This study presents a first-of-its-kind comprehensive data fusion approach combining hyperspectral microwave (HMW) with backscatter lidar (BSL) measurements for improved atmospheric thermodynamic sounding, with particular emphasis on the Earth’s Planetary Boundary Layer (PBL). This is a simulation-based trade study to demonstrate the enhancement of HMW over traditional microwave (MW) only measurements and the additional benefits of incorporating BSL with both approaches. This pioneering HMW+BSL fusion methodology represents a major advancement, achieving superior performance compared to traditional thermodynamic remote sensing approaches. Specifically, this configuration demonstrates significant enhancement in PBL temperature bias vertical stability and reduces standard deviation error (SDV) by 30% compared to traditional MW-only performance. Water vapor retrievals show similar improvements, with SDV reductions of 50% in the PBL and bias values consistently maintained below the 10% requirement threshold of the PBL DSI program, compared to PoR errors exceeding 30% bias in challenging cloudy regimes. Case studies across diverse oceanic regions reveal particular advantages of this data fusion approach in complex atmospheric conditions, especially in regions dominated by marine stratocumulus clouds and strong temperature inversions where conventional passive-only retrievals are challenging. Beyond thermodynamic profile improvements, our analysis demonstrates remarkable advances in the detection of PBL height (PBLH), with the HMW+BSL configuration achieving mean absolute errors within the 100 meter requirement threshold of the PBL DSI program, representing a step-change improvement over passive-only approaches. This work directly addresses observational gaps identified in the 2017 Earth Science Decadal Survey, positioning our integrated sensing approach as both a near-term enhancement to existing Earth observation capabilities and a pathfinder for future PBL mission architectures.

The proliferation of machine learning models, architectures, and datasets for Earth observation (EO) continues to rise dramatically. This pattern is expected to continue growing bringing with it an increase in the generation of remote sensing derived data products powered by geospatial artificial intelligence (GeoAI) techniques. Rigorous quality assessments and accuracy analysis needs to be undertaken for the science community to adopt many of these data products for scientific discovery of changes of the Earth’s land surface. While there is existing literature supporting and documenting best practices for the validation of GeoAI data products, the software to support large-scale collaborative validation efforts is limited. In this study we present the design and software implementation of a flexible commodity-based framework for large-scale global to regional validation of GeoAI data products. This framework’s main purpose is to enable, speed up, and optimize the acquisition of validation data for large-scale science projects with support across multiple sensors and spatial resolutions with little to no code. In addition, we present several use cases where this framework has enabled and streamlined the validation of global to regional data products at different spatial resolutions and within different computational platforms.

Human-induced land cover land use (LCLU) changes such as agricultural extensification and forest degradation and loss have extensive negative impacts including biodiversity loss, land degradation, and a disruption to ecological services. In Senegal, where people are heavily reliant on dryland agricultural production, climate change and land degradation pose particularly significant threats especially as rapid population growth continues to fuel frequent LCLU change. Considering these challenges, approaches that facilitate increased insight into the spatial and temporal dynamics of land use are needed to implement sustainable land management practices and mitigation strategies. However, difficulties associated with Senegal’s highly variable phenology, sparse woody cover and small, irregular fields necessitate the use of Very High Resolution (VHR; < 3 m spatial resolution) data and modern techniques for modeling land use at sufficient scales.We take advantage of VHR data’s spatial resolution and Sentinel-1’s high temporal resolution by implementing an object-based data fusion strategy to model land use. By generating high resolution vector objects from single-date WorldView imagery and using the corresponding Synthetic Aperture Radar (SAR) time series to train a One-Dimensional Convolutional Neural Network (1D CNN), we can effectively leverage deep learning techniques to extract land use signals from multi-resolution and multi-temporal data in a near-autonomous manner.

