scholarly journals Twelve years of SIFTER Sun-Induced Fluorescence retrievals from GOME-2 as an independent constraint on photosynthesis across continents and biomes

2020 ◽  
Author(s):  
Maurits L. Kooreman ◽  
K. Folkert Boersma ◽  
Erik van Schaik ◽  
Anteneh G. Mengistu ◽  
Olaf N. E. Tuinder ◽  
...  
Keyword(s):  
Photosynthetic Activity ◽  
Gross Primary Production ◽  
Global Scale ◽  
Retrieval Algorithm ◽  
Oxygen Absorption ◽  
Water Vapor Absorption ◽  
Zonal Distribution ◽  
Spectral Window ◽  
Tropical Regions ◽  
Uncertainty Estimates

<p>Solar-Induced Fluorescence (SIF) data from satellites are increasingly used as a proxy for photosynthetic activity by vegetation, and as a constraint on gross primary production. The Royal Netherlands Meteorological Institute has developed an improved retrieval algorithm called SIFTER, to retrieve mid-morning (09:30 hrs local time) SIF estimates on the global scale from GOME-2 sensors on the Metop satellite series. The product is developed within the ACSAF network of EUMETSAT and a beta version is publicly available on www.temis.nl. The SIFTER algorithm improves over a previous version by using a narrower spectral window that avoids strong oxygen absorption and is less sensitive to water vapor absorption, by constructing stable reference spectra from a 6-year period (2007-2012) of atmospheric spectra over the Sahara, and by applying a latitude-dependent zero-level adjustment that accounts for biases in the product data. With SIFTER, we generate stable, good-quality SIF retrievals also in tropical regions that are known to suffer from high noise in other SIF products. Uncertainty estimates are included for individual observations, and the product is best used for mostly clear-sky scenes, and when spectral residuals remain below a certain threshold. The strength of SIFTER in the tropical regions was exploited to quantify the 2015/2016 drought in the Amazon, related to El Niño. We found that SIF was strongly suppressed over areas with anomalously high temperatures and decreased levels of soil moisture. SIF went below its climatological range starting from the end of the 2015 dry season and returned to normal levels by February 2016. A validation study is performed to assess the SIFTER quality against independent SIF and GPP products from other platforms, including SIF from OCO-2 and GOSAT, modeled GPP from MPI-BGC and eddy covariance derived, in-situ GPP measurements. SIFTER shows strong correlations (0.70 – 0.94) in the zonal distribution for each continent and in capturing seasonal patterns of SIF and GPP over different regions across the globe (0.62-0.99) when comparing to OCO-2 SIF and GPP from MPI-BGC. At ecosystem level, SIFTER was evaluated against OCO-2 SIF and EC GPP for five flux tower sites with varying biomes and geolocations. Regions with a homogeneous vegetation distribution show a higher correlation than heterogeneous regions. Overall, the results support the use of SIFTER data to be used as an independent constraint on photosynthetic activity on global and regional scales.</p>

scholarly journals Improved SIFTER v2 algorithm for long-term GOME-2A satellite retrievals of fluorescence with a correction for instrument degradation

2020 ◽  
Author(s):  
Erik van Schaik ◽  
Maurits L. Kooreman ◽  
Piet Stammes ◽  
L. Gijsbert Tilstra ◽  
Olaf N. E. Tuinder ◽  
...  
Keyword(s):  
Near Infrared ◽  
Photosynthetic Activity ◽  
Gross Primary Production ◽  
Global Scale ◽  
Retrieval Algorithm ◽  
Oxygen Absorption ◽  
Spectral Window ◽  
Data Product ◽  
Uncertainty Estimates ◽  
Water Vapour Absorption

