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This slide presentation reviews how our understanding of the water cycle is enhanced by our use of satellite data, and how this informs land surface hydrology and water resource management. It reviews how NASA's current and future satellite missions will provide Earth system data of unprecedented breadth, accuracy and utility for hydrologic analysis.
Huntsville, Alabama’s population has grown by 10.8% since 2010, due in part to the city’s advancing engineering industry. Rapid urban growth negatively impacts the environment by decreasing tree canopy cover and increasing impervious surface cover, which can intensify the urban heat island effect. To examine the impacts of this urban growth on the environment, the team partnered with the City of Huntsville to utilize Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Landsat 8 Thermal Infrared Sensor (TIRS), Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the International Space Station’s Global Ecosystems Dynamic Investigation (GEDI) and ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS). The team utilized these Earth observations in combination with ancillary datasets to create a suite of end products to assist in mitigating the effects of extreme heat due to urban expansion and tree canopy loss. Rasters of annual land surface temperature (LST) was calculated in Google Earth Engine from 2010 to 2019. The team derived land cover classes through supervised and threshold classification methods to distinguish trees, other vegetation types, impervious surfaces, and water. From 2010 to 2019, LST increased approximately 4 °F for all census tracts within the city and the total amount of tree cover increases less than 3%. The findings will aid the city in future decision-making processes by indicating areas that would benefit from increased green infrastructure.
This summarizes technical highlights from the DOE Early Career Project entitled “Understanding the Roles of Cloud Microphysics and Land Surface Coupling Feedbacks in Multi-Scale Predictions of Central US Summer Hydroclimate” led by PI Mike Pritchard at the University of California. The project’s original aims were to investigate trade-offs of cloud superparameterization such as the effects of microphysical assumptions on mesoscale convective systems (Section I) and the representation of land-atmosphere coupling under explicit convection (Section II). Under the same broader theme of water cycle dynamics, superparameterization, and land interaction, the project evolved to further investigate the climate dynamics of irrigation, vegetation and streamflow (Section III), the dynamics of the Madden- Julian Oscillation (Section IV), as well as the use of modern machine learning to replace traditional sub grid approaches for climate simulation (Section V). Twenty peer-reviewed journal publications were produced as a result of this project (Section VI).
Abstract The detection of soil moisture‐precipitation feedback is complicated by the dependence of feedback sign on atmospheric state and cloud types. We evaluated responses of convective clouds to changes in surface evaporative fraction (EF) from satellite and ground‐based measurements over the Southern Great Plains in summer months. Strong responses to EF were inferred only after accounting for tropospheric states supporting systematically positive or negative responses. State dependence (inferred from controlled model experiments) explains the weak response found in previous studies. The probability of transitions from shallow to deep cumulus, and subsequent precipitation onset, increases by 58% over drier surfaces in weakly stratified environments having deeper boundary layers. Boundary layer dynamics play a more important role than low‐level relative humidity in these environments. Wetter surfaces yield increased deep convection in moist and thermally stratified environments. The distinct pathways of convective initiation over dry and wet land surfaces can play important roles in drought onset and termination.
The following provides the final report on NASA IDP Project NAGW-1840 'Differentiating the Role of Land Surface Variability on Global Energy Transport within the Atmosphere and Oceans'. The project was designed to investigate the role of regional perturbations in the earth radiation budget on atmospheric and oceanic energy transports on an interannual basis. We proposed a modeling strategy based on an entropy extremum principle that could be used to separate the transports into oceanic and atmospheric components so as to better understand the effects of regional perturbations at the distinct atmospheric and oceanic time scales. The original focus was to consider the maintenance and year-to-year modulation of a large-scale, low-latitude North African-West Pacific Ocean net radiation dipole, which we had detected in the Nimbus 6 and 7 record of earth radiation budget measurements, and which necessitated significant cross-meridional energy transports to maintain global equilibrium. In addition, perturbations in the radiation balance term were to be partitioned into cloud-induced and surface-induced components to better understand the feedbacks between clouds and surface boundary conditions on interannual variability of the radiation balance.
