Global observations at 30-m ground sampling distance (GSD) are now possible at a cadence of one-three days by combining Landsat 8 and 9 with Sentinel-2A and -2B satellites. Previous studies characterizing pixel-level Landsat-class measurement frequency used data from different sources but offered little information on observation availability after rigorous quality screening. This study examined the coverage frequency of Harmonized Landsat and Sentinel-2 (HLS) V2.0 data for 2022, the first year all four satellites data were available. These data have had quality control filtering and harmonization, and therefore reflect the spatial-temporal distribution of usable observations. On average, HLS data provide observations every 1.6 days at the global scale, and 2.2 days in the data-scarce tropical regions, regardless of cloud cover. The global mean and median cloud-free observations were 69 and 64, respectively. The frequency of good-quality observations varies geographically and seasonally due to changes in satellite swath overlap, cloud frequency, and solar illumination. High latitudes ($\gt \sim 75^{\circ }$N) exhibit the highest number of cloud-free observations between March and September. However, data are unavailable during winter months due to low solar elevation angles and boreal regions have a lower number of clear observations in the summer months. The tropical regions have the lowest number of clear observations. More frequent HLS observations could improve terrestrial monitoring. We mapped the monthly and weekly number of clear observations globally to show where HLS data could support monthly or subweekly time series applications.

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.

To meet the demand for more frequent medium resolution land observation imagery, the United States National Aeronautics and Space Administration’s Harmonized Landsat and Sentinel-2 (HLS) project creates comparable 30-m surface reflectance data products from Landsat 8/9 and Sentinel-2A/B Top-of-Atmosphere measurements, by applying a series of harmonization algorithms that include atmospheric correction, cloud masking, view angle effect normalization, bandpass adjustment on Sentinel-2, and gridding in the same spatial reference system. Typically, over a hundred HLS images are available in a year for any location when all four sensors are in operation. Although cloud and cloud shadow pixels are masked in the HLS, they are retained in the distributed data, resulting in unnecessarily large data volume, and there are also a small proportion of cloud and cloud shadow pixels that were undetected. The high data volume and cloud mask omission problem combine to impose a challenge for many applications. In this pilot study we produce 10-day image composites from individual HLS observations. To eliminate the residual cloud and cloud shadow pixels, our composite algorithm takes advantage of the aerosol information derived during the atmospheric correction, the distance to the detected cloud and cloud shadow, and a few spectral screening rules. Following the screening of the observations, a maximal value Enhanced Vegetation Index 2 (EVI2) rule is used to select the best observation for each pixel location. The 10-day HLS image composites were created globally for a few growing season months, and at selected location for a few years. They are in general free of cloud and cloud shadow contamination and spatially complete, with a reduced data volume and increased overall data quality. The composited HLS images are made freely available in NASA's Visualization, Exploration, and Data Analysis (VEDA) environment for users’ evaluation and feedback on the composite window length and data quality.

Quantifying aboveground biomass (AGB) in tropical forests is a challenging but necessary task to support actions to preserve and restore these ecosystems. Recent methods have leveraged the integration of NASA’s spaceborne lidar GEDI with imaging sensors to improve AGB predictions. However, the use of high-resolution (<1 m) stereo images from spaceborne sensors still needs to be explored within these frameworks. The objective of this study was to predict AGB in a tropical forest patch by combining stereo images and GEDI data. A digital surface model (Stereo-DSM) was generated by stereophotogrammetric processing of high-resolution stereo pairs collected from a spaceborne sensor. A canopy height model (Stereo-CHM) was then derived by subtracting an airborne lidar-derived digital terrain model from the Stereo-DSM. Descriptive statistics were calculated from the Stereo-CHM to be used as predictors in the model to predict AGB. We also calculated vegetation indices from the Harmonized Landsat-Sentinel and Sentinel-1 images to compare their relative importance in the model and assess their relationship to the Stereo-CHM metrics. Finally, we trained a Random Forest model using stereogrammetry - derived, multispectral and SAR metrics as features and GEDI’s footprint-level AGB product as a reference. The model to predict AGB yielded performance metrics of r = 0.63, RMSE = 25.38 Mg/ha, and MD = 2.1 Mg/ha. The metrics from the Stereo-CHM were ranked as the most important to the model. This is an indication that these metrics can add important information related to canopy structure to inform GEDI-based models to predict AGB. It is still necessary to evaluate these results considering a variation in canopy cover, topography, and understory vegetation. The findings are important to support advances on the integration of large footprint spaceborne lidar and images with sub-meter spatial resolution to characterize vegetation in tropical forests.

