In 2017, the National Research Council released the second Earth Science Decadal Survey (ESDS). The ESDS recommended four sets of measurements referred to as the Decadal Observables. One of these was the Surface Biology and Geology (SBG) Decadal Observable (DO). The Decadal Observable measurements together with measurements from the upcoming NISAR mission are now referred to as the Earth System Observatory (ESO). The SBG-DO called for high spectral and spatial resolution measurements in the visible to shortwave infrared (VSWIR: 0.38-2.4 μm) and high spatial resolution multispectral measurements in the mid and thermal infrared (MIR: 3-5 and TIR: 7-12 μm). The MIR and TIR (MTIR) measurements would be made every few days and VSWIR measurements every couple of weeks. The VSWIR and MTIR measurements would have spatial resolutions of 30 m and 60 m respectively. These measurement requirements were based, in part, on those recommended for the Hyperspectral Infrared Imager (HyspIRI) mission recommended in the prior ESDS. After the release of the 2017 ESDS, NASA formed teams to develop architectures for each of the DO’s. The SBG team recommended the VSWIR and MTIR measurements be made from two separate platforms in a morning and afternoon orbit respectively. The SBG team also recommended a technology demonstration with a constellation of smaller spacecraft with VSWIR instruments in a morning orbit. The morning orbit was preferred for the VSWIR measurements to minimize cloud cover and the afternoon preferred for the MTIR to measure the peak temperature stress of plants typically occurring in the early afternoon. The architecture team recommended global revisit times for the VSWIR and MITIR of revisit times of 16 and 3 days respectively, which resulted in swath widths of 185 km and 935 km from the nominal altitudes chosen for the VSWIR and MTIR platforms respectively.
SBG is a global survey mission that will provide an unprecedented capability to assess how ecosystems are responding to natural and human-induced changes. It will help us assess the status of biodiversity around the world and the role of different biological communities on land and within inland water bodies, as well as coastal zones. It will help identify natural hazards, in particular volcanic eruptions, and any associated precursor activity, and it will map the mineralogical composition of the land surface. In summary SBG will advance our scientific understanding of how the Earth is changing as well as provide valuable societal benefit, in particular, in understanding and tracking dynamic events such as volcanic eruptions, wildfires and droughts.
As part of the risk reduction activities for the MTIR of HyspIRI, a space-ready Prototype -the HyspIRI Thermal Infrared Radiometer (PHyTIR) was developed in the laboratory to mature the technology readiness of the instrument and an airborne Hyperspectral Thermal Emission Spectrometer (HyTES) was developed to acquire antecedent data for science studies. PHyTIR matured the technology readiness level (TRL) of certain key subsystems of the TIR imager. In 2014 the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) was selected as part of the NASA Earth Ventures Instrument program. ECOSTRESS used the components developed with PHyTIR. ECOSTRESS addresses critical questions on plant–water dynamics and future ecosystem changes with climate. ECOSTRESS has five TIR spectral bands, a spatial resolution of 68m x 38m (crosstrack x downtrack) and a revisit of every few days at varying times of day from the International Space Station (ISS). ECOSTRESS was delivered to the ISS in 2018 and operations began shortly thereafter. ECOSTRESS was planned to operate for one year, however, due to demand as well as the instrument continuing to operate well, NASA extended the mission until 2023.
HyTES represents a new generation of airborne TIR imaging spectrometers with much higher spectral resolution and a wide swath. HyTES is a pushbroom imaging spectrometer with 512 spatial pixels over a 50-degree field of view. HyTES includes many key enabling state-of-the-art technologies including a Dyson-inspired spectrometer and high performance convex diffraction grating. The Dyson optical design allows for a very compact and optically fast system (F/1.6) and minimizes cooling requirements since a single monolithic prism-like grating design can be used which allows baffling for stray light suppression. The monolithic configuration eases mechanical tolerancing requirements which are a concern since the complete optical assembly is operated at cryogenic temperatures (~100K). HyTES originally used a Quantum Well Infrared Photodetector (QWIP) and had 256 spectral channels between 7.5μm to 12μm. In 2021 this was upgraded to a Barrier InfraRed Detector (BIRD) array with 284 spectral channels. The first science flights with the QWIP were conducted in 2013 and the first science flights with the BIRD in 2021. Many flights have been conducted, and the instrument can now be deployed on a Twin Otter or the NASA ER2 aircraft allowing a variety of pixel sizes depending on flight altitude. In 2022 the instrument will also be deployed on the NASA Gulfstream V aircraft. All the data acquired thus far has been processed and is freely available from the HyTES website (http://hytes.jpl.nasa.gov). Higher level products surface temperature and emissivity and gas maps are available for the more recent data.
This presentation will describe the current status and plans for SBG, ECOSTRESS and HyTES programs as well as provide some recent results from ECOSTRESS and HyTES.

