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Vegetation 'greenness' characterized by spectral vegetation indices (VIs) is an integrative measure of vegetation leaf abundance, biochemical properties and pigment composition. Surprisingly, satellite observations reveal that several major VIs over the US Corn Belt are higher than those over the Amazon rainforest, despite the forests having a greater leaf area. This contradicting pattern underscores the pressing need to understand the underlying drivers and their impacts to prevent misinterpretations. Here we show that macroscale shadows cast by complex forest structures result in lower greenness measures compared with those cast by structurally simple and homogeneous crops. The shadow-induced contradictory pattern of VIs is inevitable because most Earth-observing satellites do not view the Earth in the solar direction and thus view shadows due to the sun-sensor geometry. The shadow impacts have important implications for the interpretation of VIs and solar-induced chlorophyll fluorescence as measures of global vegetation changes. For instance, a land-conversion process from forests to crops over the Amazon shows notable increases in VIs despite a decrease in leaf area. Our findings highlight the importance of considering shadow impacts to accurately interpret remotely sensed VIs and solar-induced chlorophyll fluorescence for assessing global vegetation and its changes., (© 2023. The Author(s), under exclusive licence to Springer Nature Limited.)

Numerical models based on physics represent the state of the art in Earth system modeling and comprise our best tools for generating insights and predictions. Despite rapid growth in computational power, the perceived need for higher model resolutions overwhelms the latest generation computers, reducing the ability of modelers to generate simulations for understanding parameter sensitivities and characterizing variability and uncertainty. Thus, surrogate models are often developed to capture the essential attributes of the full-blown numerical models. Recent successes of machine learning methods, especially deep learning (DL), across many disciplines offer the possibility that complex nonlinear connectionist representations may be able to capture the underlying complex structures and nonlinear processes in Earth systems. A difficult test for DL-based emulation, which refers to function approximation of numerical models, is to understand whether they can be comparable to traditional forms of surrogate models in terms of computational efficiency while simultaneously reproducing model results in a credible manner. A DL emulation that passes this test may be expected to perform even better than simple models with respect to capturing complex processes and spatiotemporal dependencies. Here, we examine, with a case study in satellite-based remote sensing, the hypothesis that DL approaches can credibly represent the simulations from a surrogate model with comparable computational efficiency. Our results are encouraging in that the DL emulation reproduces the results with acceptable accuracy and often even faster performance. We discuss the broader implications of our results in light of the pace of improvements in high-performance implementations of DL and the growing desire for higher resolution simulations in the Earth sciences.

Applications of satellite data in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on the tradeoffs to spatial, spectral, and temporal resolutions of observations. In weather tracking, high-frequency temporal observations are critical and used to improve forecasts, study severe events, and extract atmospheric motion, among others. However, while the current generation of geostationary (GEO) satellites has hemispheric coverage at 10-15-min intervals, higher temporal frequency observations are ideal for studying mesoscale severe weather events. In this work, we present a novel application of deep learning-based optical flow to temporal upsampling of GEO satellite imagery. We apply this technique to 16 bands of the GOES-R/Advanced Baseline Imager mesoscale dataset to temporally enhance full-disk hemispheric snapshots of different spatial resolutions from 10 to 1 min. Experiments show the effectiveness of task-specific optical flow and multiscale blocks for interpolating high-frequency severe weather events relative to bilinear and global optical flow baselines. Finally, we demonstrate strong performance in capturing variability during convective precipitation events.

Satellite data show the Earth has been greening and identify croplands in India as one of the most prominent greening hotspots. Though India's agriculture has been dependent on irrigation enhancement to reduce crop water stress and increase production, the spatiotemporal dynamics of how irrigation influenced the satellite observed greenness remains unclear. Here, we use satellite-derived leaf area data and survey-based agricultural statistics together with results from state-of-the-art Land Surface Models (LSM) to investigate the role of irrigation in the greening of India's croplands. We find that satellite observations provide multiple lines of evidence showing strong contributions of irrigation to significant greening during dry season and in drier environments. The national statistics support irrigation-driven yield enhancement and increased dry season cropping intensity. These suggest a continuous shift in India's agriculture toward an irrigation-driven dry season cropping system and confirm the importance of land management in the greening phenomenon. However, the LSMs identify CO 2 fertilization as a primary driver of greening whereas land use and management have marginal impacts on the simulated leaf area changes. This finding urges a closer collaboration of the modeling, Earth observation, and land system science communities to improve representation of land management in the Earth system modeling., Competing Interests: Competing interestsThe authors declare no competing interests., (© This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023.)

