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Land and Earth system modeling is moving towards more explicit biophysical representations, requiring increasing variety of datasets for initialization and benchmarking. However, researchers often have difficulties in identifying and integrating non-standardized datasets from various sources. We aim towards a standardized database and one-stop distribution method of global datasets. Here, we present the GriddingMachine as (1) a database of global-scale datasets commonly used to parameterize or benchmark the models, from plant traits to vegetation indices and geophysical information and (2) a cross-platform open source software to download and request a subset of datasets with only a few lines of code. The GriddingMachine datasets can be accessed either manually through traditional HTTP, or automatically using modern programming languages including Julia, Matlab, Octave, Python, and R. The GriddingMachine collections can be used for any land and Earth modeling framework and ecological research at the regional and global scales, and the number of datasets will continue to grow to meet the increasing needs of research communities.

Three-dimensional (3D) vegetation canopy structure plays an important role in the way radiation interacts with the land surface. Accurately representing this process in Earth System models (ESMs) is crucial for the modeling of the global carbon, energy, and water cycles and hence future climate projections. Despite the importance of accounting for 3D canopy structure, the inability to represent such complexity at regional and global scales has impeded a successful implementation into ESMs. An alternative approach is to use an implicit clumping index to account for the horizontal heterogeneity in vegetation canopy representations in ESMs at global scale. This paper evaluates how modeled hyperspectral shortwave radiation partitioning of the terrestrial biosphere, as well as Sun-Induced Chlorophyll Fluorescence (SIF) are impacted when a clumping index parameterization is incorporated in the radiative transfer scheme of a new generation ESM, the Climate Model Alliance (CliMA). An accurate hyperspectral radiative transfer representation within ESMs is critical for accurately using of satellite data to confront, constrain, and improve land model processes. The newly implemented scheme is compared to Monte Carlo calculations for idealized scenes from the Radiation transfer Model Intercomparison for the Project for Intercomparison of Land-Surface Parameterizations (RAMI4PILPS), for open forest canopies both with and without snow on the ground. Results indicate that it is critical to account for canopy structural heterogeneity when calculating hyperspectral radiation transfer. The RMSE in shortwave radiation is reduced for reflectance (25%), absorptance (66%), and transmittance (75%) compared to the scenario without considering clumping. Calculated SIF is validated against satellite remote sensing data with the recently launched NASA Orbiting Carbon Observatory (OCO) 3, showing that including vertical and horizontal canopy structure when deriving SIF can improve model predictions in up to 51% in comparison to the scenario without clumping. By adding a clumping index into the CliMA-Land model, the relationship between canopy structure and SIF, Gross Primary Productivity (GPP), hyperspectral radiative transfer, and viewing geometry at the canopy scale can be explored in detail.

Agriculture (e.g., rice paddies) has been considered one of the main emission sources responsible for the sudden rise of atmospheric methane concentration (XCH4) since 2007, but remains debated. Here we use satellite-based rice paddy and XCH4 data to investigate the spatial-temporal relationships between rice paddy area, rice plant growth, and XCH4 in monsoon Asia, which accounts for ~87% of the global rice area. We find strong spatial consistencies between rice paddy area and XCH4 and seasonal consistencies between rice plant growth and XCH4. Our results also show a decreasing trend in rice paddy area in monsoon Asia since 2007, which suggests that the change in rice paddy area could not be one of the major drivers for the renewed XCH4 growth, thus other sources and sinks should be further investigated. Our findings highlight the importance of satellite-based paddy rice datasets in understanding the spatial-temporal dynamics of XCH4 in monsoon Asia.

