All-weather Land Surface Temperature Research Articles

TRIMS LST: a daily 1 km all-weather land surface temperature dataset for China's landmass and surrounding areas (2000–2022)

Abstract. 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 0.09/0.37 K and a standard deviation of bias (SD) 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 1.73 K 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 TRIMS LST is freely and conveniently available at https://doi.org/10.11888/Meteoro.tpdc.271252 (Zhou et al., 2021).

Evaluating the Reconstructed All-Weather Land Surface Temperature for Urban Heat Island Analysis

With the continuous improvement of urbanization levels in the Lhasa area, the urban heat island effect (UHI) has seriously affected the ecological environment of the region. However, the satellite-based thermal infrared land surface temperature (LST), commonly used for UHI research, is affected by cloudy weather, resulting in a lack of continuous spatial and temporal information. In this study, focusing on the Lhasa region, we combine simulated LST data obtained by the Weather Research and Forecasting (WRF) model with remote sensing-based LST data to reconstruct the all-weather LST for March, June, September, and December of 2020 at a resolution of 0.01° while using the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST as a reference (in terms of accuracy). Subsequently, based on the reconstructed LST, an analysis of the UHI was conducted to obtain the spatiotemporal distribution of UHI in the Lhasa region under all-weather LST conditions. The results demonstrate that the reconstructed LST effectively captures the expected spatial distribution characteristics with high accuracy, with an average root mean square error of 2.20 K, an average mean absolute error of 1.51 K, and a correlation coefficient consistently higher than 0.9. Additionally, the heat island effect in the Lhasa region is primarily observed during the spring and winter seasons, with the heat island intensity remaining relatively stable in winter. The results of this study provide a new reference method for the reconstruction of all-weather LST, thereby improving the research accuracy of urban thermal environment from the perspective of foundational data. Additionally, it offers a theoretical basis for the governance of UHI in the Lhasa region.

A Fast and Easy Way to Produce a 1-Km All-Weather Land Surface Temperature Dataset for China Utilizing More Ground-Based Data

A Fast and Easy Way to Produce a 1-Km All-Weather Land Surface Temperature Dataset for China Utilizing More Ground-Based Data

An Annual Temperature Cycle Feature Constrained Method for Generating MODIS Daytime All-Weather Land Surface Temperature

An Annual Temperature Cycle Feature Constrained Method for Generating MODIS Daytime All-Weather Land Surface Temperature

Spatiotemporal Analysis of Soil Moisture Variability and Its Driving Factor

Soil moisture (SM), as a crucial input variable of land surface processes, plays a pivotal role in the global hydrological cycle. The aim of this paper is to examine the spatiotemporal variability in SM in the Heihe River Basin using all-weather land surface temperature (LST) and reanalysis land surface data. Initially, we downscaled and generated daily 1 km all-weather SM data (2020) for the Heihe River Basin. Subsequently, we investigated the spatial and temporal patterns of SM using geostatistical and time stability methods. The driving forces of the monthly SM were studied using the optimal parameter-based geographical detector (OPGD) model. The results indicate that the monthly mean values of the downscaled SM data range from 0.115 to 0.146, with a consistently lower SM content and suitable temporal stability throughout the year. Geostatistical analysis revealed that months with a higher SM level exhibit larger random errors and higher variability. Driving analysis based on the factor detector demonstrated that in months with a lower SM level, the q values of each driving factor are relatively small, and the primary driving factors are land cover and elevation. Conversely, in months with a higher SM level, the q values for each driving factor are larger, and the primary driving factors are the normalized difference vegetation index and LST. Furthermore, interaction detector analysis suggested that the spatiotemporal variation in SM is not influenced by a single driving factor but is the result of the interaction among multiple driving factors, with most interactions enhancing the combined effect of two factors.

Conversion of satellite passive microwave signals to land surface “skin” temperature for extremely dry deserts

Satellite passive microwave (PMW) observations are important data sources for obtaining land surface temperature (LST) especially for cloudy conditions. Considering the generally poorer spatial representativeness of PMW observations, fusion of PMW-based and thermal infrared-(TIR-) based observations have been considered as an important approach to generate all-weather LST with appropriate resolution. However, the existing penetration difference between PMW-based and TIR-based observations has been an obstacle against this objective. This is especially the case for desert regions, where the penetration depth of PMW observations is larger than under other conditions and the relationship between TIR-based and PMW-based signals are thus more complicated. In this regard, this study develops an easy and high-efficiency model suitable for transforming PMW-based subsurface LST to TIR-like skin surface temperature in four regions across the world characterized by typical arid climate. The developed model employs microwave-derived surface soil moisture (SSM) and vegetation transmissivity (VT) as major inputs to quantify that “resampling depth difference” between TIR-based and PMW-based observations. Results show that the model can significantly reduce that difference (RMSE) from >10 K to around 2–5 K in the typical desert regions. The developed model is beneficial to obtaining high-resolution all-weather LST for the vast desert regions across the world in subsequent studies.

