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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

LEO Sensor to GEO Sensor Algorithm Transfer Models for Land Surface Temperature

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Communicating diurnal LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Marie Duffy↗
NASA NTRS2010

Estimation of Surface Air Temperature from MODIS 1km Resolution Land Surface Temperature Over Northern China

Surface air temperature is a critical variable to describe the energy and water cycle of the Earth-atmosphere system and is a key input element for hydrology and land surface models. It is a very important variable in agricultural applications and climate change studies. This is a preliminary study to examine statistical relationships between ground meteorological station measured surface daily maximum/minimum air temperature and satellite remotely sensed land surface temperature from MODIS over the dry and semiarid regions of northern China. Studies were conducted for both MODIS-Terra and MODIS-Aqua by using year 2009 data. Results indicate that the relationships between surface air temperature and remotely sensed land surface temperature are statistically significant. The relationships between the maximum air temperature and daytime land surface temperature depends significantly on land surface types and vegetation index, but the minimum air temperature and nighttime land surface temperature has little dependence on the surface conditions. Based on linear regression relationship between surface air temperature and MODIS land surface temperature, surface maximum and minimum air temperatures are estimated from 1km MODIS land surface temperature under clear sky conditions. The statistical errors (sigma) of the estimated daily maximum (minimum) air temperature is about 3.8 C(3.7 C).

Shen, Suhung↗
NASA NTRS1986

Remote sensing of land-surface temperature from HIRS/MSU data

A relaxation algorithm which permits meteorological parameters to be obtained from satellite data, without a priori assumptions about the properties of the other unknowns in the field of view, was developed. Atmospheric temperature profiles, atmospheric humidity, cloud cover, cloud top height, cloud top temperature, sea-surface temperature, land-surface temperature, snow cover, and ice cover are derived. Simultaneous determination of atmospheric and surface thermal structure and the cloud distribution provides information on heat sources and sinks, storage rates, and transport phenomena in the atmosphere. Such information is critical in determining the driving mechanisms for motions in the atmosphere and oceans and in improving numerical weather prediction.

Chahine, M. T.↗
NASA NTRSDate not supplied

A generalizable machine learning approach to predict land surface temperature

Monitoring of land surface and atmospheric states is highly reliant on satellite data. Traditionally, data products are generated using carefully tuned and validated algorithms for low-earth orbit (LEO) sensors. However, the emerging constellation of geostationary (GEO) sensors contributes global, high temporal resolution observations which can better capture the diurnal variability of key observables like land surface temperature (LST). Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from LEO and GEO satellites to develop a deep learning-based method for sensor-to-sensor algorithm emulation. Our model is trained on GOES-16 thermal bands to predict MODIS Terra LST and achieves a validation error <2K. Further, application of the model to unseen times of day and a second GEO sensor observing an unseen spatial domain demonstrate the generalization of the deep learning model across space, time and spectra. We anticipate that the synergies between a variety of active orbit configurations can be used to accelerate application of existing algorithms to new datasets.

Kate Marie Duffy↗
NASA NTRS2011

Accessing Recent Trend of Land Surface Temperature from Satellite Observations

Land surface temperature (Ts) is an important element to measure the state of terrestrial ecosystems and to study surface energy budgets. In support of the land cover/land use change-related international program MAIRS (Monsoon Asia Integrated Regional Study), we have collected global monthly Ts measured by MODIS since the beginning of the missions. The MODIS Ts time series have approximately 11 years of data from Terra since 2000 and approximately 9 years of data from Aqua since 2002, which makes possible to study the recent climate, such as trend. In this study, monthly climatology from two platforms are calculated and compared with that from AIRS. The spatial patterns of Ts trends are accessed, focusing on the Eurasia region. Furthermore, MODIS Ts trends are compared with those from AIRS and NASA's atmospheric assimilation model, MERRA (Modern Era Retrospective-analysis for Research and Applications). The preliminary results indicate that the recent 8-year Ts trend shows an oscillation-type spatial variation over Eurasia. The pattern is consistent for data from MODIS, AIRS, and MERRA, with the positive center over Eastern Europe, and the negative center over Central Siberia. The calculated climatology and anomaly of MODIS Ts will be integrated into the online visualization system, Giovanni, at NASA GES DISC for easy use by scientists and general public.

