Natural wetlands are intrinsically heterogeneous and typically composed of a mosaic of ecosystem patches with different vegetation types. Hydrological and biogeochemical processes in wetlands vary strongly among these ecosystem patches. To date, most remote sensing classification approaches for wetland vegetation either rely on coarse images that cannot capture the spatial variability of wetland vegetation or rely on very-high-resolution multi-spectral images that are detailed but very sporadic in time (less than once per year). This study aimed to use NDVI time series, generated from NASA’s HLS dataset, to classify vegetation patches. We demonstrate our approach at a temperate, coastal lake, estuarine marsh. To classify vegetation patches, a standard time series library of the four land-cover patch types was built from referencing specific locations that were identified as “pure” pixels. These were identified using a single-time high-resolution image. We calculated the distance between the HLS-NDVI time series at each pixel and the “pure”-pixel standards for each land-cover type. The resulting true-positive classified rate was >73% for all patch types other than water lily. The classification accuracy was higher in pixels of a more uniform composition. A set of vegetation maps was created for the years 2016 to 2020 at our research site to identify the vegetation changes at the site as it is affected by rapid water elevation increases in Lake Erie. Our results reveal how changes in water elevation have changed the patch distribution in significant ways, leading to the local extinction of cattail by 2019 and a continuous increase in the area cover of water lily patches.

Earth system models (ESMs) are a common tool for estimating local and global greenhouse gas emissions under current and projected future conditions. Efforts are underway to expand the representation of wetlands in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) by resolving the simultaneous contributions to greenhouse gas fluxes from multiple, different, sub-grid-scale patch-types, representing different eco-hydrological patches within a wetland. However, for this effort to be effective, it should be coupled with the detection and mapping of within-wetland eco-hydrological patches in real-world wetlands, providing models with corresponding information about vegetation cover. In this short communication, we describe the application of a recently developed NDVI-based method for within-wetland vegetation classification on a coastal wetland in Louisiana and the use of the resulting yearly vegetation cover as input for ELM simulations. Processed Harmonized Landsat and Sentinel-2 (HLS) datasets were used to drive the sub-grid composition of simulated wetland vegetation each year, thus tracking the spatial heterogeneity of wetlands at sufficient spatial and temporal resolutions and providing necessary input for improving the estimation of methane emissions from wetlands. Our results show that including NDVI-based classification in an ELM reduced the uncertainty in predicted methane flux by decreasing the model’s RMSE when compared to Eddy Covariance measurements, while a minimal bias was introduced due to the resampling technique involved in processing HLS data. Our study shows promising results in integrating the remote sensing-based classification of within-wetland vegetation cover into earth system models, while improving their performances toward more accurate predictions of important greenhouse gas emissions.

Methane emissions from freshwater, mineral-soil wetlands represent an important portion of the global greenhouse gas budgets. We use long-term observations in Old Woman Creek (OWC), an estuarine wetland at the coast of Lake Erie. OWC is characterized by a fluctuating water level controlled by a natural sand barrier. OWC water levels are high when the barrier is closed. When it breaks, OWC is directly connected to the lake. Long term water level rise of Lake Erie provides a trend of water level at OWC. These changes to hydrology drive changes to the ecology. In OWC the dominant eco-hydrological patch types are mudflats, cattails (Typha), floating-leaf vegetation (Lotus, water lily), and open water. The seasonal and long term changes to water level lead to dynamic changes in the patch type composition, and as OWC gets deeper, mudflats and cattails give way to open water and floating-leaf vegetation. We developed an approach to classify the eco-hydrological patch type from remote sensing images. We used seasonal time series of NDVI from HLS (a composite dataset of Sentinel and Landsat). These time series were classified to patch types according to their similarity to the seasonal NDVI profiles of pixels identified and ground-truthed as pure pixels of a specific patch type. We then used the DG-SWEM high resolution hydrodynamic model to simulate the flow velocity throughout the wetland. Combining the eco-hydrological patch locations and the high-resolution flow simulation allowed for the calculation of an effective patch-type-level residence time. We found differences in residence times between the different patch types. We measured the relations between methane flux and CO ₂ uptake at the whole wetland scale, the vegetation patch scale, and directly from leaves. We found different methane-CO ₂ relations among the floating leaved and emergent species. Phenological transitions throughout the growing season continued to make an important effect only in Typha. Our observations represent a valuable foundation towards a more robust models of methane fluxes in wetlands at the resolution of within-wetland vegetation patch type, and resolving the effects of seasonal and within-season vegetation phenology in ecosystem-scale models.