Effect of Satellite Temporal Resolution on Long-Term Suspended Particulate Matter in Inland Lakes

Abstract

The temporal resolution of satellite determines how well remote sensing products represent changes in the lake environments and influences the practical applications by end-users. Here, a resampling method was used to reproduce the suspended particulate matter (SPM) dataset in 43 large lakes (>50 km2) on the eastern China plain during 2003–2017 at different temporal resolutions using MODIS Aqua (MODISA) based on Google Earth Engine platform, then to address the impact of temporal resolution on the long-term SPM dataset. Differences between the MODISA-derived and reproduced SPM dataset at longer temporal resolution were higher in the areas with large water dynamics. The spatial and temporal distributions of the differences were driven by unfavorable observation environments during satellite overpasses such as high cloud cover, and rapid changes in water quality, such as water inundation, algae blooms, and macrophytes. Furthermore, the annual mean difference in SPM ranged from 5–10% when the temporal difference was less than 10 d, and the differences in summer and autumn were higher than that of other seasons and surpassed 20% when the temporal resolution was more than 16 d. To assure that difference were less than 10% for long-term satellite-derived SPM datasets, the minimal requirement of temporal resolution should be within 5 d for most of the inland lakes and 3 d for lakes with large changes in water quality. This research can be used to not only evaluate the reliability of historically remote sensing products but also provide a reference for planning field campaigns and applying of high spatial resolution satellite missions to monitor aquatic systems in the future.

Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review

“…Publicly available datasets within GEE along with its high computing performance allow for accurate monitoring of water resources with adequate temporal and spatial resolutions. Consequently, GEE was efficiently employed for surface water dynamics monitoring [93], [94], bathymetry [20], [95], shoreline and coastal studies [96], [97], lake and reservoir mapping and monitoring [98], [99], glacier studies [90], [100], snow ablation and snow mapping [92], [101], suspended sediments and river studies [102], [103], and water health assessment [104], [105]. For instance, [93] introduced a fully automatic method for water extraction in New Zealand.…”

The Estimation of Water Quality Parameters in Lentic Environments Through Remote Sensing Technologies: a Review of the Past Two Decades

“…GEE is a remote sensing information portal launched in 2010 that consists in a data catalogue providing time series of satellite images from various missions as well as vector data allowing to apply processing algorithms developed by the user or the remote sensing community (GORELICK et al, 2017;MUTANGA, 2018). Especially in the last five years, a number of studies have been carried out using GEE to monitor WQ parameters such as TSM (MARKERT et al, 2018;CAO et al, 2019;CABALLERO;NAVARRO, 2021), SDD (PAGE;OLMANSON;MISHRA, 2019;LIU et al, 2020) and chla (LIN et al, 2018;WANG et al, 2020;CABALLERO;NAVARRO, 2021;LOBO et al, 2021).…”

Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

“…Due to the constraints of existing sensors, no remote sensing data are acquired at high resolutions in the temporal, spatial, and spectral dimensions [23,24]. Some scholars have designed spatio-temporal fusion (STF) methods [25][26][27][28][29].…”