Data at medium and coarse resolution from the Landsat, MODIS, and VIIRS instruments provide crucial and indispensable time-series for the land component of the study of global change. This talk will be divided into two main sections. In the first part, a summary of the status of the processing, archiving, and early (Launch +6 months) on-orbit evaluation of the VIIRS Land Environmental Data Records (EDRs), will be presented. In the second part, results from an uncertainty analysis of MODIS- and Landsat-based albedo retrievals, based on collocated comparisons with tower and airborne multiangular measurements collected at the Cloud and Radiation Testbed (CART) site during the 2007 Cloud and Land Surface Interaction Campaign (CLASIC'07), will be discussed.
An improved fine-scale soil moisture (SM) dataset at 1 km grid spacing, covering much of the eastern continental US, was generated by assimilating 9 km Soil Moisture Active Passive (SMAP) SM data into the v4.0.1 Noah-MP land surface model. With 12 ensemble members, the assimilation was carried out using the ensemble Kalman filter algorithm within NASA's Land Information System. The SM analysis for 2016 was fully validated against in situ observations from four different networks and compared with four other existing datasets. Results indicate that this SM analysis surpasses other datasets in top-layer SM distribution, including a machine-learning-based product, despite all SM estimates being less heterogeneous than observed. The analysis of anomalous errors suggests that large similarity in intrinsic errors is likely due to overlapping data sources among the selected SM datasets. More detailed evaluations were performed over two geographic areas. The observations collected by the Atmospheric Radiation Measurement facility in Oklahoma suggest that soil temperature and surface heat fluxes are concurrently simulated with good accuracy. Investigation into the 2016 southeastern US drought response further indicates drier conditions and higher evapotranspiration estimates compared to GLEAMv4.1. Notably, large errors are associated with grids having clay soil textures, underscoring the need for refined model treatments for specific soil types to further improve SM estimates. The dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.14370563 (Tai et al., 2024).
This document outlines the theory and methodology for generating the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 daily daytime and nighttime 1-km land surface temperature (LST) and emissivity product using the Temperature Emissivity Separation (TES) algorithm. The MODIS-TES (MOD21_L2) product, will include the LST and emissivity for three MODIS thermal infrared (TIR) bands 29, 31, and 32, and will be generated for data from the NASA-EOS AM and PM platforms. This is version 1.0 of the ATBD and the goal is maintain a 'living' version of this document with changes made when necessary. The current standard baseline MODIS LST products (MOD11*) are derived from the generalized split-window (SW) algorithm (Wan and Dozier 1996), which produces a 1-km LST product and two classification-based emissivities for bands 31 and 32; and a physics-based day/night algorithm (Wan and Li 1997), which produces a 5-km (C4) and 6-km (C5) LST product and emissivity for seven MODIS bands: 20, 22, 23, 29, 31-33.
This document outlines the theory and methodology for generating the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-2 daily daytime and nighttime 1-km land surface temperature (LST) and emissivity product using the Temperature Emissivity Separation (TES) algorithm. The MODIS-TES (MOD21_L2) product, will include the LST and emissivity for three MODIS thermal infrared (TIR) bands 29, 31, and 32, and will be generated for data from the NASA-EOS AM and PM platforms. This is version 1.0 of the ATBD and the goal is maintain a ‘living’ version of this document with changes made when necessary. The current standard baseline MODIS LST products (MOD11*) are derived from the generalized split-window (SW) algorithm (Wan and Dozier 1996), which produces a 1-km LST product and two classification-based emissivities for bands 31 and 32; and a physics-based day/night algorithm (Wan and Li 1997), which produces a 5-km (C4) and 6-km (C5) LST product and emissivity for seven MODIS bands: 20, 22, 23, 29, 31–33
We propose to develop an on-line data distribution and interactive display system for the collection, archival, distribution and analysis of operational weather satellite data for applications in land surface studies. A 1,000 sq km scene of the western U.S. (centered on the Colorado Rockies) will be extracted from Advanced Very High Resolution Radiometer (AVHRR) imagery collected from morning and afternoon passes of the NOAA polar-orbiters at the direct readout stations operated by CU/CCAR. All five channels of these AVHRR data will be navigated and map registered at CU/CCAR and then be transferred to NCAR for storage in an on-line data system. Software will also be available at NCAR to process and navigate the raw AVHRR data as needed. A display workstation software, based on a Macintosh II computer, will be developed that will display and further process the AVHRR data for studies of vegetation monitoring and snowpack assessment. Various options of presently used techniques for both vegetation and snowpack monitoring will be implemented in the workstation software to provide the individual investigator with the freedom to interact with the satellite image data. The display software will be freely distributed online to interested investigators and the AVHRR data will be made available on-line to anyone interested. In addition, potential users will be sought out and connected to the on-line data archive. This experiment with an active on-line archive and interactive analysis systems will provide experience with a small scale EOSDIS.