The Amhara region, in northwest Ethiopia, has a complex topography and highly fragmented croplands (averaging half a hectare). Mapping such fragmented LCLU areas requires very high-resolution satellite imagery and robust classification methodologies. To this end, we have used multi-temporal very-high-resolution (VHR) WorldView imagery (2 meters) in combination with Convolutional Neural Networks (CNNs), to map land cover classes across the entire Amhara region. This paper presents results from domain adaptation experiments using training data from Senegal to accurately map land cover classes at 2 m resolution in the Amhara region, Ethiopia. The Attention UNet CNNs provided promising results for predicting land cover in Ethiopia imagery using domain adaptation techniques and without the addition of local training labels, with an overall accuracy of 74%. We conclude that promising future research directions exist for transfer learning implementation to finetune our land cover classes with additional model refinement to the Amhara region.

Land cover mapping has been a valuable tool in capturing changes in many developing regions in Africa. Senegal has been a hotspot of change where agricultural activity has rapidly increased. Agriculture in this region is often a complex mosaic of small fields which makes them difficult to classify using conventional land cover mapping methods and coarse-resolution satellite imagery. WorldView (WV) satellites provide very high-resolution imagery that is ideal for semantic segmentation using convolutional neural networks (CNN). In this study, we introduced training strategies that scale up the training data for the U-Net model using 2 m WV-2 and 3 imagery to overcome the challenges of regional mapping with a patchwork of hundreds of images. The proposed strategies increased the number of training data for the U-Net model in three main scenarios, (i) conventional training, (ii) model transfer, and (iii) transfer learning, and we evaluated model generalizability on test sets for two different regions in Senegal. The results showed that models rapidly reached a high level of performance with a limited increase in additional training in conventional and transfer learning strategies. In these two strategies, the U-Net consistently produced >87% average accuracy for trained images and >70% average accuracy for all test images at the final scale level. The research opens opportunities to produce regional land cover maps in West Africa without generating a prohibitively large amount of training data.

Scientific analysis of changes of the Earth's land surface benefit from well characterized, science quality remotely sensed data. This data quality is the result of models that estimate and remove atmospheric constituents and account for sun-sensor geometry [1] – [3]. Surface reflectance (SR) in commercial very high resolution (< 5 m; VHR) spaceborne imagery routinely varies for unchanged surface features because of signal variation from the combined effects of atmospheric haze and a range of sun-sensor geometric scenarios of acquisitions [4]. Consistency from this imagery must be sufficient to identify and track the change or stability of fine-scale features that, though small, may be widely distributed across remote domains, and serve as key indicators of critical broad-scale environmental change [5], [6]. Currently commercial SR products are available, but typically the model employed is proprietary and the costs for using these products over a large domain can be significant (e.g., Planet Surface Reflectance v.2). Here we describe an open source workflow for the scientific community to improve detection of fine-scale change with commercial VHR imagery.

Validating land cover classification results from a machine learning model is a vital step in ensuring that further decisions are based on sound and robust results that can be trusted. Calculating pixel-wise validating statistics from a stack of land cover classification results, while computationally trivial for low-resolution imagery with a small spatial footprint, poses a significant challenge for very-high-resolution (VHR) imagery spanning a larger spatial footprint. Here we describe an open-source unified Python framework and workflow for the compositing of VHR imagery based on climatic and spatial information leveraging hardware acceleration. We additionally describe the implementation of per-pixel reduction algorithms which are used to reduce the stacked composite into a robust and accurate composite that is validated.

Artificial intelligence and machine learning (AI/ML) have grown in popularity in recent decades as a result of advances in high-performance computing (HPC) and open-source software. Earth science research has benefited from these advancements and continues to do so. However, entry-level AI/ML projects frequently have a significant level of complexity that prevents them from being realized. Furthermore, HPC workflows oftentimes require specialized knowledge for the installation and management of software dependencies. Our group has been working on closing this gap, particularly focused in remotely sensed applications, by leveraging high performance computing containers to make code portable across environments. In this paper we present the design and implementation of a set of containers to both speed up and optimize the development of remotely sensed data applications. These containers have been made publicly available for the use of the science community and include common AI/ML frameworks and hardware acceleration libraries. Here we present several use cases and experiments of how containers can be leveraged to speed up the development and deployment of AI/ML software and some examples of containerized applications performance.