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The positive response of wetland methane (CH4) emissions to climate change is an important yet uncertain Earth-system feedback that amplifies atmospheric CH4 concentrations. Here, using a wetland model, we report intensified wetland CH4 emissions during 2000–2021, corresponding with 2020 and 2021 being exceptional years of growth. Our results highlight the need for sustained monitoring and observations of global wetland CH4 fluxes to document emerging trends, variability and underlying drivers.

With a changing climate, it is becoming increasingly critical to understand vegetation responses to limiting environmental factors. Here, we investigate the spatial and temporal patterns of light and water limitation on photosynthesis using an observational framework. Our study is unique in characterizing the nonlinear relationships between photosynthesis and water and light, acknowledging approximately two regime behaviours (no limitation and varying degrees of limitation). It is also unique in using an observational framework instead of using model-derived photosynthesis properties. We combine data from three different satellite sensors, i.e., sun-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI), surface soil moisture from the Soil Moisture Active Passive (SMAP) microwave radiometer, and vegetation greenness from the Moderate Resolution Imaging Spectroradiometer (MODIS). We find both single-regime and two-regime models describe SIF sensitivity to soil moisture and photosynthetically active radiation (PAR) across the globe. The distribution and strength of soil moisture limitation on SIF are mapped in the water-limited environments, while the distribution and strength of PAR limitations are mapped in the energy-limited environments. A two-regime behaviour is detected in 73 % of the cases for water limitation on photosynthesis, while two-regime detection is much lower at 41 % for light limitation on photosynthesis. SIF sensitivity to PAR strongly increases along moisture gradients, reflecting mesic vegetation's adaptation to making rapid usage of incoming light availability on the weekly timescales. The transition point detected between the two regimes is connected to soil type and mean annual precipitation for the SIF–soil moisture relationship and for the SIF–PAR relationship. These thresholds therefore have an explicit relation to properties of the landscape, although they may also be related to finer details of the vegetation and soil interactions not resolved by the spatial scales here. The simple functions and thresholds are emergent behaviours capturing the interaction of many processes. The observational thresholds and strength of coupling can be used as benchmark information for Earth system models, especially those that characterize gross primary production mechanisms and vegetation dynamics.

The global carbon cycle is experiencing continued perturbations via increases in atmospheric carbon concentrations. Greenhouse gas satellites that are designed to retrieve atmospheric carbon concentrations can help observe seasonal to interannual variations and sources of carbon dioxide. Recent work has shown that these satellites are further applicable beyond their design specifications for directly identifying and quantifying surface emissions at various spatial and temporal scales. For example, simple model translations of atmospheric carbon concentrations to surface carbon exchanges have been used to rapidly estimate surface methane fluxes from satellite observations of methane in the atmosphere. However, less attention has been placed on using satellite column CO2 retrievals to evaluate surface CO2 fluxes from the terrestrial biosphere at shorter timescales without more complex inversion models. Such applications could be useful to monitor, in near-real time, biosphere carbon fluxes during climatic anomalies like drought, heatwaves, and floods, before more complex terrestrial biosphere model outputs become available. Here, we explore the ability of Orbiting Carbon Observatory-2 (OCO-2) satellite retrievals of column-averaged dry air CO2 (XCO2) to directly detect and estimate terrestrial biosphere CO2 flux anomalies using a simple mass balance approach. Using a regional model simulation (CarbonTracker reanalysis) as a testbed, we first demonstrate that a previously developed, simple model can rapidly estimate monthly surface CO2 flux anomalies from atmospheric XCO2 estimates in the Western United States. The method is optimal when the chosen study domain is spatially extensive enough to account for atmospheric mixing and has favorable wind conditions with incoming wind contributions primarily from the same region. While errors in individual satellite measurements partially reduce the ability of OCO-2 satellite XCO2 to estimate more frequent, smaller surface CO2 flux anomalies, we find that OCO-2 XCO2 can often detect and estimate larger surface flux anomalies that are due to droughts and heatwaves. OCO-2 is thus useful for near real time monitoring of the monthly ecosystem behavior and health. Any noise reduction in forthcoming greenhouse gas satellites and/or the existence of large surface carbon anomalies will likely enhance the ability to rapidly estimate surface fluxes at shorter timescales scales.

