Riverine wetlands are created and transformed by geomorphological processes that determine their vegetation composition, primary production and soil accretion, all of which are likely to influence C ...stocks. Here, we compared ecosystem C stocks (trees, soil and downed wood) and soil N stocks of different types of riverine wetlands (marsh, peat swamp forest and mangroves) whose distribution spans from an environment dominated by river forces to an estuarine environment dominated by coastal processes. We also estimated soil C sequestration rates of mangroves on the basis of soil C accumulation. We predicted that C stocks in mangroves and peat swamps would be larger than marshes, and that C, N stocks and C sequestration rates would be larger in the upper compared to the lower estuary. Mean C stocks in mangroves and peat swamps (784.5 ± 73.5 and 722.2 ± 63.6 MgC ha−1, respectively) were higher than those of marshes (336.5 ± 38.3 MgC ha−1). Soil C and N stocks of mangroves were highest in the upper estuary and decreased towards the lower estuary. C stock variability within mangroves was much lower in the upper estuary (range 744–912 MgC ha−1) compared to the intermediate and lower estuary (range 537–1115 MgC ha−1) probably as a result of a highly dynamic coastline. Soil C sequestration values were 1.3 ± 0.2 MgC ha−1 yr−1 and were similar across sites. Estimations of C stocks within large areas need to include spatial variability related to vegetation composition and geomorphological setting to accurately reflect variability within riverine wetlands.
We mapped tidal wetland gross primary production (GPP) with unprecedented detail for multiple wetland types across the continental United States (CONUS) at 16‐day intervals for the years 2000–2019. ...To accomplish this task, we developed the spatially explicit Blue Carbon (BC) model, which combined tidal wetland cover and field‐based eddy covariance tower data into a single Bayesian framework, and used a super computer network and remote sensing imagery (Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index). We found a strong fit between the BC model and eddy covariance data from 10 different towers (r2 = 0.83, p < 0.001, root‐mean‐square error = 1.22 g C/m2/day, average error was 7% with a mean bias of nearly zero). When compared with NASA's MOD17 GPP product, which uses a generalized terrestrial algorithm, the BC model reduced error by approximately half (MOD17 had r2 = 0.45, p < 0.001, root‐mean‐square error of 3.38 g C/m2/day, average error of 15%). The BC model also included mixed pixels in areas not covered by MOD17, which comprised approximately 16.8% of CONUS tidal wetland GPP. Results showed that across CONUS between 2000 and 2019, the average daily GPP per m2 was 4.32 ± 2.45 g C/m2/day. The total annual GPP for the CONUS was 39.65 ± 0.89 Tg C/year. GPP for the Gulf Coast was nearly double that of the Atlantic and Pacific Coasts combined. Louisiana alone accounted for 15.78 ± 0.75 Tg C/year, with its Atchafalaya/Vermillion Bay basin at 4.72 ± 0.14 Tg C/year. The BC model provides a robust platform for integrating data from disparate sources and exploring regional trends in GPP across tidal wetlands.
Key Points
We created the Blue Carbon (BC) model, which mapped the Gross Primary Production (GPP) of all tidal wetlands within the continental United States
The BC model provides maps of tidal wetland GPP at sub‐250 m scales and at 16‐day intervals for the years 2000‐2019
The average daily GPP per m2 was 4.32 ± 2.45 g C/m2/day, and the total annual GPP for the continental United States was 39.65 ± 0.89 Tg C/year
Over the past decade, Brazil has experienced severe droughts across its territory, with important implications for soil moisture dynamics. Soil moisture variability has a direct impact on ...agriculture, water security and ecosystem services. Nevertheless, there is currently little information on how soil moisture across different biomes responds to drought. In this study, we used satellite soil moisture data from the European Space Agency, from 2009 to 2015, to analyze differences in soil moisture responses to drought for each biome of Brazil: Amazon, Atlantic Forest, Caatinga, Cerrado, Pampa and Pantanal. We found an overall soil moisture decline of −0.5 % yr−1 (p<0.01) at the national level. At the biome level, Caatinga presented the most severe soil moisture decline (−4.4 % yr−1), whereas the Atlantic Forest and Cerrado biomes showed no significant trend. The Amazon biome showed no trend but had a sharp reduction of soil moisture from 2013 to 2015. In contrast, the Pampa and Pantanal biomes presented a positive trend (1.6 % yr−1 and 4.3 % yr−1, respectively). These trends are consistent with vegetation productivity trends across each biome. This information provides insights into drought risk reduction and soil conservation activities to minimize the impact of drought in the most vulnerable biomes. Furthermore, improving our understanding of soil moisture trends during periods of drought is crucial to enhance the national drought early warning system and develop customized strategies for adaptation to climate change in each biome.
