With the ability to capture daily imagery of Earth at very high spatial resolutions, commercial smallsats are emerging as a key resource for the remote sensing community. Planet (Planet Labs, Inc., San Francisco, CA, USA) operates the largest constellation of Earth imaging smallsats, which have been capturing multispectral imagery for consumer use since 2016. Use of these images is growing in the remote sensing community, but the variation in radiometric and geometric quality compared to traditional platforms (i.e., Landsat, MODIS, etc.) means the images are not always ‘analysis ready’ upon download. Neglecting these variations can impact derived products and analyses. Users also must contend with constantly evolving technology, which improves products but can create discrepancies across sensor generations. This communication provides a technical review of Planet’s PlanetScope smallsat data streams and extant literature to provide practical considerations to the remote sensing community for utilizing these images in remote sensing research. Radiometric and geometric issues for researchers to consider are highlighted alongside a review of processing completed by Planet and innovations being developed by the user community to foster the adoption and use of these images for scientific applications.

Abstract:The lowest portion of the Earth's atmosphere, known as the atmospheric boundary layer (ABL), plays an important role in the formation of weather events. Simple meteorological measurements collected from within the ABL, such as temperature, pressure, humidity, and wind velocity, are key to understanding the exchange of energy within this region, but conventional surveillance techniques such as towers, radar, weather balloons, and satellites do not provide adequate spatial and/or temporal coverage for monitoring weather events. Small unmanned aircraft, or aerial, systems (sUAS) provide a versatile, dynamic platform for atmospheric sensing that can provide higher spatio-temporal sampling frequencies than available through most satellite sensing methods. They are also able to sense portions of the atmosphere that cannot be measured from ground-based radar, weather stations, or weather balloons and have the potential to fill gaps in atmospheric sampling. However, research on the vertical sampling scales for collecting atmospheric measurements from sUAS and the variabilities of these scales across atmospheric phenomena (e.g., temperature and humidity) is needed. The objective of this study is to use variogram analysis, a common geostatistical technique, to determine optimal spatial sampling scales for two atmospheric variables (temperature and relative humidity) captured from sUAS. Results show that vertical sampling scales of approximately 3 m for temperature and 1.5-2 m for relative humidity were sufficient to capture the spatial structure of these phenomena under the conditions tested. Future work is needed to model these scales across the entire ABL as well as under variable conditions.

Tornadoes rank among the most destructive hazards to human life and property. In the United States, two regions known as Tornado Alley and Dixie Alley are particularly prone to tornadoes, but determining exactly where tornadoes will occur remains difficult. Examining the relationship between land surface heterogeneity (i.e. variations in topography, vegetation, and land cover) and tornado occurrence can provide insight into the spatial distribution of these storms. The objective of this study is to uncover the relationships between severe storm occurrence and land surface heterogeneity variables to determine (1) whether certain land heterogeneity variables exhibit a significant relationship with tornado occurrence, and (2) whether those relationships vary by region and storm strength. We delineate the spatial extent of Tornado Alley and Dixie Alley based on clusters of tornadic activity. We then use logistic regression to investigate how land heterogeneity factors are related to tornado formation of weakly and significantly tornadic storms. Using these relationships uncovered, we map tornado probability across the two regions. Regression results show that several land surface heterogeneity variables have a statistically significant relationship with tornado occurrence, but these relationships vary with region and storm severity, supporting the need for more local studies.

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<p>With thousands of plant functional trait observations across the world, there is still a lack of spatially and temporally explicit estimates of traits that help inform how biodiversity, ecological and biogeochemical processes are changing across the globe. The Leaf Mass per Area (LMA) is a key trait that influences plant ecological strategies, and it is strongly correlated with leaf photosynthesis, plant growth, vegetation primary production and decomposition rates. Based on biophysical principles of the radiative transfer in canopies, we designed a new multispectral remote sensing index that is highly sensitive to the community weighted mean of LMA. We called this index iLMA, as an acronym for “index for LMA''. We tested and calibrated this index with ground data of a wide range of forest types and herbaceous communities  collected at 510 plots from 77 sites located in the American continent (R<sup>2</sup> = 0.64). Using Landsat imagery, iLMA and the resulting calibrating equation, we made a 30m resolution global map of LMA. The LMA spatial pattern is consistent with our current understanding of LMA variation across major terrestrial biomes but at an unprecedented high-resolution. Then, we used Landsat imagery from 1985 to 2019 to produce yearly global estimates of LMA and map its rate of change. This map of change in LMA indicates that there has been a widespread decrease in LMA across the globe over the last 35 years, with the fastest and strongest declines happening in evergreen conifer forests in boreal and mountainous regions of the world, and tropical evergreen broadleaf forests in Africa and Asia. We discuss potential causes of such widespread decrease in LMA, including climate change and widespread changes in vegetation composition and structure, and its potential consequences for biogeochemical processes.</p>