A harmonized image processing workflow using Sentinel-2/MSI and Landsat-8/OLI for mapping water clarity in optically variable lake systems

Benjamin P. Page; Leif Olmanson; Deepak R. Mishra

doi:10.1016/j.rse.2019.111284

A harmonized image processing workflow using Sentinel-2/MSI and Landsat-8/OLI for mapping water clarity in optically variable lake systems

Benjamin P. Page, Leif Olmanson, Deepak R. Mishra

Research output: Contribution to journal › Article › peer-review

92 Scopus citations

Abstract

This study demonstrates the applicability of harmonizing Sentinel-2 MultiSpectral Imager (MSI) and Landsat-8 Operational Land Imager (OLI) satellite imagery products to enable the monitoring of inland lake water clarity in the Google Earth Engine (GEE) environment. Processing steps include (1) atmospheric correction and masking of MSI and OLI imagery, and (2) generating scene-based water clarity maps in terms of Secchi depth (SD). We adopted ocean-color based atmospheric correction theory for MSI and OLI sensors modified with associated scene-specific metadata and auxiliary datasets available in GEE to generate uniform remote sensing reflectances (R_rs) products over optically variable freshwater lake surfaces. MSI-R_rs products derived from the atmospheric correction were used as input predictors in a bootstrap forest to determine significant band combinations to predict water clarity. A SD model for MSI (SD_MSI) was then developed using a calibration dataset consisting of log-transformed SD_{in situ} measurements (lnSD_{in situ}) from 79 optically variable freshwater inland lakes collected within ±1 day of satellite overpass on 23-Aug 2017 (MAE = 0.53 m) and validated with 276 samples collected within ±1 day of a 12-Sep 2017 image (MAE = 0.66 m) across three ecoregions in Minnesota, USA. A separate SD model for MSI was also developed using similar spectral bands present on the OLI sensor (SD_sOLI) where cross-sensor performance can be evaluated during coincident overpass events. SD_sOLI applied to both MSI and OLI (SD_OLI) models were further validated using two coincident overpass sets of imagery on 27-Sep 2017 (n = 18) and 13-Aug 2018 (n = 43), yielding a range of error from 0.25 to 0.67 m. Potential sources of model errors and limitations are discussed. Data derived from this multi-sensor methodology is anticipated to be used by researchers, lake resource managers, and citizens to expedite the pre-processing steps so that actionable information can be retrieved for decision making.

Original language	English (US)
Article number	111284
Journal	Remote Sensing of Environment
Volume	231
DOIs	https://doi.org/10.1016/j.rse.2019.111284
State	Published - Sep 15 2019

Bibliographical note

Funding Information:
Support for this research was provided by the University of Minnesota Water Resources Center in cooperation with the U.S. Geological Survey Water Resources Research Act Program and the Minnesota Environment and Natural Resources Trust Fund (ENRTF) through the Legislative-Citizen Commission on Minnesota Resources (LCCMR). Partial support was provided by the National Science Foundation, United States (NSF) award no. 1442672. Sentinel-2 and Landast-8 data were provided by the European Space Agency and USGS, respectively, acquired through the Google Earth Engine JavaScript API. We also thank Pamela Anderson Supervisor of the Water Quality Monitoring Unit, MPCA and the CLMP volunteers for the SD data.

Funding Information:
Support for this research was provided by the University of Minnesota Water Resources Center in cooperation with the U.S. Geological Survey Water Resources Research Act Program and the Minnesota Environment and Natural Resources Trust Fund (ENRTF) through the Legislative-Citizen Commission on Minnesota Resources (LCCMR). Partial support was provided by the National Science Foundation , United States (NSF) award no. 1442672 . Sentinel-2 and Landast-8 data were provided by the European Space Agency and USGS, respectively, acquired through the Google Earth Engine JavaScript API. We also thank Pamela Anderson Supervisor of the Water Quality Monitoring Unit, MPCA and the CLMP volunteers for the SD data.

Publisher Copyright:
© 2019 Elsevier Inc.

Keywords

Atmospheric correction
Harmonized
Lake management
Lake monitoring
Landsat-8
Remote sensing
Sentinel-2
Validation
Water clarity

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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title = "A harmonized image processing workflow using Sentinel-2/MSI and Landsat-8/OLI for mapping water clarity in optically variable lake systems",

abstract = "This study demonstrates the applicability of harmonizing Sentinel-2 MultiSpectral Imager (MSI) and Landsat-8 Operational Land Imager (OLI) satellite imagery products to enable the monitoring of inland lake water clarity in the Google Earth Engine (GEE) environment. Processing steps include (1) atmospheric correction and masking of MSI and OLI imagery, and (2) generating scene-based water clarity maps in terms of Secchi depth (SD). We adopted ocean-color based atmospheric correction theory for MSI and OLI sensors modified with associated scene-specific metadata and auxiliary datasets available in GEE to generate uniform remote sensing reflectances (Rrs) products over optically variable freshwater lake surfaces. MSI-Rrs products derived from the atmospheric correction were used as input predictors in a bootstrap forest to determine significant band combinations to predict water clarity. A SD model for MSI (SDMSI) was then developed using a calibration dataset consisting of log-transformed SDin situ measurements (lnSDin situ) from 79 optically variable freshwater inland lakes collected within ±1 day of satellite overpass on 23-Aug 2017 (MAE = 0.53 m) and validated with 276 samples collected within ±1 day of a 12-Sep 2017 image (MAE = 0.66 m) across three ecoregions in Minnesota, USA. A separate SD model for MSI was also developed using similar spectral bands present on the OLI sensor (SDsOLI) where cross-sensor performance can be evaluated during coincident overpass events. SDsOLI applied to both MSI and OLI (SDOLI) models were further validated using two coincident overpass sets of imagery on 27-Sep 2017 (n = 18) and 13-Aug 2018 (n = 43), yielding a range of error from 0.25 to 0.67 m. Potential sources of model errors and limitations are discussed. Data derived from this multi-sensor methodology is anticipated to be used by researchers, lake resource managers, and citizens to expedite the pre-processing steps so that actionable information can be retrieved for decision making.",