Investigation and validation of two all-weather land surface temperature products with in-situ measurements

ABSTRACT The need for cross-comparison and validation of all-weather Land Surface Temperature (LST) products has arisen due to the release of multiple such products aimed at providing comprehensive all-weather monitoring capabilities. In this study, we focus on validating two well-established all-weather LST products (i.e. MLST-AS and TRIMS LST) against in-situ measurements obtained from four high-quality LST validation sites: Evora, Gobabeb, KIT-Forest, and Lake Constance. For the land sites, MLST-AS exhibits better accuracy, with RMSEs ranging from 1.6 K to 2.1 K, than TRIMS LST, the RMSEs of which range from 1.9 K to 3.1 K. Because MLST-AS pixels classified as “inland water” are masked out, the validation over Lake Constance is limited to TRIMS LST: it yields a RMSE of 1.6 K. Furthermore, the validation results show that MLST-AS and TRIMS LST exhibit better accuracy under clear-sky conditions than unclear-sky conditions across all sites. Since the accuracy of the all-weather LST products is considerably affected by the input clear-sky LST products, we further compare the all-weather LST with the corresponding input clear-sky LST to conduct an error source analysis. Considering the clear-sky pixels on MLST-AS directly using the estimates from MLST, the error source analysis is limited to examining TRIMS LST and its input (i.e. MODIS LST). The findings indicate that TRIMS LST is highly correlated with MODIS LST. The investigation and validation of the two selected all-weather LST products objectively evaluate their accuracy and stability, which provides important information for applications of these all-weather LST products.

A Downscaling Method Based on MODIS Product for Hourly ERA5 Reanalysis of Land Surface Temperature

Land surface temperature (LST) is a critical parameter for the dynamic simulation of land surface processes and for analyzing variations on regional or global scales. Obtaining LST with high spatiotemporal resolution is a subject of intensive and ongoing research. This study proposes a pixel-wise temporal alignment iterative linear regression model for downscaling based on MODIS LST products. This approach allows us to address the problem of high temporal resolution but low spatial resolution of the ERA5 reanalysis LST product while remaining immune to the pixel loss caused by clouds. The hourly ERA5 LST of the study area for 2012–2021 was downscaled to a 1000 m resolution, and its accuracy was verified by comparison with measured data from meteorological stations. The downscaled LST offers intricate details and is faithful to the LST characteristics of distinct land-cover categories. In comparison with other downscaling techniques, the proposed technique is more stable and preserves the spatial distribution of the ERA5 LST with minimal missing pixels. The pixel-wise average R2 and mean absolute error for the MODIS view times are 0.87 and 2.7 K, respectively, for cloud-free conditions on a 1000 m scale. The accuracy verification using data from meteorological stations indicates that the overall error is lower during cloudless periods rather than during overcast periods, during the night rather than during the day, and at MODIS view times rather than at non-view times. The maximum and minimum mean errors are 0.13 K for cloud-free periods and −0.98 K for cloudy periods, indicating a slight underestimation and overestimation, respectively. Conversely, the maximum and minimum mean absolute errors are 2.01 K for the daytime and 0.85 K for the nighttime. Therefore, the model ensures higher accuracy during cloudy periods with only the clear-sky LST used as input data, making it suitable for long-term, all-weather ERA5 LST downscaling.