Variation in canopy water content (CWC) that can be detected from microwave remote sensing of vegetation optical depth (VOD) has been proposed as an important measure of vegetation water stress. However, the contribution of leaf surface water (LWs ), arising from dew formation and rainfall interception, to CWC is largely unknown, particularly in tropical forests and other high-humidity ecosystems. We compared VOD data from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) and CWC predicted by a plant hydrodynamics model at four tropical sites in Brazil spanning a rainfall gradient. We assessed how LWs influenced the relationship between VOD and CWC. The analysis indicates that while CWC is strongly correlated with VOD (R2 = 0.62 across all sites), LWs accounts for 61-76% of the diurnal variation in CWC despite being < 10% of CWC. Ignoring LWs weakens the near-linear relationship between CWC and VOD and reduces the consistency in diurnal variation. The contribution of LWs to CWC variation, however, decreases at longer, seasonal to inter-annual, time scales. Our results demonstrate that diurnal patterns of dew formation and rainfall interception can be an important driver of diurnal variation in CWC and VOD over tropical ecosystems and therefore should be accounted for when inferring plant diurnal water stress from VOD measurements.

Plant hydraulic and photosynthetic responses to individual rain pulses are not well understood because field experiments of pulse behavior are sparse. Understanding individual pulse responses would inform how rainfall intermittency impacts terrestrial biogeochemical cycles, especially in drylands, which play a large role in interannual global atmospheric carbon uptake variability. Using satellite-based estimates of predawn plant and soil water content from the Soil Moisture Active Passive (SMAP) satellite, we quantify the timescales of plant water content increases following rainfall pulses, which we expect bear the signature of whole-plant mechanisms. In wetter regions, we find that plant water content increases rapidly and dries along with soil moisture, which we attribute to predawn soil–plant water potential equilibrium. Global drylands, by contrast, show multi-day plant water content increases after rain pulses. Shorter increases are more common following dry initial soil conditions. These are attributed to slow plant rehydration due to high plant resistances using a plant hydraulic model. Longer multi-day dryland plant water content increases are attributed to pulse-driven growth, following larger rain pulses and wetter initial soil conditions. These dryland responses reflect widespread drought recovery rehydration responses and individual pulse-driven growth responses, as supported by previous isolated field experiments. The response dependence on moisture pulse characteristics, especially in drylands, also shows ecosystem sensitivity to intra-annual rainfall intensity and frequency, which are shifting with climate change.

Understanding and reducing the uncertainties in the inversion of the first-order radiative transfer models at the L-band are important for the improved spaceborne retrievals of soil moisture (SM) and vegetation optical depth (VOD) over dense canopy. This article quantifies and compares the sensitivity of dual-channel inversion of the two-stream (2S) and $\tau $ – $\omega $ models and proposes a new inversion approach for simultaneous retrievals of SM, VOD, and vegetation-scattering albedo ( $\omega $ ) from a single satellite overpass. In particular, the inversion algorithm incorporates the information of the nearby spatial observations, assuming that the values of VOD and $\omega $ remain locally invariant, and constrains its solutions to high-resolution a priori physical/climatological knowledge of the retrieval variables. The results demonstrate that the uncertainty in the inversion of 2S model is slightly higher than the $\tau $ – $\omega $ model under noisy observations and remains homoscedastic for SM and $\omega $ , while grows heteroscedastically for higher VOD values due to the shape of the cost function. The results are validated using the SMAP data, the dense Mesonet SM network, the in situ measurements from the International SM Network (ISMN), and the derived VOD from the Moderate Resolution Imaging Spectroradiometer (MODIS)-normalized difference vegetation index (NDVI) over the state of Oklahoma in the United States. It is shown that the new approach can recover simultaneously high-resolution features of SM, VOD, and $\omega $ only from a single Soil Moisture Active Passive (SMAP) overpass, where the unbiased root-mean-squared error (ubRMSE) of SM and VOD is reduced by 30% and 70%, respectively, when compared with an unconstrained time-windowed inversion approach.

A retrieval methodology for forest water potential from ground-based L-band radiometry is proposed. It contains the estimation of the gravimetric and the relative water content of a forest stand and tests in situ- and model-based functions to transform these estimates into forest water potential. The retrieval is based on vegetation optical depth data from a tower-based experiment of the SMAPVEX 19–21 campaign for the period from April to October 2019 at Harvard Forest, MA, USA. In addition, comparison and validation with in situ measurements on leaf and xylem water potential as well as on leaf wetness and complex permittivity are foreseen to understand limitations and potentials of the proposed approach. As a first result the radiometer-based water potential estimates of the forest stand are concurrent in time and similar in value with their in situ (xylem) counterparts from single trees in the radiometer footprint.

