Conference Agenda

This work describes a new technique for the calibration and validation (Cal/Val) of radar satellite altimeters using reference points on the ground in the form of corner reflectors. Two such corner reflectors (i.e., ALX1 and ALX2) have been set up in Crete, Greece as an upgrade of the Permanent Facility for Altimetry Calibration (PFAC) of the European Space Agency. Corner reflectors are passive targets with well-determined radar properties, placed at known locations on the ground. Their response is recorded by satellite altimeters and subsequently used to calibrate measurements for the radar cross sections, range, and time-tagging. In particular, measurements of the radar cross section are valuable parameters for determining wind at the sea surface, and when such satellite observations are properly calibrated, they constitute an important contribution to calibrating climate change models.

The main advantages of corner reflectors, in relation to active microwave transponders, is that (a) they do not require activation and power supply in the field, (b) their measurements are not contaminated by errors arising from radio frequency electronics and antennas (i.e., aging, antenna misalignment, electronic gain stability, etc.), and finally (c) they can be used for calibrating satellite altimeters in different frequencies (e.g., Ku-band and Ka-band).

This work presents the steps followed for the design, manufacturing, installation, and operation of that pair of ALX1 and ALX2 corner reflectors to calibrate the sigma-0 parameters of Sentinel-6 and Sentinel-3 altimeters. Such steps include:

1. Define type of corner reflector, dimensions, material, orientation to meet the requirements for calibrating Sentinel-6 and Sentinel-3A/B altimetry missions,
2. Specify corner reflector’s planarity, orthogonality, and curvature to operate for both Ku-band and Ka-band calibration,
3. Select an optimal location for the corner reflectors installation to support multi-mission Cal/Val,
4. Define the horizontal distance and height difference that the two corner reflectors should have to be discriminant in satellite altimeter’s records,
5. Manufacture and install the corner reflectors,
6. Evaluate satellite altimeters’ records and derive the backscatter coefficient bias using the corner reflectors,
7. Compare the results with the ones derived by the microwave transponders operating at the PFAC of ESA.

The European Space Agency and the Dragon 5 project (ID 59198) are acknowledged for supporting a young European scientist to carry out the vast majority of this work.

China has been establishing a major infrastructure, called the China Altimetry Calibration Cooperation Plan, to monitor all its altimetry missions. The Wanshan ground reference site is a one such site dedicated to the calibration and validation (Cal/Val) of all the Chinese HaiYang-2 satellite altimeters. It was built in late 2019 and has been running for over 4 years. The Wanshan Cal/Val site is in the south of Zhuhai, China and surrounded by a large sea area and several small islands around it.

To support precise, reliable, and objective CAL/VAL service for the HaiYang-2 satellite altimeters, this ground reference Cal/Val site is supported by different and diverse technology instrumentation, observations, settings, and processing and thus complies to the strategy for fiducial reference measurements (FRM). It is meant to become the Permanent Facility for Altimetry Calibration (PFAC) in China.

At present, various ground reference instrumentation has been established in this CAL/VAL site, including four GNSS receivers, three acoustic tide gauges, and an automatic weather station. In addition, a GNSS buoy is ready to be set up on a nearby ocean location at the crossover point of HY-2B and HY-2C. The Data Reception and Maintenance Center has been established in the Guishan Island and is intended for the collection, evaluation, management, and dissemination of all observation reference data.

Specifically, the ground infrastructure of this PFAC in Wanshan consists of: (1) Four GNSS reference stations established, respectively, in the Wailingding Island, the Dangan Island, the Zhiwan Island, and the Miaowan Island; which provides the absolute elevation reference for the tide-gauges and the GNSS buoy. (2) Three acoustic tide gauges are in Wailingding Island, Dangan Island, Zhiwan Island. (3) An Automatic weather station established in the Dangan Island to provide wind, atmospheric pressure, air temperature, water vapor observation parameters and other auxiliary observation parameters for the estimation of the tropospheric delay. Besides the HY-2B and HY-2C calibration, the Wanshan CAL/VAL site supports the calibration of other spaceborne radar altimeters, such as Jason-3 and Sentinel-3.

