Caroline M. Gevaert et al. REMOTE SENSING OF ENVIRONMENT
- NDVI-based vegetation responses to climate change in an arid area of China
- (2015) Yufeng Xu et al. THEORETICAL AND APPLIED CLIMATOLOGY
- An improved indicator of simulated grassland production based on MODIS NDVI and GPP data: A case study in the Sichuan province, China
- (2014) Xinyu Fu et al. ECOLOGICAL INDICATORS
- Land Cover Classification of Landsat Data with Phenological Features Extracted from Time Series MODIS NDVI Data
- (2014) Kun Jia et al. Remote Sensing
- Mountain vegetation change quantification using surface landscape metrics in Lancang watershed, China
- (2012) Zhiming Zhang et al. ECOLOGICAL INDICATORS
- Greenness in semi-arid areas across the globe 1981–2007 — an Earth Observing Satellite based analysis of trends and drivers
- (2012) Rasmus Fensholt et al. REMOTE SENSING OF ENVIRONMENT
- Trend analysis of vegetation dynamics in Qinghai–Tibet Plateau using Hurst Exponent
- (2011) Jian Peng et al. ECOLOGICAL INDICATORS
- Trend changes in global greening and browning: contribution of short-term trends to longer-term change
- (2011) Rogier Jong et al. GLOBAL CHANGE BIOLOGY
- Generation of high spatial and temporal resolution NDVI and its application in crop biomass estimation
- (2011) Jihua Meng et al. International Journal of Digital Earth
- Unmixing-Based Landsat TM and MERIS FR Data Fusion
- (2008) R. Zurita-Milla et al. IEEE Geoscience and Remote Sensing Letters
- North American forest disturbance mapped from a decadal Landsat record
- (2008) Jeffrey G. Masek et al. REMOTE SENSING OF ENVIRONMENT
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