Conference Agenda

In recent years, ESA, following a proposal of the UK Space Agency, USA, and China have started to implement the concept of creating an SI traceable Satellite – SITSat. The main idea is to establish in-orbit reference radiometric calibration of other sensors based on a SITSat. In this concept the SITSat’s are must have the property that they have a robust documented uncertainty to SI in-flight and can be considered a benchmark for radiation measurement and in this case only a few such satellites need to exist to provide the space based calibration reference for transference to other satellites. However, as the high-resolution spaceborne sensor, with small swath is concerned, the cross-points (between the benchmark satellite and monitored satellite) is rarer to find if very strict matching conditions are required. So, this project explores a complimentary method of benchmark transfer calibration for the high-resolution space-borne sensor, which uses RadCalNet sites measurements as the intermediate radiometric reference value. It will benefit to solve the problem of increasing cross-calibration uncertainty and limited cross-calibration frequency caused by the relaxation of matching constraints. In the past year, great achievement has been done in designing overall research scheme and analyzing the sources of uncertainty, exploring preliminary cross-calibration of optical satellite with high-precision radiometric reference satellite over Baotou site in China as demonstration.

(1) Transfer the benchmark from the SI-traceable sensor to the RadCalNet TOA reflectance. The TOA reflectance model of ground target (e.g. Baotou site) was constructed using satellite observation data with high radiometric calibration accuracy. Then the model was used to correct the RadCalNet standard TOA reflectance products. The corrected RadCalNet TOA reflectance was used as a radiometric reference benchmark, which can be traced back to reference satellite sensor. Finally, the corrected RadCalNet TOA reflectance was used to calibrate the to-be-calibrated satellite sensors. Through this method, the uncertainty of cross-calibration between the reference satellite and the satellite to be calibrated caused by the relaxation of the time matching constraints can be reduced.

(2) The proposed method was validated and analyzed by taking Landsat8/OLI as the radiometric reference satellite, TOA reflectance products of sand target in Baotou site as the ground target and Sentinel-2A/2B and SV-01 satellite as the be-calibrated satellite. The results showed that the accuracy of Baotou sand target TOA reflectance model established in this study is quite good, which the average relative difference between the predicted values of the model and the observed values of Landsat8/OLI satellite is less than 1% (the band 4 is less than 2%). By using this model, the relative difference between TOA reflectance product of Baotou site and the actual TOA reflectance observed by Sentinel-2 and SV-1 can be reduced effectively from 6% to less than 3%. These experiments and results validated the effective of proposed method.

(3) This project will develop transfer calibration for ESA and TPM satellites: using Landsat-8 and sentinel-2A/B as reference satellites, and Chinese satellites (such as GF series, ZY series and SV series satellites) as to-be-calibrated satellites, carry out transfer calibration demonstration based on RadCalNet sites. At present, 1 postgraduate student will get Master Degree in 2021, and 2 postgraduate student students and 2 young scholars already participate in the Dragon 5 program. And the in-situ data measurements of Baotou site already provide standard calibration product to support this research. In the future, the data of other sites in China and other RadCalNet sites in Europe will provide radiometric calibration data, to improve and guarantee the radiometric calibration accuracy and data quality of Chinese and European satellites by carrying out application demonstration of automated radiometric calibration based on RadCalNet and the proposed method.

Surface reflectance is the fundamental quantity required in the majority of optical Earth Observation analyses, and as an essential input to derive biophysical products. These products, which include essential climate variables (ECVs) such as leaf area index (LAI) and the fraction of absorbed photo synthetically active radiation (FAPAR), in addition to parameters such as the fraction of vegetation cover (FCOVER) and Canopy Chlorophyll Content (CCC) , provide insight into the state and function of the terrestrial environment. In turn, they are crucial in understanding vegetation productivity/yield, biogeochemical cycles, and the weather and climate systems. Therefore, validation of such products are of great importance to ensure they meet the accuracy requirements for specific applications.

