Temperature Retrieval Research Articles

Improving remote sensing of extreme events with machine learning: land surface temperature retrievals from IASI observations

Retrieving weather extremes from observations is critical for weather forecasting and climate impact studies. Statistical and machine learning methods are increasingly popular in the remote sensing community. However, these models act as regression tools when dealing with regression problems and as such, they are not always well-suited for the estimation of the extreme weather states. This study firstly introduces two error types that arise from such statistical methods: (a) ‘dampening’ refers to the reduction of the range of variability in the retrieved values, a natural behavior for regression models; (b) ‘inflating’ is the opposite effect (i.e. larger ranges) due to data pooling. We then introduce the concept of localization that intends to better take into account local conditions in the statistical model. Localization largely improves the retrievals of extreme states, and can be used both for retrieval at the pixel level or in image processing techniques. This approach is tested on the retrieval of land surface temperature using infrared atmospheric sounding interferometer observations: the dampening is reduced from 1.9 K to 1.6 K, and the inflating from 1.1 K to 0.5 K, respectively.

Upscaling dryland carbon and water fluxes with artificial neural networks of optical, thermal, and microwave satellite remote sensing

Abstract. Earth's drylands are home to more than two billion people, provide key ecosystem services, and exert a large influence on the trends and variability in Earth's carbon cycle. However, modeling dryland carbon and water fluxes with remote sensing suffers from unique challenges not typically encountered in mesic systems, particularly in capturing soil moisture stress. Here, we develop and evaluate an approach for the joint modeling of dryland gross primary production (GPP), net ecosystem exchange (NEE), and evapotranspiration (ET) in the western United States (US) using a suite of AmeriFlux eddy covariance sites spanning major functional types and aridity regimes. We use artificial neural networks (ANNs) to predict dryland ecosystem fluxes by fusing optical vegetation indices, multitemporal thermal observations, and microwave soil moisture and temperature retrievals from the Soil Moisture Active Passive (SMAP) sensor. Our new dryland ANN (DrylANNd) carbon and water flux model explains more than 70 % of monthly variance in GPP and ET, improving upon existing MODIS GPP and ET estimates at most dryland eddy covariance sites. DrylANNd predictions of NEE were considerably worse than its predictions of GPP and ET likely because soil and plant respiratory processes are largely invisible to satellite sensors. Optical vegetation indices, particularly the normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv), were generally the most important variables contributing to model skill. However, daytime and nighttime land surface temperatures and SMAP soil moisture and soil temperature also contributed to model skill, with SMAP especially improving model predictions of shrubland, grassland, and savanna fluxes and land surface temperatures improving predictions in evergreen needleleaf forests. Our results show that a combination of optical vegetation indices and thermal infrared and microwave observations can substantially improve estimates of carbon and water fluxes in drylands, potentially providing the means to better monitor vegetation function and ecosystem services in these important regions that are undergoing rapid hydroclimatic change.

DirecTES: A Direct Method for Land and Sea Surface Temperature and Emissivity Separation for Thermal Infrared Sensors—Application to TRISHNA and ECOSTRESS

The coming years will see the launch of several missions (TRISHNA, LSTM, SBG), which will acquire images in four or more spectral bands in thermal infrared (TIR) at high spatial resolution (~50–60 m) and with high temporal revisit (~2–3 days). The derivation of surface temperature and emissivity values from top-of-atmosphere radiances is not straightforward, as it is a non-deterministic process requiring additional information. In this paper, we propose the algorithm DirecTES to efficiently separate surface temperature and emissivity. This algorithm is based on the use of a comprehensive spectral database of emissivity, resulting in a well-posed deterministic problem while not assuming strong hypotheses. The algorithm can also benefit from non-TIR information, such as the acquisitions from the same satellite but in the visible and near-infrared domains, or exogenous data—land/sea mask or soil-occupation map. These would help identify the nature of the surface and therefore improve the temperature and emissivity retrievals. After the complete description of the method, we evaluate the performances of DirecTES on theoretical landscapes in TRISHNA’s context under a large range of atmospheric conditions. The retrievals of surface temperature reach RMSEs of 0.8 K over vegetation and 0.5 K over water, including both sensor and atmospheric uncertainties. We then evaluate DirecTES on ECOSTRESS images on sites where the ECOSTRESS Land Surface Temperature (LST) performance has been documented; DirecTES surface temperature retrievals are consistent with the ECOSTRESS LST product and the in-situ data.