Shen, Suhung↗
NASA NTRS2004

Application of Satellite-Derived Land Surface Temperature to Minimum Temperature Forecasting

Satellite-derived land surface temperature (LST) is studied for the purpose of understanding regional skin temperature dependency and variability, and its relationship to corresponding, site-specific air temperature. Skin temperature is highly correlated with surface-air temperature although it differs depending on land surface characteristics, terrain, and atmospheric conditions on a diurnal and seasonal scale. The high temporal resolution of the Geostationary Operational Environmental Satellite (GOES) -12 sounder is used to compare the diurnal cycles of LST and surface-air temperature. The minimum for both temperatures occurs near sunrise and LST is found to agree closely with surface-air temperatures a period of hours before sunrise on clear sky nights. The Moderate Resolution Imaging Spectroradiometer (MODIS)-derived LST renders more horizontal temperature structure - with its high spatial resolution (1 km at nadir) compared to the GOES-12 sounder (10 km). Nighttime MODIS-derived LST is extrapolated to the time of minimum temperature for a number of case study days and these are grouped by season and atmospheric conditions. These composites show that the variation in LST mirror the variation in minimum surface-air temperature under similar conditions.

Jones, P. R.↗
NASA NTRS2011

Estimation of Surface Air Temperature Over Central and Eastern Eurasia from MODIS Land Surface Temperature

Surface air temperature (T(sub a)) is a critical variable in the energy and water cycle of the Earth.atmosphere system and is a key input element for hydrology and land surface models. This is a preliminary study to evaluate estimation of T(sub a) from satellite remotely sensed land surface temperature (T(sub s)) by using MODIS-Terra data over two Eurasia regions: northern China and fUSSR. High correlations are observed in both regions between station-measured T(sub a) and MODIS T(sub s). The relationships between the maximum T(sub a) and daytime T(sub s) depend significantly on land cover types, but the minimum T(sub a) and nighttime T(sub s) have little dependence on the land cover types. The largest difference between maximum T(sub a) and daytime T(sub s) appears over the barren and sparsely vegetated area during the summer time. Using a linear regression method, the daily maximum T(sub a) were estimated from 1 km resolution MODIS T(sub s) under clear-sky conditions with coefficients calculated based on land cover types, while the minimum T(sub a) were estimated without considering land cover types. The uncertainty, mean absolute error (MAE), of the estimated maximum T(sub a) varies from 2.4 C over closed shrublands to 3.2 C over grasslands, and the MAE of the estimated minimum Ta is about 3.0 C.

Shen, Suhung↗
NASA NTRS2016

Cloud Tolerance of Remote-Sensing Technologies to Measure Land Surface Temperature

Conventional methods to estimate land surface temperature (LST) from space rely on the thermal infrared(TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive microwave(MW) observations. The MW-LST product is informed by six polar-orbiting satellites to create a global record with up to eight observations per day for each 0.25resolution grid box. For days with sufficient observations, a continuous diurnal temperature cycle (DTC) was fitted. The main characteristics of the DTC were scaled to match those of a geostationary TIR-LST product. This paper tests the cloud tolerance of the MW-LST product. In particular, we demonstrate its stable performance with respect to flux tower observation sites (four in Europe and nine in the United States), over a range of cloudiness conditions up to heavily overcast skies. The results show that TIR based LST has slightly better performance than MW-LST for clear-sky observations but suffers an increasing negative bias as cloud cover increases. This negative bias is caused by incomplete masking of cloud-covered areas within the TIR scene that affects many applications of TIR-LST. In contrast, for MW-LST we find no direct impact of clouds on its accuracy and bias. MW-LST can therefore be used to improve TIR cloud screening. Moreover, the ability to provide LST estimates for cloud-covered surfaces can help expand current clear-sky-only satellite retrieval products to all-weather applications.

polar-orbiting↗
NASA NTRSDate not supplied

Communicating Metrics of Land Surface Temperature Variability Using Multi-sensor Machine Learning

