Estimation of spruce needle-leaf chlorophyll content based on DART and PARAS canopy reflectance models
Yanez Rausell, L. ; Malenovsky, Z. ; Rautiainen, M. ; Clevers, J.G.P.W. ; Lukes, P. ; Hanus, J. ; Schaepman, M.E. - \ 2015
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (2015)4. - ISSN 1939-1404 - p. 1534 - 1544.
photon recollision probability - area index - spectral invariants - forest - prospect - stands - simulations - resolution - retrieval - lai-2000
Needle-leaf chlorophyll content (Cab) of a Norway spruce stand was estimated from CHRIS-PROBA images using the canopy reflectance simulated by the PROSPECT model coupled with two canopy reflectance models: 1) discrete anisotropic radiative transfer model (DART); and 2) PARAS. The DART model uses a detailed description of the forest scene, whereas PARAS is based on the photon recollision probability theory and uses a simplified forest structural description. Subsequently, statistically significant empirical functions between the optical indices ANCB670-720 and ANMB670-720 and the needle-leaf Cab content were established and then applied to CHRIS-PROBA data. The Cab estimating regressions using ANMB670_720 were more robust than using ANCB670-720 since the latter was more sensitive to LAI, especially in case of PARAS. Comparison between Cab estimates showed strong linear correlations between PARAS and DART retrievals, with a nearly perfect one-to-one fit when using ANMB670-720 (slope = 1.1, offset = 11 µg · cm-2). Further comparison with Cab estimated from an AISA Eagle image of the same stand showed better results for PARAS (RMSE = 2.7 µg · cm-2 for ANCB670-720; RMSE = 9.5 µg · cm-2 for ANMB670_720) than for DART (RMSE = 7.5 µg · cm-2 for ANCB670-720; RMSE = 23 µg · cm-2 for ANMB670-720). Although these results show the potential for simpler models like PARAS in estimating needle-leaf Cab from satellite imaging spectroscopy data, further analyses regarding parameterization of radiative transfer models are recommended.
Derivation of Land Surface Albedo at High Resolution by Combining HJ-1A/B Reflectance Observations with MODIS BRDF Products
Gao, B. ; Jia, L. ; Wang, T.X. - \ 2014
Remote Sensing 6 (2014)9. - ISSN 2072-4292 - p. 8966 - 8985.
remote-sensing data - bidirectional reflectance - retrieval - algorithm - meteosat - polder/adeos - simulation - models
Land surface albedo is an essential parameter for monitoring global/regional climate and land surface energy balance. Although many studies have been conducted on global or regional land surface albedo using various remote sensing data over the past few decades, land surface albedo product with a high spatio-temporal resolution is currently very scarce. This paper proposes a method for deriving land surface albedo with a high spatio-temporal resolution (space: 30 m and time: 2-4 days). The proposed method works by combining the land surface reflectance data at 30 m spatial resolution obtained from the charge-coupled devices in the Huanjing-1A and -1B (HJ-1A/B) satellites with the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface bidirectional reflectance distribution function (BRDF) parameters product (MCD43A1), which is at a spatial resolution of 500 m. First, the land surface BRDF parameters for HJ-1A/B land surface reflectance with a spatial-temporal resolutions of 30 m and 2-4 day are calculated on the basis of the prior knowledge from the MODIS BRDF product; then, the calculated high resolution BRDF parameters are integrated over the illuminating/viewing hemisphere to produce the white-and black-sky albedos at 30 m resolution. These results form the basis for the final land surface albedo derivation by accounting for the proportion of direct and diffuse solar radiation arriving at the ground. The albedo retrieved by this novel method is compared with MODIS land surface albedo products, as well as with ground measurements. The results show that the derived land surface albedo during the growing season of 2012 generally achieved a mean absolute accuracy of +/- 0.044, and a root mean square error of 0.039, confirming the effectiveness of the newly proposed method.
