The Race To Understand A Changing Planet

The Earth's climate is changing rapidly. In some respects, the rate of change is outpacing the predictions of only a few years ago. The challenge to Earth Science is to put forward credible projections of possible future climates so that the public and policy makers can make science-based decisions about energy development strategies. Models, observations and experiments all play strong roles in improving knowledge and increasing confidence in our predictions. The models have progressed from simple, coarse-resolution descriptions of atmospheric dynamics and physics only twenty years ago, to full-up Earth System models (ESMs) that include complete descriptions of the oceans and cryosphere. It has been convincingly argued that such complexity - the construction of realistic "toy" Earth's - is necessary to address the complex processes involved in climate change, including not only the physical atmosphere, oceans and cryosphere, but also the carbon cycle - both its natural and anthropogenic components - and the biosphere. Observations, particularly satellite observations, have more or less kept pace with the demands of the modelers, being able to observe progressively more and different facets of the Earth system, but the global satellite fleet is in need of an overhaul very soon. Lastly, field experiments and process studies confront the models with facts and allow us to develop more sophisticated and accurate satellite data algorithms. The challenges facing our relatively small Earth and planetary science communities are considerable and the stakes are significant. The stakeholders, now numbering 7 billion but soon to be 10 billion, will be relying on our results and capabilitie's to guide them into the future

A Vision for the Exploration of Mars: Robotic Precursors Followed by Humans to Mars Orbit in 2033

The reformulation of the Mars program gives NASA a rare opportunity to deliver a credible vision in which humans, robots, and advancements in information technology combine to open the deep space frontier to Mars. There is a broad challenge in the reformulation of the Mars exploration program that truly sets the stage for: 'a strategic collaboration between the Science Mission Directorate (SMD), the Human Exploration and Operations Mission Directorate (HEOMD) and the Office of the Chief Technologist, for the next several decades of exploring Mars'.Any strategy that links all three challenge areas listed into a true long term strategic program necessitates discussion. NASA's SMD and HEOMD should accept the President's challenge and vision by developing an integrated program that will enable a human expedition to Mars orbit in 2033 with the goal of returning samples suitable for addressing the question of whether life exists or ever existed on Mar

Remote Sensing of Tropical Ecosystems: Atmospheric Correction and Cloud Masking Matter

Tropical rainforests are significant contributors to the global cycles of energy, water and carbon. As a result, monitoring of the vegetation status over regions such as Amazonia has been a long standing interest of Earth scientists trying to determine the effect of climate change and anthropogenic disturbance on the tropical ecosystems and its feedback on the Earth's climate. Satellite-based remote sensing is the only practical approach for observing the vegetation dynamics of regions like the Amazon over useful spatial and temporal scales, but recent years have seen much controversy over satellite-derived vegetation states in Amaznia, with studies predicting opposite feedbacks depending on data processing technique and interpretation. Recent results suggest that some of this uncertainty could stem from a lack of quality in atmospheric correction and cloud screening. In this paper, we assess these uncertainties by comparing the current standard surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4 and MYD13A2 - Vegetation Index) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to results obtained from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. MAIAC uses a new cloud screening technique, and novel aerosol retrieval and atmospheric correction procedures which are based on time-series and spatial analyses. Our results show considerable improvements of MAIAC processed surface reflectance compared to MYD09/MYD13 with noise levels reduced by a factor of up to 10. Uncertainties in the current MODIS surface reflectance product were mainly due to residual cloud and aerosol contamination which affected the Normalized Difference Vegetation Index (NDVI): During the wet season, with cloud cover ranging between 90 percent and 99 percent, conventionally processed NDVI was significantly depressed due to undetected clouds. A smaller reduction in NDVI due to increased aerosol levels was observed during the dry season, with an inverse dependence of NDVI on aerosol optical thickness (AOT). NDVI observations processed with MAIAC showed highly reproducible and stable inter-annual patterns with little or no dependence on cloud cover, and no significant dependence on AOT (p less than 0.05). In addition to a better detection of cloudy pixels, MAIAC obtained about 20-80 percent more cloud free pixels, depending on season, a considerable amount for land analysis given the very high cloud cover (75-99 percent) observed at any given time in the area. We conclude that a new generation of atmospheric correction algorithms, such as MAIAC, can help to dramatically improve vegetation estimates over tropical rain forest, ultimately leading to reduced uncertainties in satellite-derived vegetation products globally