Commercial stereo imagery has provided unprecedented planar detail (< 2 m) of the Earth’s surface. However, a number compounding factors, including view angle, convergence angle, imaging detector design, ground sampling distance (GSD), time of day/year etc., induce bias in surface reconstructions. BlackSky 1 m GSD data and its unique frame detector/telescope configuration provides a novel resource with sensor capabilities of staring, and motion imagery/video to benchmark existing commercial digital elevation model (DEM) products. The BlackSky constellation with its high repeat collection capability also allows for dense imagery collection and enables multi-view stereo reconstruction. We evaluated the ability of BlackSky data for vegetation surface reconstruction using a multi-view stereo methodology and compared products to airborne LiDAR data. We found an Absolute Median Error of 1.77 m and normalized mean absolute deviation (NMAD) of 1.54 m in areas with sufficient volume of available digital surface models (DSMs). Our results indicate the potential benefit of this workflow and resulting products for vegetation analyses.

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.

Human induced forest degradation can reduce aboveground biomass (AGB) and carbon stock of forest fragments. Developing approaches to assess these effects in highly-degraded tropical forests is necessary, especially at large scales. In this study, we developed a framework to upscale NASA’s GEDI spaceborne lidar AGB products using data from imaging sensors in the Brazilian Atlantic Forest – one of the most degraded and fragmented ecosystems in the world. A Random Forest model was trained using GEDI footprint level AGB as response and vegetation indices from Landsat 8/OLI and ALOS/PALSAR-2 images as predictors. The models were used to map and assess the AGB at the core and edge of 8783 fragments. The model had r = 0.83 and RMSE = 34.06 Mg/ha. The AGB in the fragments’ edges were significantly lower than in the fragments’ cores. The results demonstrated the potential of the developed framework to assess fragmentation effects on highly degraded tropical forest ecosystems.

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.

Circumboreal forests are currently experiencing rapid climate warming which is altering their structure, productivity, and status as a carbon sink. Very high-resolution (VHR; < 2 m) stereo-derived digital surface models are available to monitor these forests, but a similar resolution digital terrain model (DTM) is required to extract information about tree height, which is often used to estimate carbon content. To the best of our knowledge, no openly available VHR DTM currently exists. To address this need, we developed approaches to extract DTMs by filtering VHR stereo point clouds (PCs) in sparse canopies of Alaska. Our evaluation consisted of two stereo processing methods with three PC search radii at six different tree canopy cover (TCC) intervals. We found that VHR DTMs were robust for estimating bare ground at TCC intervals less than 40% with vertical errors <1.6 m using airborne small footprint light detection and ranging (LiDAR) as reference.

Smallholder farms in South East Asia are characterized by small irregular field patterns, dense cloud cover and haze which limits our ability to observe changes in agriculture land-use. Very-high resolution (VHR,30 m) in regions with dense persistent cloud cover and haze from biomass burning.