Photosynthesis of the Amazon rainforest plays an important role in the regional and global carbon cycles, but, despite considerable in situ and space-based observations, it has been intensely debated whether there is a dry-season increase in greenness and photosynthesis of the moist tropical Amazonian forests. Solar-induced chlorophyll fluorescence (SIF), which is emitted by chlorophyll, has a strong positive linear relationship with photosynthesis at the canopy scale. Recent advancements have allowed us to observe SIF globally with Earth observation satellites. Here we show that forest SIF did not decrease in the early dry season and increased substantially in the late dry season and early part of wet season, using SIF data from the Tropospheric Monitoring Instrument (TROPOMI), which has unprecedented spatial resolution and near-daily global coverage. Using in situ CO2 eddy flux data, we also show that cloud cover rarely affects photosynthesis at TROPOMI's midday overpass, a time when the forest canopy is most often light-saturated. The observed dry-season increases of forest SIF are not strongly affected by sun-sensor geometry, which was attributed as creating a pseudo dry-season green-up in the surface reflectance data. Our results provide strong evidence that greenness, SIF, and photosynthesis of the tropical Amazonian forest increase during the dry season.

Multiple ecological engineering projects have been implemented in semiarid and subhumid Northern China since 1978 with the purpose to combat desertification, control dust storms, and improve vegetation cover. Although a plethora of local studies exist, the effectiveness of these projects has not been studied in a systematic and comprehensive way. Here, we used multiple satellite-based time-series data as well as breakpoint analysis to assess shifts in leaf area index (a proxy for green vegetation cover), gross primary production, and aboveground biomass in Northern China. We documented increased vegetation growth in northwest and southeastern parts of the region, despite drought anomalies as documented by the standardized precipitation-evapotranspiration index during 1982–2016. Significant breakpoints in leaf area index were observed for over 72.5% of the southeastern and northwestern regions, and 70.6% of these breakpoints were detected after 1999, which correspond well to the areas with the highest ecological engineering efforts. Areas with negative trends were mainly located in the Inner Mongolian Plateau, Hulun Biur, Horqin Sand Land, and urban areas. The Loess Plateau had the largest increase in vegetation growth, followed by the north parts of Northern China where biomass increased more in the provinces of Shanxi, Liaoning, Shannxi, Hebei, and Beijing than Xinjiang, Inner Mongolia, Tianjin, and Qinghai. Our results show that multiple ecological engineering projects in the region have increased vegetation cover, production, and aboveground biomass that have led to improved environmental conditions in the study area.

The data, information and knowledge on the tropical forest area and its dynamics in the Brazilian Amazon remain contentious. We use time-series satellite images to quantify annual forest area, loss and gain in the Brazilian Amazon during 2000–2017. We find that forest area was ~15% higher than the estimate by the official Brazilian forest dataset (PRODES), but annual forest-loss rates were twice the PRODES estimates (~0.027 × 106 km2 yr–1 during 2001–2016). Forest-loss rates increased again after 2013. The El Niño and drought year (2015/2016) drove large forest area loss. The cumulative forest-loss area within the protected areas (which include ~50% of forests in the region) was ~11% of the total forest-loss area, which highlights the roles of protected areas in forest conservation.

The article "Waking Before the Snow Melts: Detecting the Spring Transition in Evergreen Needleleaf Forests" published in the Bulletin of the Ecological Society of America discusses the challenges of determining the start and end of the growing season in evergreen needleleaf forests (ENFs) due to limited field measurements and issues with interpreting satellite data. The study used multi‐scale remote sensing and photosynthesis measurements in Colorado, Saskatchewan, and Alaska to develop spectral methods for detecting photosynthesis during transition seasons. The research aims to link plant physiology with remote sensing to better understand the seasonal timing of photosynthesis in ENFs and its implications for the carbon cycle. [Extracted from the article]