By using the ground Cal/Val infrastructure in the Zhiwan Island, we have calibrated the HY-2B and HY-2C altimeters over 4 years continuously. Over this 4- year period, the bias of HY-2B comes to be +1.72cm±0.47cm and the bias of HY-2C is –0.42cm±0.50cm. During the year 2023, our cross-over analysis of HY-2B and HY-2C versus Jason-3 gives the same results as well. The bias of HY-2B versus Jason-3 is +0.49cm±4.87cm and the relative bias in the sea-surface height of HY-2C versus Jason-3 is 0.81cm±4.65cm.

Leaf Area Index (LAI) is a crucial vegetation canopy parameter that quantifies the total leaf surface area per unit ground area. It serves as a fundamental metric for characterizing vegetation structure and functioning in terrestrial ecosystems. Traditional retrieval method of LAI from remote sensing relies on the empirical regression between ground measurement and vegetation indices (VIs). These data-driven statistical relationships are simple and computationally efficient but lacks a physical basis and has few universalities. The Radiative Transfer Model (RTM) mechanistically describes the physical principles of the interaction between electromagnetic radiation and vegetation canopy. It is widely used in the release of land surface products from satellite imagery such as LAI from Copernicus Sentinel-2 and 3.

The project goal is to provide reliable and high-resolution LAI ground truth using UAS as a validation tool that can replace field surveys. To our knowledge, the most common used way to map LAI from UAS is still based on VIs. The uncertainty come from different LAI inversion methods will be transferred to the later verification process. To reduce the uncertainties in LAI inversion and verification. In this study, we implement an RTM-based inversion through Look Up Table (LUT) technique for UAS imagery and compare it with the VIs-based approaches.

The results show that the UAV LAI inversion based on radiative transfer model has a nicer match with GF-6 LAI product with R² of 0.68, compared with the best Vis-based LAI retrieval from Atmospherically Resistant Vegetation Index (ARVI), the RMSE is reduced from 0.76 to 0.7 and the MAE is reduced from 0.59 to 0.55.

In September 2020, enormous wildfires occurred in the western United State of America (mainly in California). Huge quantities of produced biomass burning aerosol (i.e., smoke aerosol, includes organic carbon and black carbon) were injected into the atmosphere and then lifted into the free troposphere. The worldwide first wind lidar satellite Aeolus with the capability of simultaneously measuring global wind and aerosol profiles, provided the opportunity to investigate the whole smoke aerosol transport vertically. Using data or results from multi-platform, involving ALADIN onboard Aeolus, CALIOP onboard CALIPSO, MODIS onboard Aqua and Terra, MERRA-2 reanalysis model, HYSPILT model, the large-scale long-term smoke aerosol transport event from western America over the Atlantic Ocean to the northern Europe is captured and analyzed. Firstly, the temporal range (11st to 21st September 2020) and the horizontal study region (30°N to 70°N, 140°W to -40°E) are selected as at this period the distinct high AOD values (partly larger than 5) at 550 nm appeared from California to Northern European. Then, with the use of the aerosol classification data from CALIOP, the selected study region is found dominated by smoke-related aerosol, which verifies the smoke transport event. To acquire smoke aerosol profiles from Aeolus observation, after quality control, cloud screening and outlier elimination, the profiles with the corresponding smoke column mass concentrations (CMC, provided by MERRA-2) > 10 mg/m2 are retained. Six Aeolus cross-sections on 11st, 15th, 16th, 18th, 19th and 21st September (called CS11 to CS21), located from western America over the Atlantic Ocean to Northern Europe capturing smoke aerosol layers are discovered. These six cross-sections are considered describe the whole smoke aerosol transport, while the spatial and temporal positions are verified by the HYSPLIT model. The averaged extinction coefficients from CS11 to CS18 increased from 55 Mm-1 to 86 Mm-1, and then decreased to lower than 50 Mm-1 in CS19 and CS21. The averaged lidar ratios decreased from 77 sr (CS15) to 65 sr (CS18) and increased back to 77sr on 21st. This may result from the relatively high relative humidity in CS21. Assuming the directly proportional relationship between smoke mass concentration (MC) and its extinction of an individual layer, the smoke MC of each cross sections are calculated with extinctions and CMC. The MC results are verified by the conversion factor method reported from previous study. The maximum averaged MC up to around 35 μg/m3 appeared in CS16 and CS18. At northern Europe, the averaged MC was 19 μg/m3 in the end phase of the transport. Wind vector products from Aeolus provided the dynamic information of the transport. The smoke transport tunnel from western America to northern Europe is found and the transport fluxes of each cross-section are calculated to represent the transport intensity. This study demonstrates the capability of Aeolus on observing huge smoke transport event. A new strategy for spaceborne lidars to estimate smoke MC is proposed. The aerosol properties and transport intensities during a large-scale long-term smoke transport event are calculated and analyzed.

