The Thermal Emission Spectrometer (TES) investigation on Mars Global Surveyor (MGS) is aimed at determining (1) the composition of surface minerals, rocks, and ices; (2) the temperature and dynamics of the atmosphere; (3) the properties of the atmospheric aerosols and clouds; (4) the nature of the polar regions; and (5) the thermophysical properties of the surface materials. These objectives are met using an infrared (5.8‐ to 50‐μm) interferometric spectrometer, along with broadband thermal (5.1‐ to 150‐μm) and visible/near‐IR (0.3‐ to 2.9‐μm) radiometers. The MGS TES instrument weighs 14.47 kg, consumes 10.6 W when operating, and is 23.6×35.5×40.0 cm in size. The TES data are calibrated to a 1‐σ precision of 2.5⁻⁶×10⁻⁸ W cm⁻² sr⁻¹/cm⁻¹, 1.6×10⁻⁶ W cm⁻² sr⁻¹, and ∼0.5 K in the spectrometer, visible/near‐IR bolometer, and IR bolometer, respectively. These instrument subsections are calibrated to an absolute accuracy of ∼4×10⁻⁸ W cm⁻² sr⁻¹/cm⁻¹ (0.5 K at 280 K), 1–2%, and ∼1–2 K, respectively. Global mapping of surface mineralogy at a spatial resolution of 3 km has shown the following: (1) The mineralogic composition of dark regions varies from basaltic, primarily plagioclase feldspar and clinopyroxene, in the ancient, southern highlands to andesitic, dominated by plagioclase feldspar and volcanic glass, in the younger northern plains. (2) Aqueous mineralization has produced gray, crystalline hematite in limited regions under ambient or hydrothermal conditions; these deposits are interpreted to be in‐place sedimentary rock formations and indicate that liquid water was stable near the surface for a long period of time. (3) There is no evidence for large‐scale (tens of kilometers) occurrences of moderate‐grained (>50‐μm) carbonates exposed at the surface at a detection limit of ∼10%. (4) Unweathered volcanic minerals dominate the spectral properties of dark regions, and weathering products, such as clays, have not been observed anywhere above a detection limit of ∼10%; this lack of evidence for chemical weathering indicates a geologic history dominated by a cold, dry climate in which mechanical, rather than chemical, weathering was the significant form of erosion and sediment production. (5) There is no conclusive evidence for sulfate minerals at a detection limit of ∼15%. The polar region has been studied with the following major conclusions: (1) Condensed CO₂ has three distinct end‐members, from fine‐grained crystals to slab ice. (2) The growth and retreat of the polar caps observed by MGS is virtually the same as observed by Viking 12 Martian years ago. (3) Unique regions have been identified that appear to differ primarily in the grain size of CO₂; one south polar region appears to remain as black slab CO₂ ice throughout its sublimation. (4) Regional atmospheric dust is common in localized and regional dust storms around the margin and interior of the southern cap. Analysis of the thermophysical properties of the surface shows that (1) the spatial pattern of albedo has changed since Viking observations, (2) a unique cluster of surface materials with intermediate inertia and albedo occurs that is distinct from the previously identified low‐inertia/bright and high‐inertia/dark surfaces, and (3) localized patches of high‐inertia material have been found in topographic lows and may have been formed by a unique set of aeolian, fluvial, or erosional processes or may be exposed bedrock.

The LandTrendr (LT) algorithm has been used widely for analysis of change in Landsat spectral time series data, but requires significant pre-processing, data management, and computational resources, and is only accessible to the community in a proprietary programming language (IDL). Here, we introduce LT for the Google Earth Engine (GEE) platform. The GEE platform simplifies pre-processing steps, allowing focus on the translation of the core temporal segmentation algorithm. Temporal segmentation involved a series of repeated random access calls to each pixel’s time series, resulting in a set of breakpoints (“vertices”) that bound straight-line segments. The translation of the algorithm into GEE included both transliteration and code analysis, resulting in improvement and logic error fixes. At six study areas representing diverse land cover types across the U.S., we conducted a direct comparison of the new LT-GEE code against the heritage code (LT-IDL). The algorithms agreed in most cases, and where disagreements occurred, they were largely attributable to logic error fixes in the code translation process. The practical impact of these changes is minimal, as shown by an example of forest disturbance mapping. We conclude that the LT-GEE algorithm represents a faithful translation of the LT code into a platform easily accessible by the broader user community.

Sentinel-1 satellites provide temporally dense and high spatial resolution synthetic aperture radar (SAR) imagery. The open data policy and global coverage of Sentinel-1 make it a valuable data source for a wide range of SAR-based applications. In this regard, the Google Earth Engine is a key platform for large area analysis with preprocessed Sentinel-1 backscatter images available within a few days after acquisition. To preserve the information content and user freedom, some preprocessing steps (e.g., speckle filtering) are not applied on the ingested Sentinel-1 imagery as they can vary by application. In this technical note, we present a framework for preparing Sentinel-1 SAR backscatter Analysis-Ready-Data in the Google Earth Engine that combines existing and new Google Earth Engine implementations for additional border noise correction, speckle filtering and radiometric terrain normalization. The proposed framework can be used to generate Sentinel-1 Analysis-Ready-Data suitable for a wide range of land and inland water applications. The Analysis Ready Data preparation framework is implemented in the Google Earth Engine JavaScript and Python APIs.