GCM Studies on the Interactions Between Photosynthesis and Climate at Diurnal to Decadal Time Scales

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

Remote sensing of tropical ecosystems: Atmospheric correction and cloud masking matter

Tropical rainforests are significant contributors to the global cycles of energy, water and carbon. As a result, monitoring of the vegetation status over regions such as Amazônia has been a long standing interest of Earth scientists trying to determine the effect of climate change and anthropogenic disturbance on the tropical ecosystems and its feedback on the Earth's climate. Satellite-based remote sensing is the only practical approach for observing the vegetation dynamics of regions like the Amazon over useful spatial and temporal scales, but recent years have seen much controversy over satellite-derived vegetation states in Amazônia, with studies predicting opposite feedbacks depending on data processing technique and interpretation. Recent results suggest that some of this uncertainty could stem from a lack of quality in atmospheric correction and cloud screening. In this paper, we assess these uncertainties by comparing the current standard surface reflectance products (MYD09, MYD09GA) and derived composites (MYD09A1, MCD43A4 and MYD13A2 — Vegetation Index) from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite to results obtained from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. MAIAC uses a new cloud screening technique, and novel aerosol retrieval and atmospheric correction procedures which are based on time-series and spatial analyses. Our results show considerable improvements of MAIAC processed surface reflectance compared to MYD09/MYD13 with noise levels reduced by a factor of up to 10. Uncertainties in the current MODIS surface reflectance product were mainly due to residual cloud and aerosol contamination which affected the Normalized Difference Vegetation Index (NDVI): During the wet season, with cloud cover ranging between 90% and 99%, conventionally processed NDVI was significantly depressed due to undetected clouds. A smaller reduction in NDVI due to increased aerosol levels was observed during the dry season, with an inverse dependence of NDVI on aerosol optical thickness (AOT). NDVI observations processed with MAIAC showed highly reproducible and stable inter-annual patterns with little or no dependence on cloud cover, and no significant dependence on AOT (p < 0.05). In addition to a better detection of cloudy pixels, MAIAC obtained about 20–80% more cloud free pixels, depending on season, a considerable amount for land analysis given the very high cloud cover (75–99%) observed at any given time in the area. We conclude that a new generation of atmospheric correction algorithms, such as MAIAC, can help to dramatically improve vegetation estimates over tropical rain forest, ultimately leading to reduced uncertainties in satellite-derived vegetation products globally.Keywords: MODIS, Time series, Amazon, Atmospheric correction, Land surface product validation, Cloud screening, NDV

A review of the US Global Change Research Program and NASA's Mission to Planet Earth/Earth Observing System

This report reflects the results of a ten-day workshop convened at the Scripps Institution of Oceanography July 19-28, 1995. The workshop was convened as the first phase of a two part review of the U.S. Global Change Research Program (USGCRP). The workshop was organized to provide a review of the scientific foundations and progress to date in the USGCRP and an assessment of the implications of new scientific insights for future USGCRP and Mission to Planet Earth/Earth Observing System (MTPE/EOS) activities; a review of the role of NASA's MTPE/EOS program in the USGCRP observational strategy; a review of the EOS Data and Information System (EOSDIS) as a component of USGCRP data management activities; and an assessment of whether recent developments in the following areas lead to a need to readjust MTPE/EOS plans. Specific consideration was given to: proposed convergence of U.S. environmental satellite systems and programs, evolving international plans for Earth observation systems, advances in technology, and potential expansion of the role of the private sector. The present report summarizes the findings and recommendations developed by the Committee on Global Change Research on the basis of the presentations, background materials, working group deliberations, and plenary discussions of the workshop. In addition, the appendices include summaries prepared by the six working groups convened in the course of the workshop

