Can Multispectral Information Improve Remotely Sensed Estimates of Total Suspended Solids? A Statistical Study in Chesapeake Bay

Abstract

Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include TSS measurements. Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only the red and near-infrared bands due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various statistical and machine learning models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing multispectral model, a Random Forest regression, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. We find that the Random Forest model modestly outperforms the single-band algorithm on a holdout cross-validation dataset and offers particular advantages under high TSS conditions. We also find that both methods are similarly generalizable throughout various partitions of space and time. The multispectral Random Forest model is, however, more data intensive than the single band algorithm, so the objectives of the application will ultimately determine which method is more appropriate.

Long‐Term Trends in Chesapeake Bay Remote Sensing Reflectance: Implications for Water Clarity

“…The red-band Rrs approach has historically been the most widely used satellite-derived index of water clarity for estuaries having similar turbidity conditions as the Chesapeake Bay (Crooke et al, 2017;Constantin et al, 2016;DeLuca et al, 2018;Hudson et al, 2017;Ondrusek et al, 2012;Stumpf & Pennock, 1989;Yunus et al, 2021). In moderately turbid estuaries like the Chesapeake Bay, red wavelengths (∼640-660 nm) are most commonly used to estimate suspended sediments or TSS concentrations due to the relatively lower influence of phytoplankton and CDOM in this red portion of the visible spectrum (Tzortziou et al, 2007).…”

“…Of the past approaches for estimating Chesapeake Bay water clarity from remote sensing, the most widely used method involves the MODIS high-resolution land band at 250 m resolution for the 645 nm wavelength (Aurin et al, 2013;Crooke et al, 2017;DeLuca et al, 2018;Hasan & Benninger, 2017;Ondrusek et al, 2012;Yunus et al, 2021). In the present study, we consider more wavelengths and band ratios in addition to this widely used MODIS red-band approach.…”

Open-Source, Low-Cost, In-Situ Turbidity Sensor for River Network Monitoring

“…Dissolved and suspended sediment concentrations together with gross biological activity affect the intrinsic colour of natural waters 21,22 , which makes optical satellite remote sensing of oceans, coastal areas, large lakes/rivers possible. When calibrated with ground measurements, such satellite data can be very useful for SSC estimates 23 and can give a range of additional water quality parameters 24 at large scales but not with high temporal resolutions (repeatability given by satellite overpasses) and with poor point accuracy. In rivers, satellite-based analyses are only possible if the spatial footprint is sufficiently large.…”

Combining Artificial Neural Networks with Causal Inference for Total Phosphorus Concentration Estimation and Sensitive Spectral Bands Exploration Using MODIS

“…Remote sensing is widely used in the assessment of water quality [14][15][16][17] due to its ability to provide a synoptic view of the entire water region [18][19][20]. Visible light and infrared bands of remote-sensing images are useful for the estimation of water quality parameters, such as TP [4,5]; however, no unified models have been created for the estimation of TP concentration in different lakes [21,22]. Differences in water characteristics and components between lakes lead to the low transferability of TP concentration estimation methods.…”

“…We used an ANN to establish the correlation between the in situ TP concentration of inland lakes and the remote-sensing reflectance of MODIS due to its powerful learning ability. Although water quality parameters vary in a few hours under the conditions of wind and rain [21,26,34], MODIS has high temporal resolution and can follow the daily change of TP concentration in inland lakes [43]. MODIS has a broad spectrum, spanning visible, infrared spectra, and thermal infrared.…”