Constraints on ship NOx emissions in Europe using GEOS-Chem and OMI satellite NO2 observations
Vinken, G.C.M. ; Boersma, K.F. ; Donkelaar, A. van; Zhang, W. - \ 2014
Atmospheric Chemistry and Physics 14 (2014). - ISSN 1680-7316 - p. 1353 - 1369.
ozone monitoring instrument - nitrogen-oxide emissions - marine boundary-layer - tropospheric no2 - interannual variability - power-plants - global-model - inventories - retrieval - columns
We present a top-down ship NOx emission inventory for the Baltic Sea, the North Sea, the Bay of Biscay and the Mediterranean Sea based on satellite-observed tropospheric NO2 columns of the Ozone Monitoring Instrument (OMI) for 2005–2006. We improved the representation of ship emissions in the GEOS-Chem chemistry transport model, and compared simulated NO2 columns to consistent satellite observations. Relative differences between simulated and observed NO2 columns have been used to constrain ship emissions in four European seas (the Baltic Sea, the North Sea, the Bay of Biscay and the Mediterranean Sea) using a mass-balance approach, and accounting for nonlinear sensitivities to changing emissions in both model and satellite retrieval. These constraints are applied to 39% of total top-down European ship NOx emissions, which amount to 0.96 TgN for 2005, and 1.0 TgN for 2006 (11–15% lower than the bottom-up EMEP ship emission inventory). Our results indicate that EMEP emissions in the Mediterranean Sea are too high (by 60 %) and misplaced by up to 150 km, which can have important consequences for local air quality simulations. In the North Sea ship track, our top-down emissions amount to 0.05 TgN for 2005 (35% lower than EMEP). Increased top-down emissions were found for the Baltic Sea and the Bay of Biscay ship tracks, with totals in these tracks of 0.05 TgN (131% higher than EMEP) and 0.08 TgN for 2005 (128% higher than EMEP), respectively. Our study explicitly accounts for the (non-linear) sensitivity of satellite retrievals to changes in the a priori NO2 profiles, as satellite observations are never fully independent of model information (i.e. assumptions on vertical NO2 profiles). Our study provides for the first time a space-based, top-down ship NOx emission inventory, and can serve as a framework for future studies to constrain ship emissions using satellite NO2 observations in other seas.
How much CO was emitted by the 2010 fires around Moscow?
Krol, M.C. ; Peters, W. ; Hooghiemstra, P. ; George, M. ; Clerbaux, C. ; Hurtmans, D. ; McInerney, D. ; Sedano, F. ; Bergamaschi, P. ; Hajj, M. El; Kaiser, J.W. ; Fisher, D. ; Yeshov, V. ; Muller, J.P. - \ 2013
Atmospheric Chemistry and Physics 13 (2013). - ISSN 1680-7316 - p. 4737 - 4747.
russian wildfires - emissions - pollution - summer - iasi - assimilation - retrieval - satellite - algorithm - transport
The fires around Moscow in July and August 2010 emitted a large amount of pollutants to the atmosphere. Here we estimate the carbon monoxide (CO) source strength of the Moscow fires in July and August by using the TM5-4DVAR system in combination with CO column observations of the Infrared Atmospheric Sounding Interferometer (IASI). It is shown that the IASI observations provide a strong constraint on the total emissions needed in the model. Irrespective of the prior emissions used, the optimised CO fire emission estimates from mid-July to mid-August 2010 amount to approximately 24 TgCO. This estimate depends only weakly (<15 %) on the assumed diurnal variations and injection height of the emissions. However, the estimated emissions might depend on unaccounted model uncertainties such as vertical transport. Our emission estimate of 22-27 TgCO during roughly one month of intense burning is less than suggested by another recent study, but substantially larger than predicted by the bottom-up inventories. This latter discrepancy suggests that bottom-up emission estimates for extreme peat burning events require improvements.
Remote sensing image processing
Camps-Valls, Gustavo ; Tuia, Devis ; Gómez-Chova, Luis ; Jiménez, Sandra ; Malo, Jesús - \ 2012
Morgan and Claypool Publishers (Synthesis Lectures on Image, Video, and Multimedia Processing ) - ISBN 9781608458196 - 194 p.
