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NASA NTRS2012

Thermal Infrared Radiometric Calibration of the Entire Landsat 4, 5, and 7 Archive (1982-2010)

Landsat's continuing record of the thermal state of the earth's surface represents the only long term (1982 to the present) global record with spatial scales appropriate for human scale studies (i.e., tens of meters). Temperature drives many of the physical and biological processes that impact the global and local environment. As our knowledge of, and interest in, the role of temperature on these processes have grown, the value of Landsat data to monitor trends and process has also grown. The value of the Landsat thermal data archive will continue to grow as we develop more effective ways to study the long term processes and trends affecting the planet. However, in order to take proper advantage of the thermal data, we need to be able to convert the data to surface temperatures. A critical step in this process is to have the entire archive completely and consistently calibrated into absolute radiance so that it can be atmospherically compensated to surface leaving radiance and then to surface radiometric temperature. This paper addresses the methods and procedures that have been used to perform the radiometric calibration of the earliest sizable thermal data set in the archive (Landsat 4 data). The completion of this effort along with the updated calibration of the earlier (1985 1999) Landsat 5 data, also reported here, concludes a comprehensive calibration of the Landsat thermal archive of data from 1982 to the present

Schott, John R.↗

NASA NTRS1982

Comparison of Landsat MSS, Nimbus 7 CZCS, and NOAA 6/7 AVHRR features for land use analysis

Spectral characteristics of the Coastal Zone Color Scanner (CZCS) on board Nimbus 7, the Advanced Very High Resolution Radiometer (AVHRR) on NOAA 6 and 7 and the Multispectral Scanner (MSS) on Landsat 1-3 are analyzed to comparatively assess their utility for land use analysis through remote sensing. The examination of simulated in-band radiances suggests that each sensor would respond to incident radiation reflected from a typical agricultural scene in a highly comparable manner, with most of the variation captured in two physically related variables. Several measures of green vegetation are examined and features are proposed for crop condition assessment with consideration of the course resolution characteristics of AVHRR and CZCS.

Cicone, R. C.↗

NASA NTRS1977

Development of remote sensing technology in New Zealand, part 1. Seismotectonic, structural, volcanologic and geomorphic study of New Zealand, part 2. Indigenous forest assessment, part 3. Mapping land use and environmental studies in New Zealand, part 4. New Zealand forest service LANDSAT projects, part 5. Vegetation map and landform map of Aupouri Peninsula, Northland, part 6. Geographical applications of LANDSAT mapping, part 7

The author has identified the following significant results. Inspection of pixels obtained from LANDSAT of New Zealand revealed that not only can ships and their wakes be detected, but that information on the size, state of motion, and direction of movement was inferred by calculating the total number of pixels occupied by the vessel and wake, the orientation of these pixels, and the sum of their radiance values above the background level. Computer enhanced images showing the Waimihia State Forest and much of Kaingaroa State Forest on 22 December 1975 were examined. Most major forest categories were distinguished on LANDSAT imagery. However, the LANDSAT imagery seemed to be most useful for updating and checking existing forest maps, rather than making new maps with many forest categories. Snow studies were performed using two basins: Six Mile Creek and Mt. Robert. The differences in radiance levels indicated that a greater areal snow cover in Six Mile Creek Basin with the effect of lower radiance values from vegetation/snow regions. A comparison of the two visible bands (MSS 4 and 5) demonstrate this difference for the two basins.

Probine, M. C.↗

NASA NTRS1981

Manual versus digital Landsat analysis for delineating river flooding

It has been found that flood boundary information derived from Landsat images, acquired at different flood stages, could be used to develop an empirical model for estimating the extent of flooding on the basis of in situ measurements of river discharge. An investigation was undertaken to determine whether improved results might have been obtained through digital image analysis or by including other Landsat spectral bands. The study area encompasses a highly flood-prone reach of the Black River in Lewis County, NY. It was found that visual analysis of aerial photographs and a Landsat band 7 image gave similar results. Visual and digital analysis of Landsat band 7 data gave similar results, and digital analysis of Landsat band 7 data gave results which were at least as good as digital analysis of combinations of spectral bands.