Vegetation cover absorbs and scatters L-band microwave emission measured by SMOS and SMAP satellites. Misrepresentation of this phenomena results in uncertainties when inferring, for instance, surface soil moisture in retrieval algorithms that commonly utilize the tau-omega model which is most applicable for a weakly scattering medium. In this study, we investigate the degree to which multiple-scattering is prevalent over a range of land cover classifications (from lightly vegetated grasslands to dense forests) at the satellite scale by explicitly accounting for multiple-scattering in a first-order radiative transfer model, developed here. Even though the tau-omega model with effective parameters can possibly capture higher-order scattering contributions, deliberately partitioning scattering into different components is required to estimate multiple-scattering properties. Specifically, we aim to determine how one can partition between zeroth and first-order radiative transfer terms within a retrieval algorithm without ancillary information, determine whether this method can detect first-order scattering at the SMAP measurement scale without ancillary information, and quantify the magnitude of detected scattering. A simplified first-order radiative transfer model which characterizes single interactions of microwaves with a scattering medium is developed for implementation within retrieval algorithms. This new emission model is implemented within a recently developed retrieval algorithm, the multi-temporal dual channel algorithm (MT-DCA), which does not require ancillary land use information. Scattering parameters as well as SM and vegetation optical depth (τ) are retrieved simultaneously in Africa and South America using the first year of SMAP brightness temperature measurements on a 36 km grid. Specifically, an introduced time invariant first-order scattering coefficient (ω1) is retrieved representing microwave emission interaction with the canopy. We find that ω1 is typically zero in lightly vegetated biomes and non-zero (~0.06) in 74% of the forest pixels. In forest-dominated pixels, the median first-order emissivity is 0.04, or about 4.3% of a given SMAP radiometer brightness temperature measurement. Additionally, explicitly accounting for first-order scattering terms in the radiative transfer model tends to increase SM and τ retrievals by a median of 0.02 m3/m3 and 0.1, respectively, only in forested regions. This study demonstrates the first attempt to explicitly partition higher-order scattering terms in a retrieval algorithm at a satellite scale and ultimately provides a fundamental understanding and quantification of multiple-scattering from grasslands to forests.

The attenuation of microwave emissions through the canopy is quantified by the vegetation optical depth (VOD), which is related to the amount of water, the biomass and the structure of vegetation. To provide microwave-derived plant water estimates, one must account for biomass/structure contributions in order to extract the water component from the VOD. This study uses Aquarius scatterometer data to build an L-band global seasonality of vegetation volume fraction (d), representative of biomass/structure dynamics. The dynamic range of d is adapted for its application in a gravimetric moisture (Mg) retrieval model. Results show that d ranging from 0 to 3.35.10- 4 is needed for modelling physically reasonable Mg values. The global average of d shows consistent spatial patterns across vegetation distributions, and d seasonality is coherent with the phenology of the studied vegetation types. These findings enable the separation of information on vegetation water and biomass/structure inherent within VOD. This work was supported by “la Caixa” Foundation (ID 100010434), under agreement LCF /PR/MIT19/51840001 (MIT -Spain Seed Fund), and by the Spanish Ministry of Science, Innovation and Universities and the European Regional Development Fund (ERDF, EU) through projects ESP2017-89463-C3-3-R, RTI2018-096765-A-100, CAS19/00264 and MDM-2016-0600. Also, the authors are grateful to MIT for supporting this research with the MIT-Germany Seed Fund (D. Entekhabi, T. Jagdhuber).

Satellite soil moisture and vegetation optical depth [(VOD); related to the total vegetation water mass per unit area] are increasingly being used to study water relations in the soil-plant continuum across the globe. However, soil moisture and VOD are typically jointly estimated, where errors in the optimization approach can cause compensation between both variables and confound such studies. It is thus critical to quantify how satellite microwave measurement errors propagate into soil moisture and VOD. Such a study is especially important for VOD given limited investigations of whether VOD reflects in situ plant physiology. Furthermore, despite new approaches that constrain (or regularize) VOD dynamics to reduce soil moisture errors, there is limited study of whether regularization reduces VOD errors without obscuring true vegetation temporal dynamics. Here, we find that, across the globe, VOD is less robust to measurement error (more difficult for optimization methods to find the true solution) than soil moisture in their joint estimation. However, a moderate degree of regularization (via time-constrained VOD) reduces errors in VOD to a greater degree than soil moisture and reduces spurious soil moisture-VOD coupling. Furthermore, despite constraining VOD time dynamics, regularized VOD variations on subweekly scales are both closer to simulated true VOD time series and have global VOD post-rainfall responses with reduced error signatures compared to VOD retrievals without regularization. Ultimately, we recommend moderately regularized VOD for use in large scale studies of soil-plant water relations because it suppresses noise and spurious soil moisture-VOD coupling without removing the physical signal., SMAP L1C brightness temperatures used to retrieve soil moisture and vegetation optical depth are available from the National Snow and Ice Data Center (NSIDC) (https://nsidc.org/data/SPL1CTB_E). The MT-DCA soil moisture and VOD datasets are freely available at https://doi.org/10.5281/zenodo.5579549. The DCA retrievals generated for this study are freely available from the authors upon request.