Performance of the Large Field of View Airborne Infrared Scanner and its application potential in land surface temperature retrieval

Performance of the Large Field of View Airborne Infrared Scanner and its application potential in land surface temperature retrieval

CHANGING LANDSCAPE PATTERNS AND ITS EFFECT ON RISING LAND SURFACE TEMPERATURE OF LAHORE-PAKISTAN

The study intaimsto evaluate the impact of landscape (Land use land cover) changeson Land surface temperature (LST) by using GIS and Remote sensing (RS) techniques in Lahoreduring 2017–2021.Supervised Maximum Likelihood classification method was used to classify theland use and land cover classes. For retrieval of Land surface temperature, the Landsat 8 (Band 10)product level 2 was used. Barren land and built- up area were identified as the most leading LULCclasses respectively in the study area at the cost of vegetation cover and water bodies. The barren landclass increased from 55.9% to 63.81% ,while the Built-up class also increases from 13.5% to 18.46%during 2017-2021.Whereas , vegetation and water bodies both are exhibiting the decline. The declinein vegetation class was reported from 29.58% to 17.6% over the time 2017 to 2021.Overall, 0.3 %decrease is observed in water bodies. In 2017 the 3◦C decline is observed in land surface temperature(LST) value than 2021.Built-up and Vegetation classes can contribute a significant role in variation ofLST in comparison to the water bodies. These results will be very helpful to understand the LULCchanges and eventually it will assist the land management authorities, policy maker and town plannersas well.

Estimation of Moist Atmospheric Profiles from Refraction and Attenuation Measurements by Using Centimeter and Millimeter Wave Links between LEO Satellites

Microwave occultation using centimeter and millimeter wave links between low Earth orbit (LEO) satellites provide a potential way to estimate the moist atmospheric profiles based on refraction and attenuation measurements, which is called the LEO-LEO microwave occultation (LMO) technique. It has not yet been implemented in orbit. In this paper, we analyzed the attenuation properties at different heights of centimeter and millimeter waves based on simulations. The observing capabilities with different frequency combinations at the X, K, and M bands were analyzed. The results show that LMO may improve the retrieval accuracy of bending angles above 35 km. By using several appropriate frequencies at the X+K+M band, water vapor profiles from the near-surface to the lower stratosphere (~24 km) can be obtained. When the M-band frequencies were added, the temperature retrieval accuracy does not change obviously, but the accuracy of water vapor retrieval can significantly improve above 15 km, especially at about 17–24 km, and the RMS errors decrease from over 20% to less 10%. For promoting the LMO mission in the real world, a frequency combination at the X+K band is proposed, which can provide the potential to observe the temperature profiles at about 2.5–50 km and water vapor profiles at about 2.5–15 km accurately under clear and cloudy conditions. This study demonstrates that LMO can greatly extend the capabilities of the radio occultation technique and improve our ability to measure the moist atmospheric profiles globally.

Observation based climatology Martian atmospheric waves perturbation Datasets

The Martian atmospheric waves perturbation Datasets (MAWPD) version 2.0 is the first observation-based climatology dataset of Martian atmospheric waves. It contains climatology-gridded temperature, gravity waves, and tides spanning the whole Martian year. MAWPD uses the Data INterpolating Empirical Orthogonal Functions method (DINEOF) reconstruction method for data assimilation with the observational data from the Mars Global Surveyor (MGS), Mars Reconnaissance Orbiter (MRO), Mars Atmosphere and Volatile EvolutioN (MAVEN), Mars Pathfinder (MP), Mars Phoenix Lander (MPL), Mars Exploration Rover (MER) and Mars Express (MEX) temperature retrievals. The dataset includes gridded fields of temperature (Level 1 data) as well as the physical quantities of GWs (Level 2 data, amplitude, and potential energies), SPWs and tides (Level 2 data, amplitude, and phase). The MAWPD, based entirely on multiple reliable observations, provides climatological background atmospheric information of temperature and wave disturbances on Mars. The dataset is not only useful for observation-based scientific studies concerning Martian atmospheric waves, e.g., circulation, dust storms, and wave excitation mechanism, but also for cross-validating with model-based datasets or model results.

