Dryland ecosystems cover 40% of our planet’s land surface, support the lives of billions of people, and are responding dramatically to the combined effects of climate and land use change. These expansive and diverse systems also dominate core aspects of Earth’s climate, storing and exchanging vast amounts of water, carbon, and energy with the atmosphere. Despite the indispensable natural resources and ecosystem services provided by drylands and their high vulnerability to change, drylands are one of the most, if not the most, poorly understood ecosystem types. Such lack of study has been in part due to incorrect historical assumptions that drylands are unproductive “wastelands”. This lack of understanding results in notably poor model representation and forecasting capacity, hindering our representation and decision making for these vulnerable ecosystems. The NASA Terrestrial Ecology Program solicited proposals for a multi-year field campaign, of which Adaptation and Response in Drylands (ARID) was one of two scoping studies selected. With the goal of gathering input from the scientific and data end-user communities, we provide an overview of our ARID kick-off meeting with over 300 in-person and virtual participants held in October 2023 at the University of Arizona. This meeting gathered insights from public and private data end-users and scientists. We also report on follow-up activities that have taken place since then, including town halls, community surveys, and international engagements.

Chemical and biological composition of surface materials and physical structure and arrangement of those materials determine the intrinsic reflectance of Earth’s land surface. The apparent reflectance—as measured by a spaceborne or airborne sensor that has been corrected for atmospheric attenuation—depends also on topography, surface roughness, and the atmosphere. Especially in Earth’s mountains, estimating properties of scientific interest from remotely sensed data requires compensation for topography. Doing so requires information from digital elevation models (DEMs). Available DEMs with global coverage are derived from spaceborne interferometric radar and stereo-photogrammetry at ~30 m spatial resolution. Locally or regionally, lidar altimetry, interferometric radar, or stereo-photogrammetry produces DEMs with finer resolutions. Characterization of their quality typically expresses the root-mean-square (RMS) error of the elevation, but the accuracy of remotely sensed retrievals is sensitive to uncertainties in topographic properties that affect incoming and reflected radiation and that are inadequately represented by the RMS error of the elevation. The most essential variables are the cosine of the local solar illumination angle on a slope, the shadows cast by neighboring terrain, and the view factor, the fraction of the overlying hemisphere open to the sky. Comparison of global DEMs with locally available fine-scale DEMs shows that calculations with the global products consistently underestimate the cosine of the solar angle and underrepresent shadows. Analyzing imagery of Earth’s mountains from current and future spaceborne missions requires addressing the uncertainty introduced by errors in DEMs on algorithms that analyze remotely sensed data to produce information about Earth’s surface.

Observations of Planet Earth from space are a critical resource for science and society. Satellite measurements represent very large investments and United States (US) agencies organize their effort to maximize the return on that investment. The US National Research Council conducts a survey of earth science and applications to prioritize observations for the coming decade. The most recent survey prioritized a visible to shortwave infrared imaging spectrometer and a multi-spectral thermal infrared imager to meet a range of needs. First, and perhaps, foremost, it will be the premier integrated observatory for observing the emerging impacts of climate change . It will characterize the diversity of plant life by resolving chemical and physiological signatures. It will address wildfire, observing pre-fire risk, fire behavior and post-fire recovery. It will inform responses to hazards and disasters guiding responses to a wide range of events, including oil spills, toxic minerals in minelands, harmful algal blooms, landslides and other geological hazards. The SBG team analyzed needed instrument characteristics (spatial, temporal and spectral resolution, measurement uncertainty) and assessed the cost, mass, power, volume, and risk of different architectures. The Research and Applications team examined available algorithms, calibration and validation and societal applications and used end-to-end modeling to assess uncertainty. The team also identified valuable opportunities for international collaboration to increase the frequency of revisit through data sharing, adding value for all partners. Analysis of the science, applications, architecture and partnerships led to a clear measurement strategy and a well-defined observing system architecture.