biophysical parameter - classification - computer vision - Earth observation - feature selection and extraction - image statistics - machine learning - manifold learning - morphology - pattern recognition - regression - remote sensing - retrieval - segmentation - spectral signature - spectroscopy - statistical learning - unmixing - vision science
Earth observation is the field of science concerned with the problem of monitoring and modeling the processes on the Earth surface and their interaction with the atmosphere.The Earth is continuously monitored with advanced optical and radar sensors.The images are analyzed and processed to deliver useful products to individual users, agencies and public administrations.To deal with these problems, remote sensing image processing is nowadays a mature research area, and the techniques developed in the field allow many real-life applications with great societal value.For instance, urban monitoring, fire detection or flood prediction can have a great impact on economical and environmental issues. To attain such objectives, the remote sensing community has turned into a multidisciplinary field of science that embraces physics, signal theory, computer science, electronics and communications. From a machine learning and signal/image processing point of view, all the applications are tackled under specific formalisms, such as classification and clustering, regression and function approximation, data coding, restoration and enhancement, source unmixing, data fusion or feature selection and extraction. This book covers some of the fields in a comprehensive way.
Estimating total suspended matter concentration in tropical waters of the Berau estuary, Indonesia
Ambarwulan, W. ; Verhoef, W. ; Mannaerts, C.M. ; Salama, M.S. - \ 2012
International Journal of Remote Sensing 33 (2012)16. - ISSN 0143-1161 - p. 4919 - 4936.
ocean color - atmospheric correction - meris measurements - baltic sea - products - validation - algorithm - simulation - retrieval - skagerrak
This study presents the application of a semi-empirical approach, based on the Kubelka–Munk (K-M) model, to retrieve the total suspended matter (TSM) concentration of water bodies from ocean colour remote sensing. This approach is validated with in situ data sets compiled from the tropical waters of Berau estuary, Indonesia. Compared to a purely empirical approach, the K-M model provides better results in the retrieval of TSM concentration on both data sets (in situ and Medium Resolution Imaging Spectrometer (MERIS)). In this study, the K-M model was calibrated with in situ measurements of remote-sensing reflectance (R rs) and TSM concentration. Next, the inverse K-M model was successfully applied to images taken by the MERIS instrument by generating regional maps of TSM concentration. MERIS top-of-atmosphere radiances were atmospherically corrected using the Moderate Spectral Resolution Atmospheric Transmittance (MODTRAN) radiative transfer model. The best correlation between R rs measured in situ and R rs MERIS was found to be at a wavelength of 620 nm. The TSM concentrations retrieved using the K-M model showed a lower root mean square error (RMSE), a higher coefficient of determination and a smaller relative error than those retrieved by the purely empirical approach.
Observation uncertainty of satellite soil moisture products determined with physically-based modeling
Wanders, N. ; Karssenberg, D. ; Bierkens, M.F.P. ; Parinussa, R. ; Jeu, R. de; Dam, J.C. van; Jong, S. de - \ 2012
Remote Sensing of Environment 127 (2012). - ISSN 0034-4257 - p. 341 - 356.
passive microwave measurements - improving runoff prediction - vegetation optical depth - ers scatterometer - amsr-e - retrieval - assimilation - validation - algorithm - index
Accurate estimates of soil moisture as initial conditions to hydrological models are expected to greatly increase the accuracy of flood and drought predictions. As in-situ soil moisture observations are scarce, satellite-based estimates are a suitable alternative. The validation of remotely sensed soil moisture products is generally hampered by the difference in spatial support of in-situ observations and satellite footprints. Unsaturated zone modeling may serve as a valuable validation tool because it could bridge the gap of different spatial supports. A stochastic, distributed unsaturated zone model (SWAP) was used in which the spatial support was matched to these of the satellite soil moisture retrievals. A comparison between point observations and the SWAP model was performed to enhance understanding of the model and to assure that the SWAP model could be used with confidence for other locations in Spain. A timeseries analysis was performed to compare surface soil moisture from the SWAP model to surface soil moisture retrievals from three different microwave sensors, including AMSR-E, SMOS and ASCAT. Results suggest that temporal dynamics are best captured by AMSR-E and ASCAT resulting in an averaged correlation coefficient of 0.68 and 0.71, respectively. SMOS shows the capability of capturing the long-term trends, however on short timescales the soil moisture signal was not captured as well as by the other sensors, resulting in an averaged correlation coefficient of 0.42. Root mean square errors for the three sensors were found to be very similar (± 0.05 m3m- 3). The satellite uncertainty is spatially correlated and distinct spatial patterns are found over Spain.