Changing climate patterns have increased hydrological extremes in many regions [1]. This impacts water and carbon cycles, potentially modifying vegetation processes and thus terrestrial carbon uptake. It is therefore crucial to understand the relationship between the main water pools linked to vegetation (i.e., soil moisture, plant water storage, and atmospheric water deficit), and how vegetation responds to changes of these pools. Hence, the goal of this research is to understand the water pools and fluxes in the soil-plant-atmosphere continuum (SPAC) and their relationship with vegetation responses.Our study spans from April 2015 to March 2019 and is structured in two parts:Firstly, relative water content (RWC) is estimated using a multi-sensor approach to monitor water storage in plants. This is at the core of our research approach towards water pool monitoring within SPAC. Here, we will present a RWC dataset derived from gravimetric moisture content (mg) estimates using the method first proposed in [2], and further validated in [3]. This allows retrieving RWC and mg independently from biomass influences. Here, we apply this method using a sensor synergy including (i) vegetation optical depth from SMAP L-band radiometer (L-VOD), (ii) vegetation height (VH) from ICESat-2 Lidar and (iii) vegetation volume fraction (d) from AQUARIUS L-band radar. RWC status and temporal dynamics will be discussed.Secondly, water dynamics in the SPAC and their impact on leaf changes are analyzed. We will present a global, time-lag correlation analysis among: (i) the developed RWC maps, (ii) surface soil moisture from SMAP (SM), (iii) vapor pressure deficit (VPD; from MERRA reanalysis [4]), and (iv) leaf area index (LAI; from MODIS [5]). Resulting time-lag and correlation maps, as well as analyses of LAI dynamics as a function of SPAC, will be presented at the conference. References[1] IPCC. (2013). Annex I: Atlas of global and regional climate projections. In: van Oldenborgh, et al. (Eds.) Climate Change 2013: The Physical Science Basis (pp. 1311-1393). Cambridge University Press.[2] Fink, A., et al. (2018). Estimating Gravimetric Moisture of Vegetation Using an Attenuation-Based Multi-Sensor Approach. In IGARSS 2018 (pp. 353-356). IEEE.[3] Meyer, T., et al. Estimating Gravimetric Water Content of a Winter Wheat Field from L-Band Vegetation Optical Depth, Remote Sens. 2019, 11(20), 2353[4] NASA (2019). Modern-Era Retrospective analysis for Research and Applications, Version 2. Accessed 2020-01-14 from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/.[5] Myneni, R., et al. (2015). MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. Accessed 2020-01-14 from https://doi.org/10.5067/MODIS/MOD15A2H.006.

To determine hydrologic changes in a warmer climate, we impose precipitation and potential evaporation perturbations on hydrologic response functions constructed from precipitation and satellite so...

A newly constructed rain garden in Shoelace Park in Bronx, New York, USA, was monitored between October 2014 and July 2015 as a pilot study aimed at testing the effectiveness of using urban...

Remote sensing of geophysical parameters often requires parameter estimation from mutually dependent measurements, reducing retrieval robustness when the number of retrieved parameters equals the number of unknowns. The actual number of parameters that can be retrieved from the total information (e.g., degrees of information (DOI)) is reviewed here in the context of current L-band (1.4 GHz) satellite measurements for retrieving soil and vegetation water content. A limitation of DOI metric is noted where measurements at the noise floor (i.e., in vegetated regions) tend to spuriously decrease estimated mutual information. Thus, the signal-to-noise ratio must be considered together with DOI. A proposed retrieval technique that overcomes the mutually-dependent information, called the multi-temporal dual-channel algorithm, is reviewed and its microwave vegetation parameter retrievals are discussed.

The multi-temporal dual-channel algorithm (MT-DCA) is reviewed in its approach in simultaneously estimating soil moisture and vegetation optical depth (VOD) from SMAP brightness temperature (TB) measurements. At a single incidence angle, the two polarized TB measurements from SMAP at a given location do not provide two degrees of information (DOI). Therefore, this approach assumes a constant VOD between SMAP overpasses to increase DOI and stabilize the soil moisture-VOD estimation. Retrieved time-mean VOD covaries spatially with vegetation biomass, but its temporal dynamics have yet to be validated with in-situ measurements. Recent SMAP VOD applications in plant ecology and crop monitoring are discussed. Ultimately, these studies suggest insightful vegetation information is present in both weekly and seasonal SMAP VOD variations. Further study and ground monitoring campaigns will continue to reveal the extent of geophysical information in the VOD signal.