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Delta-X: AVIRIS-NG L3-derived Water Quality, TSS, and Turbidity, MRD, Louisiana, 2021

Documentation Revision Date: 2022-06-17

Dataset Version: 1

Summary

This dataset includes estimates of total suspended solids (TSS) concentration and turbidity for waters of the Atchafalaya River and Terrebonne Basins of the Mississippi River Delta in coastal Louisiana. Estimates were derived from Level 2 (L2) BRDF-corrected imagery from the NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). AVIRIS-NG imagery was collected on March 27 to April 6 (spring) and August 20-25 (fall), 2021, as part of the 2021 Delta-X campaign. Algorithms for TSS and turbidity estimation were developed using in-situ remote-sensing reflectance measured at field sampling stations paired with in-situ measures of turbidity from a water quality probe and TSS from water samples. Using the in-situ data, a partial least squares regression (PLSR) model was developed for each AVIRIS-NG wavelength. A subset of the in-situ data, collected during relatively clear AVIRIS-NG overflights, was held out to validate the PLSR model. The PLSR algorithm was then applied to AVIRIS-NG imagery to retrieve TSS and turbidity across the study area. The measurement units for TSS and turbidity estimates are mg L-1 and Nephelometric Turbidity Units (NTU), respectively, and the spatial resolution is 3.8 to 5.4 m as determined by the AVIRIS-NG imagery. The dataset includes binary cloud and master masks. Gridded estimates are provided NetCDF format, and regression coefficients are included in a comma-separated values (CSV) file.

There are 43 NetCDF data files and one file in comma-separated values (.csv) format with this dataset.

Figure 1. Map of total suspended solids (TSS) in the Wax Lake Delta, Terrebonne basin of the Mississippi River Delta of coastal Louisiana. TSS was estimated from AVIRIS-NG imagery using a regression model derived from in-situ measurements of surface reflectance and TSS.

Citation

Fichot, C.G., and J. Harringmeyer. 2022. Delta-X: AVIRIS-NG L3-derived Water Quality, TSS, and Turbidity, MRD, Louisiana, 2021. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/2084

Table of Contents

  1. Dataset Overview
  2. Data Characteristics
  3. Application and Derivation
  4. Quality Assessment
  5. Data Acquisition, Materials, and Methods
  6. Data Access
  7. References

Dataset Overview

This dataset includes estimates of total suspended solids (TSS) concentration and turbidity for waters of the Atchafalaya River and Terrebonne Basins of the Mississippi River Delta in coastal Louisiana. Estimates were derived from Level 2 (L2) BRDF-corrected imagery from the NASA's Next Generation Airborne Visible Infrared Imaging Spectrometer (AVIRIS-NG). AVIRIS-NG imagery was collected on March 27 to April 6 (spring) and August 20-25 (fall), 2021, as part of the 2021 Delta-X campaign. Algorithms for TSS and turbidity estimation were developed using in-situ remote-sensing reflectance measured at field sampling stations paired with in-situ measures of turbidity from a water quality probe and TSS from water samples. Using the in-situ data, a partial least squares regression (PLSR) model was developed for each AVIRIS-NG wavelength. A subset of the in-situ data, collected during relatively clear AVIRIS-NG overflights, was held out to validate the PLSR model. The PLSR algorithm was then applied to AVIRIS-NG imagery to retrieve TSS and turbidity across the study area. The measurement units for TSS and turbidity estimates are mg L-1 and Nephelometric Turbidity Units (NTU), respectively, and the spatial resolution is 3.8 to 5.4 m as determined by the AVIRIS-NG imagery. The dataset includes binary cloud and master masks.

ProjectDelta-X

The Delta-X mission is a 5-year NASA Earth Venture Suborbital-3 mission to study the Mississippi River Delta in the United States, which is growing and sinking in different areas. River deltas and their wetlands are drowning as a result of sea level rise and reduced sediment inputs. The Delta-X mission will determine which parts will survive and continue to grow, and which parts will be lost. Delta-X begins with airborne and in situ data acquisition and carries through data analysis, model integration, and validation to predict the extent and spatial patterns of future deltaic land loss or gain.

Related Datasets

Fichot, C.G., N. Ghosh, J. Harringmeyer, and M. Weiser. 2021a. Delta-X: Total Suspended Solids Concentration across MRD, LA, USA, 2021. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1991

Fichot, C.G., J. Harringmeyer, and M. Weiser. 2021b. Delta-X: In Situ Water Quality Indicators across MRD, LA, USA, 2021. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1992

Fichot, C.G., and J. Harringmeyer. 2021c. Delta-X: In Situ Spectral Reflectance of Water Surface at MRD at LA, USA, Spring 2021. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1994

  • These three datasets hold the in-situ data used to develop the TSS and turbidity retrieval algorithms.

Thompson, D.R., D.J. Jensen, J. Chapman, E. Greenberg, and M. Simard. 2021. Delta-X AVIRIS-NG L2 Surface Reflectance Products across the MRD, LA, USA, 2021. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1988

  • This dataset holds the L2 BRDF-corrected AVIRIS-NG imagery employed to create the included TSS and turbidity maps.