Abstract. Solar-induced fluorescence (SIF) data from satellites are increasingly used as a proxy for photosynthetic activity by vegetation, and as a constraint on gross primary production. Here we develop an improved retrieval algorithm to retrieve mid-morning (09:30 hrs local time) SIF estimates on the global scale from GOME-2 sensor on the Metop-A satellite (GOME-2A) for the period 2007–2019. Our new SIFTER v2 algorithm improves over a previous version by using a narrower spectral window that avoids strong oxygen absorption and is less sensitive to water vapour absorption, by constructing stable reference spectra from a 6-year period (2007–2012) of atmospheric spectra over the Sahara, and by applying a latitude-dependent zero-level adjustment that accounts for biases in the data product. We generated stable, good-quality SIF retrievals between January 2007 and June 2013, when GOME-2A degradation in the near infrared was still limited. After the narrowing of the GOME-2A swath in July 2013, we characterized the throughput degradation of the level-1 data in order to derive reflectance corrections and apply these for the SIF retrievals between July 2013 and December 2018. SIFTER v2 data compares well with the independent NASA v2.8 data product. Especially in the evergreen tropics, SIFTER v2 no longer shows the underestimates against other satellite products that were seen in SIFTER v1. The new data product includes uncertainty estimates for individual observations, and is best used for mostly clear-sky scenes, and when spectral residuals remain below a certain spectral autocorrelation threshold. Our results support the use of SIFTER v2 data to be used as an independent constraint on photosynthetic activity on regional to global scales.

scholarly journals Improved SIFTER v2 algorithm for long-term GOME-2A satellite retrievals of fluorescence with a correction for instrument degradation

2020 ◽  
Vol 13 (8) ◽  
pp. 4295-4315
Author(s):  
Erik van Schaik ◽  
Maurits L. Kooreman ◽  
Piet Stammes ◽  
L. Gijsbert Tilstra ◽  
Olaf N. E. Tuinder ◽  
...  
Keyword(s):  
Near Infrared ◽  
Photosynthetic Activity ◽  
Gross Primary Production ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
Oxygen Absorption ◽  
Spectral Window ◽  
Data Product ◽  
Uncertainty Estimates ◽  
Water Vapour Absorption

Abstract. Solar-induced fluorescence (SIF) data from satellites are increasingly used as a proxy for photosynthetic activity by vegetation and as a constraint on gross primary production. Here we report on improvements in the algorithm to retrieve mid-morning (09:30 LT) SIF estimates on the global scale from the GOME-2 sensor on the MetOp-A satellite (GOME-2A) for the period 2007–2019. Our new SIFTER (Sun-Induced Fluorescence of Terrestrial Ecosystems Retrieval) v2 algorithm improves over a previous version by using a narrower spectral window that avoids strong oxygen absorption and being less sensitive to water vapour absorption, by constructing stable reference spectra from a 6-year period (2007–2012) of atmospheric spectra over the Sahara and by applying a latitude-dependent zero-level adjustment that accounts for biases in the data product. We generated stable, good-quality SIF retrievals between January 2007 and June 2013, when GOME-2A degradation in the near infrared was still limited. After the narrowing of the GOME-2A swath in July 2013, we characterised the throughput degradation of the level-1 data in order to derive reflectance corrections and apply these for the SIF retrievals between July 2013 and December 2018. SIFTER v2 data compare well with the independent NASA v2.8 data product. Especially in the evergreen tropics, SIFTER v2 no longer shows the underestimates against other satellite products that were seen in SIFTER v1. The new data product includes uncertainty estimates for individual observations and is best used for mostly clear-sky scenes and when spectral residuals remain below a certain spectral autocorrelation threshold. Our results support the use of SIFTER v2 data being used as an independent constraint on photosynthetic activity on regional to global scales.

scholarly journals SIFSpec: Measuring Solar-Induced Chlorophyll Fluorescence Observations for Remote Sensing of Photosynthesis

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 3009 ◽  
Author(s):  
Shanshan Du ◽  
Liangyun Liu ◽  
Xinjie Liu ◽  
Jian Guo ◽  
Jiaochan Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Seasonal Variations ◽  
Photosynthetic Activity ◽  
Gross Primary Production ◽  
Atmospheric Correction ◽  
Gansu Province ◽  
Optical Measurements ◽  
Calibration Data ◽  
Flux Measurements