Inversion of a coupled canopy–atmosphere model using multi-angular top-of-atmosphere radiance data: A forest case study
Laurent, V.C.E. ; Verhoef, W. ; Clevers, J.G.P.W. ; Schaepman, M.E. - \ 2011
Remote Sensing of Environment 115 (2011)10. - ISSN 0034-4257 - p. 2603 - 2612.
leaf-area index - radiative-transfer models - remote-sensing data - vegetation structure - misr data - reflectance - retrieval - variables - products - prospect
Since the launch of sensors with angular observation capabilities, such as CHRIS and MISR, the additional potential of multi-angular observations for vegetation structural and biochemical variables has been widely recognised. Various methods have been successfully implemented to estimate forest biochemical and biophysical variables from atmospherically-corrected multi-angular data, but the use of physically based radiative transfer (RT) models is still limited. Because both canopy and atmosphere have an anisotropic behaviour, it is important to understand the multi-angular signal measured by the sensor at the top of the atmosphere (TOA). Coupled canopy–atmosphere RT models allow linking surface variables directly to the TOA radiance measured by the sensor and are therefore very interesting tools to use for estimating forest variables from multi-angular data. We investigated the potential of TOA multi-angular radiance data for estimating forest variables by inverting a coupled canopy–atmosphere physical RT model. The case study focussed on three Norway spruce stands located at the Bily Kriz experimental site (Czech Republic), for which multi-angular CHRIS and field data were acquired in September 2006. The soil–leaf–canopy RT model SLC and the atmospheric model MODTRAN4 were coupled using a method allowing to make full use of the four canopy angular reflectance components provided by SLC. The TOA radiance simulations were in good agreement with the spectral and angular signatures measured by CHRIS. Singular value decompositions of the Jacobian matrices showed that the dimensionality of the variable estimation problem increased from 3 to 6 when increasing the number of observation angles from 1 to 4. The model inversion was conducted for two cases: 4 and 7 variables. The most influential parameters were chosen as free variables in the look-up tables, namely: vertical crown cover (Cv), fraction of bark material (fB), needle chlorophyll content (needleCab), needle dry matter content (needleCdm) for the 4-variable case, and additionally, tree shape factor (Zeta), dissociation factor (D), and needle brown pigments content (needleCs) in the 7-variable case. All angular combinations were tested, and the best estimates were obtained with combinations using two or three angles, depending on the number of variables and on the stand used. Overall, this case study showed that, although making use of its full potential is still a challenge, TOA multi-angular radiance data do have a higher potential for variable estimation than mono-angular data.
Satellite estimates of wide-range suspended sediment concentrations in Changjiang (Yangtze) estuary using MERIS data
Shen, F. ; Verhoef, W. ; Zhou, Y. ; Salama, M.S. ; Liu, X. - \ 2010
Estuaries and coasts 33 (2010)6. - ISSN 1559-2723 - p. 1420 - 1429.
remote-sensing reflectance - coastal waters - matter - algorithm - seawifs - images - ocean - retrieval - dynamics - radiance
The Changjiang (Yangtze) estuarine and coastal waters are characterized by suspended sediments over a wide range of concentrations from 20 to 2,500 mg l-1. Suspended sediment plays important roles in the estuarine and coastal system and environment. Previous algorithms for satellite estimates of suspended sediment concentration (SSC) showed a great limitation in that only low to moderate concentrations (up to 50 mg l-1) could be reliably estimated. In this study, we developed a semi-empirical radiative transfer (SERT) model with physically based empirical coefficients to estimate SSC from MERIS data over turbid waters with a much wider range of SSC. The model was based on the Kubelka–Munk two-stream approximation of radiative transfer theory and calibrated using datasets from in situ measurements and outdoor controlled tank experiments. The results show that the sensitivity and saturation level of remote-sensing reflectance to SSC are dependent on wavelengths and SSC levels. Therefore, the SERT model, coupled with a multi-conditional algorithm scheme adapted to satellite retrieval of wide-range SSC, was proposed. Results suggest that this method is more effective and accurate in the estimation of SSC over turbid waters
Performance of high-resolution X-band radar for rainfall measurement in the Netherlands
Beek, C.Z. van de; Leijnsel, H. ; Stricker, J.N.M. ; Uijlenhoet, R. ; Russchenberg, H.W.J. - \ 2010
Hydrology and Earth System Sciences 14 (2010)2. - ISSN 1027-5606 - p. 205 - 221.
