Developing actionable early detection and warning systems for agricultural stakeholders is crucial to reduce the annual \$200B USD losses and environmental impacts associated with crop diseases. Agricultural stakeholders primarily rely on labor-intensive, expensive scouting and molecular testing to detect disease. Spectroscopic imagery (SI) can improve plant disease management by offering decision-makers accurate risk maps derived from Machine Learning (ML) models. However, training and deploying ML requires significant computation and storage capabilities. This challenge will become even greater as global scale data from the forthcoming Surface Biology \& Geology (SBG) satellite becomes available. This work presents a cloud-hosted architecture to streamline plant disease detection with SI from NASA’s AVIRIS-NG platform, using grapevine leafroll associated virus complex 3 (GLRaV-3) as a model system. Here, we showcase a pipeline for processing SI to produce plant disease detection models and demonstrate that the underlying principles of a cloud-based disease detection system easily accommodate model improvements and shifting data modalities. Our goal is to make the insights derived from SI available to agricultural stakeholders via a platform designed with their needs and values in mind. The key outcome of this work is an innovative, responsive system foundation that can empower agricultural stakeholders to make data-driven plant disease management decisions, while serving as a framework for others pursuing use-inspired application development for agriculture to follow that ensures social impact and reproducibility while preserving stakeholder privacy.

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.

Mixed pixels are the rule, not the exception, in decameter terrestrial imaging. By definition, the reflectance spectrum of a mixed pixel is a function of more than one generative process. Physically-based surface biology or geology retrievals must therefore isolate the component of interest from a myriad of unrelated processes, heterogenously distributed across hundreds of square meters. Foliar traits, for example, must be isolated from canopy structure and substrate composition which can dominate overall variance of spatially integrated reflectance. We propose a new approach to isolate low-variance spectral signatures. The reflectance of each pixel is modeled assuming linear geographic mixing due to a small library of generic endmembers. The difference between the modeled and observed spectra is deemed the Mixture Residual (MR). The MR, a residual reflectance spectrum that is presumed to carry the subtler and variable signals of interest, is then leveraged as a source of signal. We illustrate the approach using three datasets: synthetic composites computed from field reflectance spectra, NEON AOP airborne image compilations, and DESIS satellite data. The MR discriminates between land cover versus plant trait signals and accentuates subtle absorption features. Mean band-to-band correlations within the visible, NIR, and SWIR wavebands decrease from 0.97, 0.94, and 0.97 to 0.95, 0.04 and 0.31. The number of dimensions required to explain 99% of image variance increases from 4 to 13. We focus on vegetation as an illustrative example, but note that the concept can be extended to other applications and used as an input to other algorithms.

Bidirectional reflectance distribution function (BRDF) effects are a persistent issue for the analysis of vegetation in airborne imaging spectroscopy data, especially when mosaicking results from adjacent flightlines. With the advent of large airborne imaging efforts from NASA and the US National Ecological Observatory Network (NEON), there is increasing need for methods that are both flexible and automatable across numerous images with diverse land cover. FlexBRDF corrects for BRDF effects in groups of flightlines, with key user-selectable features including kernel selection, land cover stratification (we employ NDVI), and use of a reference solar zenith angle (SZA). We demonstrate FlexBRDF using a series of nine long (150-400 km) AVIRIS-Classic flightlines collected on 22 May 2013 over Southern California, where rough terrain, diverse land cover, and a wide range of solar illumination yield significant BRDF effects, and then test the approach on additional AVIRIS-Classic data from California, AVIRIS-Next Generation data from the Arctic and India, and NEON imagery from Wisconsin. Based on comparisons of overlap areas between adjacent flightlines, correction algorithms built from multiple flightlines concurrently performed better than corrections built for single images (RMSE improved up to 2.3% and mean absolute deviation 2.5%). Standardization to a common SZA among a group of flightlines also improved performance. While BRDF corrections tailored to individual sites may be preferred for local studies, FlexBRDF is compatible with bulk processing of large datasets covering diverse land cover needed for calibration/validation of forthcoming spaceborne imaging spectroscopy missions.