Solar-induced chlorophyll fluorescence (SIF) is regarded as a proxy for photosynthesis in terrestrial vegetation. Tower-based long-term observations of SIF are very important for gaining further insight into the ecosystem-specific seasonal dynamics of photosynthetic activity, including gross primary production (GPP). Here, we present the design and operation of the tower-based automated SIF measurement (SIFSpec) system. This system was developed with the aim of obtaining synchronous SIF observations and flux measurements across different terrestrial ecosystems, as well as to validate the increasing number of satellite SIF products using in situ measurements. Details of the system components, instrument installation, calibration, data collection, and processing are introduced. Atmospheric correction is also included in the data processing chain, which is important, but usually ignored for tower-based SIF measurements. Continuous measurements made across two growing cycles over maize at a Daman (DM) flux site (in Gansu province, China) demonstrate the reliable performance of SIF as an indicator for tracking the diurnal variations in photosynthetically active radiation (PAR) and seasonal variations in GPP. For the O2–A band in particular, a high correlation coefficient value of 0.81 is found between the SIF and seasonal variations of GPP. It is thus concluded that, in coordination with continuous eddy covariance (EC) flux measurements, automated and continuous SIF observations can provide a reliable approach for understanding the photosynthetic activity of the terrestrial ecosystem, and are also able to bridge the link between ground-based optical measurements and airborne or satellite remote sensing data.

scholarly journals Measurement Characteristics of an airborne Microwave Temperature Profiler (MTP)

2020 ◽  
Author(s):  
Mareike Kenntner ◽  
Andreas Fix ◽  
Matthias Jirousek ◽  
Franz Schreier ◽  
Jian Xu ◽  
...  
Keyword(s):  
The State ◽  
Retrieval Algorithm ◽  
Oxygen Absorption ◽  
Transfer Model ◽  
Flight Altitude ◽  
Standard Measurement ◽  
Noise Characteristics ◽  
Measurement Strategy ◽  
Temperature Profiler ◽  
Calibration Parameters

Abstract. The Microwave Temperature Profiler (MTP), an airborne passive microwave radiometer, records radiances in order to estimate temperature profiles around flight altitude. From these data the state of the atmosphere can be derived and important dynamical processes (e.g. gravity waves) assessed. DLR has acquired a copy of the MTP from NASA-JPL, which was designed as a wing-canister instrument and is deployed on the German research aircraft HALO. For this instrument a thorough analysis of instrument characteristics has been made. This is necessary to correctly determine the accuracy and precision of MTP measurements, and crucial for a retrieval algorithm to derive vertical profiles of absolute atmospheric temperatures. Using a laboratory set-up, the frequency response function and antenna diagram of the instrument was carefully characterised. A cold-chamber was used to simulate the changing in-flight conditions and to derive noise characteristics as well as reliable calibration parameters for brightness temperature calculations, which are compared to those calculated from campaign data. Furthermore, using the radiative transfer model Py4CAtS, the sensitivity to the atmospheric layers around flight altitude was investigated. It was found that using the standard measurement settings, the DLR-MTP’s vertical range of sensitivity is limited to 3 km around flight altitude, but can be significantly increased by adjusting the standard measurement strategy, including slightly weaker oxygen absorption lines and a different set of viewing angles. Calibration parameters do clearly depend on the state of the instrument; using a built-in heated target for calibration may yield large errors in brightness temperatures, due to a misinterpretation of the measured absolute temperature. With here presented corrections to the calibration parameter calculations, the measurement noise becomes the dominant source of uncertainty and it is possible to measure the atmospheric temperature around flight level with a precision of 0.38 K. This is the first time such a thorough instrument characterisation of a MTP instrument is published. With the presented results, it is now possible to identify significant temperature fluctuation signals in MTP data and choose the best possible measurement strategy fitting the purpose of the measurement campaign.

scholarly journals Evaluation of modeled global carbon dynamics: analysis based on global carbon flux and above-ground biomass data

2016 ◽  
Author(s):  
Bao-Lin Xue ◽  
Qinghua Guo ◽  
Tianyu Hu ◽  
Yongcai Wang ◽  
Shengli Tao ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Carbon Flux ◽  
Large Scale ◽  
Gross Primary Production ◽  
Carbon Dynamics ◽  
Global Scale ◽  
Above Ground Biomass ◽  
Carbon Cycles ◽  
Ground Biomass ◽  
Global Carbon