neerslag - regen - meteorologische waarnemingen - hydrologische gegevens - gegevensanalyse - precipitation - rain - meteorological observations - hydrological data - data analysis - weather radar - nonprecipitating echoes - size distribution - mountainous area - reflectivity - attenuation - hydrology - calibration - retrieval
This study presents an analysis of 195 rainfall events gathered with the X-band weather radar SOLIDAR and a tipping bucket rain gauge network near Delft, The Netherlands, between May 1993 and April 1994. The aim of this paper is to present a thorough analysis of a climatological dataset using a high spatial (120 m) and temporal (16 s) resolution X-band radar. This makes it a study of the potential for high-resolution rainfall measurements with non-polarimetric X-band radar over flat terrain. An appropriate radar reflectivity – rain rate relation is derived from measurements of raindrop size distributions and compared with radar – rain gauge data. The radar calibration is assessed using a long-term comparison of rain gauge measurements with corresponding radar reflectivities as well as by analyzing the evolution of the stability of ground clutter areas over time. Three different methods for ground clutter correction as well as the effectiveness of forward and backward attenuation correction algorithms have been studied. Five individual rainfall events are discussed in detail to illustrate the strengths and weaknesses of high-resolution X-band radar and the effectiveness of the presented correction methods. X-band radar is found to be able to measure the space-time variation of rainfall at high resolution, far greater than what can be achieved by rain gauge networks or a typical operational C-band weather radar. On the other hand, SOLIDAR can suffer from receiver saturation, wet radome attenuation as well as signal loss along the path. During very strong convective situations the signal can even be lost completely. In combination with several rain gauges for quality control, high resolution X-band radar is considered to be suitable for rainfall monitoring over relatively small (urban) catchments. These results offer great prospects for the new high resolution polarimetric doppler X-band radar IDRA
Estimating canopy water content using hyperspectral remote sensing data
Clevers, J.G.P.W. ; Kooistra, L. ; Schaepman, M.E. - \ 2010
International Journal of applied Earth Observation and Geoinformation 12 (2010)2. - ISSN 0303-2434 - p. 119 - 125.
leaf optical-properties - radiative-transfer models - imaging spectrometer data - dynamic vegetation model - reflectance data - indexes - retrieval - information - variables - products
Hyperspectral remote sensing has demonstrated great potential for accurate retrieval of canopy water content (CWC). This CWC is defined by the product of the leaf equivalent water thickness (EWT) and the leaf area index (LAI). In this paper, in particular the spectral information provided by the canopy water absorption feature at 970 nm for estimating and predicting CWC was studied using a modelling approach and in situ spectroradiometric measurements. The relationship of the first derivative at the right slope of the 970 nm water absorption feature with CWC was investigated with the PROSAIL radiative transfer model and tested for field spectroradiometer measurements on two test sites. The first site was a heterogeneous floodplain with natural vegetation like grasses and various shrubs. The second site was an extensively grazed fen meadow. PROSAIL simulations (using coupled SAIL/PROSPECT-5 models) showed a linear relationship between the first derivative over the 1015–1050 nm spectral interval and CWC (R2 = 0.97). For 8 plots at the floodplain site the spectral derivative over the 1015–1050 nm interval obtained with an ASD FieldSpec spectroradiometer yielded an R2 of 0.51 with CWC. For 40 plots at the fen meadow ASD FieldSpec spectral measurements yielded an R2 of 0.68 for the derivative over the 1015–1050 nm interval with CWC. Consistency of the results confirmed the potential of using simulation results for calibrating the relationship between this first derivative and CWC
Scatterometer-Derived Soil Moisture Calibrated for Soil Texture With a One-Dimensional Water-Flow Model
Lange, R. de; Beck, R. ; Giesen, N. van de; Friesen, J. ; Wit, A.J.W. de; Wagner, W. - \ 2008
IEEE Transactions on Geoscience and Remote Sensing 46 (2008)12. - ISSN 0196-2892 - p. 4041 - 4049.
