Remote Sensing Retrieval of Water Clarity in Clear Oceanic to Extremely Turbid Coastal Waters From Multiple Spaceborne Sensors

Water clarity (
${Z} _{\text {SD}}$
) is a critical water quality parameter that requires remote sensing mapping. Although great progress has been made in
${Z} _{\text {SD}}$
retrieval over clear waters during past decades, challenges remain over turbid waters. To address this issue, a new model was proposed to retrieve
${Z} _{\text {SD}}$
in clear oceanic to extremely turbid coastal waters by improving the
${Z} _{\text {SD}}$
retrieval in turbid waters. First, waters were optically classified into three classes (clear, moderately turbid, and extremely turbid waters) with a band ratio of remote sensing reflectance [
${R} _{\text {rs}}$
(
$\lambda $
)]
${f} ={R}_{\text {rs}}$
(670)/
${R} _{\text {rs}}$
(490). Second, class-specific algorithms were adopted to retrieve the spectral diffuse attenuation coefficient
${K} _{d}$
(
$\lambda $
) from
${R} _{\text {rs}}$
(
$\lambda $
). Finally,
${Z} _{\text {SD}}$
was semianalytically estimated from minimum
${K} _{d}$
(
$\lambda $
) in the visible domain. Data from oceanic and coastal waters (
${N}$
= 2260) were used for the model parameterization, test, and validation. To demonstrate the model’s applicability to major satellite sensors, 1299 images from six spaceborne sensors were matched up with independent in situ
${Z} _{\text {SD}}$
(
${N}$
= 1464 and
${Z} _{\text {SD}}$
= 0.2–51 m) from global oceans. The results indicate that the new model has a good performance with mean absolute percentage error (MAPE) and root mean square difference (RMSD) of 21%–26% and 0.3–2.8 m. Even over extremely turbid waters, the model still performs robustly (MAPE = 22%–25%) and significantly better than the existing ones. Finally, the model application indicates that
${Z} _{\text {SD}}$
derived from six sensors shows good agreement in both spatial distribution and temporal consistency. The model shows the potential to construct high-accuracy
${Z} _{\text {SD}}$
records from multiple sensors for global oceans and can support sustainable management of the marine ecological environment.