Publications

Terrestrial ecosystems absorb about 2.8 Gt C yr, which is estimated to be about a quarter of the carbon emitted from fossil fuel combustion. However, the uncertainties of this sink are large, in the order of  40%, with spatial and temporal variations largely unknown. One of the largest factors contributing to the uncertainty is photosynthesis, the process by which plants absorb carbon from the atmosphere. Currently, photosynthesis, or gross ecosystem productivity (GEP), can only be inferred from flux‐towers by measuring the exchange of CO2 in the surrounding air column. Consequently, carbon models suffer from a lack of spatial coverage of accurate GEP observations. Here, we show that photosynthetic light‐ use efficiency (e), hence photosynthesis can be directly inferred from spaceborne measurements of reflectance. We demonstrate that the differential between reflectance measurements in bands associated with the vegetation xanthophyll cycle and estimates of canopy shading obtained from multi‐ angular satellite observations (using the CHRIS/Proba sensor) permits us to infer plant photosynthetic efficiency, independently of vegetation type and structure (r=0.68, compared to flux measurements). This is a significant advance over previous approaches seeking to model global scale photosynthesis indirectly from a combination of growth limiting factors, most notably pressure deficit and temperature. When combined with modeled global scale photosynthesis, satellite inferred e can improve model estimates through data assimilation. We anticipate that our findings will guide the development of new spaceborne approaches to observe vegetation carbon uptake and improve current predictions of global CO2 budgets and future climate scenarios by providing regularly timed calibration points for modeling plant photosynthesis consistently at a global scale.

Surface energy balance is a major determinant of land surface temperature and the Earth's climate. To date, there is no approach that can produce effective, physically consistent, global and multi-decadal energy–water flux data over land. Net radiation (Rn ) can be quantified regionally using satellite retrievals of surface reflectance and thermal emittance with errors <10%. However, consistent, useful retrieval of latent heat flux (λE) from remote sensing is not yet possible. In theory, λE could be inferred as a residual of Rn , ground heat (G) and sensible heat (H) fluxes (Rn–H–G). However, large uncertainties in remote sensing of both H and G result in low accuracies for λE. Where vegetation is the dominant surface cover, λE is largely driven by transpiration of intercellular water through leaf stomata during the photosynthetic uptake of carbon. In these areas, satellite retrievals of photosynthesis (GPP) could be used to quantify transpiration rates through stomatal conductance. Here, we demonstrate how remote sensing of GPP could be applied to obtain λE from passive optical measurements of vegetation leaf reflectance related to the photosynthetic rate independent of knowledge of H, Rn and G. We validate the algorithm using five structurally and physiologically diverse eddy flux sites in western and central Canada. Results show that transpiration and H were accurately predicted from optical data and highly significant relationships were found between the energy budget obtained from eddy flux measurements and remote sensing (0.64≤r2 ≤0.85). We conclude that spaceborne estimates of GPP could significantly improve not only estimates of the carbon balance but also the energy balance over land.

Over the past two decades, remote sensing has made possible the routine global monitoring of surface soil moisture. Regional agricultural drought monitoring is one of the most logical application areas for such monitoring. However, remote sensing alone provides soil moisture information for only the top few centimeters of the soil profile, while agricultural drought monitoring requires knowledge of the amount of water present in the entire root zone. The assimilation of remotely sensed soil moisture products into continuous soil water balance models provides a way of addressing this shortcoming. Here, we describe the assimilation of NASA's soil moisture active passive (SMAP) surface soil moisture data into the United States Department of Agriculture Foreign Agricultural Service (USDA FAS) Palmer model and assess the impact of SMAP on USDA FAS drought monitoring capabilities. The assimilation of SMAP is specifically designed to enhance the model skill and the USDA FAS drought capabilities by correcting for random errors inherent in its rainfall forcing data. The performance of this SMAP-based assimilation system is evaluated using two approaches. At global scale, the accuracy of the system is assessed by examining the lagged correlation agreement between soil moisture and the normalized difference vegetation index (NDVI). Additional regional-scale evaluation using in situ-based soil moisture estimates is carried out at seven of the SMAP core Cal/Val sites located in the USA. Both types of analysis demonstrate the value of assimilating SMAP into the USDA FAS Palmer model and its potential to enhance operational USDA FAS root-zone soil moisture information.

