Yong Q. Tian

Estimation of chromophoric dissolved organic matter in the Mississippi and Atchafalaya river plume regions using above‐surface hyperspectral remote sensing

[1] A method for the inversion of hyperspectral remote sensing was developed to determine the absorption coefficient for chromophoric dissolved organic matter (CDOM) in the Mississippi and Atchafalaya river plume regions and the northern Gulf of Mexico, where water types vary from Case 1 to turbid Case 2. Above‐surface hyperspectral remote sensing data were measured by a ship‐mounted spectroradiometer and then used to estimate CDOM. Simultaneously, water absorption and attenuation coefficients, CDOM and chlorophyll fluorescence, turbidities, and other related water properties were also measured at very high resolution (0.5–2 m) using in situ, underwater, and flow‐through (shipboard, pumped) optical sensors. We separate ag, the absorption coefficient a of CDOM, from adg (a of CDOM and nonalgal particles) based on two absorption‐ backscattering relationships. The first is between ad (a of nonalgal particles) and bbp (total particulate backscattering coefficient), and the second is between ap (a of total particles) and bbp. These two relationships are referred as ad‐based and ap‐based methods, respectively. Consequently, based on Lee’s quasi‐analytical algorithm (QAA), we developed the so‐called Extended Quasi‐Analytical Algorithm (QAA‐E) to decompose adg, using both ad‐based and ap‐based methods. The absorption‐backscattering relationships and the QAA‐E were tested using synthetic and in situ data from the International Ocean‐Colour Coordinating Group (IOCCG) as well as our own field data. The results indicate the ad‐based method is relatively better than the ap‐based method. The accuracy of CDOM estimation is significantly improved by separating ag from adg (R 2 = 0.81 and 0.65 for synthetic and in situ data, respectively). The sensitivities of the newly introduced coefficients were also analyzed to ensure QAA‐E is robust.

Functional linear analysis of in situ hyperspectral data for assessing CDOM in rivers.

Turbidity and chlorophyll introduce high uncertainty in remote sensing of Chromophoric Dissolved Organic Matter (CDOM) in riverine and coastal water. To reduce the uncertainty, we developed a functional linear model (FLM) to analyze spectral responses to CDOM concentrations observed in a cruise along two rivers and a tidal bay. The analysis was supported with the measurement of high spatial resolution underwater CDOM concentrations and concurrent in situ above-surface hyperspectral remote sensing reflectance. The functional linear model is able to explain up to 91 percent of CDOM observations (H 2 = 0.91, RMSE = 0.0206). The dummy variables of local environmental factors included in the estimation improve CDOM assessment in coastal water. Our analysis suggests that the pattern changes of the FLM coefficient curves provide useful information for understanding the spectral signal interference from turbidity and chlorophyll. This work presents a feasibility study of in situ remote sensing of CDOM on a shipboard platform.

Issues and Potential Improvement of Multiband Models for Remotely Estimating Chlorophyll-a in Complex Inland Waters

Remote estimation of chlorophyll-a (chl-a) in complex freshwaters remains a challenging problem due to the rapid spatial variability and wide range as influenced by terrestrial constituents. A controversial issue is whether or not 2-B models possess sufficient wavelength information for accurately estimating Chl-a concentrations from remote sensing data for freshwater environments. This study introduced a systemic approach and proved that adding additional wavelength information to 2-B model could not significantly improve the estimation of freshwater chl-a, but acted to increase model uncertainty. This convincing solution was based on a large synthetic data set (38 937 samples) combined with a set of in situ data (51 samples) collected in three cruises in Lake Huron. The synthetic data set has two distinct features: 1) large data items and 2) covers a broad range of chl-a (0-1000 mg/m3), colored dissolved organic matter (CDOM) (0-50 m-1), and NAP (nonalgal particles) (0-500 mg/l). Additionally, this study reveals how hyperspectral wavelength selection, number of bands, bandwidth, and parameter calibration are associated with the uncertainty in remote sensing of chl-a. The systematic analysis approach was used to evaluate 34 chl-a algorithms by using optimal location and number of wavelengths as well as calibrated parameters. The study introduced a set of new 2-B, 3-B, and 4-B models derived also from using optimized parameters, suggested wavelengths, and bands available in MERIS and MODIS satellite images. Validation results demonstrated that these models are suitable to general freshwater environments because of broad ranges of biochemical and physical properties in both synthetic and in situ data.

