New algorithms for atmospheric correction and retrieval of biophysical parameters in earth observation : application to ENVISAT/MERIS data

An algorithm for the derivation of atmospheric and surface biophysical products from the MEdium Resolution Imaging Spectrometer Instrument (MERIS) on board ENVIronmental SATellite (ENVISAT/MERIS) Level 1b data over land has been developed. Georectified aerosol optical thickness (AOT), columnar water vapor (CWV), spectral surface reflectance and chlorophyll fluorescence (CF) maps are generated. Emphasis has been put on implementing a robust software able to provide those products on an operational manner, making no use of ancillary parameters apart from those attached to MERIS images. For this reason, it has been named Self-Contained Atmospheric Parameters Estimation from MERIS data (SCAPE-M). The fundamentals of the algorithm and the validation of the derived products are presented in this thesis. Errors of ±0.03, ±4% and ±8% have been estimated for AOT, CWV and surface reflectance retrievals, respectively, by means of a sensitivity analysis. More than 200 MERIS images have been processed in order to assess the method performance under a range of atmospheric and geographical conditions. A good comparison is found between SCAPE-M AOT retrievals and ground-based measurements taken during the SPectra bARrax Campaigns (SPARC) 2003 and 2004, except for a date when an episode of Saharan dust intrusion was detected. Comparison of SCAPE-M retrievals with data from AErosol RObotic NETwork (AERONET) stations showed a square Pearson's correlation coefficient R2 of about 0.7-0.8. Those values grow up to more than 0.9 in the case of CWV after comparison with the same stations. A good correlation is also found with the ESA Level 2 official CWV product, although slight different performances with varying surface elevation are detected. Retrieved surface reflectance maps have been intercompared with reflectance data derived from MERIS images by the Bremen AErosol Retrieval (BAER) method in the first place

Remote sensing of terrestrial chlorophyll fluorescence from space

High-resolution spectrometers enable new avenues in global carbon cycle research, including the first accurate retrievals of chlorophyll fluorescence from space as an indicator of photosynthetic activity

Understanding the potential of Sentinel-2 for monitoring methane point emissions

The use of satellite instruments to detect and quantify methane emissions from fossil fuel production activities is highly beneficial to support climate change mitigation. Different hyperspectral and multispectral satellite sensors have recently shown potential to detect and quantify point-source emissions from space. The Sentinel-2 (S2) mission, despite its limited spectral design, supports the detection of large emissions with global coverage and high revisit frequency thanks to coarse spectral coverage of methane absorption lines in the shortwave infrared. Validation of S2 methane retrieval algorithms is instrumental in accelerating the development of a systematic and global monitoring system for methane point sources. Here we develop a benchmarking framework for such validation. We first develop a methodology to generate simulated S2 datasets including methane point-source plumes. These benchmark datasets have been created for scenes in three oil and gas basins (Hassi Messaoud, Algeria; Korpeje, Turkmenistan; Permian Basin, USA) under different scene heterogeneity conditions and for simulated methane plumes with different spatial distributions. We use the simulated methane plumes to validate the retrieval for different flux rate levels and define a minimum detection threshold for each case study. The results suggest that for homogeneous surfaces, the detection limit of the proposed S2 methane retrieval ranges from 1000 kg h&minus;1 to 2000 kg h&minus;1, whereas for areas with large surface heterogeneity, the retrieval can only detect plumes in excess of 5000 kg h&minus;1. The different sources of uncertainty in the flux rate estimates have also been examined. Dominant quantification errors are either wind-related or plume mask-related, depending on the surface type. Uncertainty in wind speed, both in the 10-m wind (U10) and in mapping U10 to the effective wind (Ueff ) driving plume transport, is the dominant source of error for quantifying individual plumes in homogeneous scenes. For heterogeneous scenes, the surface structure underlying the methane plume affects the plume masking and can become a dominant source of uncertainty.</p

Global Retrievals of Solar-Induced Chlorophyll Fluorescence With TROPOMI: First Results and Intersensor Comparison to OCO-2

In recent years, solar‐induced chlorophyll fluorescence (SIF) retrieved from spaceborne spectrometers has been extensively used as a proxy for terrestrial photosynthesis at relatively sparse temporal and spatial scales. The near‐infrared band of the recently launched TROPOspheric Monitoring Instrument (TROPOMI) features the required spectral resolution and signal‐to‐noise ratio to retrieve SIF in a spectral range devoid of atmospheric absorption features. We find that initial TROPOMI spectra meet high expectations for a substantially improved spatiotemporal resolution (up to 7‐km × 3.5‐km pixels with daily revisit), representing a step change in SIF remote sensing capabilities. However, interpretation requires caution, as the broad range of viewing‐illumination geometries covered by TROPOMI's 2,600‐km‐wide swath needs to be taken into account. A first intersensor comparison with OCO‐2 (Orbiting Carbon Observatory‐2) SIF shows excellent agreement, underscoring the high quality of TROPOMI's SIF retrievals and the notable radiometric performance of the instrument