Average net ecosystem exchange (NEE) and CH4 exchange in grams of carbon per square meter across different plant phenological phases in this temperate salt marsh dominated by grass species. Plant ...phenological phases are: Greenup (˜April to June), when grasses start to grow, Maturity (˜July to September), when grasses reach their peak of growth and greenness, Senescence (˜September to October), when grasses start to decrease in greenness, and Dormancy (˜November to March), when grasses are inactive. Solid blue arrows represent carbon uptake by the ecosystem; solid red arrows represent carbon emissions from CO2; and red dashed arrows carbon emissions from CH4. This salt marsh was a net source of carbon during the annual cycle.
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•Plant phenological phases influence NEE and CH4 exchange•CO2 and CH4 emissions during senescence and dormancy overshadowed annual carbon uptakes•Light availability partially explained NEE variability•Lower water table level increased ecosystem-scale CH4 emissions•This temperate tidal salt marsh was a net source of carbon to the atmosphere
Salt marshes are large carbon reservoirs as part of blue carbon ecosystems. Unfortunately, there is limited information about the net ecosystem (NEE) and methane (CH4) exchange between salt marshes and the atmosphere to fully understand their carbon dynamics. We tested the influence of biophysical drivers by plant phenological phases (i.e., Greenup, Maturity, Senescence and Dormancy) on NEE and CH4 exchange in a grass-dominated temperate tidal salt marsh. We used three years of data derived from eddy covariance, PhenoCam (to measure vegetation phenology), and ancillary meteorological and water/soil variables. Overall, NEE showed significant differences among all phenological phases (p < 0.05), while CH4 exchange had significant differences among all phases except for Greenup and Dormancy. Net CO2 uptake was higher across Maturity (-61 g C-CO2 m2), while CO2 emissions were higher during Dormancy (182 g C-CO2 m2). The lower but constant CO2 emissions during Dormancy overshadowed the CO2 uptake during the growing season and contributed to >72% of the annual CO2 emissions in this ecosystem. Net CH4 emissions were higher during Maturity (3.7 g C-CH4 m2) and Senescence (4.2 g C-CH4 m2). Photosynthetically active radiation (PAR) substantially influenced (r2 > 0.57) daytime NEE across phenological phases, but a combination of variables including water table level (WTL), water temperature and atmospheric pressure were relevant to explain CH4 exchange. The study site was an overall net carbon source to the atmosphere with annual emissions of 13-201 g C-CO2 m−2yr−1 and 8.5-15.2 g C-CH4 m−2yr−1. Our findings provide insights on: a) the role of plant phenological phases on ecosystem-scale CO2 and CH4 fluxes; b) challenges for modeling ecosystem-scale CO2 and CH4 fluxes in salt marshes; and c) the potential net loss of carbon to the atmosphere that should be considered for carbon management and accounting in these ecosystems.
•Proximal remote sensing tools can be used for estimating gross primary productivity.•The most relevant electromagnetic regions were ∼550 nm and ∼710 nm.•Daily gross primary productivity was related ...to Plant Senescence Reflectance Index (PSRI) and the Greenness Index (GCC).•Sun Induced Fluorescence (SIF) was not a good predictor for GPP.