The Land Surface Temperature Monitoring (LSTM) mission aims to address water, agriculture and food security issues by monitoring the variability of Land Surface Temperature (and hence evapotranspiration) at the European field scale enabling more robust estimates of water productivity. The LSTM mission observations will support the Copernicus land monitoring service, related European and also global and international policies as well as downstream applications.

In this study, land surface temperature (LST) estimates with the Temperature and Emissivity Separation (TES) method and evapotranspiration (ET) with the simplified surface energy balance index (S-SEBI) model have been obtained from airborne data acquired in the framework of the SurfSense 2018 and LIAISE 2020 experiments in support of the LSTM mission.

The chosen test areas for data collection are located in Italy and Spain presenting both Mediterranean climate with very mild wet winters and very hot dry summers. The experimental sites consists of a large irrigated flat areas with growing crops (mainly corn and alfalfa) in which in situ measurements of LST, radiation fluxes and evapotranspiration were taken during the campaign generating a large database for validation purposes. Acquisition of images was performed by Thermal Airborne Spectrographic Imager (TASI) hyperspectral thermal sensor in the range of 8 to 11.5 microns and across 32 spectral bands. For Visible Near Infrared (VNIR) data, HyPlant airborne sensor provided the spectral information from 370 nm to 2500 nm necessary for evapotranspiration retrievals.

LST performance was analyzed by the application of TES algorithm to different band configurations of TASI sensor and simulated bands configuration of the LSTM mission. Integrating LST and VNIR data, instantaneous values of evapotranspiration were also estimated and validated against eddy covariance measurements.

One of the obvious effects of global change is reflected in land surface temperature (LST) anomalies and interannual variability of evaporation (E). LST carries the imprints of surface water availability and is immensely sensitive to evaporative cooling and soil moisture variations. It constrains the magnitude and variability of the surface energy balance (SEB) components and is a preeminent variable for retrieving E in the terrestrial ecosystems. To understand the terrestrial ecosystem functioning, advanced monitoring of the terrestrial biosphere response to water stress and further agricultural water consumptive use is overarching. ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) has been providing high spatio-temporal thermal infrared (TIR) observations (~70 m, multiple revisits per day) since its launch in 2018. Taking advantage of the ECOSTRESS images accessible to the public, the European ECOSTRESS Hub (EEH) aims at producing LST and E data from models with different structures and parameterization schemes over Europe and Africa. In the EEH, LST products are retrieved from the Split Window (SW) and Temperature Emissivity Separation (TES) algorithms. Retrieval of evaporation in the EEH is based on three models, namely Surface Energy Balance System (SEBS) and Two Source Energy Balance (TSEB) parametric models, as well as the analytical Surface Temperature Initiated Closure (STIC) model. Along with the analysis ready data stored on a cloud platform, users are also provided with access to running the selection of models through an interactive interface backed by a supercomputing system. A preliminary evaluation of the EEH LST and ET products in 2018 showed promising results, in good agreement with the official products from NASA/JPL and in-situ measurements. The unique feature of EEH is that both the LST algorithms are driven by homogenized radiance and environmental datasets, and all the evaporation models are forced by uniform upper boundary and lower boundary conditions. This characteristic enables appropriate comparisons among different models for a large spectrum of energy and water availability scenarios. Overall, the EEH will serve as a support to the next generation Copernicus High Priority Candidate Land Surface Temperature Monitoring (LSTM) mission.