Lunar Surface Temperature and Emissivity Retrieval From SDGSAT-1 Thermal Imager Spectrometer

The lunar surface temperature is one of the important thermophysical parameters of the Moon, which helps to study the radiative properties of the lunar surface. Thermal infrared emission spectra is sensitive to the thermophysical properties of the lunar surface material, and the emissivity data can be used for lunar surface composition inversion. In the past, humans have conducted hundreds of lunar exploration missions, but only a small number have been conducted in the infrared band, typical examples include the Apollo program and the Diviner lunar radiometer experiment of the LRO satellite. Only part of the lunar surface has been explored by these missions. SDGSAT-1 carries out a complete observation of the lunar surface in three infrared wavelength bands (B1: 8-10.5μm; B2: 10.3-11.3μm; B3: 11.5-12.5μm) by its thermal imager as part of its mission. In this study, the temperature-emissivity separation (TES) algorithm is used to retrieve the lunar surface temperature and calculate the thermal infrared band emissivity using the radiometric data obtained by SDGSAT-1 thermal imager, the temperature distribution of full-disk Moon and the emissivities distribution of three bands are also mapped. The temperature retrieval error is verified less than 1K.

Retrieval and Verification of Mid-Upper Atmospheric Temperature from MIGHTI/ICON Satellite

Retrieval and Verification of Mid-Upper Atmospheric Temperature from MIGHTI/ICON Satellite

Retrieval and validation of sea surface temperature from HY-1D COCTS

Retrieval and validation of sea surface temperature from HY-1D COCTS

Land Surface Temperature Retrieval From Landsat 9 TIRS-2 Data Using Radiance-Based Split-Window Algorithm

The thermal infrared sensor-2 (TIRS-2) carried on Landsat 9 is the newest thermal infrared (TIR) sensor for the Landsat project and provides two adjacent TIR bands, which greatly benefits the land surface temperature (LST) retrieval at high spatial resolution. In this article, a radiance based split window (RBSW) algorithm for retrieving LST from Landsat 9 TIRS-2 data was proposed. In addition, the split-window covariance-variance ratio (SWCVR) algorithm was improved and applied to Landsat 9 TIRS-2 data for estimating atmospheric water vapor (AWV) that is required for accurate LST retrieval. The performance of the proposed method was assessed using the simulation data and satellite observations. Results reveal that the retrieved LST using the RBSW algorithm has a bias of 0.06 K and root-mean-square error (RMSE) of 0.51 K based on validation with the simulation data. The sensitivity analysis exhibited a LST error of <1.75 K using the RBSW algorithm when the uncertainties in input parameters (i.e., AWV, emissivity, and at-sensor radiance) were considered. There is a marginal discrepancy between LST retrievals using the estimated AWV and moderate resolution imaging spectroradiometer AWV, with a difference in bias of −0.14 K and RMSE of 0.22 K, which indicates that the improved SWCVR method can provide an optional means to obtain AWV inputted in LST retrieval. With regard to the validation using the in situ measurements, the retrieved LST from Landsat 9 TIRS-2 data exhibits a bias of 0.44 K and RMSE of 1.98 K, respectively, showing a higher accuracy than the USGS Landsat LST product with bias of 1.21 and RMSE of 2.56 K. In conclusion, the proposed algorithm combining RBSW algorithm and improved SWCVR algorithm for LST retrieval from Landsat 9 has a good accuracy without dependence on external atmospheric data, and it is expected to be a reliable method for LST generation from Landsat 9 TIRS-2 data.