Salt marshes are highly productive ecosystems relevant for Blue Carbon assessments, but information for estimating gross primary productivity (GPP) from proximal remote sensing (PRS) is limited. Temperate salt marshes have seasonal canopy structure and metabolism changes, defining different canopy phenological phases, GPP rates, and spectral reflectance. We combined multi-annual PRS data (i.e., PhenoCam, discrete hyperspectral measurements, and automated spectral reflectance sensors) with GPP derived from eddy covariance. We tested the performance of empirical models to predict GPP from 12 common vegetation indices (VIs; e.g., NDVI, EVI, PSRI, GCC), Sun-Induced Fluorescence (SIF), and reflectance from different areas of the electromagnetic spectrum (i.e., VIS-IR, RedEdge, IR, and SIF) across the annual cycle and canopy phenological phases (i.e., Greenup, Maturity, Senescence, and Dormancy). Plant Senescence Reflectance Index (PSRI) from hyperspectral data and the Greenness Index (GCC) from PhenoCam, showed the strongest relationship with daily GPP across the annual cycle and within phenological phases (r2=0.30–0.92). Information from the visible-infrared electromagnetic region (VIS-IR) coupled with a partial least square approach (PLSR) showed the highest data-model agreement with GPP, mainly because of its relevance to respond to physiological and structural changes in the canopy, compared with indices (e.g., GCC) that particularly react to changes in the greenness of the canopy. The most relevant electromagnetic regions to model GPP were ∼550 nm and ∼710 nm. Canopy phenological phases impose challenges for modeling GPP with VIs and the PLSR approach, particularly during Maturity, Senescence, and Dormancy. As more eddy covariance sites are established in salt marshes, the application of PRS can be widely tested. Our results highlight the potential to use canopy reflectance from the visible spectrum region for modeling annual GPP in salt marshes as an example of advances within the AmeriFlux network.
•Visible wavelength indices from phenocam best captured overall season length.•Indices containing infrared bands consistently overestimated onset of dormancy.•Visible wavelength indices and NDVI from ...phenocam best tracked daily NEP.•Proximal reflectance provides an ecologically meaningful link to carbon dynamics.
Salt marshes constitute an important terrestrial-aquatic interface that remains underrepresented in Earth System Models due to constraining biophysical controls and spatially limited land cover. One promising approach to improve representativeness is the application of proximal remote sensing to generate phenological information, yet we lack detailed knowledge on how proximal sensors and indices perform within these ecosystems. We use measurements of net ecosystem productivity (NEP) from eddy covariance (EC) and derive ecologically-relevant phenology parameters (i.e., phenoperiods) to use as carbon phenology benchmarks. These benchmarks are compared against vegetation indices and spectral bands derived from spaceborne (i.e., MODIS) or common proximal sensors (i.e., phenocam and spectral reflectance sensors; SRS).
Phenocam derived indices, which exclude infrared wavelengths (i.e., vegetation contrast index; VCI and greenness chromatic coordinate; GCC), aligned closely with NEP benchmarks and provided best predictions of carbon sink season length (within 1–6 days of benchmark). Although isolating infrared from vegetation (NIRv) offered improvements, other indices utilizing infrared bands (i.e., normalized difference vegetation index; NDVI and enhanced vegetation index; EVI) primarily underestimated season start dates (5–30 days prior to benchmark) while overestimating season end dates (7–47 days after benchmark). These discrepancies are greatest for indices derived from MODIS and SRS sensors, which have narrower full width half maximum spectral bandwidths and sharper orientation angles. The phenocam (VCI and GCC) provides the most accurate phenology parameters while offering near-infrared (NIR) response which can generate additional information on seasonal changes in canopy structure and function.
The distinctive characteristics of the salt marsh environment and vegetation properties including standing dead biomass can introduce interpretation challenges for commonly used vegetation indices (NDVI, EVI). Incorporating information from proximal sensors utilizing only visible wavelengths (VCI, GCC) or isolating the near-infrared reflectance of vegetation (NIRv) offers improvements for studying carbon phenology within salt marshes.