Commercial very high-resolution (VHR) Earth observing (EO) satellites have grown into constellations with global repeat coverage that can support existing NASA EO missions with stereo and multispectral capabilities. Sub-meter data from these instruments exceeds petabytes per year and the cost for data, storage systems and compute power have all dropped exponentially. Concurrently, through agreements with the National Geospatial-Intelligence Agency, NASA-Goddard Space Flight Center is acquiring VHR EO imagery from DigitalGlobe's WorldView-1, 2, 3 Quickbird-2, GeoEye-1 and IKONOS-2 satellites. To enhance the utility of these data we are developing an Application Program Interface (API) to produce on-demand user defined science ready products to support NASA's EO missions. These enhancements include two primary foci: 1) surface reflectance 1/2° ortho mosaics - multi-temporal 2 m multispectral imagery that can be used to investigate biodiversity, horizontal forest structure, surface water fraction, and land-cover land-use at the human scale; and 2) VHR digital elevation models (DEMs) - derived with the NASA Ames Stereo Pipeline. These enhanced products benefit Earth surface studies on the cryosphere (glacier mass balance, flow rates and snow depth), hydrology (lake/waterbody levels, landslides, subsidence) and the biosphere (vertical forest structure, tree canopy height and cover) among others. Here we present current API capabilities and recent examples of derived products used in NASA Earth Science projects.

In February 2017, the Earth Observing One (EO-1) satellite mission successfully completed sixteen years and three months of Earth imaging by its two unique instruments, the Hyperion and the Advanced Land Imager (ALI). Both instruments have served as prototypes for new orbital sensors. Hyperion has provided the only available global sample of the Earth's surface with: (i) passive optical mid-morning observations at moderate spatial resolution (30 m) to match the Landsat series; and (ii) spectral coverage over almost the full optical spectrum in 10 nm contiguous bands, in visible through shortwave infrared (VSWIR, 0.4-2.5 μm) wavelengths. Consequently, Hyperion is a heritage platform for future full-spectrum VSWIR orbital spectrometers, including the German mission, EnMAP (2019), and the NASA pre-Phase A (yet unscheduled) mission, the Hyperspectral InfraRed Imager (HyspIRI), defined by the 2007 Decadal Survey conducted by the US National Research Council. We provide an overview of the mission's lifetime and Hyperion's scientific and application accomplishments, including calibration & validation activities, data quality evaluations during end of mission precession changes to the orbit and overpass time, and the development of a user-friendly science quality archive.

Spaceborne spectrometers require spectral-temporal stability characterization to aid in validation of derived data products. Earth Observation 1 (EO-1) began orbital precession in 2011 after exhausting onboard fuel resources. In the Libya-4 pseudoinvariant calibration site (PICS), this resulted in a progressive shift from a mean local equatorial crossing time of ~10:00 A.M. in 2011 to ~8:30 A.M. in late 2015. Here, we studied precession impacts to Hyperion surface reflectance products using three atmospheric correction approaches from 2004 to 2015. Combined difference estimates of surface reflectance were2) in VNIR from 0.25 to 0.94 and in SWIR from 0.12 to 0.88 (p <; 0.01). The uncertainties in all the models increased with a terrain slope up to 15° and selecting dune flats could reduce errors. We conclude that these data remain a valuable resource over this period for sensor intercalibration despite orbital decay.

The land surface imaging (LSI) virtual constellation approach promotes the concept of increasing Earth observations from multiple but disparate satellites. We evaluated this through spectral and spatial domains, by comparing surface reflectance from 30-m Hyperion and 2-m resolution WorldView-2 (WV-2) data in the Libya-4 pseudoinvariant calibration site. We convolved and resampled Hyperion to WV-2 bands using both cubic convolution and nearest neighbor (NN) interpolation. Additionally, WV-2 and WV-1 same-date imagery were processed as a cross-track stereo pair to generate a digital terrain model to evaluate the effects from large (>70 m) linear dunes. Agreement was moderate to low on dune peaks between WV-2 and Hyperion (R22> 0.6). Our results provide a satellite sensor intercomparison protocol for an LSI virtual constellation at high spatial resolution, which should start with geolocation of pixels, followed by NN interpolation to avoid tall dunes that enhance surface reflectance differences across this internationally utilized site.