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note = "Funding Information: Support for this research was provided by the University of Minnesota Water Resources Center in cooperation with the U.S. Geological Survey Water Resources Research Act Program and the Minnesota Environment and Natural Resources Trust Fund (ENRTF) through the Legislative-Citizen Commission on Minnesota Resources (LCCMR). Partial support was provided by the National Science Foundation, United States (NSF) award no. 1442672. Sentinel-2 and Landast-8 data were provided by the European Space Agency and USGS, respectively, acquired through the Google Earth Engine JavaScript API. We also thank Pamela Anderson Supervisor of the Water Quality Monitoring Unit, MPCA and the CLMP volunteers for the SD data. Funding Information: Support for this research was provided by the University of Minnesota Water Resources Center in cooperation with the U.S. Geological Survey Water Resources Research Act Program and the Minnesota Environment and Natural Resources Trust Fund (ENRTF) through the Legislative-Citizen Commission on Minnesota Resources (LCCMR). Partial support was provided by the National Science Foundation , United States (NSF) award no. 1442672 . Sentinel-2 and Landast-8 data were provided by the European Space Agency and USGS, respectively, acquired through the Google Earth Engine JavaScript API. We also thank Pamela Anderson Supervisor of the Water Quality Monitoring Unit, MPCA and the CLMP volunteers for the SD data. Publisher Copyright: {\textcopyright} 2019 Elsevier Inc.",

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N2 - This study demonstrates the applicability of harmonizing Sentinel-2 MultiSpectral Imager (MSI) and Landsat-8 Operational Land Imager (OLI) satellite imagery products to enable the monitoring of inland lake water clarity in the Google Earth Engine (GEE) environment. Processing steps include (1) atmospheric correction and masking of MSI and OLI imagery, and (2) generating scene-based water clarity maps in terms of Secchi depth (SD). We adopted ocean-color based atmospheric correction theory for MSI and OLI sensors modified with associated scene-specific metadata and auxiliary datasets available in GEE to generate uniform remote sensing reflectances (Rrs) products over optically variable freshwater lake surfaces. MSI-Rrs products derived from the atmospheric correction were used as input predictors in a bootstrap forest to determine significant band combinations to predict water clarity. A SD model for MSI (SDMSI) was then developed using a calibration dataset consisting of log-transformed SDin situ measurements (lnSDin situ) from 79 optically variable freshwater inland lakes collected within ±1 day of satellite overpass on 23-Aug 2017 (MAE = 0.53 m) and validated with 276 samples collected within ±1 day of a 12-Sep 2017 image (MAE = 0.66 m) across three ecoregions in Minnesota, USA. A separate SD model for MSI was also developed using similar spectral bands present on the OLI sensor (SDsOLI) where cross-sensor performance can be evaluated during coincident overpass events. SDsOLI applied to both MSI and OLI (SDOLI) models were further validated using two coincident overpass sets of imagery on 27-Sep 2017 (n = 18) and 13-Aug 2018 (n = 43), yielding a range of error from 0.25 to 0.67 m. Potential sources of model errors and limitations are discussed. Data derived from this multi-sensor methodology is anticipated to be used by researchers, lake resource managers, and citizens to expedite the pre-processing steps so that actionable information can be retrieved for decision making.

AB - This study demonstrates the applicability of harmonizing Sentinel-2 MultiSpectral Imager (MSI) and Landsat-8 Operational Land Imager (OLI) satellite imagery products to enable the monitoring of inland lake water clarity in the Google Earth Engine (GEE) environment. Processing steps include (1) atmospheric correction and masking of MSI and OLI imagery, and (2) generating scene-based water clarity maps in terms of Secchi depth (SD). We adopted ocean-color based atmospheric correction theory for MSI and OLI sensors modified with associated scene-specific metadata and auxiliary datasets available in GEE to generate uniform remote sensing reflectances (Rrs) products over optically variable freshwater lake surfaces. MSI-Rrs products derived from the atmospheric correction were used as input predictors in a bootstrap forest to determine significant band combinations to predict water clarity. A SD model for MSI (SDMSI) was then developed using a calibration dataset consisting of log-transformed SDin situ measurements (lnSDin situ) from 79 optically variable freshwater inland lakes collected within ±1 day of satellite overpass on 23-Aug 2017 (MAE = 0.53 m) and validated with 276 samples collected within ±1 day of a 12-Sep 2017 image (MAE = 0.66 m) across three ecoregions in Minnesota, USA. A separate SD model for MSI was also developed using similar spectral bands present on the OLI sensor (SDsOLI) where cross-sensor performance can be evaluated during coincident overpass events. SDsOLI applied to both MSI and OLI (SDOLI) models were further validated using two coincident overpass sets of imagery on 27-Sep 2017 (n = 18) and 13-Aug 2018 (n = 43), yielding a range of error from 0.25 to 0.67 m. Potential sources of model errors and limitations are discussed. Data derived from this multi-sensor methodology is anticipated to be used by researchers, lake resource managers, and citizens to expedite the pre-processing steps so that actionable information can be retrieved for decision making.

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A harmonized image processing workflow using Sentinel-2/MSI and Landsat-8/OLI for mapping water clarity in optically variable lake systems

Abstract

Bibliographical note

Keywords

UN SDGs

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