Quantifying Cloud-Free Observations from Landsat Missions: Implications for Water Environment Analysis

Feng, Lian; Wang, Xinchi

2024-02-22

10.34133/remotesensing.0110

JOURNAL OF REMOTE SENSING   影响因子和分区

2694-1589

Since the launch of the Landsat missions, they have been widely employed for monitoring water environments. However, the designed revisiting period of Landsat satellites is 16 days, leading to large uncertainties when tracking long-term changes in water environmental parameters characterized by high spatiotemporal dynamics. Given this challenge, comprehensive assessments of the global distribution of cloud-free observations (NCOs) obtained from Landsat missions and their applications in water environments and hydrology are currently unavailable. In this study, we utilized >4.8 million images acquired from Landsat-5, Landsat-7, and Landsat-8 to quantify and analyze the spatiotemporal variations of NCOs on a global scale. Our findings indicate that while NCOs demonstrate substantial spatial and temporal heterogeneities, Landsat-8 provides nearly twice as many mean annual NCOs (21.8 +/- 14.7 year(-1)) compared to Landsat-7 (10.8 +/- 4.8 year(-1)) and Landsat-5 (8.3 +/- 5.6 year(-1)). Moreover, we examined how the overlap area of adjacent orbits contributes to improving NCOs, noting that nearly all Landsat observation areas above 45 degrees N are covered by overlapping paths in the east-west direction. Additionally, we conducted an analysis of the potential uncertainties arising from Landsat NCOs in obtaining long-term trends of various water parameters, including total suspended sediment (TSS) concentration, water level, water surface temperature (WST), and ice cover phenology. The results revealed that the uncertainty in water quality parameters (i.e., TSS) from Landsat is much higher than that in hydrological parameters (i.e., water level and WST). The quantification of NCOs and assessment of their impact on water parameter estimations contribute to enhancing our understanding of the limitations and opportunities associated with utilizing Landsat data in water environmental and hydrological studies.

ESCI ; EI

英语

第一 ; 通讯

National Natural Science Foundation of China["42271322","42321004"] ; Guangdong Provincial Higher Education Key Technology Innovation Project[2020ZDZX3006]

Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology

Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology

WOS:001235451200001

AMER ASSOC ADVANCEMENT SCIENCE

20241015695333

Suspended sediments ; Uncertainty analysis ; Water levels ; Water quality

Water Analysis:445.2 ; Soil Mechanics and Foundations:483 ; Satellites:655.2 ; Probability Theory:922.1

Web of Science

Southern Univ Sci & Technol, Sch Environm Sci & Engn, Shenzhen, Peoples R China

Feng, Lian,Wang, Xinchi. Quantifying Cloud-Free Observations from Landsat Missions: Implications for Water Environment Analysis[J]. JOURNAL OF REMOTE SENSING,2024,4.

Feng, Lian,&Wang, Xinchi.(2024).Quantifying Cloud-Free Observations from Landsat Missions: Implications for Water Environment Analysis.JOURNAL OF REMOTE SENSING,4.

Feng, Lian,et al."Quantifying Cloud-Free Observations from Landsat Missions: Implications for Water Environment Analysis".JOURNAL OF REMOTE SENSING 4(2024).