ers scatterometer - near-surface - assimilation - retrieval - validation - space
Current global satellite scatterometer-based soil moisture retrieval algorithms do not take soil characteristics into account. In this paper, the characteristic time length of the soil water index has been calibrated for ten sampling frequencies and for different soil conductivity associated with 12 soil texture classes. The calibration experiment was independently performed from satellite observations. The reference soil moisture data set was created with a I-D water-flow model and by making use of precipitation measurements. The soil water index was simulated by applying the algorithm to the modeled soil moisture of the upper few centimeters. The resulting optimized characteristic time lengths T increase with longer sampling periods. For instance, a T of 7 days was found for sandy soil when a sampling period of I day was applied, whereas an optimized T-value of 18 days was found for a sampling period of 10 days. A maximum rmse improvement of 0.5% vol. can be expected when using the calibrated T-values instead of T = 20. The soil water index and the differentiated T-values were applied to European Remote Sensing (ERS) satellite scatterometer data and were validated against in situ soil moisture measurements. The results obtained using calibrated T-values and T = 20 did not differ (r = 0.39, rmse = 5.4% vol.) and can be explained by the averaged sampling period of 4-5 days. The soil water index obtained with current operational microwave sensors [Advanced Wind Scatterometer (ASCAT) and Advanced Microwave Scanning Radiometer-Earth Observation System] and future sensors (Soil Moisture and Ocean Salinity and Soil Moisture Active Passive) should benefit from soil texture differentiation, as they can record on a daily basis either individually or synergistically using several sensors. The proposed differentiated characteristic time length enables the continuation of the soil water index of sensors with varying sampling periods (e.g., ERS-ASCAT).
Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland
Darvishzadeh, R. ; Skidmore, A.K. ; Schlerf, M. ; Atzberger, C. - \ 2008
Remote Sensing of Environment 112 (2008)5. - ISSN 0034-4257 - p. 2592 - 2604.
leaf-area index - canopy biophysical variables - remote-sensing data - reflectance data - optical-properties - neural-network - ancillary information - precision agriculture - satellite data - retrieval
Radiative transfer models have seldom been applied for studying heterogeneous grassland canopies. Here, the potential of radiative transfer modeling to predict LAI and leaf and canopy chlorophyll contents in a heterogeneous Mediterranean grassland is investigated. The widely used PROSAIL model was inverted with canopy spectral reflectance measurements by means of a look-up table (LUT). Canopy spectral measurements were acquired in the field using a GER 3700 spectroradiometer, along with simultaneous in situ measurements of LAI and leaf chlorophyll content. We tested the impact of using multiple solutions, stratification (according to species richness), and spectral subsetting on parameter retrieval. To assess the performance of the model inversion, the normalized RMSE and R-2 between independent in situ measurements and estimated parameters were used. Of the three investigated plant characteristics, canopy chlorophyll content was estimated with the highest accuracy (R-2 = 0.70, NRMSE = 0.18). Leaf chlorophyll content, on the other hand, could not be estimated with acceptable accuracy, while LAI was estimated with intermediate accuracy (R-2 = 0.59, NRMSE = 0.18). When only sample plots with up to two species were considered (n = 107), the estimation accuracy for all investigated variables (LAI, canopy chlorophyll content and leaf chlorophyll content) increased (NRMSE=0.14, 0.16, 0.19, respectively). This shows the limits of the PROSAIL radiative transfer model in the case of very heterogeneous conditions. We also found that a carefully selected spectral subset contains sufficient information for a successful model inversion. Our results confirm the potential of model inversion for estimating vegetation biophysical parameters at the canopy scale in (moderately) heterogeneous grasslands using hyperspectral measurements. (C) 2008 Elsevier Inc. All rights reserved.
