1,721,170 research outputs found
Data assimilation of photosynthetic light-use efficiency using multi-angular satellite data: I. Model formulation
No full textForest photosynthetic exchange rates at landscape scales have proven difficult to either accurately measure or estimate. Recent developments (Hall et al., 2011, 2008; Hilker et al., 2011a, 2010a) permit us to infer photosynthetic forest light use efficiency () using multi-angle measurements of photochemical reflectance index (PRI) from the CHRIS/PROBA satellite imaging spectrometer, thus completing a long sought-after capability to remotely sense the major inputs driving gross primary production GPP i.e., and absorbed photosynthetically active radiation (APAR). In this first of two companion papers we introduce the theoretical underpinnings of an innovative approach that utilizes our recent developments to produce remotely sensed and spatially explicit maps of and GPP from space, and a data assimilation approach to extend the spatially explicit maps to diurnal, daily and annual time scales. We quantify GPP using the traditional radiation-limited approach of Monteith (1972); however we apply it in an innovative way. [I] Using CHRIS/PROBA we quantify at each satellite overpass for a 625km2 area at 30m resolution. [II] We use a novel physiologically-based multivariate function of APAR, temperature and water vapor pressure deficit model (described herein) and use it to down-regulate at 30 minute intervals. [III] We use the CHRIS/PROBA images of spatial variation in , and NDVI to quantify APAR, hence produce snapshots of GPP. We use a data assimilation approach to extend and GPP to temporally continuous and spatially contiguous maps of vegetation carbon uptake. In the second part of this study (Hilker et al., 2011b) we demonstrate and validate our approach over eight different forest flux tower sites in North America
PHOTOSYNSAT, photosynthesis from space: Theoretical foundations of a satellite concept and validation from tower and spaceborne data
No full textWe develop herein the theoretical foundations for a new satellite concept, utilizing multi-angle, along track spectral measurements to infer photosynthesis and gross primary production, at the landscape level over time. We validate the theory using both tower and space-borne sensors. The concept, originated in Hall et al. (2008), and Hilker et al. (2008a) and is based on two principles: (1) The first derivative of the photochemical reflectance index (PRI) with respect to shadow fraction viewed by the sensor ?PRI/??s, is proportional to light-use efficiency ?. (2) This behavior can be shown both theoretically and empirically to be independent of vegetation structure and optical properties. These two principles provide the basis for a robust photosynthesis algorithm that can be applied consistently both spatially and temporally. We develop the general theoretical concept using a canopy reflectance model that incorporates a dependence of leaf reflectance on illumination strength, permitting the leaf reflectance at 531 nm to depend on the intensity of photosynthetic down-regulation. Using this model we are able to show that using PRI alone to infer ? is confounded by the shadow fraction viewed by a sensor, the PRI value in a non-down-regulated physiological state, and the sunlit canopy reflectance. We are able to demonstrate that these difficulties are mitigated by using ?PRI/??s—not PRI—as the primary measure of canopy level ?. We demonstrate our concept using tower and satellite data acquired over three years, in two distinct biomes and vegetation types to show that PRI/??s and ? are related by a single function. Building on these ideas we propose the development of a new satellite concept that can utilize a spatially and temporally robust algorithm to map photosynthesis at landscape scales and its temporal variation
Remote Sensing of Tropical Ecosystems: Atmospheric Correction and Cloud Masking Matter
Full text linkTropical 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
An assessment of photosynthetic light use efficiency from space: modeling the atmospheric and directional impacts on PRI reflectance
No full textEstimation of photosynthetic light use efficiency (?) from satellite observations is an important component of climate change research. The photochemical reflectance index, a narrow waveband index based on the reflectance at 531 and 570 nm, allows sampling of the photosynthetic activity of leaves; upscaling of these measurements to landscape and global scales, however, remains challenging. Only a few studies have used spaceborne observations of PRI so far, and research has largely focused on the MODIS sensor. Its daily global coverage and the capacity to detect a narrow reflectance band at 531 nm make it the best available choice for sensing ? from space. Previous results however, have identified a number of key issues with MODIS-based observations of PRI. First, the differences between the footprint of eddy covariance (EC) measurements and the MODIS footprint, which is determined by the sensor's observation geometry make a direct comparison between both data sources challenging and second, the PRI reflectance bands are affected by atmospheric scattering effects confounding the existing physiological signal. In this study we introduce a new approach for upscaling EC based ? measurements to MODIS. First, EC-measured ? values were “translated” into a tower-level optical PRI signal using AMSPEC, an automated multi-angular, tower-based spectroradiometer instrument. AMSPEC enabled us to adjust tower-measured PRI values to the individual viewing geometry of each MODIS overpass. Second, MODIS data were atmospherically corrected using a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, which uses a time series approach and an image-based rather than pixel-based processing for simultaneous retrievals of atmospheric aerosol and surface bidirectional reflectance (BRDF). Using this approach, we found a strong relationship between tower-based and spaceborne reflectance measurements (r2 = 0.74, p < 0.01) throughout the vegetation period of 2006. Swath (non-gridded) observations yielded stronger correlations than gridded data (r2 = 0.58, p < 0.01) both of which included forward and backscatter observations. Spaceborne PRI values were strongly related to canopy shadow fractions and varied with different levels of ?. We conclude that MAIAC-corrected MODIS observations were able to track the site-level physiological changes from space throughout the observation period
On the measurability of change in Amazon vegetation from MODIS
