Mapping Phenology of Complicated Wetland Landscapes through Harmonizing Landsat and Sentinel-2 Imagery

被引:4
|
作者
Fan, Chang [1 ]
Yang, Jilin [2 ]
Zhao, Guosong [3 ]
Dai, Junhu [2 ]
Zhu, Mengyao [2 ]
Dong, Jinwei [2 ]
Liu, Ruoqi [1 ]
Zhang, Geli [1 ]
机构
[1] China Agr Univ, Coll Land Sci & Technol, Beijing 100193, Peoples R China
[2] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China
[3] China Univ Geosci, Sch Geog & Informat Engn, Wuhan 430074, Peoples R China
基金
中国国家自然科学基金;
关键词
wetlands; phenology; PhenoCam; Landsat; Sentinel-2; MODIS; DECIDUOUS BROADLEAF FOREST; TIME-SERIES; SURFACE PHENOLOGY; VEGETATION PHENOLOGY; MODIS; DYNAMICS; CANOPY; SATELLITE; VIIRS; CONTINUITY;
D O I
10.3390/rs15092413
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Wetlands are important CO2 sinks and methane sources, and their seasonality and phenological cycle play an essential role in understanding the carbon budget. However, given the spatial heterogeneity of wetland landscapes and the coarser spatial resolution of satellites, the phenological retrievals of wetlands are challenging. Here we examined the phenology of wetlands from 30 m harmonized Landsat/Sentinel-2 (LandSent30) and 500 m MODIS satellite observations using the ground phenology network PhenoCam as a benchmark. This study used all 11 available wetland PhenoCam sites (about 30 site years), covering diverse wetland types from different climate zones. We found that the LandSent30-based phenology results were in overall higher consistency with the PhenoCam results compared to MODIS, which could be related to the better explanation capacity of LandSent30 data in the heterogeneous landscapes of wetlands. This also means that the LandSent30 has an advantage over the 500 m MODIS regarding wetland vegetation phenological retrievals. It should be noted that the LandSent30 did not show a greatly improved performance, which could be related to the specificity and complexity of the wetlands landscape. We also illustrated the potential effects of the location and observation direction of PhenoCam cameras, the selection of Region of Interest (ROI), as well as the landscape composition of the site. Overall, this study highlights the complexity of wetland phenology from both ground and remote sensing observations at different scales, which paves the road for understanding the role of wetlands in global climate change and provides a basis for understanding the real phenological changes of wetland surfaces.
引用
收藏
页数:19
相关论文
共 50 条
  • [21] An evaluation of Landsat, Sentinel-2, Sentinel-1 and MODIS data for crop type mapping
    Song, Xiao-Peng
    Huang, Wenli
    Hansen, Matthew C.
    Potapov, Peter
    SCIENCE OF REMOTE SENSING, 2021, 3
  • [22] Coastal Wetland Mapping with Sentinel-2 MSI Imagery Based on Gravitational Optimized Multilayer Perceptron and Morphological Attribute Profiles
    Zhang, Aizhu
    Sun, Genyun
    Ma, Ping
    Jia, Xiuping
    Ren, Jinchang
    Huang, Hui
    Zhang, Xuming
    REMOTE SENSING, 2019, 11 (08)
  • [23] In-Season Mapping of Sugarcane Planting Based on Sentinel-2 Imagery
    Li, Hui
    Di, Liping
    Zhang, Chen
    Lin, Li
    Guo, Liying
    Li, Ruopu
    Zhao, Haoteng
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2025, 18 : 1410 - 1421
  • [24] Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery
    Ibrahim, Esther Shupel
    Rufin, Philippe
    Nill, Leon
    Kamali, Bahareh
    Nendel, Claas
    Hostert, Patrick
    REMOTE SENSING, 2021, 13 (17)
  • [25] Efficacy of Multiseason Sentinel-2 Imagery for Classifying and Mapping Grassland Condition
    Guevara-Torres, Diego R.
    Facelli, Jose M.
    Ostendorf, Bertram
    JOURNAL OF SENSORS, 2024, 2024
  • [26] META-LEARNING FOR WETLAND CLASSIFICATION USING A COMBINATION OF SENTINEL-1 AND SENTINEL-2 IMAGERY
    Jafarzadeh, Hamid
    Mahdianpari, Masoud
    Gill, Eric
    XXIV ISPRS CONGRESS: IMAGING TODAY, FORESEEING TOMORROW, COMMISSION III, 2022, 5-3 : 47 - 52
  • [27] Phenology Modelling and Forest Disturbance Mapping with Sentinel-2 Time Series in Austria
    Low, Markus
    Koukal, Tatjana
    REMOTE SENSING, 2020, 12 (24) : 1 - 27
  • [28] Analysis-ready satellite data mosaics from Landsat and Sentinel-2 imagery
    Orka, Hans Ole
    Gailis, Janis
    Vege, Mathias
    Gobakken, Terje
    Hauglund, Kenneth
    METHODSX, 2023, 10
  • [29] Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region
    Astola, Heikki
    Hame, Tuomas
    Sirro, Laura
    Molinier, Matthieu
    Kilpi, Jorma
    REMOTE SENSING OF ENVIRONMENT, 2019, 223 : 257 - 273
  • [30] Semantic Segmentation of Sentinel-2 Imagery for Mapping Irrigation Center Pivots
    Graf, Lukas
    Bach, Heike
    Tiede, Dirk
    REMOTE SENSING, 2020, 12 (23) : 1 - 19