LakeCoSTR: A tool to facilitate use of Landsat Collection 2 to estimate lake surface water temperatures

AbstractAlthough remote sensing of lake surface water temperature has been ongoing for decades and has led to important discoveries regarding the warming of lake surface temperature in some regions, the ability to access and use remote sensing data is still primarily limited to remote sensing experts. Effective use of remote sensing data involves technical skills in coding (often in multiple programming languages), application of appropriate atmospheric corrections, and integrating spatially heterogeneous remote sensing data with in situ data obtained at specific geographic locations. To improve access to remote sensing data and broaden the understanding of changes in lake surface water temperature over the past four decades, we created lakeCoSTR (lake Collection 2 Surface Temperature Retrieval), a user‐friendly, cloud‐based script that gives ecologists lacking specialized training in remote sensing the ability to access the Landsat Collection 2 temperature estimates for lakes with a surface area of at least 0.4 ha. Additionally, if in situ data are provided, a paired dataset can be created within the tool. To demonstrate lakeCoSTR, we retrieved surface water temperature data for a lake with a long monitoring history, Lake Sunapee, NH, USA, and compared long‐term surface temperature trends between data obtained via lakeCoSTR and in situ measurements. When compared with Landsat Collection 1 temperature estimates derived from a single‐channel algorithm, the Landsat Collection 2 data from lakeCoSTR required no calibration to in situ data. This suggests that lakeCoSTR can be used to document temporal trends in lakes and to reprocess analyses that relied on Collection 1 data. From 1983 until 2020, remotely sensed surface water temperature estimates at Lake Sunapee increased by 0.07–0.09°C year−1 during July and August, a trend that agreed with long‐term in situ record. As climate change continues to impact freshwater systems, a better understanding of long‐term temperature data will be vital. Remotely sensed data, like those acquired using lakeCoSTR, may provide a window into temperature trends for lakes.

Urban Land Surface Temperature Retrieval From High Spatial Resolution Thermal Infrared Image Using a Modified Split-Window Algorithm

Urban Land Surface Temperature Retrieval From High Spatial Resolution Thermal Infrared Image Using a Modified Split-Window Algorithm

Land Surface Temperature Retrieval From Sentinel-3A SLSTR Data: Comparison Among Split-Window, Dual-Window, Three-Channel, and Dual-Angle Algorithms

Land surface temperature (LST) is a vital parameter for studying global ecological, climatic, and environmental changes. Although various LST retrieval algorithms have been proposed, including split-window (SW), dual-window (DW), three-channel (TC), and dual-angle (DA) algorithms, few studies have compared these algorithms using the same satellite observations. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3A provides a unique opportunity to conduct this comparison owing to its dual-angle viewing capability and multiple thermal infrared (TIR) and mid-infrared (MIR) channels. Here, we implemented two SW algorithms, one DW algorithm, two TC algorithms and one DA algorithm for the SLSTR data. The LST retrievals from these six algorithms were validated, along with the SLSTR operational LST product based on an emissivity-implicit SW algorithm. Temperature-based and radiance-based validation methods were used to evaluate different LST retrievals across different land cover types. The results indicated that the proposed SW algorithm had the highest accuracy, followed by the Pérez-Planells SW and the official algorithms. The overall root-mean-square errors (RMSEs) of these three SW algorithms were 1.42 K, 1.79 K and 2.05 K, respectively. The three algorithms involving the MIR channel (one DW and two TC algorithms) were more suitable for nighttime LST retrieval and had similar performances to the three SW algorithms, with a nighttime RMSE of approximately 1.36 K. The LST retrieval accuracy of the DA algorithm had the highest uncertainty and was closely related to the angular variation in surface emissivity and brightness temperature. The findings of this study contribute to a better understanding of the different LST retrieval algorithms and facilitate potential improvements in the official LST retrieval algorithm for SLSTR.

