We present the latest development in the Digital Earth Africa (DE Africa) program on application development using analysis-ready data, development of tools for accessing commercial imagery, and evaluation of additional analysis-ready data sources. We discuss the significant achievements and challenges encountered and highlight future work essential for the program’s goal to empower African countries in climate actions.

OBJECTIVE: 2017 blood pressure (BP) categories focus on cardiac risk. We hypothesize that studying the balance between mechanisms that increase or decrease BP across the medical phenome will lead to new insights. We devised a classifier that uses BP measures to assign individuals to mutually exclusive categories centered in the upper (Htn), lower (Hotn) and middle (Naf) zones of the BP spectrum; and examined the epidemiologic and phenotypic patterns of these BP-categories. METHODS: We classified a cohort of 832,560 deidentified electronic health records by BP-category; compared the frequency of BP-categories and four subtypes of Htn and Hotn by sex and age-decade; visualized the distributions of systolic, diastolic, mean arterial and pulse pressures stratified by BP-category; and ran Phenome-wide Association Studies (PheWAS) for Htn and Hotn. We paired knowledgebases for hypertension and hypotension and computed aggregate knowledgebase status (KB-status) indicating known associations. We assessed alignment of PheWAS results with KB-status for phecodes in the knowledgebase, and paired PheWAS correlations with KB-status to surface phenotypic patterns. RESULTS: BP-categories represent distinct distributions within the multimodal distributions of systolic and diastolic pressure. They are centered in the upper, lower, and middle zones of mean arterial pressure and provide a different signal than pulse pressure. For phecodes in the knowledgebase, 85% of positive correlations align with KB-status. Phenotypic patterns for Htn and Hotn overlap for several phecodes and are separate for others. Our analysis suggests five candidates for hypothesis testing research, two where the prevalence of the association with Htn or Hotn may be under appreciated, three where mechanisms that increase and decrease blood pressure may be affecting one another's expression. CONCLUSION: PairedPheWAS methods may open a phenome-wide path to disentangling hypertension and chronic hypotension. Our classifier provides a starting point for assigning individuals to BP-categories representing the upper, lower, and middle zones of the BP spectrum. 4.7 % of individuals matching 2017 BP categories for normal, elevated BP or isolated hypertension, have diastolic pressure < 60. Research is needed to fine-tune the classifier, provide external validation, evaluate the clinical significance of diastolic pressure < 60, and test the candidate hypotheses.

MOTIVATION: Phecodes are widely used and easily adapted phenotypes based on International Classification of Diseases codes. The current version of phecodes (v1.2) was designed primarily to study common/complex diseases diagnosed in adults; however, there are numerous limitations in the codes and their structure. RESULTS: Here, we present phecodeX, an expanded version of phecodes with a revised structure and 1,761 new codes. PhecodeX adds granularity to phenotypes in key disease domains that are under-represented in the current phecode structure-including infectious disease, pregnancy, congenital anomalies, and neonatology-and is a more robust representation of the medical phenome for global use in discovery research. AVAILABILITY AND IMPLEMENTATION: phecodeX is available at https://github.com/PheWAS/phecodeX.

OBJECTIVE: Knowledgebases are needed to clarify correlations observed in real-world electronic health record (EHR) data. We posit design principles, present a unifying framework, and report a test of concept. MATERIALS AND METHODS: We structured a knowledge framework along 3 axes: condition of interest, knowledge source, and taxonomy. In our test of concept, we used hypertension as our condition of interest, literature and VanderbiltDDx knowledgebase as sources, and phecodes as our taxonomy. In a cohort of 832 566 deidentified EHRs, we modeled blood pressure and heart rate by sex and age, classified individuals by hypertensive status, and ran a Phenome-wide Association Study (PheWAS) for hypertension. We compared the correlations from PheWAS to the associations in our knowledgebase. RESULTS: We produced PhecodeKbHtn: a knowledgebase comprising 167 hypertension-associated diseases, 15 of which were also negatively associated with blood pressure (pos+neg). Our hypertension PheWAS included 1914 phecodes, 129 of which were in the PhecodeKbHtn. Among the PheWAS association results, phecodes that were in PhecodeKbHtn had larger effect sizes compared with those phecodes not in the knowledgebase. DISCUSSION: Each source contributed unique and additive associations. Models of blood pressure and heart rate by age and sex were consistent with prior cohort studies. All but 4 PheWAS positive and negative correlations for phecodes in PhecodeKbHtn may be explained by knowledgebase associations, hypertensive cardiac complications, or causes of hypertension independently associated with hypotension. CONCLUSION: It is feasible to assemble a knowledgebase that is compatible with EHR data to aid interpretation of clinical correlation research.

