Vegetation water content (VWC) plays a key role in transpiration, plant mortality, and wildfire risk. Although land surface models now often contain plant hydraulics schemes, there are few direct VWC measurements to constrain these models at global scale. One proposed solution to this data gap is passive microwave remote sensing, which is sensitive to temporal changes in VWC. Here, we test that approach by using synthetic microwave observations to constrain VWC and surface soil moisture within the CliMA Land model. We further investigate the possible utility of sub-daily observations of VWC, which could be obtained through a satellite in geostationary orbit or combinations of multiple satellites. These high-temporal-resolution observations could allow for improved determination of ecosystem parameters, carbon and water fluxes, and subsurface hydraulics, relative to the currently available twice-daily sun-synchronous observational patterns. We find that incorporating observations at four different times in the diurnal cycle (such as could be available from two sun-synchronous satellites) provides a significantly better constraint on water and carbon fluxes than twice-daily observations do. For example, the root mean square errors (RMSE) of projected evapotranspiration and gross primary productivity during drought periods was reduced by approximately 40%, when using four-times-daily relative to twice-daily observations. Adding hourly observations of the entire diurnal cycle did not further improve the inferred parameters and fluxes. Our comparison of observational strategies may be informative in the design of future satellite missions to study plant hydraulics, as well as when using existing remotely sensed data to study vegetation water stress response.

Incorporating observational data in carbon-cycle models provides a systematic framework for understanding complex ecosystem carbon dynamics, contributing essential insights for climate change mitigation and land ability to continue acting as a carbon sink. This study addresses the challenge of accurately quantifying carbon fluxes and pools, focusing on the information content of remote sensing observations. The research explores the impact of assimilating multiple observational datasets into the CARbon DAta MOdel fraMework (CARDAMOM). Satellite observations such as solar-induced fluorescence (SIF) and vegetation optical depth (VOD) are used as proxies for photosynthesis and aboveground biomass, respectively. The study aims to answer key questions about the reliability of remote sensing data in constraining the ecosystem respiration flux and sizes and dynamics of carbon pools and the relative usefulness of SIF and VOD across five FLUXNET sites. We conclude that assimilating remote SIF and VOD instead of site-based net ecosystem exchange did not deteriorate and even improved model predictions for all metrics except for interannual variability. Notably, the improved results correspond to a consistent shift in values for crucial model parameters across all five investigated sites.

The relationships between water potential and water content in plants and soil have long been of interest, and there is increasing focus on understanding how these fundamental measures are linked at larger spatial and temporal scales. In this Perspective, we explore how the theory of pressure-volume (PV) relationships can be applied at ecosystem scale. We define and evaluate the concept and limitations of the ecosystem and vegetation pressure-volume curves and discuss its application using existing data. As a proof of concept, plot-scale aboveground vegetation PV curves were generated from equilibrium (e.g. predawn) water potentials and water content of the above ground biomass of nine plots including tropical rainforest, savanna, temperate forest, and a long-term Amazonian rainforest drought experiment. Initial findings suggest high consistency among sites where the steady-state water:biomass ratio is approximately 1:3, while the relative values of ecosystem hydraulic capacitance and accessible water storage (the water volume between saturation and a threshold) do not vary systematically with biomass. The ecosystem-scale PV relationship provides a thermodynamically consistent steady-state view of ecosystem form and function and a biophysically robust basis for the interpretation of remote sensing data of vegetation and soil water content, with promise for revealing useful trends across ecosystems.

The spatio-temporal variation of stomatal conductance directly regulates photosynthesis, water partitioning, and biosphere-atmosphere interactions. While many studies have focused on stomatal response to stresses, the spatial variation of unstressed stomatal conductance remains poorly determined, and is usually characterized in land surface models (LSMs) simply based on plant functional type (PFT). Here, we derived unstressed stomatal conductance at the ecosystem-scale using observations from 115 global FLUXNET sites. When aggregated by PFTs, the across-PFT pattern was highly consistent with the parameterizations of LSMs. However, PFTs alone captured only 17\% of the variation in unstressed stomatal conductance across sites. Within the same PFT, unstressed stomatal conductance was negatively related to climate dryness and canopy height, which explained 45\% of the total spatial variation. Our results highlight the importance of plant-environment interactions in shaping stomatal traits. The trait-environment relationship established here provides an empirical approach for improved parameterizations of stomatal conductance in LSMs.

Drainage canals associated with logging and agriculture dry out organic soils in tropical peatlands, thereby threatening the viability of long-term carbon stores due to increased emissions from decomposition, fire, and fluvial transport. In Southeast Asian peatlands, which have experienced decades of land use change, the exact extent and spatial distribution of drainage canals are unknown. This has prevented regional-scale investigation of the relationships between drainage, land use, and carbon emissions. Here, we create the first regional map of drainage canals using high resolution satellite imagery and a convolutional neural network. We find that drainage is widespread-occurring in at least 65% of peatlands and across all land use types. Although previous estimates of peatland carbon emissions have relied on land use as a proxy for drainage, our maps show substantial variation in drainage density within land use types. Subsidence rates, and corresponding carbon losses from decomposition, are 3.2 times larger in intensively drained areas than in non-drained areas, highlighting the central role of drainage in mediating peat carbon fluxes. Accounting for drainage canals was found to improve a subsidence prediction model by 30%, suggesting that canals contain information about subsidence not captured by land use alone. Thus, our dataset can be used to improve carbon emissions predictions in peatlands and to target areas for hydrologic restoration.

When organic peat soils are sufficiently dry, they become flammable. In Southeast Asian peatlands, widespread deforestation and associated drainage create dry conditions that, when coupled with El Niño-driven drought, result in catastrophic fire events that release large amounts of carbon and deadly smoke to the atmosphere. While the effects of anthropogenic degradation on peat moisture and fire risk have been extensively demonstrated, climate change impacts to peat flammability are poorly understood. These impacts are likely to be mediated primarily through changes in soil moisture. Here, we used neural networks (trained on data from the NASA SMAP satellite) to model soil moisture as a function of climate, degradation, and location. The neural networks were forced with regional climate model projections for 1985-2005 and 2040-2060 climate under RCP8.5 forcing to predict changes in soil moisture. We find that reduced precipitation and increased evaporative demand will lead to median soil moisture decreases about half as strong as those observed during recent El Niño droughts. Such reductions may be expected to accelerate peat emissions. Our results also suggest that soil moisture in degraded areas with less tree cover may be more sensitive to climate change than in other land use types, motivating urgent peatland restoration. Climate change may play an important role in future soil moisture regimes and by extension, future peat fire in Southeast Asian peatlands.