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
  • [11] Mapping the spatiotemporal variability of salinity in the hypersaline Lake Urmia using Sentinel-2 and Landsat-8 imagery
    Bayati, Majid
    Danesh-Yazdi, Mohammad
    JOURNAL OF HYDROLOGY, 2021, 595
  • [12] Harmonizing Landsat 8 and Sentinel-2: A time-series-based reflectance adjustment approach
    Shang, Rong
    Zhu, Zhe
    REMOTE SENSING OF ENVIRONMENT, 2019, 235
  • [13] Mapping Shallow Waters of the Baltic Sea with Sentinel-2 Imagery
    Kutser, T.
    Paavel, B.
    Kaljurand, K.
    Ligi, M.
    Randla, M.
    2018 IEEE/OES BALTIC INTERNATIONAL SYMPOSIUM (BALTIC), 2018,
  • [14] Mapping mangrove in Dongzhaigang, China using Sentinel-2 imagery
    Chen, Na
    JOURNAL OF APPLIED REMOTE SENSING, 2020, 14 (01)
  • [15] MAPPING AND MONITORING WETLANDS USING SENTINEL-2 SATELLITE IMAGERY
    Kaplan, G.
    Avdan, U.
    4TH INTERNATIONAL GEOADVANCES WORKSHOP - GEOADVANCES 2017: ISPRS WORKSHOP ON MULTI-DIMENSIONAL & MULTI-SCALE SPATIAL DATA MODELING, 2017, 4-4 (W4): : 271 - 277
  • [16] Fusion of Landsat 8 and Sentinel-2 data for mangrove phenology information extraction and classification
    Xue Z.
    Qian S.
    National Remote Sensing Bulletin, 2022, 26 (06) : 1121 - 1142
  • [17] Capability of Phenology-Based Sentinel-2 Composites for Rubber Plantation Mapping in a Large Area with Complex Vegetation Landscapes
    Li, Hongzhong
    Zhao, Longlong
    Sun, Luyi
    Li, Xiaoli
    Wang, Jin
    Han, Yu
    Liang, Shouzhen
    Chen, Jinsong
    REMOTE SENSING, 2022, 14 (21)
  • [18] A robust and unified land surface phenology algorithm for diverse biomes and growth cycles in China by using harmonized Landsat and Sentinel-2 imagery
    Yang, Jilin
    Dong, Jinwei
    Liu, Luo
    Zhao, Miaomiao
    Zhang, Xiaoyang
    Li, Xuecao
    Dai, Junhu
    Wang, Huanjiong
    Wu, Chaoyang
    You, Nanshan
    Fang, Shibo
    Pang, Yong
    He, Yingli
    Zhao, Guosong
    Xiao, Xiangming
    Ge, Quansheng
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2023, 202 : 610 - 636
  • [19] Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine
    Pan, Li
    Xia, Haoming
    Yang, Jia
    Niu, Wenhui
    Wang, Ruimeng
    Song, Hongquan
    Guo, Yan
    Qin, Yaochen
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2021, 102
  • [20] Integrating GEDI, Sentinel-2, and Sentinel-1 imagery for tree crops mapping
    Adrah, Esmaeel
    Wong, Jesse Pan
    Yin, He
    REMOTE SENSING OF ENVIRONMENT, 2025, 319