The aim of this project is to evaluate the capability of UAVs as a source of reference data for validating decametric surface reflectance and vegetation biophysical products, with a specific focus on the European Sentinel-2 and Chinese Gaofen-6 missions. The project will provide an opportunity to transfer knowledge gained from existing ESA-funded projects on fiducial reference measurements (FRM), which focus on traceability and uncertainty evaluation in Earth Observation validation efforts.

In-situ measurements shall be collected using a combination of instruments, LAI-2200C plant canopy analyser and digital hemispherical photography for obtaining LAI, FAPAR and FCOVER data. Leaf chlorophyll content (LCC) obtained with the Minolta SPAD-502 chlorophyll meter shall be combined with LAI data to derive in-situ measurements of CCC.

In the first year of the project, existing ground measurements obtained during previous field campaigns at three sites (Wytham Woods, UK (51.774°, -1.338°) and Taizi Mountain and Wangmang Cave, China (30.916°, 112.866°) , will be used to calibrate and validate a prototype processor for deriving biophysical variables from Gaofen-6 data. These products will be compared with the biophysical variable derived from Sentinel 2 L2 processor to evaluate their similarity and differences, with a view to exploit the complementary from both satellite sensors. In addition, the suitability of drone images collected over the Chinese sites will be evaluate to bridge the scale gap between the ground measurements and Sentinel 2 image.

The schedule for the project, detailing the field campaigns, processing chains and planned academic exchange activities shall be presented.

In August 2018, ESA’s Earth Explorer mission Aeolus has been successfully launched to space. Since then Aeolus has been demonstrating its capability to accurately measure atmospheric wind profiles from the ground to the lower stratosphere on a global scale deploying the first ever satellite borne wind lidar system ALADIN. In order to validate Aeolus wind products several airborne campaigns were performed over Central Europa and the North Atlantic region (most recently in autumn 2019 in Iceland), employing the ALADIN Airborne Demonstrator (A2D) developed by DLR (Deutsches Zentrum für Luft- und Raumfahrt). Ground-based direct-detection and heterodyne Doppler wind lidar and ocean lidar are developed by the Ocean University of China (OUC) and deployed during several field campaigns, including the sailing competition within the Olympic Games in 2008 in Qingdao and the atmospheric explorer in Tibetan Plateau Experiment of Atmospheric Sciences (TIPEX III). The Shanghai Institute of Optics and Fine Mechanics (SIOM) of the Chinese Academy of Sciences (CAS) developed a ground based direct-detection wind lidar in 355nm and a airborne coherent Doppler wind lidar. SIOM is responsible for several ground validation stations for future spaceborne atmospheric lidar in China, which may provide useful aerosol and wind profiles data for Aeolus validation. The National Satellite Meteorological Center (NSMC), China Meteorological Administration (CMA) is responsible for receiving, processing the data of Chinese FY meteorological satellites, and distributing the data and information products to users for application. Apart from that, it is envisaged to investigate the capability of measuring the marine boundary layer with Aeolus and to measure marine optical properties with co-located shipborne ocean lidar systems during overpasses of Aeolus. The first part of this proposal covers the validation of Aeolus wind and aerosol data products by means of ground and airborne observations with the objective to improve the quality of Aeolus operational data products. Global observations of column carbon dioxide concentrations and aerosol extinction profiles are important for climate study and environment monitoring which is why China decided to implement the lidar mission ACDL (Aerosol and Carbon dioxide Detection Lidar) to measure CO2 and aerosol from space - currently scheduled for 2021. Within this framework a spaceborne engineering prototype of the ACDL lidar is being developed and an airborne lidar prototype for column carbon dioxide concentration measurements was developed by Shanghai Institute of Optics and Fine Mechanics (SIOM) of the Chinese Academy of Sciences (CAS). The second part of the proposal covers the preparation of the ACDL mission with the objectives to analyse requirements for column carbon dioxide concentration and aerosol extinction profile measurements of the ACDL lidar for science applications and to validate the retrieval algorithms for carbon dioxide and aerosol parameters for the future space mission.