A framework for reconstructing 1km all-weather hourly LST from MODIS data

ABSTRACT Land surface temperature (LST) is an essential parameter in environmental monitoring. However, due to the cloud contamination and the limitations of sensors, existing thermal infrared LST products are challenging to provide all-weather LST with high spatiotemporal resolution. Therefore, this paper presents a framework for reconstructing hourly LST under clouds based on the moderate resolution imaging spectroradiometer (MODIS) LST products. This framework consists of two main steps: (1) Instantaneous LST estimation for MODIS using a modified annual temperature cycle (ATCM) model and (2) Hourly LST reconstruction under all-weather conditions from atmospheric reanalysis data and MODIS instantaneous LST with the linear model (LM). The proposed framework is evaluated with the full year data of 2019 with tile number H26V05. The results show that the spatial distribution of the estimated LST is similar with the MODIS LST. Additionally, it could provide an accurate indication of the spatial and temporal variability of LST. Comparing the MODIS LST of the selected area for tile number H26V05 in 2019 with the estimated LST of the ATCM, the root mean squared errors (RMSEs) of the model are around 1K ~ 3K. The hourly LST is then reconstructed based on the LM method, and the RMSE is around 2K during the daytime and around 1K during the night-time. Finally, to verify the robustness of the hourly LST reconstruction method, the accuracy of the hourly LST was evaluated by the MODIS LST at different situations in the day. The results show that the missing data at one moment will cause the accuracy of the model decreasing by 0.09 ~ 1.67K at the corresponding moment. In general, the proposed framework has the potential to reconstruct the 1 km all-weather hourly LST with high accuracy and a certain level of robustness.

Reconstruction of all-weather land surface temperature based on a combined physical and data-driven model.

At present, the remote sensing (RS) thermal infrared (TIR) images that are commonly used to obtain land surface temperature (LST) are contaminated by clouds and thus cannot obtain spatiotemporal integrity of LST. To solve this problem, this study combined a physical model with strong interpretability with a data-driven model with high data adaptability. First, the physical model (Weather Research and Forecast (WRF) model) was used to generate LST source data. Then, combined with multisource RS data, a data-driven method (random forest (RF)) was used to improve the accuracy of the LST, and a model framework for a data-driven auxiliary physical model was formed. Finally, all-weather MODIS-like data with a spatial resolution of 1km were generated. Beijing, China, was used as the study area. The results showed that in cases of more clouds and fewer clouds, the reconstructed all-weather LST had a high spatial continuity and could restore the spatial distribution details of the LST well. The mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (ρ) in the case of more (fewer) clouds were ranked as follows: MAE < 1K (< 2K), RMSE < 2K (< 2K), and ρ > 0.9. The errors obeyed an approximately normal distribution. The total MAE, RMSE, and ρ were 0.80K, 1.09K, and 0.94K, respectively. Generally, the LST reconstructed in this paper had a high accuracy, and the model could provide all-weather MODIS-like LST to compensate for the disadvantages of satellite TIR images (i.e., contamination by clouds and inability to obtain complete LST values).

A stepwise framework for interpolating land surface temperature under cloudy conditions based on the solar-cloud-satellite geometry

Thermal infrared land surface temperature (LST) data from satellites often contain extensive missing values due to high cloudiness degree, which severely hinders their use in applications. Despite the many methods developed, common methods, such as fusion with microwave or reanalysis data and the surface energy budget approach, still remain subject to important limitations and uncertainties, such as dependency on coarse resolution data and difficulty in interpolation for large-scale missing LST data. In this study, we proposed a stepwise framework for estimating missing cloudy-sky LST values of Moderate Resolution Imaging Spectroradiometer (MODIS) from informative samples owing to the solar-cloud-satellite geometry (SCSG) effect, by which satellite imagery records the cloudy-sky LST values of a portion of pixels. We first estimated the clear-sky LST equivalents for all cloud-affected pixels via a similarity-based approach and then determined unknown LSTs for cloudy pixels by training a machine-learning model on cloudy-sky LST values observed owing to the SCSG effect. We demonstrated the utility of this approach by using MODIS/Aqua daytime LST data over Qinghai-Tibet Plateau (QTP) and validated the interpolation results against representative in-situ LST observations and two recently published all-weather LST datasets. When compared to the corresponding in-situ measurements, the interpolated cloudy-sky LST values showed satisfactory accuracy with a mean absolute error (MAE) value of 3.99 °C and a coefficient of determination (R2) value of 0.74, while the MODIS/Aqua clear-sky LST values led to an MAE value of 2.66 °C and an R2 value of 0.86. Compared to the two all-weather LST datasets, results of this study showed the highest accuracy over the data-gap-filled regions in terms of all quantitative performance metrics, more natural transition textures, and better representation of seasonal characteristics. The proposed framework has the advantage of relying on the MODIS family data and handling extensive missing data as well as triggers opportunities to leverage the SCSG effect to produce high-quality all-weather LST data.