Satellite normalized difference vegetation index (NDVI) observations reveal large and geographically extensive decreases in vegetation activity in Eurasia and North America between 1999 and 2002. In 2001, 73% of central southwest Asia exhibited NDVI anomalies that were more than one standard deviation below 21‐year average conditions, and in 2002, fully 95% of North America exhibited below‐average NDVI. This episode of large‐scale vegetation browning coincided with a prolonged period of below‐normal precipitation in the Northern Hemisphere, which limited moisture availability for plant growth. Spatio‐temporal dynamics of NDVI, precipitation, and sea surface temperature data reveal that synchronous patterns of ocean circulation anomalies in the Pacific, Atlantic, and Indo‐Pacific are strongly correlated with observed joint variability in NDVI and precipitation in the Northern Hemisphere during this period.

Using satellite-derived normalized difference vegetation index (NDVI) data, several previous studies have indicated that vegetation growth significantly increased in most areas of China during the period 1982–99. In this letter, we extended the study period to 2010. We found that at the national scale the growing season (April–October) NDVI significantly increased by 0.0007 yr −1 from 1982 to 2010, but the increasing trend in NDVI over the last decade decreased in comparison to that of the 1982–99 period. The trends in NDVI show significant seasonal and spatial variances. The increasing trend in April and May (AM) NDVI (0.0013 yr −1 ) is larger than those in June, July and August (JJA) (0.0003 yr −1 ) and September and October (SO) (0.0008 yr −1 ). This relatively small increasing trend of JJA NDVI during 1982–2010 compared with that during 1982–99 (0.0012 yr −1 ) (Piao et al 2003 J. Geophys. Res.—Atmos. 108 4401) implies a change in the JJA vegetation growth trend, which significantly turned from increasing (0.0039 yr −1 ) to slightly decreasing ( − 0.0002 yr −1 ) in 1988. Regarding the spatial pattern of changes in NDVI, the growing season NDVI increased (over 0.0020 yr −1 ) from 1982 to 2010 in southern China, while its change was close to zero in northern China, as a result of a significant changing trend reversal that occurred in the 1990s and early 2000s. In northern China, the growing season NDVI significantly increased before the 1990s as a result of warming and enhanced precipitation, but decreased after the 1990s due to drought stress strengthened by warming and reduced precipitation. Our results also show that the responses of vegetation growth to climate change vary across different seasons and ecosystems.

We developed a new 6-year daily, daytime and nighttime, NOAA-14 AVHRR based land surface temperature (LST) dataset over continental Africa for the period 1995 through 2000. The processing chain was developed within the Global Inventory Modeling and Mapping System (GIMMS) at NASA's Goddard Space Flight Center. This paper describes the processing methodology used to convert the Global Area Coverage Level-1b data into LST and collateral data layers, such as sun and view geometries, cloud mask, local time of observation, and latitude and longitude. We used the Ulivieri et al. [Ulivieri, C., M.M. Castronuovo, R. Francioni, and A. Cardillo (1994), A split window algorithm for estimating land surface temperature from satellites, Adv. Space Research, 14(3):59–65.] split window algorithm to determine LST values. This algorithm requires as input values of surface emissivity in AVHRR channels 4 and 5. Thus, we developed continental maps of emissivity using an ensemble approach that combines laboratory emissivity spectra, MODIS-derived maps of herbaceous and woody fractional cover, and the UNESCO FAO soil map. A preliminary evaluation of the resulting LST product over a savanna woodland in South Africa showed a bias of <0.3 K and an uncertainty of <1.3 K for daytime retrievals (<2.5 K for night). More extensive validation is required before statistically significant uncertainties can be determined. The LST production chain described here could be adapted for any wide field of view sensor (e.g., MODIS, VIIRS), and the LST product may be suitable for monitoring spatial and temporal temperature trends, or as input to many process models (e.g., hydrological, ecosystem).

Los enfoques basados en la tierra para mejorar la productividad de las zonas degradadas pueden ayudar a abordar el triple desafio de nuestro tiempo, con lo que aseguran: un clima estable, seguridad alimentaria y espacio para la naturaleza (Baldwin-Cantello, 2018). La restauracion de bosques y paisajes es un conjunto de estrategias de uso de la tierra, como la agrosilvicultura y la regeneracion natural, que se implementan para lograr los objetivos de gestion relacionados con la conservacion del suelo y la biodiversidad, el agua, la retencion del carbono, la produccion de energia y alimentos y las necesidades de la comunidad (FAO y WRI, 2019). La restauracion de bosques y paisajes puede ayudar a mitigar el cambio climatico, mejorar la salud y la conectividad del paisaje y reducir la presion que se ejerce sobre los bosques naturales mediante la produccion de materias primas como madera, dendrocombustibles y productos forestales no madereros.