Remote sensing of Suspended Particulate Matter (SPM) is crucial for water-quality monitoring, as it influences turbidity, light availability, or nutrient transports. Here, we provide a comprehensive evaluation of 12 SPM models for the Ocean and Land Color Instrument (OLCI), based on different methods and assumptions, including estimation from water-leaving reflectance, through proxies, combination of semi-analytical equations, and machine learning algorithms (Nechad et al. (2010); Siswanto et al. (2011); Wozniak et al. (2016); Han et al. (2016); Novoa et al. (2017); Gernez et al. (2017); Balasubramanian et al. (2020); Pahlevan et al. (2020); Jiang et al. (2021); Stramski et al. (2023); and the standard SPM model for OLCI). The models are first tested on the GLORIA dataset. Sub-sequently, a matchup exercise is conducted in French coastal waters using the SOMLIT dataset, based on the standard OLCI L2 product. Additionally, we investigate the models capabilities in SPM mapping using OLCI images. Results reveal that algorithms proposed by Jiang et al. (2021) and Novoa et al. (2017) exhibit the highest correlation with in-situ measurements. However, the matchup exercise shows that while Novoa17 demonstrates more accurate SPM retrievals (Error = 76%, Bias = -3%, RMSLE = 0.41, Slope = 0.79), it yields over-estimations of low concentrations. On the other hand, Jiang21 displays a larger dynamic range (Error = 110%, Bias = -105%, RMSLE = 0.46, Slope = 0.98), making it suitable for applications in regions with diverse concentration ranges.

Land surface temperature (LST) is a key variable within Earth’s climate system and a necessary input parameter required by numerous land–atmosphere models. It can be directly retrieved from satellite thermal infrared (TIR) observations, which contain many invalid pixels mainly caused by cloud contamination. To investigate the spatial and temporal variations in LST in China, long-term, high-quality, and spatiotemporally continuous LST datasets (i.e., all-weather LST) are urgently needed. Fusing satellite TIR LST and reanalysis datasets is a viable route to obtain long time-series all-weather LSTs. Among satellite TIR LSTs, the MODIS LST is the most commonly used, and a few corresponding all-weather LST products have been reported recently. However, the publicly reported all-weather LSTs were not available during the temporal gaps of MODIS between 2000 and 2002. In this study, we generated a daily (four observations per day) 1 km all-weather LST dataset for China’s landmass and surrounding areas, the Thermal and Reanalysis Integrating Moderate-resolution Spatial-seamless (TRIMS) LST, which begins on the first day of the new millennium (1 January 2000). We used the enhanced reanalysis and thermal infrared remote sensing merging (E-RTM) method to generate the TRIMS LST dataset with the temporal gaps being filled, which had not been achieved by the original RTM method. Specifically, we developed two novel approaches, i.e., the random-forest-based spatiotemporal merging (RFSTM) approach and the time-sequential LST-based reconstruction (TSETR) approach, respectively, to produce Terra/MODIS-based and Aqua/MODIS-based TRIMS LSTs during the temporal gaps. We also conducted a thorough evaluation of the TRIMS LST. A comparison with the Global Land Data Assimilation System (GLDAS) and ERA5-Land LST demonstrates that the TRIMS LST has similar spatial patterns but a higher image quality, more spatial details, and no evident spatial discontinuities. The results outside the temporal gap show consistent comparisons of the TRIMS LST with the MODIS LST and the Advanced Along-Track Scanning Radiometer (AATSR) LST, with a mean bias deviation (MBD) of 0.09/0.37K and a standard deviation of bias (STD) of 1.45/1.55 K. Validation based on the in situ LST at 19 ground sites indicates that the TRIMS LST has a mean bias error (MBE) ranging from -2.26 to 1.73K and a root mean square error (RMSE) ranging from 0.80 to 3.68 K. There is no significant difference between the clear-sky and cloudy conditions. For the temporal gap, it is observed that RFSTM and TSETR perform similarly to the original RTM method. Additionally, the differences between Aqua and Terra remain stable throughout the temporal gap. The TRIMS LST has already been used by scientific communities in various applications such as soil moisture downscaling, evapotranspiration estimation, and urban heat island modeling.