Near-Real-Time Estimation of Hourly All-Weather Land Surface Temperature by Fusing Reanalysis Data and Geostationary Satellite Thermal Infrared Data

Near-Real-Time Estimation of Hourly All-Weather Land Surface Temperature by Fusing Reanalysis Data and Geostationary Satellite Thermal Infrared Data

Estimation of quasi-full spatial coverage soil moisture with fine resolution in China from the combined use of ERA5-Land reanalysis and TRIMS land surface temperature product

Knowledge of surface soil moisture (SSM) at various scales is essential for understanding energy and water exchange between the atmosphere and land surface. In this study, we propose a practical approach based on the synergy of all-weather land surface temperature (LST) and reanalysis data to generate a daily/1-km SSM with quasi-full spatial coverage. In this approach, the day/night LST difference and SSM derived from the ERA5-Land with a spatial resolution of 0.1° were first used to develop linear relationships between them under various vegetation densities (sparse, medium, and dense) for four dominant land cover types (forest, cropland, grassland, and barren) in China. The relationships were subsequently used in an all-weather 1-km LST product to generate an initial daily/1-km SSM dataset in 2019. In addition, two blended microwave-based SSM products were applied to correct the initial dataset by assuming that the aggregated SSM values within the coarse scale were unbiased when compared with the original microwave-based SSM products. Furthermore, two blended microwave-based SSM products were used to correct the initial dataset with the assumption that aggregated SSM values within the coarse scale are unbiased when compared to the original microwave-based SSM products. Finally, the estimated SSM were preliminarily evaluated against the ground in-situ measurements at cropland under different aridity conditions. A satisfactory accuracy was obtained with an unbiased root mean square error (ubRMSE) of ∼0.05 m3/m3, which is not only close to the ubRMSE requirements of 0.04 m3/m3 for the SSM estimates in most practical applications, but is also characterized with quasi-full spatial coverage.

A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data

High temporal resolution and spatially complete (seamless) land surface temperature (LST) play a crucial role in numerous geoscientific aspects. This paper proposes a data fusion method for producing hourly seamless LST from Himawari-8 Advanced Himawari Imager (AHI) data. First, the high-quality hourly clear-sky LST was retrieved from AHI data by an improved temperature and emissivity separation algorithm; then, the hourly spatially complete China Land Data Assimilation System (CLDAS) LST was calibrated by a bias correction method. Finally, the strengths of the retrieved AHI LST and bias-corrected CLDAS LST were combined by the multiresolution Kalman filter (MKF) algorithm to generate hourly seamless LST at different spatial scales. Validation results showed the bias and root mean square error (RMSE) of the fused LST at a finer scale (0.02°) were −0.65 K and 3.38 K under cloudy sky conditions, the values were −0.55 K and 3.03 K for all sky conditions, respectively. The bias and RMSE of the fused LST at the coarse scale (0.06°) are −0.46 K and 3.11 K, respectively. This accuracy is comparable to the accuracy of all-weather LST derived by various methods reported in the published literature. In addition, we obtained the consistent LST images across different scales. The seamless finer LST data over East Asia can not only reflect the spatial distribution characteristics of LST during different seasons, but also exactly present the diurnal variation of the LST. With the proposed method, we have produced a 0.02° seamless LST dataset from 2016 through 2021 that is freely available at the National Tibetan Plateau Data Center. It is the first time that we can obtain the hourly seamless LST data from AHI.

Estimation of evapotranspiration using all-weather land surface temperature and variational trends with warming temperatures for the River Source Region in Southwest China

• All-weather LST is used to overcome satellite TIR LST missing in cloudy conditions. • Estimation scheme for all-weather evapotranspiration (AWET) is proposed. • Response of AWET to the warming temperatures exhibits a spatiotemporal heterogeneity. Land surface temperature (LST) derived from satellite thermal infrared (TIR) remote sensing is widely used in estimating land surface evapotranspiration (ET) through energy balance theory. However, as satellite TIR remote sensing is frequently affected by clouds, the derived LST with spatial missing makes it impossible to estimate seamless ET for large areas. In this study, based on the all-weather LST (AWLST) generated through the synergistic use of TIR and passive microwave (PMW) remote sensing, we propose an estimation scheme of all-weather ET (AWET) for the River Source Region of Southwest China (RSR-SC) with complex environmental characteristics of frequent clouds and fog. Specifically, the parameters of the Surface Energy Balance System (SEBS), which have high impact on the model results, are first accurately determined by a global sensitivity analysis. Second, the parametric calculation schemes of turbulent exchange, such as surface roughness heights for momentum ( z 0m ) and heat ( z 0h ) transfer, and soil heat flux ( G 0 ) of the SEBS model are refined. Third, the daily ET temporal upscaling method is developed. Then a long-term AWET product for the RSR-SC is generated. Comparison against ground measurements from 12 eddy covariance (EC) sites indicates a good accuracy of the AWET product: the mean absolute percent error (MAPE) and root mean square error (RMSE) of the daily ET estimates are 36 % and 0.88 mm/d, respectively. From aspects of temporal variation of daily ET, monthly spatiotemporal distribution patterns, interannual variation of different land covers and spatial distribution comparison with other ET products, the generated AWET exhibits to be realistic, reflecting relatively subtle variations. Further investigation indicates that the response of surface ET to the warming temperatures exhibits the spatial heterogeneity of the change trend. This study highlights the importance of estimating daily AWET at a MODIS-like scale using AWLST data to significantly benefit water resource monitoring and conduct runoff prediction over large areas and watershed scales.