Coastal salt marshes store large amounts of carbon but the magnitude and patterns of greenhouse gas (GHG; i.e., carbon dioxide (CO2) and methane (CH4)) fluxes are unclear. Information about GHG ...fluxes from these ecosystems comes from studies of sediments or at the ecosystem‐scale (eddy covariance) but fluxes from tidal creeks are unknown. We measured GHG concentrations in water, water quality, meteorological parameters, sediment CO2 efflux, ecosystem‐scale GHG fluxes, and plant phenology; all at half‐hour intervals over 1 year. Manual creek GHG flux measurements were used to calculate gas transfer velocity (k) and parameterize a model of water‐to‐atmosphere GHG fluxes. The creek was a source of GHGs to the atmosphere where tidal patterns controlled diel variability. Dissolved oxygen and wind speed were negatively correlated with creek CH4 efflux. Despite lacking a seasonal pattern, creek CO2 efflux was correlated with drivers such as turbidity across phenological phases. Overall, nighttime creek CO2 efflux (3.6 ± 0.63 μmol/m2/s) was at least 2 times higher than nighttime marsh sediment CO2 efflux (1.5 ± 1.23 μmol/m2/s). Creek CH4 efflux (17.5 ± 6.9 nmol/m2/s) was 4 times lower than ecosystem‐scale CH4 fluxes (68.1 ± 52.3 nmol/m2/s) across the year. These results suggest that tidal creeks are potential hotspots for CO2 emissions and could contribute to lateral transport of CH4 to the coastal ocean due to supersaturation of CH4 (>6,000 μmol/mol) in water. This study provides insights for modeling GHG efflux from tidal creeks and suggests that changes in tide stage overshadow water temperature in determining magnitudes of fluxes.
Key Points
A tidal creek was a hotspot for CO2 efflux compared to the surrounding wetland
Changes in tide stage, not water temperature variability, regulated diel creek CO2 and CH4 efflux
The relative influence of nontidal drivers of creek CO2 and CH4 efflux varied by plant phenological phases
Accurate estimates of habitat extent and rates of change are crucial inputs for the global, regional, and national assessments that guide policy-making and prioritize strategies. This can contribute ...to an understanding of ecosystems in the landscape for their use, management, and preservation. Mangroves are one of the types of ecosystems in which estimation discrepancies have been analyzed to determine the impacts of data quality on conservation and policy-making. We identify significant discrepancies in the extent of the last map of Mexican mangroves (i.e., 2020) produced by the Mexican Mangrove Monitoring System (MMMS). We performed a comparative assessment between the 2020 and 2015 maps by using geographical information systems to analyze the spatial extent across these years and estimate the accuracy of map changes with airborne data. We observed a spurious gain of 129,531 ha between 2015 and 2020, including 102,610 ha (79% of total changes) in the Sian Ka'an Biosphere Reserve and its surroundings. Furthermore, the mangrove definition changed, causing the MMMS to map other coastal wetlands with the presence of Rhizophora mangle scrubs dispersed in the landscape. The analysis of MMMS airborne data demonstrates that this significant increase is due to changes in mangrove mapping criteria and definitions. The definition and spatial delimitation of "mangrove" (i.e., mangrove community or stands forest) has implications relevant to the conservation policy of these coastal wetlands/coastal resources. MMMS discrepancies in mangrove extent could generate misleading perspectives on different sectors. A cartographic solution is to separate the other coastal wetland areas with the presence of R. mangle from all MMMS products and reclassify them as "other wetlands with the presence of R. mangle." Robust ecosystem extent data is crucial for the design and implementation of efficient land use and conservation policies.
•Robust ecosystem extent data is crucial for the implementation of land use/conservation policies.•The current significant increase in mangrove extent in Mexico is due to changes in mapping criteria.•Cartographic products must be consistent to avoid discrepancies and ensure their correct use.
