<p>Satellite remote sensing data provide key information needed to understand the dynamic behavior of our planet as well as to prepare for, respond to, and recover from disasters. The Observational Products for End-Users from Remote Sensing Analysis (OPERA) project at the Jet Propulsion Laboratory, in partnership with the US Geological Survey and the University of Maryland, starts releasing near-global products that are based on Harmonized Landsat-8 Sentinel-2 A/B (HLS) optical datasets in February 2023: (1) Dynamic Surface Water eXtent (DSWx-HLS) and (2) Land Surface Disturbance (DIST-HLS) product suites. These derived products have applications including  monitoring and guiding future hazard management and recovery efforts. While OPERA does not have an urgent response requirement for disasters, the project will process and deliver the data to end-users as soon as possible. HLS has 2-3 day revisit frequency at the equator allowing the potential for OPERA products to help provide analysis ready data from before, during, and after some events to aid disaster response and recovery efforts. All the DSWx and DIST products will be freely available to the public through various Distributed Active Archive Centers (PO.DAAC for DSWx, https://podaac.jpl.nasa.gov/; LPDAAC for DIST, https://lpdaac.usgs.gov/) and NASA’s Earthdata Search platform based on their scheduled operational release.</p><p>Here, we present applications of the first provisional products from the DSWx-HLS and DIST-HLS suites to monitor changes in water bodies and vegetation cover due to droughts, floods, and wildfires. In particular, we focus our analysis on: (a) drastic extent changes in reservoirs,  such as for Lake Mead from 2014-present, (b) mapping flood extents such as the 2020 dam failures in Midland, Michigan, and (c) mapping burned areas due to wildfires such as the 2022 wildfires in New Mexico and in California. We develop open-source tutorials using GIS software and Jupyter Notebooks to visualize and showcase these applications. Both the provisional data and the tutorials are available on the OPERA website (https://www.jpl.nasa.gov/go/opera) to ensure broad access and reproducibility. </p>

We compare two planner solutions for a challenging Earth science application to plan coordinated measurements (observations) for a constellation of satellites. This problem is combinatorially explosive, involving many degrees of freedom for planner choices. Each satellite carries two different sensors and is maneuverable to 61 pointing angle options. The sensors collect data to update the predictions made by a high-fidelity global soil moisture prediction model. Soil moisture is an important geophysical variable whose knowledge is used in applications such as crop health monitoring and predictions of floods, droughts, and fires. The global soil-moisture model produces soil-moisture predictions with associated prediction errors over the globe represented by a grid of 1.67 million Ground Positions (GPs). The prediction error varies over space and time and can change drastically with events like rain/fire. The planner's goal is to select measurements which reduce prediction errors to improve future predictions. This is done by targeting high-quality observations at locations of high prediction-error. Observations can be made in multiple ways, such as by using one or more instruments or different pointing angles; the planner seeks to select the way with the least measurement-error (higher observation quality). In this paper we compare two planning approaches to this problem: Dynamic Constraint Processing (DCP) and Mixed Integer Linear Programming (MILP). We match inputs and metrics for both DCP and MILP algorithms to enable a direct apples-to-apples comparison. DCP uses domain heuristics to find solutions within a reasonable time for our application but cannot be proven optimal, while the MILP produces provably optimal solutions. We demonstrate and discuss the trades between DCP flexibility and performance vs. MILP's promise of provable optimality.

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