Acknowledgments

This work was supported by NASA's Earth Venture Suborbital-3 (EVS-3) program and the Jet Propulsion Laboratory R&TD F17-19 program.

Data Characteristics

Spatial Coverage: Atchafalaya River and Terrebonne Basins, Louisiana, USA

Spatial Resolution: 3.8 to 5.4 m

Temporal Coverage: 2021-03-27 to 2021-08-25

Temporal Resolution: One time estimates

Site Boundaries: Latitude and longitude are given in decimal degrees.

Site Westernmost Longitude Easternmost Longitude Northernmost Latitude Southernmost Latitude
Atchafalaya River and Terrebonne Basins, Louisiana  -91.4847 -90.1774 29.8090 29.0609

Data File Information

There are 43 NetCDF files and one file in comma-separated values (.csv) format with this dataset.

The NetCDFs hold gridded estimates of total suspended solids (TSS) and turbidity. The naming convention is angYYYYMMDDthhmmss_rfl_brdf_WQ.nc (e.g., ang20210825t153003_rfl_brdf_WQ.nc), where

  • YYYYMMDD = date of acquisition in year (YYYY), month (MM), and day (DD)
  • hhmmss = start time of acquisition in hour) (hh), minute (mm), and second (ss)

The file DeltaX_WQ_PLSR_Coefficients.csv holds the regression coefficients for the models used for estimating TSS and turbidity from the L2 AVIRIS-NG imagery. There is a coefficient for each AVIRIS-NG band.

Table 1. Variables in the NetCDF data files: angYYYYMMDDthhmmss_rfl_brdf_WQ.nc.

Variable Units Description
latitude degrees east Location of measurement pixel
longitude degrees north Location of measurement pixel
TSS_PLSR _380_900 mg L-1 Estimated total suspended solids (TSS) concentration
Turbidity_PLSR_ _380_900 1 Estimated turbidity in Formazin Nephelometric Units (FNU)
Water_Mask 1 Binary water mask derived from AVIRIS-NG band ratio (see Methods below): 1 indicates water pixel, 0 indicates non-water pixel
Cloud_Mask 1 Binary cloud mask derived from AVIRIS-NG band ratio (see Methods below): 1 indicates cloud, pixel 0 indicates non-cloud pixel

Table 2. Variables in DeltaX_WQ_PLSR_Coefficients.csv

Variable Units Description
AVIRIS-NG Band Center nm Center of wavelengths for each AVIRIS-NG band
TSS Algorithm Coefficients 1 Partial least squares regression coefficients (ßi) for each AVIRIS-NG band used to estimate total suspended solids concentration (mg L-1)
Turbidity Algorithm Coefficients  1 Partial least squares regression coefficients (ßi) for each AVIRIS-NG band used to estimate turbidity in Formazin Nephelometric Units (FNU)

Application and Derivation

This dataset presents maps of total suspended solids (TSS) and turbidity derived from AVIRIS-NG airborne imagery from the 2021 Delta-X campaign. During the Delta-X field efforts, in-situ above-water measurements of remote-sensing reflectance (Rrs) were collected at a number of sites across the Atchafalaya and Terrebonne basins. These sampling sites spanned large and small channels at locations chosen to cover a representative range of TSS concentration from a variety of hydrodynamic and physical settings typically encountered across this study area. The in-situ data were used to calibrate and validate Delta-X’s algorithms that relate remote-sensing reflectance to TSS concentration and turbidity. The algorithms enable remote estimation of TSS from its influence on the color of surface waters. Algorithms are then applied to atmospherically-corrected AVIRIS-NG imagery (Thompson et al., 2021) on a pixel-by-pixel basis to retrieve maps of TSS.

Remote-sensing-derived maps of TSS distribution will be compared to numerical models to calibrate and validate its parameters. The models quantify the mesoscale (i.e., on the order of 1 ha) patterns of soil accretion that control land loss and gain and predict the resilience of deltaic floodplains under projected relative sea-level rise. Understanding and mitigating the impact of the relative sea-level rise on coastal deltas is urgent. If ignored, relative sea-level rise will very soon have devastating consequences on the livelihood of the half-billion people that live in these low-lying coastal regions.

Quality Assessment

Uncertainty was evaluated by comparing water quality parameters retrieved using the partial least squares regression algorithms to field measurements collected near-simultaneously with AVIRIS-NG overflights. Reflectance was extracted from the imagery at matching pixels and used with the regression algorithms to retrieve local, remotely sensed estimates of TSS and turbidity. These validation data allowed comparison of TSS estimates from AVIRIS-NG imagery against near-simultaneous, co-located field measurements. Error statistics for end-to-end retrievals using both spring and fall 2021 field campaign data are presented in Table 3.

