Conference Agenda

Past year still saw that COVID restrictions hampered FTIR and MAX-DOAS operations, yet we reached some very important milestones. Foremost, after passing the TCCON application and evaluation process, Xianghe has become an official operational TCCON site in September 2021. Data from June 2018 onwards are publicly available from the TCCON data archive (https://tccondata.org/). Also the teams readily implemented the transition from the GG2014 to the GGG2020 TCCON retrieval processing suite, data of which became publicly available in May 2022.

Xianghe FTIR (both TCCON and NDACC-type), and UV-VIS ground-based remote sensing measurements have been and continue to be used for a wide array of research topics, be it satellite validation (S5P, OCO-2/3, TANSAT, etc.), finding novel retrieval strategies (for instance for O₃) or network retrieval strategy harmonisation studies (NDACC HCHO and NO₂). Its location next to Beijing provides an excellent testing ground for retrieval algorithms (satellite and ground-based) as one wants to test ones product under as many conditions as possible (from remote pristine to heavily urbanized, across all continents). This is important for uncovering imperfections in the algorithms, which can then be evaluated and remedied. For instance, when comparing Xianghe TCCON CO₂ dry air mole fractions with OCO-2 and OCO-3 measurements in North-China, we found that the mean bias is close to 0, but that the OCO-3 snapshot area mode (SAM) is about 1.0 ppm overestimated.

In recent years, Europe, USA, and China have started to implement the concept of creating an SI-traceable Satellite – SITSat. A key objective is to enable high-accuracy on-orbit radiometric calibration of other satellite sensors traceable to the SITSat. Cross-calibration is considered as an effective approach to transfer the radiometric reference from the high-accuracy satellite (e.g., SITSat) sensor to other satellite sensors which require periodical calibration. The Simultaneous Nadir Overpass (SNO) method based on strict matching of observations is the ideal and most accurate way to cross-calibrate and compare satellite sensors. However, high-resolution spaceborne sensors tend to have relatively small swaths and thus fewer opportunities to have ideal matching conditions with a reference sensors. However, relaxing the matching conditions; times, view/illumination angles etc to increase the number of matches inevitably leads to increased uncertainty.

The key step in cross-calibration is to establish the proper reference i.e. Top-of-Atmosphere (TOA) radiance/reflectance, corresponding to the observation value of the satellite being monitored. For pseudo invariant calibration sites (PICS), the relatively stable surface and atmospheric conditions help ensure the accuracy of cross-calibration results. Similarly, TOA reflectance models established for PICS sites can also ensure high-repeatability of such sites as a radiometric reference in cross-calibration. For RadCalNet sites, autonomous observation instruments are deployed to obtain surface and atmospheric parameters at the time of overpass of a satellite. RadCalNet can provide satellite operators with SI-traceable TOA spectrally-resolved reflectance derived over a network of sites, with associated uncertainties. Therefore, RadCalNet sites are able to serve as calibration references for high-medium resolution satellite, with uncertainties limited by the radiometric values assigned to them from characterization against another sensor, at present ground based observations. However, with the concept of SITSats the opportunity arises to provide the reference calibration to the RadCalNet site from a spaceborne sensor and use the surface instrumentation to provide a monitor/correction function instead of the absolute reference. Here we describe a new benchmark transfer calibration method for high-resolution spaceborne sensors, which uses RadCalNet sites measurements as the intermediate radiometric reference value.

In this study, the TOA reflectance models of RadCalNet sites were 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 then used as an intermediate radiometric reference, which can be traced back to the reference satellite sensor. The corrected RadCalNet TOA reflectance was then used to calibrate the monitored satellite sensors. Through the uncertainty analysis of this method, the uncertainty of cross-calibration between the reference satellite and the satellite to be calibrated caused by the relaxation of time matching constraint can be reduced.

Taking Baotou site in China and RVP site in US, the proposed method is validated, and the application demonstration is carried out by using the Chinese and European satellites (i.e., Sentinel-2A/2B, GF-1, GF-6 and SV-1). The preliminary uncertainty analysis results show that this method can achieve obtain high-precision calibration coefficients, and the calibration uncertainty is 3.5%-4%.

Plant canopy characteristics are essential indicators of plant growth status, of which the two most commonly reported are the leaf area index (LAI) and leaf chlorophyll content (LCC). They determine the interception and absorption rates of solar radiation by vegetation, thereby implying the plant productivity and yield. They are also the two main driving variables in several ecological and crop models that provide practical guidance for precision agriculture and aid monitor the state and functioning of terrestrial environments. Such biophysical products could indirectly be estimated from the optical Earth Observation dataset through surface reflectance. Nevertheless, the non-destructive estimation from different satellites have varying levels of spatial and spectral resolution. Therefore, the validation of such biophysical products parameters and improving remote sensed characterization of vegetation biophysical properties are of great importance to ensure they meet the requirements for specific applications. The UAV4VAL project objective is to evaluate the capability of UAVs as a source of reference data for validating decametric surface reflectance and vegetation biophysical products like LAI, with a specific focus on the European Sentinel-2 and Chinese Gaofen-6 missions.

In the first year, we focused on the LAI product retrieval from both Sentinel2 and GF6 data and validated with in-situ LAI collection and UAV image. We collected 17 ground LAI measurements at Taizi Mountain, China (30.916°, 112.866°) on 31th October 2020, and gathered the GF6 and Sentinel2 imagery over the same period. LAI-2200C plant canopy analyser and digital hemispherical photography was used for obtaining in-situ LAI. In addition, the drone images were collected by a P4 Multispectral camera with 5 bands. To evaluate whether UVA imagery can well bridge the scale gap between ground measurements and satellite imagery, we first generate LAI validation maps from UAV by constructing a regression model between in-situ LAI and UAV vegetation index(VI). Second, the Sentinel-2 retrieved LAI maps was implemented using SNAP and a similar hybrid LAI retrieval process was applied on GF6, the hybrid LAI retrieval combine the advantages of physical-methods with the learning accuracy and flexibility of non-parametric regression algorithms. Finally, we compared the traditional in-situ LAI validation and the validation using UAV LAI maps on Sentinel-2 imagery.

The results of this study showed Atmospherically resistant vegetation index (ARVI) is the best VI for UAV-based LAI retrieval with R2 of 0.66 and RMSE of 1.00. The validation of Sentinel-2 LAI products using the UAV LAI map outperformed traditional in-situ validation. The UVA validation on LAI has RMSE of 1.566 and MAE of 1.238 while the in-situ validation has higher RMSE and MAE of 2.17 and 1.61, respectively. The ground validation of GF6-derived LAI also got poor result with low R2, i.e., ~0.1 to ~0.15 and high RMSE, i.e., ~1.4 to ~1.6 between GF6-derived LAI and in-situ LAI. However, the GF6 image of the study area was highly influenced by cloud and the LAI retrieval from GF6 still needs further exploration. In addition, a vegetation indices (VIs) sensitivity test between NDVI, MTCI and MCARI based on PROSAIL simulated dataset. MTCI showed the highest correlation with Canopy Chlorophyll Content (CCC), with 0.8577 for UAV sensors and 0.8493 for GF6 sensor. MTCI was recommended for Chlorophyll retrieval in the future. The next step will focus on the in-situ biophysical parameters and UVA image collection in the UK site and compare the LAI-retrieval results between GF6 and Sentinel2.