Terry Arvidson

Characterization of the Landsat-7 ETM Automated Cloud-Cover Assessment (ACCA) Algorithm

A scene-average automated cloud-cover assessment (ACCA) algorithm has been used for the Landsat-7 Enhanced Thematic Mapper Plus (ETM� ) mission since its launch by NASA in 1999. ACCA assists in scheduling and confirming the acquisition of global “cloud-free” imagery for the U.S. archive. This paper documents the operational ACCA algorithm and validates its performance to a standard error of � 5 percent. Visual assessment of clouds in three-band browse imagery were used for comparison to the five-band ACCA scores from a stratified sample of 212 ETM� 2001 scenes. This comparison of independent cloud-cover estimators produced a 1:1 correlation with no offset. The largest commission errors were at high altitudes or at low solar illumination where snow was misclassified as clouds. The largest omission errors were associated with undetected optically thin cirrus clouds over water. There were no statistically significant systematic errors in ACCA scores analyzed by latitude, seasonality, or solar elevation angle. Enhancements for additional spectral bands, per-pixel masks, land/water boundaries, topography, shadows, multidate and multi-sensor imagery were identified for possible use in future ACCA algorithms.

Historical Record of Landsat Global Coverage: Mission Operations, NSLRSDA, and International Cooperator Stations

The long-term, 34� year record of global Landsat remote sensing data is a critical resource to study the Earth system and human impacts on this system. The National Satellite Land Remote Sensing Data Archive (NSLRSDA) is charged by public law to: “maintain a permanent, comprehensive Government archive of global Landsat and other land remote sensing data for long-term monitoring and study of the changing global environment” (U.S. Congress, 1992). The advisory committee for NSLRSDA requested a detailed analysis of observation coverage within the U.S. Landsat holdings, as well as that acquired and held by International Cooperator (IC) stations. Our analyses, to date, have found gaps of varying magnitude in U.S. holdings of Landsat global coverage data, which appear to reflect technical or administrative variations in mission operations. In many cases it may be possible to partially fill these gaps in U.S. holdings through observations that were acquired and are now being held at International Cooperator stations.

Landsat 7's long-term acquisition plan — an innovative approach to building a global imagery archive

Abstract The Landsat-7 Long-Term Acquisition Plan (LTAP) automates the selection of Landsat scenes to periodically refresh a global archive of sunlit, substantially cloud-free land images. Its automatic nature reduces the workload on both operations and science office management, and makes the best use of limited resources both on board the satellite and in the ground-processing systems. The core of the LTAP is the definition of seasonality for each scene of interest, where seasonality is defined as the occurrence of change over time. When significant change is occurring, acquisition frequencies are high. Conversely, if little change is occurring, acquisition frequencies are low. This definition of seasonality was augmented with suggested acquisition frequencies from the various science community niches that have historically relied on Landsat data to support their research goals. Cloud climatology for each scene was determined, as monthly historical averages, so it could be compared during scheduling with the daily cloud-cover predictions to discriminate between scenes cloudier than usual (less worthy of acquisition) and scenes clearer than normal (more worthy of acquisition). A scheduler system executes the LTAP and builds the daily schedule by evaluating many factors for each scene, including seasonality, acquisition history, cloud cover, and availability of resources. The result of this evaluation is a list of the “best” 250 scenes to be acquired that day. An important tool in the development of the scheduling system and in the evaluation of the LTAP performance is a modeling capability that allows “what if” analyses to be made and proposed changes to the LTAP or the scheduling software to be evaluated. This modeling capability has proven key to understanding the impacts of potential changes in strategy or software. By considering cloud climatology and current cloud-cover predictions, Landsat 7 has acquired imagery with less cloud contamination than previous Landsat data. After 16 months of operations, a relatively cloud-free global archive exists that captures land-cover change where and when it occurs. Additional user requests have been low, suggesting users are finding suitable data already in the archive. Short-term science campaigns and special requests can be accommodated in the LTAP without perturbing the seasonality of the archive or exceeding the available resources.

Landsat-7 Long-Term Acquisition Plan: Development and Validation

The long-term acquisition plan (LTAP) was developed to fulfill the Landsat-7 (L7) mission of building and seasonally refreshing an archive of global, essentially cloud-free, sunlit, land scenes. The LTAP is considered one of the primary successes of the mission. By incorporating seasonality and cloud avoidance into the decision making used to schedule image acquisitions, the L7 data in the U.S. Landsat archive is more complete and of higher quality than has ever been previously achieved in the Landsat program. Development of the LTAP system evolved over more than a decade, starting in 1995. From 2002 to 2004 most attention has been given to validation of LTAP elements. We find that the original expectations and goals for the LTAP were surpassed for Landsat 7. When the L7 scan line corrector mirror failed, we adjusted the LTAP operations, effectively demonstrating the flexibility of the LTAP concept to address unanticipated needs. During validation, we also identified some seasonal and geographic acquisition shortcomings of the implementation: including how the spectral vegetation index measurements were used and regional/seasonal cloud climatology concerns. Some of these issues have already been at least partially addressed in the L7 LTAP, while others will wait further attention in the development of the LTAP for the Landsat Data Continuity Mission (LDCM). The lessons learned from a decade of work on the L7 LTAP provide a solid foundation upon which to build future mission LTAPs including the LDCM.