Land surface temperature (LST) is a key climate observable used to detect changes in the Earth’s surface energy budget that influence carbon and water cycles. Land surface temperature exhibits strong diurnal variability, which geostationary satellites can observe at scale thanks to their temporal resolution. Due to anthropogenic climate and land use changes, the surface energy balance has been considerably modified and may be described by changes in diurnal temperature range and extremes. Using high performance computing and datasets from the NASA Earth Exchange, we exploit co-located, co-temporal observations from low-earth orbit (LEO) and geostationary (GEO) sensors to develop a deep learning-based method for LEO-to-GEO algorithm emulation. Our model is trained to predict MODIS Terra LST from GOES-16 thermal bands and achieves validation error <2K. Application of the model to unseen times of day (observed by MODIS Aqua) and a new GEO sensor (Himawari-8) observing an unseen spatial domain, demonstrate the generalization of the deep learning model across space, time and spectra. Further, time series clustering approaches are examined with the objective of identifying key indicators of change in diurnal cycling and extremes on a continental scale. Communicating LST variability observed by geostationary satellites can have impacts in multiple disciplines, from understanding of snow, vegetation and soil dynamics, to recognizing trends in heat events relevant to human health.

Kate Duffy↗
DOE OSTI2022

Comparing Coarse-Resolution Land Surface Temperature Products over Western Australia

Satellite-derived land surface temperature (LST) has commonly been used to monitor global temperature changes. The MODIS MYD11A2 product is the most common coarse-resolution product used for this purpose. An updated MODIS product (MYD21A2) and new VIIRS (VNP21A2) product have also recently become available. This study analyses eight-day, quality-controlled, LST imagery over Western Australia (WA) for the three products for an urban and a non-urban area for the years 2013, 2016, and 2019. An analysis of the data indicates that (i) the averaged daytime LST difference between the three products for Perth city over the three years was 1.32 °C, while at night it was 0.89 °C; (ii) the averaged daytime difference over the Kimberley region was 7.02 °C with a night average difference of 2.39 °C; and (iii) both the MYD21A2 and VNP21A2 products still appear to record anomalous monthly LST values, particularly in the humid Kimberley monsoonal months. The overall objective of the National Aeronautics and Space Administration (NASA) is to ensure that the LST values of the two satellite system products are comparable, so evidence of LST value differences will require further investigation, especially if the older product is to be replaced by the newer systems.

47 OTHER INSTRUMENTATION↗
NASA NTRS1994

Development of practical multiband algorithms for estimating land-surface temperature from EOS/MODIS data

A practical multiband, hierarchical algorithm for estimating land-surface temperature from NASA's future Earth Observing System (EOS) instruments Moderate Resolution Imaging Spectroradiometer (MODIS) and Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) is developed through comprehensive, accurate, radiative transfer simulations at moderate spectral steps of 1-5/cm for wide ranges of atmospheric and surface conditions. The algorithm will accept empirical or estimated information about the surface emissivity and reflectivity and the atmospheric temperature and water-vapor profiles. Ground-based and aircraft measurements are necessary to validate and improve the algorithm and to establish its quality. Its accuracy depends on the calibration accuracy of thermal infrared data, uncertainties in surface heterogeneity, and temperature-dependent atmospheric absorption coefficients. Better knowledge of land-surface spectral emissivities and more accurate coefficients for atmospheric molecular band absorption and water vapor continuum absorption are needed to develop global land-surface temperature algorithms accurate to 1-2 K.

Dozier, J.↗
NASA NTRS1999

Satellite Derived Land Surface Temperature for Model Assimilation

Studies have shown that land surface temperature (LST) tendencies are sensitive to the surface moisture availability which is a function of soil moisture and vegetation. The assimilation of satellite derived LST tendencies into the surface energy budget of mesoscale models has shown promise in improving the representation of the complex effects of both soil moisture and vegetation within the models for short term simulations. LST derived from geostationary satellites has the potential of providing the temporal and spatial resolution needed for an LST assimilation process. This paper presents an analysis comparing the LST derived from GOES-8 infrared measurements with LST calculated by the MM5 numerical model. The satellite derived LSTs are calculated using a physical split window approach using channels 4 and 5 of GOES-8. The differences in the LST data sets, especially the tendencies, are presented and examined. Quantifying the differences between the data sets provide insight of possible weaknesses in the model parameterizations affecting the surface energy budget calculations and an indication of the potential effectiveness o f assimilating LST into the models.