Philipson, W. R.↗

NASA NTRSDate not supplied

TPSAS-NF1676L-13210-DND

Pumice rafts are an occasional byproduct of explosive volcanic eruptions that occur near bodies of water. Rafts can be tens of kilometers across and are a hazard to shipping, as the pumice can block the seawater intake valves for ships’ engine cooling systems. In particular, this research focuses on pumice rafts that emanated from the eruptions near Tonga in 2006, Yemen in 2007, and the Chile-Argentina border in 2011 using data from Landsat 5 & 7, MODIS onboard the Aqua and Terra satellites, and ALI aboard the EO-1 satellite. False color composites were created to facilitate visual identification of pumice rafts in high resolution images. Additionally, a material of interest subpixel classification was implemented to automatically demarcate the extent of the pumice rafts. MODIS was found to be the most useful sensor for tracking larger rafts in the open ocean, while Landsat 5 & 7 and ALI were more effective in tracking smaller rafts in lakes and rivers. The pumice rafts generally took on a temperature between that of the ambient air and water temperature. The data collected for this study has been compiled to provide an effective tool for further investigation of pumice rafts.

Paula Chojnacki↗

DOE OSTI2026

Daily, 30 m Resolution NDSI Data for the East River Watershed, CO for 2000-2020

This dataset contains daily Normalized Difference Snow Index (NDSI) values at 30 m spatial resolution for the East River watershed in Colorado, USA. The temporal range of these data includes water years 2001-2020. These data were created using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). This model fuses low spatial and high temporal resolution data from MODIS (500 m, daily) with high spatial and low temporal resolution data from Landsat (30 m, 16 days) to create a 30m synthetic daily snow product. This product allows for the analysis of historical snow covered area trends in the East River Watershed at fine spatiotemporal resolutions where it was not available previously. This research was performed as a part of the Department of Energy’s Subsurface Biogeochemical Research Program with the primary intent of better understanding the timing and spatial patterns of water delivery to the Critical Zone in mountain watersheds. Each .zip file contains one "water year" of data (October 1 - September 30; i.e., water year 2010 starts October 1, 2010 and ends September 30, 2011). Each zip file contains the following: STARFM daily Normalized Difference Snow Index (NDSI) fusion data files in GeoTiff format with one layer for each day between Landsat data acquisition dates (i.e., for dates of Landsat acquisition, the Landsat image is included for that date). The study area is located in an area of Landsat path overlap, so Landsat dates acquisitions are every 7-9 days. Landsat NDSI files containing the high spatial (30m), low temporal (7-9 days due to Landsat path overlap) resolution data used as input to STARFM in GeoTiff format with one layer for each day. Dates for which no Landsat data were obtained are included as NoData layers. MODIS NDSI files containing the high temporal (daily), low spatial (500m) resolution data used as input to STARFM in GeoTiff format with one layer for each day. Please note the MODIS data were resampled to 30m pixels for input into the STARFM model. The data have a scale factor of 10,000 and a no data value of -32767. The projection of all datasets is WGS 84 (EPSG: 4326), which has a latitude/longitude based degree resolution of 0.0002694946 X 0.0002694946, and approximates to the 30 m spatial resolution mentioned above. The Layer Index files in .csv format. They contain information for each layer in the above GeoTiff files regarding the corresponding date for each layer, the fraction of pixels in the image that contain valid data (missing data is due to either cloud cover or poor data quality; these values are not percent snow cover). Dates of Landsat overpass are indicated in these files. If no Landsat data were able to be obtained due to cloud cover or lack of Landsat Tier 1 data available on Google Earth Engine, this is also noted.

EARTH SCIENCE > CRYOSPHERE > SNOW/ICE↗

NASA NTRS2021

Evaluation of VIIRS and MODIS snow cover fraction in High-Mountain Asia using Landsat 8 OLI

