TY - JOUR
T1 - Recipes for the Derivation of Water Quality Parameters Using the High-Spatial-Resolution Data from Sensors on Board Sentinel-2A, Sentinel-2B, Landsat-5, Landsat-7, Landsat-8, and Landsat-9 Satellites
AU - Tavora, Juliana
AU - Jiang, Binbin
AU - Kiffney, Thomas
AU - Bourdin, Guillaume
AU - Gray, Patrick Clifton
AU - de Carvalho, Lino Sander
AU - Hesketh, Gabriel
AU - Schild, Kristin M.
AU - de Souza, Luiz Faria
AU - Brady, Damian C.
AU - Boss, Emmanuel
N1 - Publisher Copyright:
© 2023 Juliana Tavora et al. Exclusive licensee Aerospace Information Research Institute, Chinese Academy of Sciences.
PY - 2023/6
Y1 - 2023/6
N2 - Satellites have provided high-resolution (<100 m) water color (i.e., remote sensing reflectance) and thermal emission imagery of aquatic environments since the early 1980s; however, global operational water quality products based on these data are not readily available (e.g., temperature, chlorophyll-a, turbidity, and suspended particle matter). Currently, because of the postprocessing required, only users with expressive experience can exploit these data, limiting their utility. Here, we provide paths (recipes) for the nonspecialist to access and derive water quality products, along with examples of applications, from sensors on board Landsat-5, Landsat-7, Landsat-8, Landsat-9, Sentinel-2A, and Sentinel-2B. We emphasize that the only assured metric for success in product derivation and the assigning of uncertainties to them is via validation with in situ data. We hope that this contribution will motivate nonspecialists to use publicly available high-resolution satellite data to study new processes and monitor a variety of novel environments that have received little attention to date.
AB - Satellites have provided high-resolution (<100 m) water color (i.e., remote sensing reflectance) and thermal emission imagery of aquatic environments since the early 1980s; however, global operational water quality products based on these data are not readily available (e.g., temperature, chlorophyll-a, turbidity, and suspended particle matter). Currently, because of the postprocessing required, only users with expressive experience can exploit these data, limiting their utility. Here, we provide paths (recipes) for the nonspecialist to access and derive water quality products, along with examples of applications, from sensors on board Landsat-5, Landsat-7, Landsat-8, Landsat-9, Sentinel-2A, and Sentinel-2B. We emphasize that the only assured metric for success in product derivation and the assigning of uncertainties to them is via validation with in situ data. We hope that this contribution will motivate nonspecialists to use publicly available high-resolution satellite data to study new processes and monitor a variety of novel environments that have received little attention to date.
UR - http://www.scopus.com/inward/record.url?scp=85166425416&partnerID=8YFLogxK
U2 - 10.34133/remotesensing.0049
DO - 10.34133/remotesensing.0049
M3 - Review article
AN - SCOPUS:85166425416
SN - 2097-0064
VL - 3
JO - Journal of Remote Sensing (United States)
JF - Journal of Remote Sensing (United States)
M1 - 49
ER -