Suggs, Ronnie J.↗
NASA NTRS2002

Monitoring Thermal Status of Ecosystems with MODIS Land-Surface Temperature and Vegetation Index Products

The global land-surface temperature (LST) and normalized difference vegetation index (NDVI) products retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) data in 2001 were used in this study. The yearly peak values of NDVI data at 5km grids were used to define six NDVI peak zones from -0.2 to 1 in steps of 0.2, and the monthly NDVI values at each grid were sorted in decreasing order, resulting in 12 layers of NDVI images for each of the NDVI peak zones. The mean and standard deviation of daytime LSTs and day-night LST differences at the grids corresponding to the first layer of NDVI images characterize the thermal status of terrestrial ecosystems in the NDVI peak zones. For the ecosystems in the 0.8-1 NDVI peak zone, daytime LSTs distribute from 0-35 C and day-night LST differences distribute from -2 to 22 C. The daytime LSTs and day-night LST differences corresponding to the remaining layers of NDVI images show that the growth of vegetation is limited at low and high LSTs. LSTs and NDVI may be used to monitor photosynthetic activity and drought, as shown in their applications to a flood-irrigated grassland in California and an unirrigated grassland in Nevada.

Wan, Zhengming↗
DOE OSTI2022

Ten Years of VIIRS Land Surface Temperature Product Validation

The Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature (LST) has been operationally produced for a decade since the Suomi National Polar-orbiting Partnership (SNPP) launched in October 2011. A comprehensive evaluation of its accuracy and precision will be helpful for product users in climate studies and atmospheric models. In this study, the VIIRS LST is validated with ground observations from multiple high-quality radiation networks, including six stations from the Surface Radiation budget (SURFRAD) network, two stations from the Baseline Surface Radiation Network (BSRN), and 13 stations from the Atmospheric Radiation Measurement (ARM) network, to evaluate its performance over various land-cover types. The VNP21A1 LST was validated against the same ground observations as a reference. The results yield a close agreement between the SNPP VIIRS LST and ground LSTs with a bias of -0.4 K and a RMSE of 1.96 K over six SURFRAD sites; a bias of -0.2 K and a RMSE of 1.93 K over two BSRN sites; and a bias of -0.1 K and a RMSE of 1.7 K over the 13 ARM sites. The time series of the LST errors over individual sites indicate seasonal cycles. The data anomaly over the BSRN site in Cabauw and the SURFRAD site in Desert Rock is revealed and discussed in this study. In addition, a method using Landsat-8 data is applied to quantify the heterogeneity level of each ground station and the results provide promising insights. The validation results demonstrate the maturity of the JPSS VIIRS LST products and their readiness for various application studies.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2022

An Analysis of Spatio-Temporal Relationship between Satellite-Based Land Surface Temperature and Station-Based Near-Surface Air Temperature over Brazil

A better understanding of the relationship between land surface temperature (Ts) and near-surface air temperature (Ta) is crucial for improving the simulation accuracy of climate models, developing retrieval schemes for soil and vegetation moisture, and estimating large-scale Ta from satellite-based Ts observations. In this study, we investigated the relationship between multiple satellite-based Ts products, derived from the Atmospheric Infrared Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Aqua satellite, and Ta from 204 meteorological stations over Brazil during 2003–2016. Monthly satellite-based Ts products used in this study include: (1) AIRS Version 6 with 1° spatial resolution, (2) AIRS Version 7 with 1° spatial resolution, (3) MODIS Collection 6 with 0.05° spatial resolution, and (4) MODIS Collection 6 with 1° spatial resolution re-sampled from (3) for a direct comparison with AIRS products. We found that satellite-based Ts is lower than Ta over the forest area, but higher than Ta over the non-forest area. Nevertheless, the correlation coefficients (R) between monthly Ta and four Ts products during 2003–2016 are greater than 0.8 over most stations. The long-term trend analysis shows a general warming trend in temperatures, particularly over the central and eastern parts of Brazil. The satellite products could also observe the increasing Ts over the deforestation region. Furthermore, we examined the temperature anomalies during three drought events in the dry season of 2005, 2010, and 2015. All products show similar spatio-temporal patterns, with positive temperature anomalies expanding in areal coverage and magnitude from the 2005 to 2015 event. The above results show that satellite-based Ts is sensitive in reflecting environmental changes such as deforestation and extreme climatic events, and can be used as an alternative to Ta for climatological studies. Moreover, the observed differences between Ts and Ta may inform how thermal assumptions can be improved in satellite-based retrievals of soil and vegetation moisture or evapotranspiration.

land surface temperature↗