In this work, Land Surface Temperature (LST) estimation techniques, applied to remote sensing data, offer a systematic and precise detection of thermal anomalies in Italian geothermal areas. LST was retrieved by means of two different methodologies and results were validated thanks to ground measurements collected during field campaigns and/or cross-validation methods. The main analyses were conducted using nighttime satellite data, in order to reduce the solar effects and comparison results showed very good agreements. Moreover, the comparison between ground data collected during the morning and LST retrieved by daytime satellite data showed also good agreements.
The use of three sensors (ASTER, ECOSTRESS and Landsat-8), despite the different GSD and LST estimation methodologies, have produced results with high correlation offering the possibility to extend the LST time series. ASTER is one of the more versatile satellite imagers used for studies of thermal anomalies; it can estimate surface temperatures with several thermal infrared spectral channels. TIRS/Landsat 8 images, while having fewer spectral channels and slightly lower spatial resolution than ASTER, provide additional temperature data for estimating and monitoring LST on active volcanoes as well as geothermal areas. The recent ECOSTRESS sensor, very similar to ASTER, could increase the number of data. The employing of these three space sensors has proven cost-effective technical method for generating products for the detection of geothermal anomalies.
The well-known methodologies have been used to evaluate the surface temperature on two test sites characterized by different geological features: the volcanic area of Solfatara-Campi Flegrei and the geothermal area of Parco delle Biancane. A cross-comparison test has been conducted comparing the LST estimated by ASTER, ECOSTRESS and Landsat 8 with the surface temperature estimated by NASA-HyTES sensor and UAV survey. During the last field campaign (June 2019), data acquired by Twin-Otter Aircraft of HyTES project (NASA/JPL/ESA) have been collected thanks to a collaboration with NASA/JPL.
Moreover, measuring gas emissions of eruptive volcanoes is a risky task that cannot be performed by hand portable or backpack carried gas analysis systems. Satellite based remote sensing and near remote sensing instruments are useful to provide gas flux information when is not possible to perform in situ sampling, but not all gases of interest can be achieved with this method and they still require in situ data validation to provide a proper measurements of the gas fluxes emitted by the volcano. The measurement of volcanic gases as CO2, H2S and SO2 emitted from summit craters and fumaroles is a crucial parameter to monitor the volcano activity. The measurements of passing degassing plumes (non-eruptive) has been achieved by combining satellite data with local airborne measurements using UAV (Unoccupied Aerial Vehicle) and ground field in-situ measurements. We include the measurements of a miniature multi-Gas analysis system (called miniGAS) to measure H2O, CO2, H2S and SO2 gases, and a field portable backpack mass spectrometer system (called MPH Explorer) designed for in situ volcanic gas analysis.

Land surface temperature (LST) is a key variable in the study of the thermal environment, modelling of surface energy fluxes, estimation of evapotranspiration and soil moisture, and the characterization of urban heat island effects [1,2]. LST can be effectively retrieved from remotely sensed data in the thermal infrared part of the spectrum (TIR).
To properly capture the high variability of complex areas, such as an urban environment, both spatially and temporally dense LST data are needed. Unfortunately, sensors on board satellites with high revisit time usually cannot adequately provide detailed spatial information, whereas high spatial resolution sensors have typically a low revisit time. Among the satellite TIR sensors in operation, Landsat-8 TIR sensor provides 100m spatial resolution imagery, which is well suited to capturing surface details, but its long revisit cycle of 16 day has limited its use in generating a temporally continuous LST dataset. In this context, downscaling low-resolution imagery is necessary to bridge the existing gap and make available frequent thermal data at a fine spatial resolution.
In the literature, several methods have been developed to generate daily LST at fine spatial resolution, which blend information from different sensors and/or different spectroscopic bands[3,4]. A widely studied strategy is the use of daily TIR observations provided by the Moderate-resolution Imaging Spectroradiometer (MODIS) at 1 km nominal spatial resolution as precursor variables. Despite all the progress, existing algorithms are still subject to several key limitations, among which: (1) the need of a high resolution image as reference; (2) mixing effects due to the heterogeneity of neighboring pixels and which are difficult to control; (3) not taking into account the change in the local variance; (4) sudden changes and disturbances are hardly detected. Moreover, in recent studies the use of linear models in the downscaling procedure has been criticized, advocating the need of highly non linear approaches to obtain accurate results, coupled with the use of a large number of predictors from different spectral indices[5].
This study focuses on the relations between the Landsat and daily MODIS TIR data, with the aim of analyzing, and possibly overcoming, these difficulties found in the downscaling process. We show that there are linear correlations between the two datasets when the data are properly aggregated. We then propose an effective downscaling strategy for the reconstruction of the missing Landsat LST images over an area of interest, which only makes use of the coarse resolution MODIS images at the prediction days, and the Landsat/MODIS correlations retrieved from the analysis of the Landsat/MODIS historical series. The new approach is tested over urban and non-urban environment. This methodology could be used in the constellation composed of Sentinel-3 SLSTR and the future LSTM mission.

[1] D. Quattrochi, et al., Thermal Remote Sensing in Land Surface Processing, CRC Press (2004).
[2] Q. Weng, Techniques and Methods in Urban Remote Sensing, Wiley (2019).
[3] F. Gao, et al., Fusing Landsat and MODIS Data for Vegetation Monitoring, IEEE Geoscience and Remote Sensing Magazine, 3 (2015) 47.
[4] J. Wang, et al., Thermal unmixing based downscaling for fine resolution diurnal land surface temperature analysis, ISPRS Journal of Photogrammetry and Remote Sensing, 161 (2020) 76.
[5] V. Moosavi, et al., A wavelet-artificial intelligence fusion approach (WAIFA) for blending Landsat and MODIS surface temperature, Remote Sensing of Environment, 169 (2015) 243.