The Lofoten Basin (LB) is a dynamic region in the Nordic Seas (NS). It lies between the two branches of the Norwegian Atlantic Front Current that transport the Atlantic water northward. It is typically defined as a hot spot of mesoscale activity in the NS and it is also characterized by a permanent vortex trapped by the deep bathymetry in the western part of the basin. The ocean dynamics in the LB is extremely important for the ocean circulation system in the NS, as it behaves as a reservoir of the Atlantic water that will reach the Arctic. The strong mesoscale activity in the LB plays a crucial role in the heat and salt distribution. Eddy-induced anomalies also influence the sea-level signal in the LB, where the largest positive trend in the NS can be observed from 2004 onwards. Recent studies suggest that this tendency can be explained by a change in large-scale atmospheric circulation patterns over the Arctic. In this study, we investigate how these patterns are influencing the mesoscale activity and heat and salt transport in the LB, building on satellite multi-sensor synergy combined with eddy detection and singular analysis. The analysis was developed considering different subregions East and West Lofoten Basin (ELB, WLB), and three different periods aligned with the occurrence of the leading atmospheric patterns.

The results show that the number of detected eddies and kinetic energy have increased in the whole basin. This aligns with the previously reported positive sea level trend and freshening of the NS. Fresher waters can enhance stronger density gradients and baroclinic conditions in favor of the eddy formation. The mesoscale eddies clearly show their impact on the ocean surface temperature and salinity anomalies and their role in modulating the heat exchange with the atmosphere. Interestingly, the eddy-induced anomalies on ocean surface temperature detected during the last years of the analysis were larger compared to other periods. We have also found differences in the intensity and distribution of the anomalies in the ELB and WLB. The ELB is strongly influenced by the Norwegian Atlantic Front Current while ELB is more influenced by the topographically steered Norwegian Atlantic Slope Current. The results show that the eddy kinetic energy exhibits a temporal offset of four months between the two regions due to the different occurrences of mesoscale eddies.