Remote estimation of colored dissolved organic matter and chlorophyll-a in Lake Huron using Sentinel-2 measurements

Abstract. Colored dissolved organic matter (CDOM) and chlorophyll-a (Chla) are important water quality parameters and play crucial roles in aquatic environment. Remote sensing of CDOM and Chla concentrations for inland lakes is often limited by low spatial resolution. The newly launched Sentinel-2 satellite is equipped with high spatial resolution (10, 20, and 60 m). Empirical band ratio models were developed to derive CDOM and Chla concentrations in Lake Huron. The leave-one-out cross-validation method was used for model calibration and validation. The best CDOM retrieval algorithm is a B3/B5 model with accuracy coefficient of determination (R2)=0.884, root-mean-squared error (RMSE)=0.731  m−1, relative root-mean-squared error (RRMSE)=28.02%, and bias=−0.1  m−1. The best Chla retrieval algorithm is a B5/B4 model with accuracy R2=0.49, RMSE=9.972  mg/m3, RRMSE=48.47%, and bias=−0.116  mg/m3. Neural network models were further implemented to improve inversion accuracy. The applications of the two best band ratio models to Sentinel-2 imagery with 10  m×10  m pixel size presented the high potential of the sensor for monitoring water quality of inland lakes.

Estimating of chromophoric dissolved organic matter (CDOM) with in-situ and satellite hyperspectral remote sensing technology

The influence of land surface characteristics and climate conditions on the source, quantity, quality, and timing of dissolved organic carbon (DOC) fluxes to coastal waters is not well understood. The significant correlation between DOC and chromophoric dissolved organic matter (CDOM) leads to an increasing need of CDOM monitoring for understanding the DOC land-water dynamics. This study is to report the results of using hyperspectral in-situ data and satellite images to estimate riverine CDOM from rivers to oceans and Great Lakes. A major research goal is to demonstrate that advancement of hyperspectral and high spatial resolution remote sensing technology is vital to the study of interactive processes between terrestrial ecosystems and aquatic environments. Our research results confirm that hyperspectral remote sensing is effective in extracting riverine CDOM loading.

Effects of Landcover, Soil Property, and Temperature on Covariations of DOC and CDOM in Inland Waters

Significant uncertainty exists in the estimation of dissolved organic carbon (DOC) concentration via remote sensing from colored dissolved organic matter (CDOM) absorption in inland waters pointing to a need for more process-based understanding of the relationship between CDOM and DOC. In this study, we examine the factors affecting the covariations of DOC and CDOM using controlled experiments combined with field measurements at subbasin scale that have varying environmental and biological conditions. Our analysis reveals that the DOC:CDOM ratio is mainly related to landcover types. Higher DOC:CDOM linear regression slopes observed in evergreen leaf litter leachate suggest that CDOM comprises a smaller fraction of the DOC pool in evergreen sites in comparison to agricultural and deciduous leaf litter leachates. Given the same DOC concentrations, the range of CDOM levels from deciduous forest plant varied 3 times greater than that from other plant types. Results indicate that soil narrows the slope differences in the linear regressions of DOC from CDOM for all plant types (by 19% of evergreen, 18% of agriculture, and 77% of deciduous). Raising soil temperature by 5°C could double the range of DOC concentration and CDOM absorption for all scenarios. We present a mathematical model to estimate DOC concentration in freshwater environment via CDOM variations with reference to land cover and soil effects. The model was able to explain 95% of field measurements of multiple years in four subbasins. This improved understanding is critical for the remote sensing of DOC directly via observations of CDOM.