The TRISHNA mission (Thermal infraRed Imaging Satellite for High-resolution Natural resource Assessment) is a cooperation between the French (CNES) and Indian (ISRO) space agencies, to be launched in 2025. It is intended to measure during 5 years approximately twice a week the thermal infrared signal of the surface-atmosphere system globally and at 60-meter resolution for the continents and the coastal ocean, and a resolution of 1000 meters over deep ocean.
The mission focuses on the estimation of evapotranspiration, and will contribute to the detection of water stress, the monitoring of irrigation and its management. It will provide direct information on the use of water, and in particular its agricultural consumption. These data will also indirectly contribute to water availability assessments and studies on the natural or anthropogenic causes of the variation in groundwater level.
TRISHNA and its frequent high-resolution measurements raise major scientific, economic and societal issues through the 6 major themes that the mission addresses from the angle of research and development of applications: ecocystem stress and water use (through the monitoring of agriculture and water content of natural vegetation), coastal and inland waters (sea, lakes, rivers), urban ecosystem monitoring, cryosphere, solid Earth, atmosphere.
The main requirements which are at the origin of the definition of the mission are the following: temporal resolution (capacity to produce an observable every 3 days), spatial (access to the scale of the agricultural plot), spectral (visible and near infrared capacity in support of thermal infrared, with the following spectral bands, radiometric (measurement precision). 7 spectral bands will be available in the visible to short-wave infrared part of the spectrum: blue, green, red, Near Infrared (865nm), water vapor (910nm), cirrus (1.38µm) and Short-wave infrared (1.6µm). 4 spectral bands will be available in the thermal infrared.
Moreover, the system design is also driven by the objective of demonstrating that this kind of data, when delivered with a low latency, can be used to raise an early warning signal, in order to prevent the effects of droughts on agricultural surfaces or for issues related to the health of agro-forest ecosystems, but also of wetlands and coral reefs.
As far as products are concerned, the specificities of the mission play an important role: there will be one mission center in India and one in France, each mission center being able to deliver all types of data, with a full coordination including common Algorithm Theoretical Basis Documents and a coordinated process implying frequent and in-depth cross-calibration from early validation phases to the operational phase. TRISHNA mission center will deliver level 1C, level 2 and level 3 data.
The TRISHNA products are defined to answer a wide range of use of the data, from science to applications.
Following the CEOS definition, level 1C data consist of Top-Of-Atmosphere radiometrically and geometrically calibrated reflectances in each of the 7 visible and whort-wave infrared channels, and radiance in each of the 4 thermal infrared channels. A raw cloud mask is also provided, based on spectral thresholds. From this level on, the data are orthorectified and resampled on a uniform spatial grid. In order to ease the use of TRISHNA data together with other missions and especially with the present and future Copernicus data, it has been decided to use Sentinel-2 tiles and Copernicus Digital Elevation Model.
Level 2A data are surface radiative variables: surface reflectances in 5 visible and short-wave infrared channels, Land Surface Temperature or Sea Surface Temperature, and Land Surface Emissivity in the 4 thermal infrared channels. Level 2A products also include Total Water Vapor Column and a refined cloud mask from multi-spectral and multi-temporal processings.
Level 2B variables are still under definition: albedo; vegetation indices computed from visible and near infrared data: NDVI, Leaf Area Index (LAI) and Fraction of Vegetation Cover. The variables necessary to compute the energy budget at the time of the acquisition will also be delivered: Net radiation, Ground Heat Flux and Evaporative Fraction. Finally, daily evapotranspiration and water stress will also be available for each day for which a TRISHNA acquisition is available.
Level 3 products, also under definition, are constituted of temporal and spatial synthesis of level 2 data. Moreover, as Soil-Vegetation-Atmosphere Transfer Models (SVAT) and Crop Simulation Models provide continuous simulation of evapotranspiration, they can be used for interpolating evapotranspiration between remote sensing data acquisitions with the objective of delivering daily evapotranspiration and daily water stress on a day-to-day basis as level 3 products.
As far as data production and distribution in the French mission ground segment are concerned, CNES Computing Center offers a user-oriented service and will include by TRISHNA launch a computing platform (next version of the current HPC – High Performance Computing) and a new storage platform (DataLake). A simplified and unified access to spatial data will be ensured through the GeoDataHub, an organization around Earth Observation data hosted at CNES. It will allow to supply new services for distributing ready-to-use information to the land surfaces and hydrology user community, extracted from TRISHNA products, but also from other high resolution multi spectral optical and thermal data. In order to build this, an open data policy is a huge asset, and the economic and social benefit of this kind of policy is becoming more and more accepted, especially in the scientific community.