Quantitative analysis of SCIAMACHY carbon monoxide total column measurements
Laat, A.T.J. de; Gloudemans, A.M.S. ; Schrijver, H. ; Broek, M.M.P. van den; Meirink, J.F. ; Aben, I. ; Krol, M.C. - \ 2006
Geophysical Research Letters 33 (2006). - ISSN 0094-8276 - 5 p.
co - instrument - resolution - retrieval - emissions - mopitt - trends - impact - ch4
Global tropospheric ozone distributions, budgets, and radiative forcings from an ensemble of 26 state-of-the-art atmospheric chemistry models have been intercompared and synthesized as part of a wider study into both the air quality and climate roles of ozone. Results from three 2030 emissions scenarios, broadly representing “optimistic,” “likely,” and “pessimistic” options, are compared to a base year 2000 simulation. This base case realistically represents the current global distribution of tropospheric ozone. A further set of simulations considers the influence of climate change over the same time period by forcing the central emissions scenario with a surface warming of around 0.7K. The use of a large multimodel ensemble allows us to identify key areas of uncertainty and improves the robustness of the results. Ensemble mean changes in tropospheric ozone burden between 2000 and 2030 for the 3 scenarios range from a 5% decrease, through a 6% increase, to a 15% increase. The intermodel uncertainty (±1 standard deviation) associated with these values is about ±25%. Model outliers have no significant influence on the ensemble mean results. Combining ozone and methane changes, the three scenarios produce radiative forcings of -50, 180, and 300 mW m-2, compared to a CO2 forcing over the same time period of 800–1100 mW m-2. These values indicate the importance of air pollution emissions in short- to medium-term climate forcing and the potential for stringent/lax control measures to improve/worsen future climate forcing. The model sensitivity of ozone to imposed climate change varies between models but modulates zonal mean mixing ratios by ±5 ppbv via a variety of feedback mechanisms, in particular those involving water vapor and stratosphere-troposphere exchange. This level of climate change also reduces the methane lifetime by around 4%. The ensemble mean year 2000 tropospheric ozone budget indicates chemical production, chemical destruction, dry deposition and stratospheric input fluxes of 5100, 4650, 1000, and 550 Tg(O3) yr-1, respectively. These values are significantly different to the mean budget documented by the Intergovernmental Panel on Climate Change (IPCC) Third Assessment Report (TAR). The mean ozone burden (340 Tg(O3)) is 10% larger than the IPCC TAR estimate, while the mean ozone lifetime (22 days) is 10% shorter. Results from individual models show a correlation between ozone burden and lifetime, and each model's ozone burden and lifetime respond in similar ways across the emissions scenarios. The response to climate change is much less consistent. Models show more variability in the tropics compared to midlatitudes. Some of the most uncertain areas of the models include treatments of deep tropical convection, including lightning NO x production; isoprene emissions from vegetation and isoprene's degradation chemistry; stratosphere-troposphere exchange; biomass burning; and water vapor concentrations.
Evidence for long-range transport of Carbon Monoxide in the Southern Hemisphere from SCIAMACHY observations
Gloudemans, A.M.S. ; Krol, M.C. ; Meirink, J.F. ; Laat, A.T.J. de; Werf, G.R. van der; Schrijver, H. ; Broek, M.M.P. van den; Aben, I. - \ 2006
Geophysical Research Letters 33 (2006). - ISSN 0094-8276 - 5 p.
wfm-doas - total columns - data set - co - retrieval - ch4 - calibration - instrument - emissions - pacific
The SCIAMACHY satellite instrument shows enhanced carbon monoxide (CO) columns in the Southern Hemisphere during the local Spring. Chemistry-transport model simulations using the new GFEDv2 biomass-burning emission database show a similar temporal and spatial CO distribution, indicating that the observed enhancements are mainly due to biomass burning (BB). Large differences between the year 2003 and 2004 are observed in both the measurements and the model for South America and Australia. This study analyzes the origin of these observed enhancements in the Southern Hemisphere. The fact that SCIAMACHY is sensitive to surface CO allows for the observation of enhanced CO columns in both emission areas and in areas that are affected by long-range transport of CO. Model results show a large contribution of South American BB CO over Australian BB regions during the 2004 BB season of up to similar to 30-35% and up to 55% further south, with smaller contributions for 2003. BB CO transported from southern Africa contributes up to similar to 40% in 2003 and similar to 30% in 2004. The results indicate that differences between SCIAMACHY CO and the model simulations over Australian BB areas are probably not only caused by uncertainties in local emissions but also in overseas emissions.