Woody plant encroachment (WPE) into grasslands has been occurring globally and may be accelerated by climate change in the future. This land cover change is expected to alter the carbon and water cycles, but it remains uncertain how and to what extent the carbon and water cycles may change with WPE into grasslands under current climate. In this study, we examined the difference of vegetation indices (VIs), evapotranspiration (ET), gross primary production (GPP), and solar-induced chlorophyll fluorescence (SIF) during 2000-2010 between grasslands and juniper-encroached grasslands. We also quantitatively assessed the changes of GPP and ET for grasslands with different proportions of juniper encroachment (JWPE). Our results suggested that JWPE increased the GPP, ET, greenness-related VIs, and SIF of grasslands. Mean annual GPP and ET were, respectively, ~55% and ~45% higher when grasslands were completely converted into juniper forests under contemporary climate during 2000-2010. The enhancement of annual GPP and ET for grasslands with JWPE varied over years ranging from about +20% GPP (~+30% for ET) in the wettest year (2007) to about twice as much GPP (~+55% for ET) in the severe drought year (2006) relative to grasslands without encroachment. Additionally, the differences in GPP and ET showed significant seasonal dynamics. During the peak growing season (May-August), GPP and ET for grasslands with JWPE were ~30% and ~40% higher on average. This analysis provided insights into how and to what degree carbon and water cycles were impacted by JWPE, which is vital to understanding how JWPE and ecological succession will affect the regional and global carbon and water budgets in the future.

Large spatial-scale effects of climate extremes on gross primary production (GPP), the largest terrestrial carbon flux, are highly uncertain even as these extremes increase in frequency and extent. Here we report the impacts of spring warming and summer drought in 2012 on GPP across the contiguous United States (CONUS) using estimates from four GPP models: Vegetation Photosynthesis Model (VPM), MOD17A2H V006, Carnegie-Ames-Stanford Approach, and Simple Biosphere/Carnegie-Ames-Stanford Approach. VPM simulations are driven by Moderate Resolution Imaging Spectroradiometer, North American Regional Reanalysis climate data, and C3 and C4 cropland maps from the United States Department of Agriculture Cropland Data Layer data set. Across 25 eddy covariance flux tower sites, GPP estimates from VPM (GPPVPM) showed better accuracy in terms of cross-site variability and interannual variability (R2 = 0.84 and 0.46, respectively) when compared to MOD17 GPP. We further assessed the spatial and temporal (seasonal) consistency between GPP products and the Global Ozone Monitoring Experiment-2 solar-induced chlorophyll fluorescence over CONUS during 2008–2014. The results suggested that GPPVPM agrees best with solar-induced chlorophyll fluorescence across space and time, capturing seasonal dynamics and interannual variations. Anomaly analyses showed that increased GPP during the spring compensated for the reduced GPP during the summer, resulting in near-neutral changes in annual GPP for the CONUS. This study demonstrates the importance of assessing the impacts of different types and timing of climate extremes on GPP and the need to improve light use efficiency models by incorporating C3 and C4 plant functional types.

As the biggest carbon flux of terrestrial ecosystems from photosynthesis, gross primary productivity (GPP) is an important indicator in understanding the carbon cycle and biogeochemical process of terrestrial ecosystems. Despite advances in remote sensing-based GPP modeling, spatial and temporal variations of GPP are still uncertain especially under extreme climate conditions such as droughts. As the only official products of global spatially explicit GPP, MOD17A2H (GPPMOD) has been widely used to assess the variations of carbon uptake of terrestrial ecosystems. However, systematic assessment of its performance has rarely been conducted especially for the grassland ecosystems where inter-annual variability is high. Based on a collection of GPP datasets (GPPEC) from a global network of eddy covariance towers (FluxNet), we compared GPPMOD and GPPEC at all FluxNet grassland sites with more than five years of observations. We evaluated the performance and robustness of GPPMOD in different grassland biomes (tropical, temperate, and alpine) by using a bootstrapping method for calculating 95% confident intervals (CI) for the linear regression slope, coefficients of determination (R2), and root mean square errors (RMSE). We found that GPPMOD generally underestimated GPP by about 34% across all biomes despite a significant relationship (R2 = 0.66 (CI, 0.63-0.69), RMSE = 2.46 (2.33-2.58) g Cm-2 day-1) for the three grassland biomes. GPPMOD had varied performances with R2 values of 0.72 (0.68-0.75) (temperate), 0.64 (0.59-0.68) (alpine), and 0.40 (0.27-0.52) (tropical). Thus, GPPMOD performed better in low GPP situations (e.g., temperate grassland type), which further indicated that GPPMOD underestimated GPP. The underestimation of GPP could be partly attributed to the biased maximum light use efficiency (εmax) values of different grassland biomes. The uncertainty of the fraction of absorbed photosynthetically active radiation (FPAR) and the water scalar based on the vapor pressure deficit (VPD) could have other reasons for the underestimation. Therefore, more accurate estimates of GPP for different grassland biomes should consider improvements in εmax, FPAR, and the VPD scalar. Our results suggest that the community should be cautious when using MODIS GPP products to examine spatial and temporal variations of carbon fluxes.