We present thefirst application of the Snow Covered Area and Grain size model (SCAG) tothe Visible Infrared imaging Radiometer Suite (VIIRS) and assess these retrievals withfiner-resolution fractional snow cover maps from Landsat 8 Operational Land Imager (OLI).Because Landsat 8 OLI avoids saturation issues common to Landsat 1–7 in the visiblewavelengths, we re-assess the accuracy of the SCAG fractional snow cover maps fromModerate Resolution Imaging Spectroradiometer (MODIS) that were previously evaluatedusing data from earlier Landsat sensors. Use of the fractional snow cover maps fromLandsat 8 OLI shows a negative bias of−0.5% for MODSCAG and−1.3% for VIIRSCAG,whereas previous MODSCAG evaluations found a bias of−7.6% in the Himalaya. Wefindsimilar root mean squared error (RMSE) values of 0.133 and 0.125 for MODIS and VIIRS,respectively. The Recall statistic (probability of detection) for cells with more than 15%snow cover in this challenging steep topography was found to be 0.90 for both MODSCAGand VIIRSCAG, significantly higher than previous evaluations based on Landsat 5Thematic Mapper (TM) and 7 Enhanced Thematic Mapper Plus (ETM+). In addition,daily retrievals from MODIS and VIIRS are consistent across gradients of elevation, slope,and aspect. Different native resolutions of the gridded products at 1 km and 500 m forVIIRS and MODIS, respectively, result in snow cover maps showing a slightly differentdistribution of values with VIIRS having more mixed pixels and MODIS having 7% morepure snow pixels. Despite the resolution differences, the snow maps from both sensorsproduce similar total snow-covered areas and snow-line elevations in this region, withR2values of 0.98 and 0.88, respectively. Wefind that the SCAG algorithm performsconsistently across various spatial resolutions and that fractional snow cover mapsfrom the VIIRS instruments aboard Suomi NPP, JPPS–1, and JPPS–2 can be asuitable replacement as MODIS sensors reach their ends of life.

Karl Rittger↗

NASA NTRS1980

Appendix E: Research papers. Manual versus digital LANDSAT analysis for modeling river flooding

The comparative value of manual versus digital image analysis for determining flood boundaries is being examined in a study of the use of LANDSAT data for modeling flooding of the Black River, in northern New York. The work is an extension of an earlier study in which Black River flooding was assessed through visually interpreted, multi-date LANDSAT band 7 images. Based on the results to date, it appears that neither color-additive viewing nor digital analysis of LANDSAT data provide improvement in accuracy over visual analysis of band 7 images, for delineating the boundaries of flood-affected areas.

Philipson, W. R.↗

NASA NTRS1981

Manual versus digital Landsat analysis for modeling river flooding

The comparative value of manual versus digital image analysis for determining flood boundaries is being examined in a study of the use of Landsat data for modeling flooding of the Black River, in northern New York. The work is an extension of an earlier study in which Black River flooding was assessed through visually interpreted, multi-date Landsat band 7 images. Based on the results to date, it appears that neither color-additive viewing nor digital analysis of Landsat data provide improvement in accuracy over visual analysis of band 7 images, for delineating the boundaries of flood-affected areas.

Philipson, W. R.↗

NASA NTRS1985

Study of spectral/radiometric characteristics of the Thematic Mapper for land use applications

Progress during ERIM's tenth quarter of effort under the LANDSAT-4 and 5 Image Data Quality Assessment program for the Thematic Mapper is described. Coincident LANDSAT-4 and 5 fully corrected (CCT-PT) TM data are analyzed in more detail and revised band-by-band relationships between the two sensors derived. An analysis technique employing the matching of cumulative distributions is developed and used and is believed to offer advantages over the histogram matching procedure currently used to produce LANDSAT data. Multiplicative factors ranging from 0.987 to 1.145 and offsets ranging from -2.7 to -6.2 video quantum levels are required to cause LANDSAT-5 data to match LANDSAT-4 data values. Evidence of low level clipping is found in TM Bands 5 and 7 of LANDSAT-5 but not LANDSAT-4. Analysis of the information content of LANDSAT TM and MSS data is continued. Components of information loss are identified and quantified and the effects of coarsened quantization are explored.

Malila, W. A.↗

NASA NTRS1996

The effects of cloud inhomogeneities upon radiative fluxes, and the supply of a cloud truth validation dataset

The ASTER polar cloud mask algorithm is currently under development. Several classification techniques have been developed and implemented. The merits and accuracy of each are being examined. The classification techniques under investigation include fuzzy logic, hierarchical neural network, and a pairwise histogram comparison scheme based on sample histograms called the Paired Histogram Method. Scene adaptive methods also are being investigated as a means to improve classifier performance. The feature, arctan of Band 4 and Band 5, and the Band 2 vs. Band 4 feature space are key to separating frozen water (e.g., ice/snow, slush/wet ice, etc.) from cloud over frozen water, and land from cloud over land, respectively. A total of 82 Landsat TM circumpolar scenes are being used as a basis for algorithm development and testing. Numerous spectral features are being tested and include the 7 basic Landsat TM bands, in addition to ratios, differences, arctans, and normalized differences of each combination of bands. A technique for deriving cloud base and top height is developed. It uses 2-D cross correlation between a cloud edge and its corresponding shadow to determine the displacement of the cloud from its shadow. The height is then determined from this displacement, the solar zenith angle, and the sensor viewing angle.