Overview of Global Monitoring of Terrestrial Chlorophyll Fluorescence from Space

Despite the critical importance of photosynthesis for the Earth system, understanding how it is influenced by factors such as climate variability, disturbance history, and water or nutrient availability remains a challenge because of the complex interactions and the lack of GPP measurements at various temporal and spatial scales. Space observations of the sun-induced chlorophyll fluorescence (SIF) electromagnetic signal emitted by plants in the 650-850nm spectral range hold the promise of providing a new view of vegetation photosynthesis on a global basis. Global retrievals of SIF from space have recently been achieved from a number of spaceborne spectrometers originally intended for atmospheric research. Despite not having been designed for land applications, such instruments have turned out to provide the necessary spectral and radiometric sensitivity for SIF retrieval from space. The first global measurements of SIF were achieved in 2011 from spectra acquired by the Japanese GOSAT mission launched in 2009. The retrieval takes advantage of the high spectral resolution provided by GOSATs Fourier Transform Spectrometer (FTS) which allows the evaluation of the in-filling of solar Fraunhofer lines by SIF. Unfortunately, GOSAT only provides a sparse spatial sampling with individual soundings separated by several hundred kilometers. Complementary, the Global Ozone Monitoring Experiment-2 (GOME-2) instruments onboard MetOp-A and MetOp-B enable SIF retrievals since 2007 with a continuous and global spatial coverage. GOME-2 measures in the red and near-infrared (NIR) spectral regions with a spectral resolution of 0.5 nm and a pixel size of up to 40x40 km2. Most recently, another global and spatially continuous data set of SIF retrievals at 740 nm spanning the 2003-2012 time frame has been produced from ENVISATSCIAMACHY. This observational scenario has been completed by the first fluorescence data from the NASA-JPL OCO-2 mission (launched in July 2014) and the upcoming Copernicus' Sentinel 5-Precursor to be launched in early 2016. OCO-2 and TROPOMI offer the possibility of monitoring SIF globally with a 100-fold improvement in spatial and temporal resolution with respect to the current measurements from the GOSAT, GOME-2 and SCIAMACHY missions. In this contribution, we will provide an overview of existing global SIF data sets derived from space-based atmospheric spectrometers and will demonstrate the potential of such data to improve our knowledge of vegetation photosynthesis and gross primary production at the synoptic scale. We will show examples of ongoing research exploiting SIF data for an improved monitoring of photosynthetic activity in different ecosystems, including large crop belts worldwide, the Amazon rainforest and boreal evergreen forests

A spatially downscaled sun-induced fluorescence global product for enhanced monitoring of vegetation productivity

Sun-induced chlorophyll fluorescence (SIF) retrieved from satellite spectrometers can be a highly valuable proxy for photosynthesis. The SIF signal is very small and notoriously difficult to measure, requiring sub-nanometre spectral-resolution measurements, which to date are only available from atmospheric spectrometers sampling at low spatial resolution. For example, the widely used SIF dataset derived from the GOME-2 mission is typically provided in 0.5∘ composites. This paper presents a new SIF dataset based on GOME-2 satellite observations with an enhanced spatial resolution of 0.05∘ and an 8 d time step covering the period 2007–2018. It leverages on a proven methodology that relies on using a light-use efficiency (LUE) modelling approach to establish a semi-empirical relationship between SIF and various explanatory variables derived from remote sensing at higher spatial resolution. An optimal set of explanatory variables is selected based on an independent validation with OCO-2 SIF observations, which are only sparsely available but have a high accuracy and spatial resolution. After bias correction, the resulting downscaled SIF data show high spatio-temporal agreement with the first SIF retrievals from the new TROPOMI mission, opening the path towards establishing a surrogate archive for this promising new dataset. We foresee this new SIF dataset becoming a valuable asset for Earth system science in general and for monitoring vegetation productivity in particular. The dataset is available at https://doi.org/10.2905/21935FFC-B797-4BEE-94DA-8FEC85B3F9E1 (Duveiller et al., 2019)

Estimating Crop Primary Productivity with Sentinel-2 and Landsat 8 using Machine Learning Methods Trained with Radiative Transfer Simulations