Reconstructing all-weather daytime land surface temperature based on energy balance considering the cloud radiative effect

Land surface temperature (LST) has been used in many applications as its strong relationships with land surface processes. However, the greatest limitation of the use of LST is the missing data caused by cloud contamination and weather conditions. In this study, we first used the XGBoost method to describe complex relationships of LST with surface characteristics from clear-sky pixels, and applied the model to retrieve hypothetical clear-sky LST under cloudy sky. Secondly, cloud radiative effect (CRE) on the LST was calculated based on energy balance using the reanalysis data. The models were applied to reconstruct the all-weather LST over the Tibetan Plateau (TP). The spatial patterns of reconstructed LST indicated that our model could produce completely spatial-seamless LST and depict the detailed information. The accuracy of the XGBoost LST was validated against the clear-sky MODIS LST (average R2 = 0.92, MAPE = 0.52, RMSE = 2.32 K). The CRE-EB LST was evaluated using data from six in-situ sites from the TP. The validation results were separated into three conditions: clear sky (RMSE = 3.01 K–3.52 K, R2 = 0.88–0.93, bias = −1.08 K-1.88 K), cloudy sky (RMSE = 3.31 K–4.06 K, R2 = 0.87–0.92, bias = −0.21 K-1.11 K), and overall (RMSE = 3.31 K–3.82 K, R2 = 0.88–0.93, bias = −0.42 K-1.24 K). Compared to existing all-weather LST datasets, the temporal variability of our LST data shows similar seasonal and daily changes, and the CRE-EB LST has advantages in terms of image quality and accuracies under cloudy condition. This study demonstrated the utility of proposed models to reconstruct all-weather LST.

A novel TIR-derived three-source energy balance model for estimating daily latent heat flux in mainland China using an all-weather land surface temperature product

A reliable thermal-derived method for spatially and temporally continuous latent heat flux (LE) estimates is vital for agricultural water resource management. In this study, we proposed a novel three-source LE model (TSLEM) derived by all-weather thermal infrared (TIR) land surface temperature (LST) to generate all-sky daily LE in mainland China. In this approach, LE was partitioned into the energy fluxes from canopy transpiration, soil evaporation, and interception water evaporation, respectively. Importantly, a new strategy was used to decompose radiational temperature into soil temperature (Ts), canopy temperature (Tc) and interception water temperature (Ti). Then soil evaporation was estimated by Penman-Monteith (PM) equation that parameterizes a soil resistance as a function of the normalized difference temperature index (NDTI) derived from Ts. A simplified MOD16 algorithm framework was used to estimate canopy transpiration and a Priestley-Taylor (PT) model to estimate interception evaporation. The proposed method was validated using 26 eddy covariance (EC) tower sites in mainland China across various vegetation types and applied to generate spatial continuous daily LE in mainland China. The results show that TSLEM accurately yielded daily LE with an average coefficient of determination (R2) of 0.53 (p<0.01) and root-mean-square-error (RMSE) of 27.37 W/m2, indicating TSLEM is a promising method for generating daily LE using all-weather LST at the regional scale.

Application of a Machine Learning Algorithm in Generating an Evapotranspiration Data Product From Coupled Thermal Infrared and Microwave Satellite Observations.