Multi-angle implementation of atmospheric correction for MODIS (MAIAC): 3. Atmospheric correction

This paper describes the atmospheric correction (AC) component of the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) which introduces a new way to compute parameters of the Ross-Thick Li-Sparse (RTLS) Bi-directional reflectance distribution function (BRDF), spectral surface albedo and bidirectional reflectance factors (BRF) from satellite measurements obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS). MAIAC uses a time series and spatial analysis for cloud detection, aerosol retrievals and atmospheric correction. It implements a moving window of up to 16 days of MODIS data gridded to 1 km resolution in a selected projection. The RTLS parameters are computed directly by fitting the cloud-free MODIS top of atmosphere (TOA) reflectance data stored in the processing queue. The RTLS retrieval is applied when the land surface is stable or changes slowly. In case of rapid or large magnitude change (as for instance caused by disturbance), MAIAC follows the MODIS operational BRDF/albedo algorithm and uses a scaling approach where the BRDF shape is assumed stable but its magnitude is adjusted based on the latest single measurement. To assess the stability of the surface, MAIAC features a change detection algorithm which analyzes relative change of reflectance in the Red and NIR bands during the accumulation period. To adjust for the reflectance variability with the sun-observer geometry and allow comparison among different days (view geometries), the BRFs are normalized to the fixed view geometry using the RTLS model. An empirical analysis of MODIS data suggests that the RTLS inversion remains robust when the relative change of geometry-normalized reflectance stays below 15%. This first of two papers introduces the algorithm, a second, companion paper illustrates its potential by analyzing MODIS data over a tropical rainforest and assessing errors and uncertainties of MAIAC compared to conventional MODIS products.Keywords: MAIAC, Atmospheric correction, Time series, Multi-angle implementation of atmospheric correction, MODIS, Aerosols, Surface reflectanc

Remote sensing of transpiration and heat fluxes using multi-angle observations

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 (R[subscript n]) can be quantified regionally using satellite retrievals of surface reflectance and thermal emittance with errors b10%. 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 R[subscript n], ground heat (G) and sensible heat (H) fluxes (R[subscript n]–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, R[subscript n] 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.This is the publisher’s final pdf. The published article is copyrighted by Elsevier and can be found at: http://www.elsevier.com/Keywords: Multi-angle remote sensing AMSPEC, Ball–Berry relationship, Transpiration, GPP, Stomatal conductanc

Vegetation dynamics and rainfall sensitivity of the Amazon

We show that the vegetation canopy of the Amazon rainforest is highly sensitive to changes in precipitation patterns and that reduction in rainfall since 2000 has diminished vegetation greenness across large parts of Amazonia. Large-scale directional declines in vegetation greenness may indicate decreases in carbon uptake and substantial changes in the energy balance of the Amazon. We use improved estimates of surface reflectance from satellite data to show a close link between reductions in annual precipitation, El Niño southern oscillation events, and photosynthetic activity across tropical and subtropical Amazonia. We report that, since the year 2000, precipitation has declined across 69% of the tropical evergreen forest (5.4 million km²) and across 80% of the subtropical grasslands (3.3 million km²). These reductions, which coincided with a decline in terrestrial water storage, account for about 55% of a satellite-observed widespread decline in the normalized difference vegetation index (NDVI). During El Niño events, NDVI was reduced about 16.6% across an area of up to 1.6 million km² compared with average conditions. Several global circulation models suggest that a rise in equatorial sea surface temperature and related displacement of the intertropical convergence zone could lead to considerable drying of tropical forests in the 21st century. Our results provide evidence that persistent drying could degrade Amazonian forest canopies, which would have cascading effects on global carbon and climate dynamics