We use statistical techniques to quantify the effect of interannual variations in vegetation within land covers on surface temperature in North America and Eurasia from satellite measures of surface greenness and ground based meteorological observations. During the winter, reductions in the extent of snow cover cause (in a statistical sense) temperature to rise. During the summer, increases in terrestrial vegetation within land covers cause (in a statistical sense) temperature to fall. Temperature‐induced increases in vegetation have slowed increases in surface temperature, but this feedback may be limited by the range over which temperature has a positive effect on vegetation.

Increases in atmospheric CO2 and CH4 result from a combination of forcing from anthropogenic emissions and Earth System feedbacks that reduce or amplify the effects of those emissions on atmospheric concentrations. Despite decades of research carbon-climate feedbacks remain poorly quantified. The impact of these uncertainties on future climate are of increasing concern, especially in the wake of recent climate negotiations. Emissions, long concentrated in the developed world, are now shifting to developing countries, where the emissions inventories have larger uncertainties. The fraction of anthropogenic CO2 remaining in the atmosphere has remained remarkably constant over the last 50 years. Will this change in the future as the climate evolves? Concentrations of CH4, the 2nd most important greenhouse gas, which had apparently stabilized, have recently resumed their increase, but the exact cause for this is unknown. While greenhouse gases affect the global atmosphere, their sources and sinks are remarkably heterogeneous in time and space, and traditional in situ observing systems do not provide the coverage and resolution to attribute the changes to these greenhouse gases to specific sources or sinks. In the past few years, space-based technologies have shown promise for monitoring carbon stocks and fluxes. Advanced versions of these capabilities could transform our understanding and provide the data needed to quantify carbon-climate feedbacks. A new observing system that allows resolving global high resolution fluxes will capture variations on time and space scales that allow the attribution of these fluxes to underlying mechanisms.

Transpiration, a major component of total evaporation from vegetated surfaces, is an unavoidable consequence of photosynthetic carbon fixation. Because of limiting soil moisture and competition for solar radiation plants invest most of their fixed carbon into structural and hydraulic functions (roots and stems) and solar radiation absorption (leaves). These investments permit individuals to overshadow competitors and provide for transport of water from the soil to the leaves where photosynthesis and transpiration occur. Often low soil moisture or high evaporative demand limit the supply of water to leaves reducing photosynthesis and thus transpiration. The absorption of solar radiation for photosynthesis and dissipation of this energy via radiation, heat, mass and momentum fluxes represents the link between photosynthesis and climate. Recognition of these relationships has led to the development of hydro/energy balance models that are based on the physiological ecology of photosynthesis. We discuss an approach to study vegetation-climate interactions using photosynthesis-centric models embedded in a GCM. The rate at which a vegetated area transpires and photosynthesizes is determined by the physiological state of the vegetation, its amount and its type. The latter two are specified from global satellite data collected since 1982. Climate simulations have been carried out to study how this simulated climate system responds to changes in radiative forcing, physiological capacity, atmospheric CO2, vegetation type and variable vegetation cover observed from satellites during the 1980's. Results from these studies reveal significant feedbacks between the vegetation activity and climate. For example, vegetation cover and physiological activity increases cause the total latent heat flux and precipitation to increase while mean and maximum air temperatures decrease. The reverse occurs if cover or activity'decreases. In general climate response of a particular region was dominated by local processes but we also find evidence that plausible climate-vegetation scenarios lead to changes in global atmospheric circulation and strong non-local influences in some cases.

Using Landsat images acquired since early 1970s, we have mapped the forest cover and change between 1989 and 2000, and estimated forest area in 1973 in Paraguay's Atlantic Forest Ecoregion (PAFE). The results revealed that as of 1973, 73.4% of the PAFE region was covered by forest. Since then, the proportion of forested area was quickly reduced to 40.7% by 1989 and further down to 24.9% by 2000. Two competing deforestation processes contributed to this rapid forest loss, with the first being driven by settlers and the second by large private land owners. During the 1989–2000 period, 80% of deforested areas were cleared by private land owners and 20% by the settlers. Protected areas slowed down forest loss within their boundaries, but not in their surrounding areas. The average percent forest loss in the area within 5 km from the boundary of Paraguay's major forested protected areas was 39% during the 1989–2000 period, which was essentially the same as that for the entire PAFE region during the same period. The high rates of forest loss in the areas surrounding the protected areas not only left the protected areas highly isolated as ecological “islands”, they may also be precursors to rapid forest loss within the protected areas. These protected areas are critical to the conservation of many species endemic or limited to the PAFE region and surrounding areas, and should be continuously monitored using recent and future satellite observations.