Satellite remote sensing has been widely used in the last decades for agricultural applications, {both for assessing vegetation condition and for subsequent yield prediction.} Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for advanced monitoring of crop productivity. In particular, we combine process-based modeling with the soil-canopy energy balance radiative transfer model (SCOPE) with Sentinel-2 {and Landsat 8} optical remote sensing data and machine learning methods in order to estimate crop GPP. Our model successfully estimates GPP across a variety of C3 crop types and environmental conditions even though it does not use any local information from the corresponding sites. This highlights its potential to map crop productivity from new satellite sensors at a global scale with the help of current Earth observation cloud computing platforms

Satellites Detect Abatable Super-Emissions in One of the World¿s Largest Methane Hotspot Regions

[EN] Reduction of fossil fuel-related methane emissions has been identified as an essential means for climate change mitigation, but emission source identification remains elusive for most oil and gas production basins in the world. We combine three complementary satellite data sets to survey single methane emission sources on the west coast of Turkmenistan, one of the largest methane hotspots in the world. We found 29 different emitters, with emission rates >1800 kg/h, active in the 2017¿2020 time period, although older satellite data show that this type of emission has been occurring for decades. We find that all sources are linked to extraction fields mainly dedicated to crude oil production, where 24 of them are inactive flares venting gas. The analysis of time series suggests a causal relationship between the decrease in flaring and the increase in venting. At the regional level, 2020 shows a substantial increase in the number of methane plume detections concerning previous years. Our results suggest that these large venting point sources represent a key mitigation opportunity as they emanate from human-controlled facilities, and that new satellite methods promise a revolution in the detection and monitoring of methane point emissions worldwide.The authors thank the team that realized the TROPOMI instrument and its data products, consisting of the partnership between Airbus Defense and Space Netherlands, KNMI, SRON, and TNO, commissioned by NSO and ESA. Sentinel-5 Precursor is part of the EU Copernicus program, Copernicus (modified) Sentinel-5P data (2018-2020) have been used. We thank the Sentinel Hub service for providing the EO Browser service. Thanks to the Environmental Defense Fund (EDF) for providing data about the O&G fields of the study area, and the Carbon Limits group for contributing to the verification of the emission sources. We thank the Italian Space Agency for the PRISMA data used in this work. Dr. Yongguang Zhang from the University of Nanjing is also thanked for his support to get access to ZY1 AHSI data, and Dr. Javier Gorrono from Universitat Politecnica de Valencia for his assistance in the uncertainty estimations. Authors Itziar Irakulis-Loitxate and Luis Guanter received funding from ESA Contract 4000134929.Irakulis-Loitxate, I.; Guanter-Palomar, LM.; Joannes D. Maasakkers; Daniel Zavala-Araiza; Ilse Aben (2022). Satellites Detect Abatable Super-Emissions in One of the World¿s Largest Methane Hotspot Regions. Environmental Science & Technology (Online). 56(4):2143-2152. https://doi.org/10.1021/acs.est.1c048732143215256

Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2

The Orbiting Carbon Observatory-2 (OCO-2), scheduled to launch in July 2014, is a NASA mission designed to measure atmospheric CO_2. Its main purpose is to allow inversions of net flux estimates of CO_2 on regional to continental scales using the total column CO_2 retrieved using high-resolution spectra in the 0.76, 1.6, and 2.0 μm ranges. Recently, it was shown that solar-induced chlorophyll fluorescence (SIF), a proxy for gross primary production (GPP, carbon uptake through photosynthesis), can be accurately retrieved from space using high spectral resolution radiances in the 750 nm range from the Japanese GOSAT and European GOME-2 instruments. Here, we use real OCO-2 thermal vacuum test data as well as a full repeat cycle (16 days) of simulated OCO-2 spectra under realistic conditions to evaluate the potential of OCO-2 for retrievals of chlorophyll fluorescence and also its dependence on clouds and aerosols. We find that the single-measurement precision is 0.3–0.5 W m^(− 2) sr^(− )1 μm^(−1) (15–25% of typical peak values), better than current measurements from space but still difficult to interpret on a single-sounding basis. The most significant advancement will come from smaller ground-pixel sizes and increased measurement frequency, with a 100-fold increase compared to GOSAT (and about 8 times higher than GOME-2). This will largely decrease the need for coarse spatial and temporal averaging in data analysis and pave the way to accurate local studies. We also find that the lack of full global mapping from the OCO-2 only incurs small representativeness errors on regional averages. Eventually, the combination of net ecosystem exchange (NEE) derived from CO_2 source/sink inversions and SIF as proxy for GPP from the same satellite will provide a more process-based understanding of the global carbon cycle