The gross primary production (GPP) of vegetation in urban areas plays an important role in the study of urban ecology. It is difficult however, to accurately estimate GPP in urban areas, mostly due to the complexity of impervious land surfaces, buildings, vegetation, and management. Recently, we used the Vegetation Photosynthesis Model (VPM), climate data, and satellite images to estimate the GPP of terrestrial ecosystems including urban areas. Here, we report VPM-based GPP (GPPvpm) estimates for the world's ten most populous megacities during 2000-2014. The seasonal dynamics of GPPvpm during 2007-2014 in the ten megacities track well that of the solar-induced chlorophyll fluorescence (SIF) data from GOME-2 at 0.5° × 0.5° resolution. Annual GPPvpm during 2000-2014 also shows substantial variation among the ten megacities, and year-to-year trends show increases, no change, and decreases. Urban expansion and vegetation collectively impact GPP variations in these megacities. The results of this study demonstrate the potential of a satellite-based vegetation photosynthesis model for diagnostic studies of GPP and the terrestrial carbon cycle in urban areas.

The seasonal timing and magnitude of photosynthesis in evergreen needleleaf forests (ENFs) has major implications for the carbon cycle and is increasingly sensitive to changing climate. Earlier spring photosynthesis can increase carbon uptake over the growing season or cause early water reserve depletion that leads to premature cessation and increased carbon loss. Determining the start and the end of the growing season in ENFs is challenging due to a lack of field measurements and difficulty in interpreting satellite data, which are impacted by snow and cloud cover, and the pervasive "greenness" of these systems. We combine continuous needle‐scale chlorophyll fluorescence measurements with tower‐based remote sensing and gross primary productivity (GPP) estimates at three ENF sites across a latitudinal gradient (Colorado, Saskatchewan, Alaska) to link physiological changes with remote sensing signals during transition seasons. We derive a theoretical framework for observations of solar‐induced chlorophyll fluorescence (SIF) and solar intensity‐normalized SIF (SIFrelative) under snow‐covered conditions, and show decreased sensitivity compared with reflectance data (~20% reduction in measured SIF vs. ~60% reduction in near‐infrared vegetation index [NIRv] under 50% snow cover). Needle‐scale fluorescence and photochemistry strongly correlated (r2 = 0.74 in Colorado, 0.70 in Alaska) and showed good agreement on the timing and magnitude of seasonal transitions. We demonstrate that this can be scaled to the site level with tower‐based estimates of LUEP and SIFrelative which were well correlated across all sites (r2 = 0.70 in Colorado, 0.53 in Saskatchewan, 0.49 in Alaska). These independent, temporally continuous datasets confirm an increase in physiological activity prior to snowmelt across all three evergreen forests. This suggests that data‐driven and process‐based carbon cycle models which assume negligible physiological activity prior to snowmelt are inherently flawed, and underscores the utility of SIF data for tracking phenological events. Our research probes the spectral biology of evergreen forests and highlights spectral methods that can be applied in other ecosystems. [ABSTRACT FROM AUTHOR]