We propose to develop an on-line data distribution and interactive display system for the collection, archival, distribution and analysis of operational weather satellite data for applications in land surface studies. A 1,000 square km scene of the western U.S. (centered on the Colorado Rockies) will be extracted from Advanced Very High Resolution Radiometer (AVHRR) imagery collected from morning and afternoon passes of the NOAA polar-orbiters at the direct readout stations operated by CU/CCAR. All five channels of these AVHRR data will be navigated and map registered at CU/CCAR and then be transferred to NCAR for storage in an on-line data system. Software will also be available at NCAR to process and navigate the raw AVHRR data as needed. A display workstation software, based on a Macintosh II computer, will be developed that will display and further process the AVHRR data for studies of vegetation monitoring and snowpack assessment. Various options of presently used techniques for both vegetation and snowpack monitoring will be implemented in the workstation software to provide the individual investigator with the freedom to interact with the satellite image data. The display software will be freely distrributed online to interested investigators, and the AVHRR data will be made available on-line to anyone interested. In addition, potential users will be sought out and connected to the on-line data archive. This experiment with an active on-line archive and interactive analysis systems will provide experience with a small scale EOSDIS.
Groundwater is a major source of water in the western US. However, there are limited recharge estimates available in this region due to the complexity of recharge processes and the challenge of direct observations. Land surface Models (LSMs) could be a valuable tool for estimating current recharge and projecting changes due to future climate change. In this study, simulations of three LSMs (Noah, Mosaic and VIC) obtained from the North American Land Data Assimilation System (NLDAS-2) are used to estimate potential recharge in the western US Modeled recharge was compared with published recharge estimates for several aquifers in the region. Annual recharge to precipitation ratios across the study basins varied from 0.01-15% for Mosaic, 3.2-42% for Noah, and 6.7-31.8% for VIC simulations. Mosaic consistently underestimates recharge across all basins. Noah captures recharge reasonably well in wetter basins, but overestimates it in drier basins. VIC slightly overestimates recharge in drier basins and slightly underestimates it for wetter basins. While the average annual recharge values vary among the models, the models were consistent in identifying high and low recharge areas in the region. Models agree in seasonality of recharge occurring dominantly during the spring across the region. Overall, our results highlight that LSMs have the potential to capture the spatial and temporal patterns as well as seasonality of recharge at large scales. Therefore, LSMs (specifically VIC and Noah) can be used as a tool for estimating future recharge rates in data limited regions.
Extensive areas in the Mediterranean region are subject to land degradation and desertification. The high variability of the coupling between the surface and the atmosphere affects the regional climate. Relevant surface characteristics, such as spectral reflectance, surface emissivity in the thermal-infrared region, and vegetation indices, serve as "primary" level indicators for the state of the surface. Their spatial, seasonal and interannual variability can be monitored from satellites. Using relationships between these primary data and combining them with prior information about the land surfaces (such as topography, dominant soil type, land use, collateral ground measurements and models), a second layer of information is built up which specifies the land surfaces as a component of the regional climate system. To this category of parameters which are directly involved in the exchange of energy, momentum and mass between the surface and the atmosphere, belong broadband albedo, thermodynamic surface temperature, vegetation types, vegetation cover density, soil top moisture, and soil heat flux. Information about these parameters finally leads to the computation of sensible and latent heat fluxes. The methodology was tested with pilot data sets. Full resolution, properly calibrated and normalized NOAA-AVHRR multi-annual primary data sets are presently compiled for the whole Mediterranean area, to study interannual variability and longer term trends.
