The goal of this study was to develop a suite of inter-related water quality monitoring approaches capable of modeling and estimating spatial and temporal gradients of particulate and dissolved total mercury (THg) concentration, and particulate and dissolved methyl mercury (MeHg), concentration, in surface waters across the Sacramento / San Joaquin River Delta (SSJRD). This suite of monitoring approaches included: a) data collection at fixed continuous monitoring stations (CMS) outfitted with in-situ sensors, b) spatial mapping using boat-mounted flow-through sensors, and c) satellite-based remote sensing. The focus of this specific Child Page is to document a series of derived remote sensing products for turbidity and fluorescent dissolved organic matter (fDOM) based on Sentinel 2 (S2) A/B Multispectral Imager (MSI) imagery acquired between June 1, 2019 and May 31, 2021 for the SSJRD. These remote sensing products were developed using S2 A/B Level 1C input data with less than 25% cloud cover over the SSJRD. Each image in the archive was atmospherically corrected to Level 2 remote sensing reflectance with the open source ACOLITE software package. The turbidity and fDOM products were developed using machine learning to generate SSJRD – specific models based on S2 A/B remote sensing reflectance and in situ measurements collected at USGS continuous monitoring stations. The specific products presented herein consists of 154 Geographic Tagged Image File Format (GeoTIFF) files, with one folder of 77 turbidity files and one folder of 77 fDOM files. Each GeoTIFF file has the following naming convention: AA_BBBBBB_yyyy_mm_dd_CCCCCC_xxxx.tif, where AA indicates the sensor (S2) that acquired the data, BBBBBB indicates the tile identifying the remote sensing image used, yyyy_mm_dd indicates the year, month and day that the image was acquired, CCCCCC indicates the spatial area (SFBDelta) and xxxx indicates the water quality parameter (turbidity or fDOM).