We describe the latest version of the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6). The archive contains downscaled historical and future projections for 1950-2100 based on output from Phase 6 of the Climate Model Intercomparison Project (CMIP6). The downscaled products were produced using a daily variant of the monthly bias correction/spatial disaggregation (BCSD) method and are at 1/4-degree horizontal resolution. Currently, eight variables from five CMIP6 experiments (historical, SSP126, SSP245, SSP370, and SSP585) are provided as procurable from thirty-five global climate models., (© 2022. The Author(s).)

Assessing the seasonal patterns of the Amazon rainforests has been difficult because of the paucity of ground observations and persistent cloud cover over these forests obscuring optical remote sensing observations. Here, we use data from a new generation of geostationary satellites that carry the Advanced Baseline Imager (ABI) to study the Amazon canopy. ABI is similar to the widely used polar orbiting sensor, the Moderate Resolution Imaging Spectroradiometer (MODIS), but provides observations every 10-15 min. Our analysis of NDVI data collected over the Amazon during 2018-19 shows that ABI provides 21-35 times more cloud-free observations in a month than MODIS. The analyses show statistically significant changes in seasonality over 85% of Amazon forest pixels, an area about three times greater than previously reported using MODIS data. Though additional work is needed in converting the observed changes in seasonality into meaningful changes in canopy dynamics, our results highlight the potential of the new generation geostationary satellites to help us better understand tropical ecosystems, which has been a challenge with only polar orbiting satellites.

Satellite observations show widespread increasing trends of leaf area index (LAI), known as the Earth greening. However, the biophysical impacts of this greening on land surface temperature (LST) remain unclear. Here, we quantify the biophysical impacts of Earth greening on LST from 2000 to 2014 and disentangle the contributions of different factors using a physically based attribution model. We find that 93% of the global vegetated area shows negative sensitivity of LST to LAI increase at the annual scale, especially for semiarid woody vegetation. Further considering the LAI trends ( P ≤ 0.1), 30% of the global vegetated area is cooled by these trends and 5% is warmed. Aerodynamic resistance is the dominant factor in controlling Earth greening's biophysical impacts: The increase in LAI produces a decrease in aerodynamic resistance, thereby favoring increased turbulent heat transfer between the land and the atmosphere, especially latent heat flux., (Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

The growing volume of Earth science data available from climate simulations and satellite remote sensing offers unprecedented opportunity for scientific insight, while also presenting computational challenges. One potential area of impact is atmospheric correction, where physics-based numerical models retrieve surface reflectance information from top of atmosphere observations, and are too computationally intensive to be run in real time. Machine learning methods have demonstrated potential as fast statistical models for expensive simulations and for extracting credible insights from complex datasets. Here, we develop DeepEmSat: a deep learning emulator approach for atmospheric correction, and offer comparison against physics-based models to support the hypothesis that deep learning can make a contribution to the efficient processing of satellite images., (Copyright © 2019 Duffy, Vandal, Li, Ganguly, Nemani and Ganguly.)

Satellite data show increasing leaf area of vegetation due to direct (human land-use management) and indirect factors (climate change, CO 2 fertilization, nitrogen deposition, recovery from natural disturbances, etc.). Among these, climate change and CO 2 fertilization effect seem to be the dominant drivers. However, recent satellite data (2000-2017) reveal a greening pattern that is strikingly prominent in China and India, and overlapping with croplands world-wide. China alone accounts for 25% of the global net increase in leaf area with only 6.6% of global vegetated area. The greening in China is from forests (42%) and croplands (32%), but in India is mostly from croplands (82%) with minor contribution from forests (4.4%). China is engineering ambitious programs to conserve and expand forests with the goal of mitigating land degradation, air pollution and climate change. Food production in China and India has increased by over 35% since 2000 mostly due to increasing harvested area through multiple cropping facilitated by fertilizer use and surface/ground-water irrigation. Our results indicate that the direct factor is a key driver of the "Greening Earth", accounting for over a third, and likely more, of the observed net increase in green leaf area. They highlight the need for realistic representation of human land-use practices in Earth system models., Competing Interests: Competing interests The authors declare no competing financial interests.