Welch, Ronald M.↗

NASA NTRS1982

Determination of turbidity patterns in Lake Chicot from LANDSAT MSS imagery

A historical analysis of all the applicable LANDSAT imagery was conducted on the turbidity patterns of Lake Chicot, located in the southeastern corner of Arkansas. By examining the seasonal and regional turbidity patterns, a record of sediment dynamics and possible disposition can be obtained. Sketches were generated from the suitable imagery, displaying different intensities of brightness observed in bands 5 and 7 of LANDSAT's multispectral scanner data. Differences in and between bands 5 and 7 indicate variances in the levels of surface sediment concentrations. High sediment loads are revealed when distinct patterns appear in the band 7 imagery. Additionally, the upwelled signal is exponential in nature and saturates in band 5 at low wavelengths for large concentrations of suspended solids.

Lecroy, S. R.↗

NASA NTRS1982

Determination of turbidity patterns in Lake Chicot from LANDSAT MSS imagery

Lecroy, S. R.↗

NASA NTRS1981

Determination of circulation and turbidity patterns in Kerr Lake from LANDSAT MSS imagery

The LANDSAT imagery was historically analyzed to determine the circulation and turbidity patterns of Kerr Lake, located on the Virginia-North Carolina border. By examining the seasonal and regional turbidity and circulation patterns, a record of sediment transport and possible disposition can be obtained. Sketches were generated, displaying different intensities of brightness observed in bands 5 and 7 of LANDSAT's multispectral scanner data. Differences in and between bands 5 and 7 indicate variances in the levels of surface sediment concentrations. High sediment loads are revealed when distinct patterns appear in the band 7 imagery. The upwelled signal is exponential in nature and saturates in band 5 at low wavelengths for large concentrations of suspended solids.

Lecroy, S. R.↗

NASA NTRS1977

The application of LANDSAT-1 imagery for monitoring strip mines in the new river watershed in northeast Tennessee, part 2

The author has identified the following significant results. LANDSAT imagery and supplementary aircraft photography of the New River drainage basin were subjected to a multilevel analysis using conventional photointerpretation methods, densitometric techniques, multispectral analysis, and statistical tests to determine the accuracy of LANDSAT-1 imagery for measuring strip mines of common size. The LANDSAT areas were compared with low altitude measurements. The average accuracy over all the mined land sample areas mapped from LANDSAT-1 was 90%. The discrimination of strip mine subcategories is somewhat limited on LANDSAT imagery. A mine site, whether active or inactive, can be inferred by lack of vegetation, by shape, or image texture. Mine ponds are difficult or impossible to detect because of their small size and turbidity. Unless bordered and contrasted with vegetation, haulage roads are impossible to delineate. Preparation plants and refuge areas are not detectable. Density slicing of LANDSAT band 7 proved most useful in the detection of reclamation progress within the mined areas. For most state requirements for year-round monitoring of surface mined land, LANDSAT is of limited value. However, for periodic updating of regional surface maps, LANDSAT may provide sufficient accuracies for some users.

Shahrokhi, F.↗

NASA NTRS1984

Characteristics of playa deposits as seen on SIR-A, Seasat and Landsat coregistered data

A classification technique currently under development has been applied for the qualitative study of a playa located in Northeastern Algeria using coregistered SIR-A, Seasat and Landsat MSS 7 data. This classification which is texture based can be applied to only single band imagery at this time. The first results are very encouraging and reach conclusions similar to those obtained with more time consuming classifiers. The signature of each class is well expressed for the Landsat picture. Misclasifications occur with the SIR-A or Seasat images.