Abstract. Dynamic global vegetation models are useful tools for the simulation of carbon dynamics on regional and global scales. However, even the most validated models are usually hampered by the poor availability of global biomass data in the model validation, especially on regional/global scales. Here, taking the integrated biosphere simulator model (IBIS) as an example, we evaluated the modeled carbon dynamics, including gross primary production (GPP) and potential above-ground biomass (AGB), on the global scale. The IBIS model was constrained by both in situ GPP and plot-level AGB data collected from the literature. Independent validation showed that IBIS could reproduce GPP and evapotranspiration with acceptable accuracy at site and global levels. On the global scale, the IBIS-simulated total AGB was similar to those obtained in other studies. However, discrepancies were observed between the model-derived and observed spatial patterns of AGB for Amazonian forests. The differences among the AGB spatial patterns were mainly caused by the single-parameter set of the model used. This study showed that different meteorological inputs can also introduce substantial differences in AGB on the global scale. Further analysis showed that this difference is small compared with parameter-induced differences. The conclusions of our research highlight the necessity of considering the heterogeneity of key model physiological parameters in modeling global AGB. The research also shows that to simulate large-scale carbon dynamics, both carbon flux and AGB data are necessary to constrain the model. The main conclusions of our research will help to improve model simulations of global carbon cycles.

Towards a nudging of stratospheric overshoots in the 3D mesoscale BRAMS model

2021 ◽  
Author(s):  
Justine Pichon ◽  
Emmanuel Riviere ◽  
Abhinna Behera ◽  
Jeremie Burgalat
Keyword(s):  
Mesoscale Model ◽  
Water Concentration ◽  
Global Scale ◽  
Satellite Measurement ◽  
Model Categories ◽  
Tropical Regions ◽  
Water Ice ◽  
Convective Clouds ◽  
Starting Point ◽  
Ice Water

<p>Water repartition in the stratosphere is a key compound in the atmospheric chemical and<br>radiative equilibrium. Since the 80’s, an increase of the water concentration in the<br>stratosphere has been observed.This presence in the stratosphere can be explained by the<br>slow ascent of air mass above convective clouds in tropical regions. The amount of water<br>vapor entering in the stratosphere depends on the coldest temperature and countered<br>during this slow ascent because it can lead to ice cristal formation that sediment and<br>dehydrate the air masses. But some other processes may contribute to the stratospheric<br>water budget, especially to explain the increase of water vapor. Stratospheric overshoots<br>phenomenon can take part in the stratospheric hydratation, by injecting directly water ice in<br>the stratosphere. Injected ice water, by sublimation, will hydrate stratosphere locally. The<br>local role of overshoots is better known but their contributions at the global scale steal need<br>to be quantified. In order to estimate this contribution, previous studies have used the 3D<br>simulation mesoscale model BRAMS to show overshoot impact in the upper Tropical<br>Tropopause Layer (TTL). These studies are the starting point of our study.</p><p>The aim of this paper is to present the new development inside BRAMS to nudge<br>stratospheric ice injection by overshoots. It uses an overshoot occurrence climatology from<br>MHS (Microwave Humidity Sounder) satellite measurement. Ice injection in the model is<br>made according to ice model categories previously shown to be present in the overshoot<br>plumes with ratios already diagnosed in previous studies. Ice injection is made between two<br>layers of TTL’s stratospheric part: between 380 and 385K and between 385 et 400K. Nudging<br>is triggered only if, in the grid mesh (20 x 20 km) where MHS has detected an overshoot,<br>BRAMS computes a cumulonimbus with a top above 13.5km. For the layer above 385 K<br>isentrope, a subgrid box of 2 km x 2 km is considered for the computation of ice injection.<br>Sensibility test of this nudging scheme will be presented in this presentation. </p>

Evaluation of key parameters controlling phenology-induced variability of surface fluxes in land surface models

2021 ◽  
Author(s):  
Jan De Pue ◽  
José Miguel Barrios ◽  
Liyang Liu ◽  
Philippe Ciais ◽  
Alirio Arboleda ◽  
...  
Keyword(s):  
Land Surface ◽  
Gross Primary Production ◽  
Global Scale ◽  
Plant Phenology ◽  
Surface Fluxes ◽  
Terrestrial Vegetation ◽  
Land Surface Models ◽  
Area Index ◽  
Wide Range ◽  
Surface Models