Table 3. Validation error statistics 

Parameter (units) Validation R2 Mean Absolute Percent Error (MAPE) Mean Absolute Error (MAE)
Total suspended solids (mg L-1) 0.922 29.2 11.0
Turbidity (NTU) 0.890 33.6 7.2

Data Acquisition, Materials, and Methods

Airborne Imagery and In-situ Measurements

During the 2021 Delta-X field efforts, AVIRIS-NG imagery was collected to produce georeferenced surface reflectance (Rrs ) spectra across the Atchafalaya and Terrebonne Basins. The flight lines were planned to capture variability in total suspended solids (TSS) concentration and care was taken to maintain viewing and solar geometry to minimize sun glint. Field sampling was coordinated so that field measurements were collected simultaneously with imagery (Figure 2). In-situ field measurements of water-quality parameters included turbidity, above-water radiometry (in-situ Rrs), and water sample collection for laboratory analysis of TSS. Some sampling stations did not include water sample collection, and a few stations only included water quality measured with a multi-parameter probe. However, only stations where in-situ Rrs and all water quality parameters were measured were used for algorithm development.

Map of fall 2021 sampling sites.

Figure 2. Measurement stations for the fall 2021 Delta-X field efforts in Atchafalaya and Terrebonne basins of the Mississippi River Delta.

Algorithm Development and Measurement Procedure

Algorithms were developed using in-situ Rrs, TSS, and turbidity datasets measured at field stations (Fichot et al., 2021a, 2021b, 2021c). Field data from both the spring 2021 and fall 2021 field efforts were used to estimate the coefficients of a partial-least-squares (PLS) regression that estimated TSS or turbidity from in-situ Rrs in the range from 380-900 nm. TSS was estimated using the equation:

TSS regression equation

where β0 is an intercept coefficient and βλ1 to βλi are the coefficients corresponding to in-situ reflectance (λ1...λi) interpolated to the AVIRIS-NG wave bands. Coefficients were estimated for the same functional form to retrieve turbidity from in-situ Rrs. Partial-least-squares regression was conducted using the plsregress function in MatLab, which implements the SIMPLS algorithm. The optimal number of latent variables for the PLS regression was selected using leave-one-out cross-validation, resulting in seven latent variables for the TSS algorithm and eight latent variables for the turbidity algorithm. The provided CSV file holds these coefficient values.

Algorithms were then applied to Level 2 BRDF-corrected AVIRIS-NG imagery collected for Delta-X in 2021 (Thompson et al. 2021). For each L2 AVIRIS-NG image, the TSS and turbidity algorithms were applied on a pixel-wise basis to estimate water quality parameters from the remotely sensed Rrs for each cell. A 3x3 median filter was then applied to each image, to remove any residual salt-and-pepper artifacts (e.g., residual glint) in the retrieved water quality products.

The NetCDF files include water mask and cloud mask layers to exclude dry land pixels and those in which clouds prevented accurate estimation of water quality parameters. The water mask was derived from band ratio thresholds on the AVIRIS-NG imagery as described by Greenberg et al. (2022). Water pixels were identified by ...

water mask equation,
otherwise the pixel was non-water.

The cloud mask was derived from band ratio thresholds on the AVIRIS-NG imagery as described by Jensen et al. (2019). Cloud pixels were identified by ...

cloud mask equation,
otherwise the pixel was non-cloud. For some images, an additional buffer around identified clouds may beneficial to remove potential effects of cloud adjacency and cloud shadows before use.

Data Access

These data are available through the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

Delta-X: AVIRIS-NG L3-derived Water Quality, TSS, and Turbidity, MRD, Louisiana, 2021

Contact for Data Center Access Information:

References

Fichot, C.G., N. Ghosh, J. Harringmeyer, and M. Weiser. 2021a. Delta-X: Total Suspended Solids Concentration across MRD, LA, USA, 2021. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1991

Fichot, C.G., J. Harringmeyer, and M. Weiser. 2021b. Delta-X: In Situ Water Quality Indicators across MRD, LA, USA, 2021. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1992

Fichot, C.G., and J. Harringmeyer. 2021c. Delta-X: In Situ Spectral Reflectance of Water Surface at MRD at LA, USA, Spring 2021. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1994

Greenberg, E., D.R. Thompson, D.J. Jensen, P.A. Townsend, N. Queally, A. Chlus, C.G. Fichot, J.P. Harringmeyer, and M. Simard. 2022. An improved scheme for correcting remote spectral surface reflectance simultaneously for terrestrial BRDF and water‐surface sunglint in coastal environments. Journal of Geophysical Research: Biogeosciences 127:e2021JG006712. https://doi.org/10.1029/2021jg006712

Jensen, D., M. Simard, K. Cavanaugh, Y. Sheng, C. G. Fichot, T. Pavelsky, and R. Twilley. 2019. Improving the transferability of suspended solid estimation in wetland and deltaic waters with an empirical hyperspectral approach. Remote Sensing 11:1629. https://doi.org/10.3390/rs11131629

Thompson, D.R., D.J. Jensen, J. Chapman, E. Greenberg, and M. Simard. 2021. Delta-X AVIRIS-NG L2 Surface Reflectance Products across the MRD, LA, USA, 2021. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1988