The retrieval of solar induced chlorophyll fluorescence (SIF) from space is a relatively new advance in Earth observation science, having only become feasible within the last decade. Interest in SIF data has grown exponentially, and the retrieval of SIF and the provision of SIF data products has become an important and formal component of spaceborne Earth observation missions. Here, we describe the global Level 2 SIF Lite data products for the Greenhouse Gases Observing Satellite (GOSAT), the Orbiting Carbon Observatory-2 (OCO-2), and OCO-3 platforms, which are provided for each platform in daily netCDF files. We also outline the methods used to retrieve SIF and estimate uncertainty, describe all the data fields, and provide users the background information necessary for the proper use and interpretation of the data, such as considerations of retrieval noise, sun-sensor geometry, the indirect relationship between SIF and photosynthesis, and differences among the three platforms and their respective data products. OCO-2 and OCO-3 have the highest spatial resolution spaceborne SIF retrievals to date, and the target and snapshot area mode observation modes of OCO-2 and OCO-3 are unique. These modes provide hundreds to thousands of SIF retrievals at biologically diverse global target sites during a single overpass, and provide an opportunity to better inform our understanding of canopy-scale vegetation SIF emission across biomes. [ABSTRACT FROM AUTHOR]

At the leaf level, stomata control the exchange of water and carbon across the air-leaf interface. Stomatal conductance is typically modeled empirically, based on environmental conditions at the leaf surface. Recently developed stomatal optimization models show great skills at predicting carbon and water fluxes at both the leaf and tree levels. However, it has not been evaluated how well the optimization models perform at larger scales. Furthermore, stomatal models are often used with simple single-leaf representations of canopy radiative transfer (RT), such as big-leaf models. Nevertheless, the single-leaf canopy RT schemes do not have the capability to model optical properties of the leaves or the entire canopy. As a result, they are unable to evaluate the impact of vertical gradients within the canopy, or directly link canopy optical properties with light distribution within the canopy to remote sensing data observed from afar. Here we incorporated one optimization-based and two empirical stomatal models with a comprehensive RT model in the land component of a new Earth System model within CliMA, the Climate Modelling Alliance. The model allowed us to simultaneously simulate carbon and water fluxes as well as leaf and canopy reflectance and fluorescence spectra. We tested our model by comparing our modeled carbon and water fluxes and solar-induced chlorophyll fluorescence (SIF) to two flux tower observations (a gymnosperm forest and an angiosperm forest) and satellite SIF retrievals, respectively. All three stomatal models quantitatively predicted the carbon and water fluxes for both forests. The optimization model, in particular, showed increased skill in predicting the water flux given the lower error (c. 14.2% and 21.8% improvement for the gymnosperm and angiosperm forests, respectively) and better 1:1 comparison (slope increases from c. 0.34 to 0.91 for the gymnosperm forest, and from c. 0.38 to 0.62 for the angiosperm forest). Our model also predicted the SIF yield, quantitatively reproducing seasonal cycles for both forests. We found that using stomatal optimization with a comprehensive RT model showed high accuracy in simulating land surface processes. The ever-increasing number of regional and global datasets of terrestrial plants, such as leaf area index and chlorophyll contents, will help parameterize the land model and improve future Earth System modeling in general. [ABSTRACT FROM AUTHOR]