<p>Over the past decades, land surface models have evolved into advanced tools which comprise detailed process descriptions and interactions at a broad range of scales. One of the challenges in these models is the accurate simulation of plant phenology. It is a key element at the nexus of the simulated hydrological and carbon cycle, where the leaf area index (LAI) plays a major role in flux partitioning, water balance and gross primary production.<br>In this study, three well-established models are used to simulate the intrinsically coupled fluxes of water, energy and carbon from terrestrial vegetation. ORCHIDEE, ISBA-CC and the LSA-SAF algorithm each have a different approach to represent plant phenology. Whereas ISBA-CC has a fairly simple biomass allocation scheme to represent the phenological cycle, ORCHIDEE relies on a dedicated phenology module, and LSA-SAF is driven by remote-sensed forcing variables, such as LAI. Simulations were performed for a wide range of hydro-climatic biomes and plant functional types at field scale. The simulated fluxes were validated using eddy-covariance measurements, and the simulated phenology was compared to remote-sensed observations.<br>These models are tools to extrapolate leaf-level processes to global scale climate predictions. The origin of the parameters controlling phenology-induced variability in these models ranges from plant-scale lab experiments to global-scale calibration. The aim of this study is to investigate the key parameters controlling phenology-induced variability in these models.</p>

scholarly journals Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1225
Author(s):  
Lanka Karthikeyan ◽  
Ming Pan ◽  
Dasika Nagesh Kumar ◽  
Eric F. Wood
Keyword(s):  
Soil Moisture ◽  
Global Scale ◽  
Passive Microwave ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Structural Uncertainty ◽  
Three Solutions ◽  
Brightness Temperatures ◽  
Retrieval Algorithms

Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.

scholarly journals Structural uncertainty in air mass factor calculation for NO<sub>2</sub> and HCHO satellite retrievals

2017 ◽  
Vol 10 (3) ◽  
pp. 759-782 ◽  
Author(s):  
Alba Lorente ◽  
K. Folkert Boersma ◽  
Huan Yu ◽  
Steffen Dörner ◽  
Andreas Hilboll ◽  
...  
Keyword(s):  
Surface Albedo ◽  
Trace Gas ◽  
Retrieval Algorithm ◽  
Structural Uncertainty ◽  
Ancillary Data ◽  
Air Mass ◽  
Cloud Parameters ◽  
Uncertainty Estimates ◽  
Satellite Retrievals ◽  
Mass Factor

Abstract. Air mass factor (AMF) calculation is the largest source of uncertainty in NO2 and HCHO satellite retrievals in situations with enhanced trace gas concentrations in the lower troposphere. Structural uncertainty arises when different retrieval methodologies are applied within the scientific community to the same satellite observations. Here, we address the issue of AMF structural uncertainty via a detailed comparison of AMF calculation methods that are structurally different between seven retrieval groups for measurements from the Ozone Monitoring Instrument (OMI). We estimate the escalation of structural uncertainty in every sub-step of the AMF calculation process. This goes beyond the algorithm uncertainty estimates provided in state-of-the-art retrievals, which address the theoretical propagation of uncertainties for one particular retrieval algorithm only. We find that top-of-atmosphere reflectances simulated by four radiative transfer models (RTMs) (DAK, McArtim, SCIATRAN and VLIDORT) agree within 1.5 %. We find that different retrieval groups agree well in the calculations of altitude resolved AMFs from different RTMs (to within 3 %), and in the tropospheric AMFs (to within 6 %) as long as identical ancillary data (surface albedo, terrain height, cloud parameters and trace gas profile) and cloud and aerosol correction procedures are being used. Structural uncertainty increases sharply when retrieval groups use their preference for ancillary data, cloud and aerosol correction. On average, we estimate the AMF structural uncertainty to be 42 % over polluted regions and 31 % over unpolluted regions, mostly driven by substantial differences in the a priori trace gas profiles, surface albedo and cloud parameters. Sensitivity studies for one particular algorithm indicate that different cloud correction approaches result in substantial AMF differences in polluted conditions (5 to 40 % depending on cloud fraction and cloud pressure, and 11 % on average) even for low cloud fractions (<  0.2) and the choice of aerosol correction introduces an average uncertainty of 50 % for situations with high pollution and high aerosol loading. Our work shows that structural uncertainty in AMF calculations is significant and that it is mainly caused by the assumptions and choices made to represent the state of the atmosphere. In order to decide which approach and which ancillary data are best for AMF calculations, we call for well-designed validation exercises focusing on polluted conditions in which AMF structural uncertainty has the highest impact on NO2 and HCHO retrievals.

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