Land surface evapotranspiration (ET) is one of the main energy sources for atmospheric dynamics and a critical component of the local, regional, and global water cycles. Consequently, accurate measurement or estimation of ET is one of the most active topics in hydro-climatology research. With massive and spatially distributed observational data sets of land surface properties and environmental conditions being collected from the ground, airborne or space-borne platforms daily over the past few decades, many research teams have started to use big data science to advance the ET estimation methods. The Geostationary satellite Evapotranspiration and Drought (GET-D) product system was developed at the National Oceanic and Atmospheric Administration (NOAA) in 2016 to generate daily ET and drought maps operationally. The primary inputs of the current GET-D system are the thermal infrared (TIR) observations from NOAA GOES satellite series. Because of the cloud contamination to the TIR observations, the spatial coverage of the daily GET-D ET product has been severely impacted. Based on the most recent advances, we have tested a machine learning algorithm to estimate all-weather land surface temperature (LST) from TIR and microwave (MW) combined satellite observations. With the regression tree machine learning approach, we can combine the high accuracy and high spatial resolution of GOES TIR data with the better spatial coverage of passive microwave observations and LST simulations from a land surface model (LSM). The regression tree model combines the three LST data sources for both clear and cloudy days, which enables the GET-D system to derive an all-weather ET product. This paper reports how the all-weather LST and ET are generated in the upgraded GET-D system and provides an evaluation of these LST and ET estimates with ground measurements. The results demonstrate that the regression tree machine learning method is feasible and effective for generating daily ET under all weather conditions with satisfactory accuracy from the big volume of satellite observations.

A two-step deep learning framework for mapping gapless all-weather land surface temperature using thermal infrared and passive microwave data

Blending data from thermal infrared (TIR) and passive microwave (PMW) measurements is a promising solution for generating the all-weather land surface temperature (LST). However, owing to swath gaps in PMW data and the resolution inconsistence between TIR and PWM data, spatial details are often incomplete or considerable losses are generated in the all-weather LST using traditional methods. This study was conducted to develop a two-step deep learning framework (TDLF) for mapping gapless all-weather LST over the China's landmass using MODIS and AMSR-E LST data. In the TDLF, a multi-temporal feature connected convolutional neural network bidirectional reconstruction model was developed to obtain the spatially complete AMSR-E LST. A multi-scale multi-temporal feature connected generative adversarial network model was then designed to blend spatially complete AMSR-E LST and cloudy-sky MODIS LST, and generate gapless all-weather LST data. Gapless all-weather LST data were evaluated using six in-situ LST data from the Tibetan Plateau (TP) and the Heihe River Basin (HRB). The root mean squared errors (RMSEs) of the gapless all-weather LST were 1.71–2.0 K with determination coefficients (R2) of 0.94–0.98 under clear conditions, and RMSEs of 3.41–3.87 K and R2 of 0.88–0.94 were obtained under cloudy conditions. Compared to the existing PMW-based all-weather LSTs, the validation accuracy and image quality (such as spatial detail) of the generated gapless all-weather LSTs were superior. The TDLF does not require the use of any additional data and can potentially be implemented with other satellite TIR and PWM sensors to produce long-term, gapless, all-weather MODIS LST records on a global scale. Such a capability is beneficial for generating further gapless all-weather soil moisture and evapotranspiration datasets that can all be applied in global climate change research.

A new East African satellite data validation station: Performance of the LSA-SAF all-weather land surface temperature product over a savannah biome

We describe a new satellite data validation facility located in a savannah biome at the International Livestock Research Institute (ILRI) Kapiti Research Station (Kenya). The facility is focused on satellite land surface temperature (LST) and is equipped with multiple ground-viewing infrared radiometers across four sites. The in-situ LST observations are upscaled to match satellite LST products using a geometric illumination model. The in-situ sensor network represents a step-forward in LST validation in East Africa and savannah biomes. To our knowledge this is the first time that such an extensive network of LST radiometers and supporting measurements has been installed in sub-Saharan Africa, or a savannah. With this network we capture surface heterogeneity in a manner that has not previously been possible. The LST ground data from this station collected between October 2018 and March 2019 is used to evaluate the new Land Surface Analysis Satellite Application Facility (LSA-SAF) all-sky LST product (MLST-AS) that blends clear-sky infrared-retrieved LSTs with LSTs derived from a land surface energy balance model to fill gaps due to cloudy conditions. Comparison against the in-situ LSTs indicates overall accuracy, precision, and root-mean-square error (RMSE) of MLST-AS to be 2.02 K, 1.38 K and 3.64 K respectively. The infrared-retrieved LST component of MLST-AS under clear skies has an accuracy, precision and RMSE of 1.16 K, 0.8 K and 3.16 K respectively. The energy balance model-based component of MLST-AS has performance statistics of 3.02 K, 1.38 K and 4.16 K. The MLST-AS energy balance model component is observed to perform worse when surface moisture is present, underestimating night-time and daily maximum temperatures by between 2 and 4 K in the 24 h following surface water deposition as precipitation or dew.