Aerosol mapping over land with imaging spectroscopy using spectral autocorrelation
Bojinski, S. ; Schlapfer, D. ; Schaepman, M.E. ; Keller, J. ; Itten, K.I. - \ 2004
International Journal of Remote Sensing 25 (2004)22. - ISSN 0143-1161 - p. 5025 - 5047.
atmospheric correction algorithm - satellite imagery - optical-thickness - vegetation index - ocean color - eos-modis - retrieval - aviris - space
A new method for aerosol retrieval over land is proposed that makes explicit use of the contiguous, high-resolution spectral coverage of imaging spectrometers. The method is labelled Aerosol Retrieval by Interrelated Abundances (ARIA) and is based on unmixing of the short-wave infrared sensor signal by region-specific endmembers, assuming low aerosol radiative influence in this spectral region. Derived endmember abundances are transferred to the visible part of the spectrum in order to approximate surface reflectance where aerosol influence is generally strongest. Spectral autocorrelation of surface spectra is a precondition for ARIA and demonstrated using a reference spectrum database. The re-mixed surface reflectance is used as input quantity for the inversion of aerosol optical depth tau(a) at 0.55 mum wavelength on a pixel basis. Except for the choice of endmembers and the atmospheric vertical profile, no a priori assumptions on the image scene are required. The potential of the presented method for aerosol retrieval is demonstrated for an Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) scene, collected in California in 2000. Comparisons with existing aerosol retrieval methods showed encouraging results in terms of achieved spatial smoothness and degree of uncertainty of aerosol optical depth across the scene.
A practical algorithm to infer soil and foliage component temperatures from bi-angular ATSR-2 data
Jia, L. ; Li, Z.L. ; Menenti, M. ; Su, Z. ; Verhoef, W. ; Wan, Z. - \ 2003
International Journal of Remote Sensing 24 (2003)23. - ISSN 0143-1161 - p. 4739 - 4760.
surface-temperature - bidirectional reflectance - vegetation temperatures - atmospheric correction - solar spectrum - retrieval
An operational algorithm is proposed to retrieve soil and foliage component temperatures over heterogeneous land surface based on the analysis of bi-angular multi-spectral observations made by ATSR-2. Firstly, on the basis of the radiative transfer theory in a canopy, a model is developed to infer the two component temperatures using six channels of ATSR-2. Four visible, nearinfrared and short wave infrared channels are used to estimate the fractional vegetation cover within a pixel. A split-window method is developed to eliminate the atmospheric effects on the two thermal channels. An advanced method using all four visible, near-infrared and short wave channel measurements at two view angles is developed to perform atmospheric corrections in those channels allowing simultaneous retrieval of aerosol opacity and land surface bi-directional reflectance. Secondly, several case studies are undertaken with ATSR-2 data. The results indicate that both foliage and soil temperatures can be retrieved from bi-angular surface temperatures measurements. Finally, limitations and uncertainties in retrieving component temperatures using the present algorithm are discussed.
Rain detection over land surfaces using passive microwave satellite data
Bauer, P. ; Burose, D. ; Schulz, J. - \ 2002
Meteorologische Zeitschrift 11 (2002)1. - ISSN 0941-2948 - p. 37 - 48.
precipitation - retrieval - ssm/i
An algorithm is presented for the detection of surface rainfall using passive microwave measurements by satellite radiometers. The technique consists of a two-stage approach to distinguish precipitation signatures from other effects: (1) Contributions from slowly varying parameters (surface type and state) are isolated by comparing observed brightness temperatures to those obtained from previous orbits only containing rain-free observations. (2) Effects of more dynamic parameters, i.e., surface temperature and moisture, are reduced by successive subtraction from the observations by means of principal component analysis. For this purpose, the general signatures of both temperature and moisture variations are deduced from radiative transfer simulations. The fundamentals of this approach are based on a methodology developed by CONNER and PETTY (1998). The technique is applied to TMI observations and compared to co-located measurements of TMI and PR as well as independent techniques over selected regions in Africa, North and South America and India, but less skill over South America. All techniques provide similar rainfall screening skill where our technique showed superior results over Africa, North America, and India. Based on HEIDKE skill scores as a function of rainfall and brightness temperature range, an efcient calibration tool to retrieve near-surface rainfall intensities is provided