A Physical-Based Method for Pixel-by-Pixel Quantifying Uncertainty of Land Surface Temperature Retrieval From Satellite Thermal Infrared Data Using the Generalized Split-Window Algorithm

Land surface temperature (LST) is an important physical parameter at the interface between the Earth’s surface and the atmosphere. Accurately quantifying LST uncertainty is essential for the generation of a long-term and consistent LST Climate Data Record (CDR) or Earth System Data Record (ESDR) from either multiple sensors or algorithms. In this study, a physical-based method was proposed to quantify the uncertainty of LST retrieval from satellite thermal infrared data using the generalized split-window (GSW) algorithm. LST uncertainties were parameterized as a function of brightness temperature at the top of the atmosphere (TOA) and surface emissivity in two split-window channels, which are two key input parameters in the GSW algorithm, as well as their uncertainties. The performance of the parameterized uncertainty model was evaluated according to simulation dataset at six prescribed viewing zenith angles (VZAs) of 0°, 33.56°, 44.42°, 51.32°, 56.25°, and 60°, with a root mean squared error (RMSE) of 0.001 K. The coefficients of the parameterized uncertainty model at arbitrary VZA within a sensor’ field of view (FOV) can be obtained by linear interpolation of the coefficients at the six prescribed VZAs. Once the coefficients of the parameterized uncertainty model for each pixel are available, total LST uncertainties can be quantified on a pixel-by-pixel basis. As an example, the parameterized uncertainty model was applied to actual MODIS data for displaying the spatial distribution of LST uncertainties. The results indicate that the parameterized uncertainty model can well characterize the spatial variation in LST uncertainties over various land cover types.

A Fast Piecewise-Defined Neural Network Method to Retrieve Temperature and Humidity Profile for the Vertical Atmospheric Sounding System of FengYun-3E Satellite

A fast piecewise-defined neural network (PDNN) method is presented to produce accurate atmospheric temperature and humidity profiles from satellite hyperspectral infrared and microwave observations in all-sky conditions. The PDNN method relies on a novel classification approach and a principal component-based neural network function to better capture the nonlinear relationship between the spectral radiances and atmospheric state vectors. The algorithm was designed to only rely on satellite measurements. Large datasets were used for network training to make the retrieval robust to random errors in the reference data. For each retrieved profile, an effective quality indicator was obtained by training against the absolute value of the retrieval error. In addition, a reliable rain cloud flag was generated based on the scattering difference of cloud particles between the 50 and 118 GHz channels. Besides the independent reanalysis field data, the algorithm’s performance was also evaluated using radiosonde measurements. Preliminary validation shows that the best temperature retrieval occurred in the mid-troposphere (around 1.0 K). Depending on the reference data, errors in the boundary layer typically ranged from 1.8 K to 2.5 K. For water vapor, the retrieval error was less than 22% in the low-troposphere and less than 35% in the mid-and upper-troposphere when validated against the reanalysis field. The humidity error was approximately 10% lower when compared to radiosondes. The PDNN is used operationally to produce atmospheric temperature and moisture soundings from the Vertical Atmospheric Sounding System of the FengYun-3E satellite, an early-morning-orbit meteorological satellite launched in 2021.

Multifrequency Microwave Radiometry for Characterizing the Internal Temperature of Biological Tissues

The analysis of near-field radiometry is described for characterizing the internal temperature of biological tissues, for which a system based on multifrequency pseudo-correlation-type radiometers is proposed. The approach consists of a new topology with multiple output devices that enables real-time calibration and performance assessment, recalibrating the receiver through simultaneous measurable outputs. Experimental characterization of the prototypes includes a well-defined calibration procedure, which is described and demonstrated, as well as DC conversion from the microwave input power. Regarding performance, high sensitivity is provided in all the bands with noise temperatures around 100 K, reducing the impact of the receiver on the measurements and improving its sensitivity. Calibrated temperature retrievals exhibit outstanding results for several noise sources, for which temperature deviations are lower than 0.1% with regard to the expected temperature. Furthermore, a temperature recovery test for biological tissues, such as a human forearm, provides temperature values on the order of 310 K. In summary, the radiometers design, calibration method and temperature retrieval demonstrated significant results in all bands, validating their use for biomedical applications.