Mangroves cover less than 0.1% of Earth's surface, store large amounts of carbon per unit area, but are threatened by global environmental change. The capacity of mangroves productivity could be ...characterized by their canopy greenness, but this property has not been systematically tested across gradients of mangrove forests and national scales. Here, we analyzed time series of Normalized Difference Vegetation Index (NDVI), mean air temperature and total precipitation between 2001 and 2015 (14 years) to quantify greenness and climate variability trends for mangroves not directly influenced by land use/land cover change across Mexico. Between 2001 and 2015 persistent mangrove forests covered 432 800 ha, representing 57% of the total current mangrove area for Mexico. We found a temporal greenness increase between 0.0030.001-0.004 and 0.0040.002-0.005 yr−1 (NDVI values 95%CI) for mangroves located over the Gulf of California and the Pacific Coast, with many mangrove areas dominated by Avicennia germinans. Mangroves developed along the Gulf of Mexico and Caribbean Sea did not show significant greenness trends, but site-specific areas showed significant negative greenness trends. Mangroves with surface water input have above ground carbon stocks (AGC) between 37.7 and 221.9 Mg C ha−1 and soil organic carbon density at 30 cm depth (SOCD) between 92.4 and 127.3 Mg C ha−1. Mangroves with groundwater water input have AGC of 12.7 Mg C ha−1 and SOCD of 219 Mg C ha−1. Greenness and climate variability trends could not explain the spatial variability in carbon stocks for most mangrove forests across Mexico. Site-specific characteristics, including mangrove species dominance could have a major influence on greenness trends. Our findings provide a baseline for national-level monitoring programs, carbon accounting models, and insights for greenness trends that could be tested around the world.
Salt marsh ecosystems are underrepresented in process‐based models due to their unique location across the terrestrial–aquatic interface. Particularly, the role of leaf nutrients on canopy ...photosynthesis (FA) remains unclear, despite their relevance for regulating vegetation growth. We combined multiyear information of canopy‐level nutrients and eddy covariance measurements with canopy surface hyperspectral remote sensing (CSHRS) to quantify the spatial and temporal variability of FA in a temperate salt marsh. We found that FA showed a positive relationship with canopy‐level N at the ecosystem scale and for areas dominated by Spartina cynosuroides, but not for areas dominated by short S. alterniflora. FA showed a positive relationship with canopy‐level P, K, and Na, but a negative relationship with Fe, for areas associated with S. cynosuroides, S. alterniflora, and at the ecosystem scale. We used partial least squares regression (PLSR) with CSHRS and found statistically significant data–model agreements to predict canopy‐level nutrients and FA. The red‐edge electromagnetic region and ∼770 nm showed the highest contribution of variance in PLSR models for canopy‐level nutrients and FA, but we propose that underlying sediment biogeochemistry can complicate interpretation of reflectance measurements. Our findings highlight the relevance of spatial variability in salt marshes vegetation and the promising application of CSHRS for linking information of canopy‐level nutrients with FA. We call for further development of canopy surface hyperspectral methods and analyses across salt marshes to improve our understanding of how these ecosystems will respond to global environmental change.
Plain Language Summary
Canopy photosynthesis in salt marshes contributes to the carbon stored in these ecosystems; however, its relationship with canopy‐level nutrients has been underrepresented in models. Reflectance from near surface remote sensing could be a cost‐effective nondestructive tool to monitor canopy photosynthesis and associated nutrients in salt marshes. We combined canopy‐level nutrient information with hyperspectral canopy reflectance to represent the spatial and temporal variability of canopy photosynthesis in a salt marsh in the Mid‐Atlantic cost of the U.S. We found that local variability such as different salt marsh species have an influence on the relationship between canopy photosynthesis and associated nutrients, in consequence the most limiting nutrients for photosynthesis were phosphorus, potassium, and sodium. We propose that underlying sediment biogeochemistry can potentially obscure the expected relationships between plant nutrients and photosynthesis in remote sensing of coastal wetlands. These results open the possibility to use similar reflectance information from airborne or spaceborne platforms to explore these relationships at broader scales.
Key Points
Local environmental variability influences the relationship of canopy nutrients with canopy photosynthesis in a salt marsh ecosystem
Sediment biogeochemistry can obscure expected relationships between plant nutrients and photosynthesis in remote sensing of coastal wetlands
Canopy surface hyperspectral remote sensing is a promising technique for studying vegetation dynamics of salt marshes
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