Previous studies have highlighted the occurrence and intensity of El Niño-Southern Oscillation as important drivers of the interannual variability of the atmospheric CO2 growth rate, but the underlying biogeophysical mechanisms governing such connections remain unclear. Here we show a strong and persistent coupling (r(2) ≈ 0.50) between interannual variations of the CO2 growth rate and tropical land-surface air temperature during 1959 to 2011, with a 1 °C tropical temperature anomaly leading to a 3.5 ± 0.6 Petagrams of carbon per year (PgC/y) CO2 growth-rate anomaly on average. Analysis of simulation results from Dynamic Global Vegetation Models suggests that this temperature-CO2 coupling is contributed mainly by the additive responses of heterotrophic respiration (Rh) and net primary production (NPP) to temperature variations in tropical ecosystems. However, we find a weaker and less consistent (r(2) ≈ 0.25) interannual coupling between CO2 growth rate and tropical land precipitation than diagnosed from the Dynamic Global Vegetation Models, likely resulting from the subtractive responses of tropical Rh and NPP to precipitation anomalies that partly offset each other in the net ecosystem exchange (i.e., net ecosystem exchange ≈ Rh - NPP). Variations in other climate variables (e.g., large-scale cloudiness) and natural disturbances (e.g., volcanic eruptions) may induce transient reductions in the temperature-CO2 coupling, but the relationship is robust during the past 50 y and shows full recovery within a few years after any such major variability event. Therefore, it provides an important diagnostic tool for improved understanding of the contemporary and future global carbon cycle.

Recent Amazonian droughts have drawn attention to the vulnerability of tropical forests to climate perturbations. Satellite and in situ observations have shown an increase in fire occurrence during drought years and tree mortality following severe droughts, but to date there has been no assessment of long-term impacts of these droughts across landscapes in Amazonia. Here, we use satellite microwave observations of rainfall and canopy backscatter to show that more than 70 million hectares of forest in western Amazonia experienced a strong water deficit during the dry season of 2005 and a closely corresponding decline in canopy structure and moisture. Remarkably, and despite the gradual recovery in total rainfall in subsequent years, the decrease in canopy backscatter persisted until the next major drought, in 2010. The decline in backscatter is attributed to changes in structure and water content associated with the forest upper canopy. The persistence of low backscatter supports the slow recovery (>4 y) of forest canopy structure after the severe drought in 2005. The result suggests that the occurrence of droughts in Amazonia at 5-10 y frequency may lead to persistent alteration of the forest canopy.

An increase in atmospheric carbon dioxide (CO(2)) concentration influences climate both directly through its radiative effect (i.e., trapping longwave radiation) and indirectly through its physiological effect (i.e., reducing transpiration of land plants). Here we compare the climate response to radiative and physiological effects of increased CO(2) using the National Center for Atmospheric Research (NCAR) coupled Community Land and Community Atmosphere Model. In response to a doubling of CO(2), the radiative effect of CO(2) causes mean surface air temperature over land to increase by 2.86 +/- 0.02 K (+/- 1 standard error), whereas the physiological effects of CO(2) on land plants alone causes air temperature over land to increase by 0.42 +/- 0.02 K. Combined, these two effects cause a land surface warming of 3.33 +/- 0.03 K. The radiative effect of doubling CO(2) increases global runoff by 5.2 +/- 0.6%, primarily by increasing precipitation over the continents. The physiological effect increases runoff by 8.4 +/- 0.6%, primarily by diminishing evapotranspiration from the continents. Combined, these two effects cause a 14.9 +/- 0.7% increase in runoff. Relative humidity remains roughly constant in response to CO(2)-radiative forcing, whereas relative humidity over land decreases in response to CO(2)-physiological forcing as a result of reduced plant transpiration. Our study points to an emerging consensus that the physiological effects of increasing atmospheric CO(2) on land plants will increase global warming beyond that caused by the radiative effects of CO(2).

Some saplings and shrubs growing in the understory of temperate deciduous forests extend their periods of leaf display beyond that of the overstory, resulting in periods when understory radiation, and hence productivity, are not limited by the overstory canopy. To assess the importance of the duration of leaf display on the productivity of understory and overstory trees of deciduous forests in the north eastern United States, we applied the simulation model, BIOME-BGC with climate data for Hubbard Brook Experimental Forest, New Hampshire, USA and mean ecophysiological data for species of deciduous, temperate forests. Extension of the overstory leaf display period increased overstory leaf area index (LAI) by only 3 to 4% and productivity by only 2 to 4%. In contrast, extending the growing season of the understory relative to the overstory by one week in both spring and fall, increased understory LAI by 35% and productivity by 32%. A 2-week extension of the growing period in both spring and fall increased understory LAI by 53% and productivity by 55%.