Satellite observations of gravity wave momentum flux in the mesosphere and lower thermosphere (MLT): feasibility and requirements

Abstract. In the recent decade it became evident that we need to revise our picture of how gravity waves (GWs) reach the mesosphere and lower thermosphere (MLT). This has consequences for our understanding not just of the properties of the GWs themselves, but in particular of the global circulation in the MLT. Information on spectral distribution, direction, and zonal mean GW momentum flux is required to test the theoretical and modeling findings. In this study, we propose a constellation of two CubeSats for observing mesoscale GWs in the MLT region by means of temperature limb sounding in order to derive such constraints. Each CubeSat deploys a highly miniaturized spatial heterodyne interferometer (SHI) for the measurement of global oxygen atmospheric band emissions. From these emissions, the 3-D temperature structure can be inferred. We propose obtaining four independent observation tracks by splitting the interferograms in the center and thus gaining two observation tracks for each satellite. We present a feasibility study of this concept based on self-consistent, high-resolution global model data. This yields a full chain of end-to-end (E2E) simulations incorporating (1) orbit simulation, (2) airglow forward modeling, (3) tomographic temperature retrieval, (4) 3-D wave analysis, and (5) GW momentum flux (GWMF) calculation. The simulation performance is evaluated by comparing the retrieved zonal mean GWMF with that computed directly from the model wind data. A major question to be considered in our assessment is the minimum number of tracks required for the derivation of 3-D GW parameters. The main result from our simulations is that the GW polarization relations are still valid in the MLT region and can thus be employed for inferring GWMF from the 3-D temperature distributions. Based on the E2E simulations for gaining zonal mean climatologies of GW momentum flux, we demonstrate that our approach is robust and stable, given a four-track observation geometry and the expected instrument noise under nominal operation conditions. Using phase speed and direction spectra we show also that the properties of individual wave events are recovered when employing four tracks. Finally, we discuss the potential of the proposed observations to address current topics in the GW research. We outline for which investigations ancillary data are required to answer science questions.

TUNER-compliant error estimation for MIPAS: methodology

Abstract. This paper describes the error estimation for temperature and trace gas mixing ratios retrieved from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) limb emission spectra. The following error sources are taken into account: measurement noise, propagated temperature and pointing noise, uncertainties in the abundances of spectrally interfering species, instrument line shape errors, and spectroscopic data uncertainties in terms of line intensities and broadening coefficients. Furthermore, both the direct impact of volatile and persistent gain calibration uncertainties, offset calibration, and spectral calibration uncertainties, as well as their impact through propagated calibration-related temperature and pointing uncertainties, are considered. An error source specific to the MIPAS upper atmospheric observation mode is the propagation of the smoothing error crosstalk of the combined NO and temperature retrieval. Whenever non-local thermodynamic equilibrium modelling is used in the retrieval, related kinetic constants and mixing ratios of species involved in the modelling of populations of excitational states also contribute to the error budget. Both generalized Gaussian error propagation and perturbation studies are used to estimate the error components. Error correlations are taken into account. Estimated uncertainties are provided for a multitude of atmospheric conditions. Some error sources were found to contribute both to the random and the systematic component of the total estimated error. The sequential nature of the MIPAS retrievals gives rise to entangled errors. These are caused by error sources that affect the uncertainty in the final data product via multiple pathways, i.e., on the one hand, directly, and, on the other hand, via errors caused in a preceding retrieval step. These errors tend to partly compensate for each other. The hard-to-quantify effect of the horizontally non-homogeneous atmosphere and unknown error correlations of spectroscopic data are considered to be the major limitations of the MIPAS error estimation.

Review for Region Localization in Large-Scale Optical Remote Sensing Images

For the massive large-scale visible image data obtained by satellite, unmanned aerial vehicles, and other reconnaissance platforms, if only relying on manual visual interpretation, there will be problems such as heavy workload, low efficiency, high repeatability, strong subjectivity, and high cost, which cannot meet the demand of modern society for efficient information. Therefore, in order to improve work efficiency, it is necessary to study the rapid automatic region localization in large-scale remote sensing images. That will play an important role in change detection, temperature retrieval, and other files. The development and present situation of the region localization algorithms are analyzed. This paper summarizes the development, improvement, and deficiency of the traditional algorithm, as well as the difficulties and challenges. And the authors make a comparison to the deep learning-based methods. Finally, a possible development direction is prospected.