Recent studies at the Global Hydrology and Climate Center (GHCC) have shown that the assimilation of land skin temperature (LST) tendencies into a mesoscale model can significantly improve short term forecasts of near surface temperature and moisture. The high spatial and temporal resolution of GOES derived land surface products provide valuable information about the spatial and temporal variability of the land surface forcing simulated in the model. In the GHCC studies LST was derived using a split window technique requiring at least two longwave infrared window channels and thus, utilized the 11 and 12 micron channels found on the GOES-8 Imager with a nadir spatial resolution of 4km. However, beginning with the launch of GOES-M (scheduled for mid 2001 ) and subsequent satellites the 12 micron channel will be removed from the Imager leaving only one longwave window channel. The GOES Sounder will continue to have more than one longwave infrared window channel (including a 12 micron channel) but, with a spatial resolution of 10 km nadir. LST retrievals from the newer GOES satellites will thus be derived from Sounder measurements at a reduced spatial resolution. This paper intercompares the LST retrievals from the GOES-8 Imager and Sounder. The effects on the LST retrievals due to the Sounder's reduced resolution from that of the Imager and its different longwave infrared channel characteristics are examined. The effects of transitioning from Imager to Sounder LST products on the results from model assimilation of these products are also examined.
Recent studies at the Global Hydrology and Climate Center (GHCC) have shown that the assimilation of land skin temperature (LST) tendencies into a mesoscale model can significantly improve short-term forecasts of near surface air temperature and moisture. Derived land surface products from the GOES satellites were used in these studies to provide high spatial and temporal resolution information about the spatial and temporal variability of the land surface forcing simulated in the model. In the model assimilation studies, LST was derived using a split window technique using the 11 and 12 pm channels found on the GOES-8 Sounder. These studies used a constant surface emissivity of 0.98 for both channels. However, this emissivity assumption over the land does not take into account emissivity variations due to varying terrain characteristics and differences between channels. These emissivity variations are seen to be significant as indicated by emissivity products from the polar orbiting MODIS instrument channels similar to the GOES-8 Sounder channels mentioned above. MODIS is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. In an attempt to improve the emissivity assumptions used in the GOES Sounder LST retrieval procedure, the incorporation of MODIS high spatial resolution (1 km) emissivity measurements into the LST procedure is being explored. This paper intercompares the LST retrievals from the GOES-8 Sounder using a constant emissivity assumption with those using MODIS retrieved emissivities. The effects of MODIS emissivities on the LST retrievals are discussed. Potential improvements in model forecasts using assimilated LST products incorporating MODIS emissivities are also examined.
Accurately capturing the impact of urban trees on temperature can help optimize urban heat mitigation strategies. Recently, there has been widespread use of remotely sensed land surface temperature (T s ) to quantify the cooling efficiency (CE) of urban trees. However, remotely sensed T s reflects emitted radiation from the surface of an object seen from the point of view of the thermal sensor, which is not a good proxy for the air temperature (T a ) perceived by humans. The extent to which the CEs derived from T s reflect the true experiences of urban residents is debatable. Therefore, this study systematically compared the T s -based CE (CE T s ) with the T a -based CE (CE T a ) in 392 European urban clusters. CE T s and CE T a were defined as the reductions in T s and T a , respectively, for every 1% increase in fractional tree cover (FTC). The results show that the increase in FTC has a substantial impact on reducing T s and T a in most cities during daytime. However, at night, the response of T s and T a to increased FTC appears to be much weaker and ambiguous. On average, for European cities, daytime CE T s reaches 0.075 °C % -1 , which is significantly higher (by an order of magnitude) than the corresponding CE T a of 0.006 °C % -1 . In contrast, the average nighttime CE T s and CE T a for European cities are similar, both approximating zero. Overall, urban trees can lower daytime temperatures, but the magnitude of their cooling effect is notably amplified when using remotely sensed T s estimates compared to in situ T a measurements, which is important to consider for accurately constraining public health benefits. Our findings provide critical insights into the realistic efficiencies of alleviating urban heat through tree planting.
The status of the data sets is discussed. Progress was made in both data analysis and modeling areas. The atmospheric and land surface contributions to the net radiation budget over the Sahara-Sahel region is being decoupled. The interannual variability of these two processes was investigated and this variability related to seasonal rainfall fluctuations. A modified Barnes objective analysis scheme was developed which uses an eliptic scan pattern and a 3-pass iteration of the difference fields.
How much of the interannual variation in the satellite derived radiation balance can be purely attributed to changes taking place at the land surface, was examined. The role of surface latent heating was examined in relation to its control of the precipitation pattern from one year to the next.