Structural complexity biases vegetation greenness measures.

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.

Capturing patterns of evolutionary relatedness with reflectance spectra to model and monitor biodiversity.

Biogeographic history can set initial conditions for vegetation community assemblages that determine their climate responses at broad extents that land surface models attempt to forecast. Numerous studies have indicated that evolutionarily conserved biochemical, structural, and other functional attributes of plant species are captured in visible-to-short wavelength infrared, 400 to 2,500 nm, reflectance properties of vegetation. Here, we present a remotely sensed phylogenetic clustering and an evolutionary framework to accommodate spectra, distributions, and traits.

Greening of human-dominated ecosystems in India.

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.

NASA Global Daily Downscaled Projections, CMIP6.

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.

A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing.

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.

Temporal Interpolation of Geostationary Satellite Imagery With Optical Flow.

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.

New generation geostationary satellite observations support seasonality in greenness of the Amazon evergreen forests.

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.

Biophysical impacts of Earth greening largely controlled by aerodynamic resistance.

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.

Changes in timing of seasonal peak photosynthetic activity in northern ecosystems.

Seasonality in photosynthetic activity is a critical component of seasonal carbon, water, and energy cycles in the Earth system. This characteristic is a consequence of plant's adaptive evolutionary processes to a given set of environmental conditions. Changing climate in northern lands (>30°N) alters the state of climatic constraints on plant growth, and therefore, changes in the seasonality and carbon accumulation are anticipated.

Deep neural networks for texture classification-A theoretical analysis.

We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks.

Deploying a quantum annealing processor to detect tree cover in aerial imagery of California.

Quantum annealing is an experimental and potentially breakthrough computational technology for handling hard optimization problems, including problems of computer vision. We present a case study in training a production-scale classifier of tree cover in remote sensing imagery, using early-generation quantum annealing hardware built by D-wave Systems, Inc. Beginning within a known boosting framework, we train decision stumps on texture features and vegetation indices extracted from four-band, one-meter-resolution aerial imagery from the state of California.

Vegetation Greening and Climate Change Promote Multidecadal Rises of Global Land Evapotranspiration.

Recent studies showed that anomalous dry conditions and limited moisture supply roughly between 1998 and 2008, especially in the Southern Hemisphere, led to reduced vegetation productivity and ceased growth in land evapotranspiration (ET). However, natural variability of Earth's climate system can degrade capabilities for identifying climate trends. Here we produced a long-term (1982-2013) remote sensing based land ET record and investigated multidecadal changes in global ET and underlying causes.

A high-resolution approach to estimating ecosystem respiration at continental scales using operational satellite data.

A better understanding of the local variability in land-atmosphere carbon fluxes is crucial to improving the accuracy of global carbon budgets. Operational satellite data backed by ground measurements at Fluxnet sites proved valuable in monitoring local variability of gross primary production at highly resolved spatio-temporal resolutions. Yet, we lack similar operational estimates of ecosystem respiration (Re) to calculate net carbon fluxes.

Variations in atmospheric CO2 growth rates coupled with tropical temperature.

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.

A large proportion of North American net ecosystem production is offset by emissions from harvested products, river/stream evasion, and biomass burning.

Diagnostic carbon cycle models produce estimates of net ecosystem production (NEP, the balance of net primary production and heterotrophic respiration) by integrating information from (i) satellite-based observations of land surface vegetation characteristics; (ii) distributed meteorological data; and (iii) eddy covariance flux tower observations of net ecosystem exchange (NEE) (used in model parameterization). However, a full bottom-up accounting of NEE (the vertical carbon flux) that is suitable for integration with atmosphere-based inversion modeling also includes emissions from decomposition/respiration of harvested forest and agricultural products, CO2 evasion from streams and rivers, and biomass burning. Here, we produce a daily time step NEE for North America for the year 2004 that includes NEP as well as the additional emissions.

Persistent effects of a severe drought on Amazonian forest canopy.

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.

Importance of carbon dioxide physiological forcing to future climate change.

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.

Global Distribution and Density of Constructed Impervious Surfaces.

We present the first global inventory of the spatial distribution and density ofconstructed impervious surface area (ISA). Examples of ISA include roads, parking lots,buildings, driveways, sidewalks and other manmade surfaces. While high spatialresolution is required to observe these features, the new product reports the estimateddensity of ISA on a one-km² grid based on two coarse resolution indicators of ISA - thebrightness of satellite observed nighttime lights and population count.

Large seasonal swings in leaf area of Amazon rainforests.

Despite early speculation to the contrary, all tropical forests studied to date display seasonal variations in the presence of new leaves, flowers, and fruits. Past studies were focused on the timing of phenological events and their cues but not on the accompanying changes in leaf area that regulate vegetation-atmosphere exchanges of energy, momentum, and mass. Here we report, from analysis of 5 years of recent satellite data, seasonal swings in green leaf area of approximately 25% in a majority of the Amazon rainforests.

Mapping and modeling the biogeochemical cycling of turf grasses in the United States.

Turf grasses are ubiquitous in the urban landscape of the United States and are often associated with various types of environmental impacts, especially on water resources, yet there have been limited efforts to quantify their total surface and ecosystem functioning, such as their total impact on the continental water budget and potential net ecosystem exchange (NEE). In this study, relating turf grass area to an estimate of fractional impervious surface area, it was calculated that potentially 163,800 km2 (+/- 35,850 km2) of land are cultivated with turf grasses in the continental United States, an area three times larger than that of any irrigated crop. Using the Biome-BGC ecosystem process model, the growth of warm-season and cool-season turf grasses was modeled at a number of sites across the 48 conterminous states under different management scenarios, simulating potential carbon and water fluxes as if the entire turf surface was to be managed like a well-maintained lawn.

Enhancement of understory productivity by asynchronous phenology with overstory competitors in a temperate deciduous forest.

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%.

Climate-driven increases in global terrestrial net primary production from 1982 to 1999.

Recent climatic changes have enhanced plant growth in northern mid-latitudes and high latitudes. However, a comprehensive analysis of the impact of global climatic changes on vegetation productivity has not before been expressed in the context of variable limiting factors to plant growth. We present a global investigation of vegetation responses to climatic changes by analyzing 18 years (1982 to 1999) of both climatic data and satellite observations of vegetation activity.