No full textThe Amazon rainforest is a critical hotspot for bio-diversity, and plays an essential role in global carbon, water and energy fluxes and the earth's climate. Our ability to project the role of vegetation carbon feedbacks on future climate critically depends upon our understanding of this tropical ecosystem, its tolerance to climate extremes and tipping points of ecosystem collapse. Satellite remote sensing is the only practical approach to obtain observational evidence of trends and changes across large regions of the Amazon forest; however, inferring these trends in the presence of high cloud cover fraction and aerosol concentrations has led to widely varying conclusions. Our study provides a simple and direct statistical analysis of a measurable change in daily and composite surface reflectance obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) based on the noise level of data and the number of available observations. Depending on time frame and data product chosen for analysis, changes in leaf area need to exceed up to 2 units leaf area per unit ground area (expressed as m2 m? 2) across much of the basin before these changes can be detected at a 95% confidence level with conventional approaches, roughly corresponding to a change in NDVI and EVI of about 25%. A potential way forward may be provided by advanced multi-angular techniques, such as the Multi-Angle Implementation of Atmospheric Correction Algorithm (MAIAC), which allowed detection of changes of about 0.6–0.8 units in leaf area (2–6% change in NDVI) at the same confidence level. In our analysis, the use of the Enhanced Vegetation Index (EVI) did not improve accuracy of detectable change in leaf area but added a complicating sensitivity to the bi-directional reflectance, or view geometry effects
Potentials and limitations for estimating daytime ecosystem respiration by combining tower-based remote sensing and carbon flux measurements
No full textVegetation carbon uptake and respiration constitute the largest carbon cycle of the planet with an annual turnover in the order of 120 GT. Currently, neither ecosystem carbon uptake (through photosynthesis) nor ecosystem carbon release (through respiration) can be measured directly during the daytime. Instead, flux-tower measurements rely on nighttime respiration based on the assumption of zero carbon uptake which are then projected to daytime using an exponential relationship to soil temperature at shallow soil depth. As an alternative to this approach, R could possibly also be determined from combining daytime eddy covariance measurements of net ecosystem production (NEP) and spectral observations of gross primary production (GPP). In previous work, we have shown that multi-angular observations can be used to determine GPP from the absorbed photosynthetically active radiation (APAR) and spectrally obtained observations of light-use efficiency (?). The difference of NEP and GPP suggests that daytime respiration is greater and more dynamic than conventional estimates derived from nighttime flux values. Our findings also suggest that an accelerated ecosystem metabolism results in an exponential increase in respiration which eventually diminishes net ecosystem production. Respiration was also closely related to air and soil temperature. We conclude that tower-level spectral measurements provide considerable new insights into ecosystem fluxes as they allow independent yet complementary measurements of different aspects of the carbon and energy cycl
Separating physiologically and directionally induced changes in PRI using BRDF models
No full textMonitoring of photosynthetic efficiency () over space and time is a critical component of climate change research as it is a major determinant of the amount of carbon accumulated by terrestrial ecosystems. While the past decade has seen progress in the remote estimation of at the leaf, canopy and stand level using the photochemical reflectance index PRI (based on the normalized difference of reflectance at 531 and 570 nm), little is known about the temporal and spatial requirements for up-scaling PRI to landscape and global levels using satellite observations. One potential way to investigate these requirements is using automated tower-based remote sensing platforms, which observe stand level reflectance with high spatial, temporal, and spectral resolution. Prediction of from PRI diurnally or over a full year requires observations of canopy reflectance over multiple view and sun-angles. As a result, these observations are subject to directional reflectance effects which can be interpreted in terms of the bidirectional reflectance distribution function (BRDF) using semi-empirical kernel driven models. These semi-empirical models use a combination of physically based BRDF shapes and empirical observations to standardize multi-angular observations to a common viewing and illumination geometry. Directional reflectance effects are thereby modeled as a linear superposition of mathematical kernels, representing the bi-direction variation in reflectance from isotropic, geometric, and volumetric scattering components of the vegetation canopy. However, because variations in plant physiological conditions can also introduce bidirectional reflectance variations, we introduce an approach to separate bidirectional effects arising purely from plant physiological status from other effects by stratifying PRI observations into categories based on environmental conditions for which the expected physiological variability is low. Within each of these PRI strata, the derived physically based BRDF shapes were used to standardize multi-angular PRI measurements to a common viewing and illumination geometry. The method significantly enhanced the relationship found between PRI and (from r2=0.38 for the directionally uncorrected case to r2=0.82 for the directionally corrected case) from data measured continuously over the course of 1 year over an evergreen conifer forest using an automated platform. Results show that isotropic PRI scattering is highly correlated to changes in , while geometric scattering can be related to canopy level shading. Instrumentation and approaches such as the one demonstrated in this study may be integrated into current efforts aiming at predicting at global scales using satellite observations
Estimation of Light-use Efficiency of Terrestrial Ecosystem from Space: A Status Report
Full text linkA critical variable in the estimation of gross primary production of terrestrial ecosystems is light-use efficiency (LUE), a value that represents the actual efficiency of a plant's use of absorbed radiation energy to produce biomass. Light-use efficiency is driven by the most limiting of a number of environmental stress factors that reduce plants' photosynthetic capacity; these include short-term stressors, such as photoinhibition, as well as longer-term stressors, such as soil water and temperature. Modeling LUE from remote sensing is governed largely by the biochemical composition of plant foliage, with the past decade seeing important theoretical and modeling advances for understanding the role of these stresses on LUE. In this article we provide a summary of the tower-, aircraft-, and satellite-based research undertaken to date, and discuss the broader scalability of these methods, concluding with recommendations for ongoing research possibilities.</p
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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