Landsat-7 Long-Term Acquisition Plan Radiometry - Evolution Over Time

The Landsat-7 Enhanced Thematic Mapper Plus instrument has two selectable gains for each spectral band. In the acquisition plan, the gains were initially set to maximize the entropy in each scene. One unintended consequence of this strategy was that, at times, dense vegetation saturated band 4 and deserts saturated all bands. A revised strategy, based on a land-cover classification and sun angle thresholds, reduced saturation, but resulted in gain changes occurring within the same scene on multiple overpasses. As the gain changes cause some loss of data and difficulties for some ground processing systems, a procedure was devised to shift the gain changes to the nearest predicted cloudy scenes. The results are still not totally satisfactory as gain changes still impact some scenes and saturation still occurs, particularly in ephemerally snow-covered regions. A primary conclusion of our experience with variable gain on Landsat-7 is that such an approach should not be employed on future global monitoring missions.

Enhanced Landsat capturing all the Earth's land areas

The latest in a series of Landsat terrestrial observatories was launched April 15 and for the first time the mission configuration captures the dream envisioned by Earth scientists more than a quarter century ago. Landsat 7 now has the capability and the operations strategy to dynamically monitor all of the Earths land areas annually paving the way toward a better understanding of the terrestrial dynamics that result from natural stresses and human activities. Landsats long term acquisition plan (LTAP) is an ambitious step forward for the satellite, which has been tracking the Earths land areas since 1972 [Short et al., 1976]. NASA and the U.S. Geological Survey are jointly operating the mission.

Large Area Scene Selection Interface (LASSI): Methodology of Selecting Landsat Imagery for the Global Land Survey 2005.

The Global Land Survey (GLS) 2005 is a cloud-free, orthorectified collection of Landsat imagery acquired during the 2004 to 2007 epoch intended to support global land-cover and ecological monitoring. Due to the numerous complexities in selecting imagery for the GLS2005, NASA and the U.S. Geological Survey (USGS) sponsored the development of an automated scene selection tool, the Large Area Scene Selection Interface (LASSI), to aid in the selection of imagery for this data set. This innovative approach to scene selection applied a user-defined weighting system to various scene parameters: image cloud cover, image vegetation greenness, choice of sensor, and the ability of the Landsat-7 Scan Line Corrector (SLC)-off pair to completely fill image gaps, among others. The parameters considered in scene selection were weighted according to their relative importance to the data set, along with the algorithm’s sensitivity to that weight. This paper describes the methodology and analysis that established the parameter weighting strategy, as well as the post-screening processes used in selecting the optimal data set for GLS2005.

Landsat and Thermal Infrared Imaging

The purpose of this chapter is to describe the collection of thermal images by Landsat sensors already on orbit and to introduce a new Landsat thermal sensor. The chapter describes the Landsat 4 and 5 thematic mapper (TM) and Landsat 7 enhanced thematic mapper plus (ETM+) sensors, the calibration of their thermal bands, and the design and prelaunch calibration of the new thermal infrared sensor (TIRS). The TIRS will be launched in February 2013 on the Landsat Data Continuity Mission (LDCM) satellite, which will be renamed to Landsat 8 after it reaches orbit. Continuity of the data record has always been a priority for the Landsat project. The TIRS will extend the unique Landsat thermal data archive begun in 1978 that supports, among other applications, water resource management in the western United States and global agricultural monitoring studies. The TIRS also introduces improved technology and data quality, both of which are discussed in the chapter.

Global vegetation - Assessing Landsat 7/ETM+ coverage of tropical rainforest and global agricultural and forest extents

This paper presents the state of the 10-month old Landsat 7 archive of high resolution, multi-spectral imagery of global vegetation, including global agricultural extent, forests, and tropical rainforests.

Landsat 8: status and on-orbit performance

Landsat 8 and its two Earth imaging sensors, the Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) have been operating on-orbit for 2 ½ years. Landsat 8 has been acquiring substantially more images than initially planned, typically around 700 scenes per day versus a 400 scenes per day requirement, acquiring nearly all land scenes. Both the TIRS and OLI instruments are exceeding their SNR requirements by at least a factor of 2 and are very stable, degrading by at most 1% in responsivity over the mission to date. Both instruments have 100% operable detectors covering their cross track field of view using the redundant detectors as necessary. The geometric performance is excellent, meeting or exceeding all performance requirements. One anomaly occurred with the TIRS Scene Select Mirror (SSM) encoder that affected its operation, though by switching to the side B electronics, this was fully recovered. The one challenge is with the TIRS stray light, which affects the flat fielding and absolute calibration of the TIRS data. The error introduced is smaller in TIRS band 10. Band 11 should not currently be used in science applications.