Impacts of Insufficient Observations on the Monitoring of Short- and Long-Term Suspended Solids Variations in Highly Dynamic Waters, and Implications for an Optimal Observation Strategy

Abstract

Coastal water regions represent some of the most fragile ecosystems, exposed to both climate change and human activities. While remote sensing provides unprecedented amounts of data for water quality monitoring on regional to global scales, the performance of satellite observations is frequently impeded by revisiting intervals and unfavorable conditions, such as cloud coverage and sun glint. Therefore, it is crucial to evaluate the impacts of varied sampling strategies (time and frequency) and insufficient observations on the monitoring of short-term and long-term tendencies of water quality parameters, such as suspended solids (SS), in highly dynamic coastal waters. Taking advantage of the first high-frequency in situ SS dataset (at 30 min sampling intervals from 2007 to 2008), collected in Deep Bay, China, this paper presents a quantitative analysis of the influences of sampling strategies on the monitoring of SS, in terms of sampling frequency and time of day. Dramatic variations of SS were observed, with standard deviation coefficients of 48.9% and 54.1%, at two fixed stations; in addition, significant uncertainties were revealed, with the average absolute percent difference of approximately 13%, related to sampling frequency and time, using nonlinear optimization and random simulation methods. For a sampling frequency of less than two observations per day, the relative error of SS was higher than 50%, and stabilized at approximately 10%, when at least four or five samplings were conducted per day. The optimal recommended sampling times for SS were at around 9:00, 12:00, 14:00, and 16:00 in Deep Bay. The "pseudo" MODIS SS dataset was obtained from high-frequency in situ SS measurements at 10:30 and 14:00, masked by the temporal gap distribution of MODIS coverage to avoid uncertainties propagated from atmospheric correction and SS models. Noteworthy uncertainties of daily observations from the Terra/Aqua MODIS were found, with mean relative errors of 19.2% and 17.8%, respectively, whereas at the monthly level, the mean relative error of Terra/Aqua MODIS observations was approximately 10.7% (standard deviation of 8.4%). Sensitivity analysis between MODIS coverage and SS relative errors indicated that temporal coverage (the percentage of valid MODIS observations for a month) of more than 70% is required to obtain high-precision SS measurements at a 5% error level. Furthermore, approximately 20% of relative errors were found with the coverage of 30%, which was the average coverage of satellite observations over global coastal waters. These results highlight the need for high-frequency measurements of geostationary satellites like GOCI and multi-source ocean color sensors to capture the dynamic process of coastal waters in both the short and long term.

High-Frequency Monitoring of Suspended Sediment Variations for Water Quality Evaluation at Deep Bay, Pearl River Estuary, China: Influence Factors and Implications for Sampling Strategy

“…Long-term series-calibrated OBS measurements were also carried out at two stationary stations (A1 and K1 in Figure 1) in 2007 and 2008 and these were treated as the "ground truth" for continuous calibration of OBS data. More details on the collection of SS can be found in [50].…”

Grouping-Based Time-Series Model for Monitoring of Fall Peak Coloration Dates Using Satellite Remote Sensing Data

“…The optimal sampling scale is highly related to the spatial and temporal characteristics of ground targets. Low or high scales will lead to insufficient information or information redundancy, which will influence the analysis and interpretations of satellite remote sensing [8,18].…”

“…Satellite remote sensing provides unprecedented opportunities to monitor vegetation dynamics, including peak foliage coloration at large scales, because of the various spatial and temporal coverages of satellite observations [8]. Over the past decade, various methods based on satellite remote sensing data have been widely used to monitor vegetation phenology [9].…”

Sampling Uncertainties of Long-Term Remote-Sensing Suspended Sediments Monitoring over China’s Seas: Impacts of Cloud Coverage and Sediment Variations

“…Important ocean color sensors orbiting the earth include the Moderate Resolution Imaging Spectroradiometer (Terra/Aqua MODIS, 1999-present), the US Visible Infrared Imager Radiometer Suite (VIIRS, 2011-present), the Ocean and Land Color Instrument (Sentinel-3A OLCI, 2016-present) and the Korean Geostationary Ocean Color Imager (GOCI, 2010-present). Prepared to launch are several other ocean color sensors, including the Chinese HY-series satellites and NASA PACE mission [7,8]. Ideally, ocean color remote sensing unprecedentedly provides data for water quality properties at various spectral, temporal and spatial scales [9][10][11].…”

“…Observations should preferably be taken every 30 min to 1 h [24], while inappropriate sampling frequencies and/or times could introduce large biases and eventually lead to unscientific decisions in water management. Previous studies have indicated that inappropriate sampling time and frequency in water quality monitoring have led to statistical errors larger than 50% [7,25,26]. The facts of missing data from CZCS remote sensing resulted in a root mean-square error (RMSE) of 8% for annual mean chlorophyll concentrations, and a RMSE of 33% when determining the peak chlorophyll values [18].…”