The Yarlung Zsangbo Grand Canyon (YGC) in the southeastern Tibetan Plateau is one of the world's deepest canyons. In this sparsely gauged region, remotely sensed precipitation products can be valuable if adequately calibrated and assessed with the help of rain gauges. A new rain gauge network was installed in the YGC in November 2018, and the three years of observations were utilized to evaluate and calibrate the Integrated Multi‐satellite Retrieval for Global Precipitation Measurements (GPM IMERG) precipitation product. The evaluation results demonstrate that the GPM IMERG data reasonably captured the observed seasonal and diurnal variations in the precipitation but with much weaker seasonal and diurnal variations compared with the gauge data. IMERG underestimated the total rainfall primarily due to under-detection of rainfall events, with misses being more prevalent than false alarms. IMERG overestimated and underestimated the light and heavy precipitation, respectively, leading to a significant underestimation of the rainfall frequency and intensity at both the daily and monthly scales. We analyzed whether bias of IMERG is associated with the topography factor. It was found that the probability of detection decreased with elevation, leading to increased underestimation of rainfall events at higher elevations, and the false alarm ratio was higher in valley sites. In terms of the hit events, IMERG overestimated the light rainfall events and underestimated the heavy rainfall events and the negative bias in the hit events decreased with elevation. IMERG could capture the early morning peak precipitation in the YGC region but underestimated the amplitude of the diurnal variation. This bias was inherent at the sensor level, and the Global Precipitation Climatology Center (GPCC) calibration partially improved the underestimation. However, this improvement was not sufficient for the YGC region. This study fills the gap in IMERG validation in a complex mountainous region and has implications for users and developers. A cumulative distribution functions (CDF) method was used to address the systematic underestimation of GPM-IMERG. This work has implications for the satellite rainfall studies for the global mountain area.

As the most complex and highest altitude plateau in the world, the Tibet Plateau has an average altitude of over 4000 meters and is known as the "Roof of the World" and "Third Pole of the Earth". Due to the strong solar radiation received by the surface of the Tibet Plateau, combined with the large terrain of the plateau's uplift, it has formed a towering and powerful power and heat source that can penetrate into the atmosphere. The dynamic and thermal effects of the Tibet Plateau not only regulate the energy and water cycles in Asia and the world, but also have a profound impact on global climate and environment. The middle and lower reaches of the Yangtze River refers to the central and eastern regions of China to the east of the Three Gorges of the Yangtze River. It is one of the regions with frequent and the intensive rainstorms and flood disasters in China. The thermal and dynamic effects of the special topography of the Tibet Plateau, as well as its complex land atmosphere interactions, play an important role in the changes of atmospheric circulation and the formation of precipitation in eastern China. Studying the land atmosphere interactions on the Tibet Plateau is crucial for understanding the characteristics of precipitation distribution and changes in eastern China, especially in the middle and lower reaches of the Yangtze River where precipitation anomalies and regional droughts and floods occur. Therefore, this study intends to conduct zoning and spatiotemporal evolution analysis of surface flux on the Tibet Plateau. By using correlation analysis, the impact of land atmosphere interaction on the weather in the middle and lower reaches of the Yangtze River will be explored, and the key source areas of land atmosphere interaction will be determined; Using the HYSPLIT model, explore the characteristics of precipitation and water vapor transport in the middle and lower reaches of the Yangtze River and its relationship with the interaction between land and atmosphere on the Tibet Plateau.

Water supply is the most critical constraint for vegetation growth and food security. The amount of water demand by plant growth is usually estimated by plant water requirement which unfortunately cannot be directly measured at any large scale in field conditions. Different estimation methods have been proposed in the past seven decades for estimating plant water requirements using the concept of reference evapotranspiration (ET0) methods or potential evapotranspiration (PET) methods. In addition, using PET or ET0 to estimate actual evapotranspiration (ETa) is a critical approach in hydrological and climate models. However, different PET or ET0 models provide diverse results for irrigation water requirement (IWR) that in turn may result in a huge waste of irrigation water. Here, we assess the suitability of six common methods for estimating PET at 170 eddy covariance flux sites and propose a practical approach for estimating the IWR using a physically consistent model STEMMUS-SCOPE.

Notably, the Priestley-Taylor and LSA_SAF method excels in providing reasonable approximations of daily PET. Consequently, in scenarios where net radiation data and ground heat flux are accessible, the Priestley-Taylor method emerges as the recommended choice. The LSA_SAF method is the better one when only net radiation data is available. Alternatively, in cases where only global radiation data is available, the Makkink and Hargreaves methods serve as viable substitutes. Although the FAO56 Penman-Monteith method is much better than the original Penman-Monteith method when wind speed and air humidity data are at hand, its suitability falls short of the preferred status. This study contributes to understanding and quantifying the applicability of different methods in estimating PET and IWR, based on input data availability and physical considerations.