Rebillard, PH.↗

NASA NTRS2020

Monitoring Water-Related Ecosystems with Earth Observation Data in Support of Sustainable Development Goal (SDG) 6 Reporting

Lack of national data on water-related ecosystems is a major challenge to achieving the Sustainable Development Goal (SDG) 6 targets by 2030. Monitoring surface water extent, wetlands, and water quality from space can be an important asset for many countries in support of SDG 6 reporting. We demonstrate the potential for Earth observation (EO) data to support country reporting for SDG Indicator 6.6.1, ‘Change in the extent of water-related ecosystems over time’ and identify important considerations for countries using these data for SDG reporting. The spatial extent of water-related ecosystems, and the partial quality of water within these ecosystems is investigated for seven countries. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 5, 7, and 8 with Shuttle Radar Topography Mission (SRTM) are used to measure surface water extent at 250 m and 30 m spatial resolution, respectively, in Cambodia, Jamaica, Peru, the Philippines, Senegal, Uganda, and Zambia. The extent of mangroves is mapped at 30 m spatial resolution using Landsat 8 Operational Land Imager (OLI), Sentinel-1, and SRTM data for Jamaica, Peru, and Senegal. Using Landsat 8 and Sentinel 2A imagery, total suspended solids and chlorophyll-a are mapped over time for a select number of large surface water bodies in Peru, Senegal, and Zambia. All of the EO datasets used are of global coverage and publicly available at no cost. The temporal consistency and long time-series of many of the datasets enable replicability over time, making reporting of change from baseline values consistent and systematic. We find that statistical comparisons between different surface water data products can help provide some degree of confidence for countries during their validation process and highlight the need for accuracy assessments when using EO-based land change data for SDG reporting. We also raise concern that EO data in the context of SDG Indicator 6.6.1 reporting may be more challenging for some countries, such as small island nations, than others to use in assessing the extent of water-related ecosystems due to scale limitations and climate variability. Country-driven validation of the EO data products remains a priority to ensure successful data integration in support of SDG Indicator 6.6.1 reporting. Multi-country studies such as this one can be valuable tools for helping to guide the evolution of SDG monitoring methodologies and provide a useful resource for countries reporting on water-related ecosystems. The EO data analyses and statistical methods used in this study can be easily replicated for country-driven validation of EO data products in the future.

water-related ecosystems; surface water extent; ma↗

NASA NTRS2020

Monitoring Water-Related Ecosystems with Earth Observation Data in Support of Sustainable Development Goal (SDG) 6 Reporting

Lack of national data on water-related ecosystems is a major challenge to achieving the Sustainable Development Goal (SDG) 6 targets by 2030. Monitoring surface water extent, wetlands, and water quality from space can be an important asset for many countries in support of SDG 6reporting. We demonstrate the potential for Earth observation (EO) data to support country reporting for SDG Indicator 6.6.1, ‘Change in the extent of water-related ecosystems over time’ and identify important considerations for countries using these data for SDG reporting. The spatial extent of water-related ecosystems, and the partial quality of water within these ecosystems is investigated for seven countries. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 5, 7, and 8 with Shuttle Radar Topography Mission (SRTM) are used to measure surface water extent at 250 m and 30 m spatial resolution, respectively, in Cambodia, Jamaica, Peru, the Philippines, Senegal, Uganda, and Zambia. The extent of mangroves is mapped at 30 m spatial resolution using Landsat 8 Operational Land Imager (OLI), Sentinel-1, and SRTM data for Jamaica, Peru, and Senegal. Using Landsat 8 and Sentinel 2A imagery, total suspended solids and chlorophyll-a are mapped overtime for a select number of large surface water bodies in Peru, Senegal, and Zambia. All of the EO datasets used are of global coverage and publicly available at no cost. The temporal consistency and long time-series of many of the datasets enable replicability over time, making reporting of change from baseline values consistent and systematic. We find that statistical comparisons between different surface water data products can help provide some degree of confidence for countries during their validation process and highlight the need for accuracy assessments when using EO-based land change data for SDG reporting. We also raise concern that EO data in the context of SDG Indicator 6.6.1reporting may be more challenging for some countries, such as small island nations, than others to use in assessing the extent of water-related ecosystems due to scale limitations and climate variability. Country-driven validation of the EO data products remains a priority to ensure successful data integration in support of SDG Indicator 6.6.1 reporting. Multi-country studies such as this one can be valuable tools for helping to guide the evolution of SDG monitoring methodologies and provide a useful resource for countries reporting on water-related ecosystems. The EO data analyses and statistical methods used in this study can be easily replicated for country-driven validation of EO data products in the future.

Raha Hakimdavar↗