Remote sensing of ocean color over coastal waters is challenging and these difficulties can be placed in 3 categories: i) adverse atmospheric conditions associated with the presence of thin clouds or thick aerosol plumes (sometimes biomass burning), ii) challenging environments found over or around the water target (boundary conditions); iii) extreme conditions associated with the water content in optically active constituents (high concentrations of sediments). Evaluation and improvements of the estimation of bio-optical and biogeochemical parameters is an indispensable task for accurately monitoring the dynamics and the quality of coastal waters through the use of ocean color remote sensing. Especially, with the improvement of sensor ability and the advent of novel retrieval algorithms/models, ocean color is playing a more and more important role in understanding, the utilization, protection and management of coastal environments. Ocean color data can thus provide biogeochemical data with known uncertainty, which is of great importance for quantitatively characterizing variation of key elements in coastal ecosystem and is required for input in modelling.

Our project aims at tackling those issues over European (mainly French) and Chinese coastal waters. The main scientific objectives concern the monitoring of the quality of the French and Chinese coastal waters using OLCI and COCTS/CZI space-borne sensors. The project is divided into different tasks: (1) Characterization of uncertainty of OLCI and COCTS/CZI ocean color products in coastal waters; (2) Development of novel regional EO datasets in coastal waters. The first task aims at evaluating the atmospheric correction and bio-optical algorithms of OLCI and COCTS/CZI in our two areas of interest using in-situ measurements collected by both teams and the second task aims at developing regional bio-optical algorithms for the Chinese/French coastal waters according to specific spectral configuration of COCTS and OLCI.

During the symposium, we will present the objectives of the project with detailed description of each task, the in-situ measurements collected by both teams that will be used to validate the different algorithms and the plan for training young scientists.

Project name: All-Weather Land Surface Temperature at High Spatial Resolution: Validation and Applications.

Project’s objectives: The main objective is to inter-compare and validate two new LST products, which provide (nearly) gap-free all-weather LST at high spatial resolution. The two all-weather LST products utilise different retrieval approaches, namely the method by
- Zhang et al. (2019): temporal component decomposition and merging of TIR LST with passive microwave (PMW) LST.
- Martins et al. (2019): merging of clear-sky MSG/SEVIRI LST with LST generated by a Soil-Vegetation-Atmosphere (SVAT) model under cloudy conditions.

Further objectives: i) Generation of long-term (global) all-weather LST data set; ii) Setting up an LST validation station in China to provide Fiducial Reference Measurements (FRM); iii) Employing all-weather LST data to simulate and study freeze/thaw on the TP.

Major progress: Land Surface Temperature (LST) is an indicator for the exchange of energy in the process of atmosphere-ground interaction. The all-weather LST at high spatial resolution is required for understanding and simulating regional processes of meteorology, hydrology, and ecology. The project team has completed a series of validation, algorithm development, and product generation.

Due to considerable temporal gaps between AMSR-E and AMSR2 observations from November 2011 to May 2012, the current version of integrated LST based on MODIS-AMSR-E/2 data is not really an all-weather product. To solve this problem, the project team used Chinese Fengyun-3B MWRI brightness temperature (BT) to reconstruct a spatial-seamless (i.e. without the two major gaps) AMSR-E/2-like microwave (MW) BT based on MWRI data for 2011–2012 over the Tibetan Plateau (TP) and to estimate a realistic 1-km all-weather LST by integrating reconstructed MW BT with Aqua-MODIS LST. Based on the in-situ measurements from Heihe Watershed Allied Telemetry Experimental Research (HiWATER) and Watershed Allied Telemetry Experimental Research (WATER) in the Heihe River basin, and networks operated by other Chinese groups on the TP, the project team validated the estimated all-weather LST with RMSEs of about 1.45–3.36 K.