The contiguous United States (CONUS), especially the West, faces challenges of increasing water stress and uncertain impacts of climate change. The historical information of surface water body distribution, variation, and multidecadal trends documented in remote-sensing images can aid in water-resource planning and management, yet is not well explored. Here, we detected open-surface water bodies in all Landsat 5, 7, and 8 images (~370,000 images, >200 TB) of the CONUS and generated 30-meter annual water body frequency maps for 1984-2016. We analyzed the interannual variations and trends of year-long water body area, examined the impacts of climatic and anthropogenic drivers on water body area dynamics, and explored the relationships between water body area and land water storage (LWS). Generally, the western half of the United States is prone to water stress, with small water body area and large interannual variability. During 1984-2016, water-poor regions of the Southwest and Northwest had decreasing trends in water body area, while water-rich regions of the Southeast and far north Great Plains had increasing trends. These divergent trends, mainly driven by climate, enlargedwater-resource gaps and are likely to continue according to climate projections. Water body area change is a good indicator of LWS dynamics in 58% of the CONUS. Following the 2012 prolonged drought, LWS in California and the southern Great Plains had a larger decrease than surface water body area, likely caused by massive groundwater withdrawals. Our findings provide valuable information for surface water-resource planning and management across the CONUS. [ABSTRACT FROM AUTHOR]

Accurately monitoring forest dynamics in the tropical regions is essential for ecological studies and forest management. In this study, images from phase-array L-band synthetic aperture radar (PALSAR), PALSAR-2, and Landsat in 2006–2010 and 2015 were combined to identify tropical forest dynamics on Hainan Island, China. Annual forest maps were first mapped from PALSAR and PALSAR-2 images using structural metrics. Those pixels with a high biomass of sugarcane or banana, which are widely distributed in the tropics and subtropics and have similar structural metrics as forests, were excluded from the SAR-based forest maps by using phenological metrics from time series Landsat imagery. The optical–SAR-based forest maps in 2010 and 2015 had high overall accuracies (OA) of 92–97% when validated with ground reference data. The resultant forest map in 2010 shows good spatial agreement with public optical-based forest maps (OA = 88–90%), and the annual forest maps (2007–2010) were spatiotemporally consistent and more accurate than the PALSAR-based forest map from the Japan Aerospace Exploration Agency (OA = 82% in 2010). The areas of forest gain, loss, and net change on Hainan Island from 2007 to 2015 were 415 000 ha (+2.17% yr–1), 179 000 ha (–0.94% yr –1), and 236 000 ha (+1.23% yr–1), respectively. About 95% of forest gain and loss occurred in those areas with an elevation less than 400 m, where deciduous rubber, eucalyptus plantations, and urbanization expanded rapidly. This study demonstrates the potential of PALSAR/PALSAR-2/Landsat image fusion for monitoring annual forest dynamics in the tropical regions. [ABSTRACT FROM PUBLISHER]

Aerosols can affect photosynthesis through radiative perturbations such as scattering and absorbing solar radiation. This biophysical impact has been widely studied using field measurements, but the sign and magnitude at continental scales remain uncertain. Solar-induced fluorescence (SIF), emitted by chlorophyll, strongly correlates with photosynthesis. With recent advancements in Earth observation satellites, we leverage SIF observations from the Tropospheric Monitoring Instrument (TROPOMI) with unprecedented spatial resolution and near-daily global coverage, to investigate the impact of aerosols on photosynthesis. Our analysis reveals that on weekends when there is more plant-available sunlight due to less particulate pollution, 64% of regions across Europe show increased SIF, indicating more photosynthesis. Moreover, we find a widespread negative relationship between SIF and aerosol loading across Europe. This suggests the possible reduction in photosynthesis as aerosol levels increase, particularly in ecosystems limited by light availability. By considering two plausible scenarios of improved air quality-reducing aerosol levels to the weekly minimum 3-d values and levels observed during the COVID-19 period-we estimate a potential of 41 to 50 Mt net additional annual CO 2 uptake by terrestrial ecosystems in Europe. This work assesses human impacts on photosynthesis via aerosol pollution at continental scales using satellite observations. Our results highlight i) the use of spatiotemporal variations in satellite SIF to estimate the human impacts on photosynthesis and ii) the potential of reducing particulate pollution to enhance ecosystem productivity., Competing Interests: Competing interests statement:The authors declare no competing interest.