Passive microwave (PMW) is an effective means to obtain surface temperature under clouds, thus the PMW-LST accuracy is critical for all-weather LST. The project team used a convolutional neural network (CNN) to estimate LST from the AMSR-E and AMSR2 data over the Chinese landmass. The intercomparison indicated that ~50% of the CNN LSTs were closer to the MODIS LSTs than ESA’s Glob Temperature AMSR-E LSTs. Validation against in-situ LSTs showed that the CNN LSTs yielded RMSEs of 2.10–4.72 K for forest and cropland sites.

Reanalyses data from Global Circulation Models (GCM) have the advantage to be spatiotemporally continuous: therefore, they offer a promising alternative to be merged with TIR data in order to reconstruct an all-weather LST product. Based on the decomposition model of LST time series, the project team proposed a novel method to reconstruct a 1-km all-weather LST, which is termed ‘reanalysis and thermal infrared remote sensing data merging’ (RTM). RTM was applied to merge (MODIS) and Global/China Land Data Assimilation System (GLDAS/CLDAS) data over the TP and the surrounding area. Validation results based on in-situ LST show that the RTM LST has RMSEs of 2.03–3.98 K.

Based on the method of Zhang et al. (2019) and RTM, the project team has produced and released two all-weather LST products: i) Daily 1-km all-weather land surface temperature dataset for Western China V1 (2003-2018) and ii) Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST-China (TRIMS LST-China; 2000-2019).

At the EUMETSAT LSA-SAF, the operational “All-Sky LST” production and distribution is now underway. The product is based on optical observations by SEVIRI (onboard MSG), delivering data every 30 min with a 3 km resolution at nadir, for the whole SEVIRI disk encompassing Europe, Africa and part of South America. The product has been thoroughly validated against in-situ data collected from 33 stations located over a wide range of biomes, distributed by global networks (e.g. BSRN, SURFRAD, KIT and EFDC), with comparable RMSEs between clear and cloud conditions (2.8 K and 2.9 K, respectively). Comparisons with AMSR-E (Martins et al., 2019) and with ERA5-Land (MLST-AS Validation Report) have highlighted that most satellite to in-situ discrepancies may be explained by land surface heterogeneities, directional effects, the presence of deep/opaque clouds and high desert aerosol loads. This information will be useful to contrain the product uncertainty, which will be one of the outcomes of this project.

Based on a highly standardized instrument package for LST validation developed for Copernicus LAW (https://law.acri-st.fr/home), the project team adapted and built an instrument package to be deployed within this Dragon 5 project on a suitable Chinese validation site. The package’s main instruments are two long-term stable, narrow-band TIR radiometers, which have been calibrated against KIT’s certified reference source; furthermore, the entire instrument package has been tested intensively. During an inter-comparison study performed in September 2020 on Lake Constance, an identical instrument package was inter-compared against the ISAR (Infrared Sea Surface Temperature Autonomous Radiometer), which is constantly calibrated against two internal blackbodies: the in-situ LST obtained with the standard instrument package only had a bias of -0.09 K and a standard deviation of 0.06 K w.r.t. the ISAR.

The next schedule: i) The project team will further inter-compare TRIMS-LST with the original two products. ii) Based on existing infrastructure, the Chinese team will make efforts to set up a new LST validation station on the TP (or its nearby areas). The station will provide highly accurate in-situ LST and can draw on KIT's technical and scientific support. iii) the project team will use all-weather LST to calibrate and evaluate the hydrological model on Tibetan Plateau. iv) the project team will inter-compare co-located all-weather LST based on the Martins et al. (2019) and Zhang et al. (2019) methods and validate both products with in-situ LST from KIT’s permanent validation station at Gobabeb, Namibia. Other new methods will also be considered.

With the support of Dragon-5 project, the Chinese team's Ph.D. student, Jin Ma, went to KIT for a one-year exchange and has now returned to China after completing the exchange.