This paper has been developed to address one deliverable in the CEOS ARD Strategy [1] and provides an initial evaluation of the views from the private sector on how the CEOS community needs to engage with the broader community to successfully advance the CEOS Analysis Ready Data (CARD) initiative to the next level. Further, the paper offers an overview of the next steps that the CEOS community should consider to ensure all three main stakeholder groups; a) Earth Observation (EO) data providers; b) Big Data hosts and c) aggregators and data users are actively involved in the CARD initiative. The full version of the paper can be found at the CEOS webpage [2]. Methods of evaluation included consultation with a small group from the private sector through an initial teleconference, follow-up emails and informal face-to-face discussions. Outcomes from this initial engagement show that the private sector recognises the CARD work as an important initiative and is open for future collaboration with the CEOS community. The next steps proposed in this paper include: –Share Information on CEOS Analysis Ready Data to build engagement support, –Understand the Industry Perspective, –Engage Industry in CARD Specifications, –Move CEOS specifications into the broader community.

Digital Earth Africa is a continental infrastructure that makes Earth observation data available to support sustainable development in Africa. Access to analysis ready data, i.e., data that are processed to a defined set of requirements and ready for immediate analysis, is key to the success and sustainability of the program. In this paper, we present our strategy around analysis ready data and outline the efforts taken to ensure operational supply of these datasets for the African continent. We also share our experience and learnings during this process.

Over the past several years Analysis Ready Data has been a major focus for the Committee on Earth Observation Satellites (CEOS). The CEOS Analysis Ready Data concept within the context of Land started in 2015 and has been led by the CEOS Land Surface Imaging Virtual Constellation (LSI-VC) community. LSI-VC has developed the definition that CEOS uses for ARD for Land and the overall framework [1]. The CEOS ARD definition identifies that the major benefit of ARD lies in enabling a broad community of non-remote sensing experts to better use CEOS data; and in increasing the interoperability of products through space and time as a foundation for `Big Data' analyses, future data architectures and the application of AI (artificial intelligence and machine learning) methods. LSI-VC also took the responsibility to coordinate the development of the Product Family Specifications (PFS). An important step in the CARD specification approach is that it is not prescriptive with regard to the data processing approach. This recognises that there are generally multiple approaches and that these will evolve through time. Four PFS have been endorsed by the CEOS LSI-VC community during 2019/2020. These endorsements represent the conclusion of over two years of work since the agreement of the CEOS ARD definition & framework at CEOS Plenary in 2016. The endorsed specifications contain inputs from experts around the world and it demonstrates the resolution of various points of view. It also shows the emerging acceptance of the concept of Analysis Ready Data and the value proposition of the CEOS ARD. As the increase of available EO data and processing platforms offer an unprecedented choice for the users, the need for harmonized data is growing [2]. Thus, over the past few years, there has been an increased interest from the private sector in the CEOS ARD initiative as well, and the expectation is that the availability of CEOS ARD datasets from both institutional and private data providers will increase in the coming years. To address the CEOS ARD long-term view and to ensure a consensus between players is achieved, the CEOS ARD strategy was developed in 2019. The strategy recognises that the private industry has different roles to play within the CEOS ARD initiative such as data users, data providers, data hosts or as providers of processing chains to produce data that meet CEOS ARD specifications. The strategy is also focused on the need, prioritization and continuity of CEOS ARD PFS development including the development of ARD specifications for other thematic areas such as ocean and atmosphere. This paper intends to give an update on the current CEOS ARD for Land accomplishments, and what activities are envisaged for the near future.

Analysis Ready Data (ARD) products are enabling users to get first hand satellite data that are `ready to use' for a wide range of applications, including time-series analysis and the way forward to multi-sensor interoperability. The Committee on Earth Observation Satellites (CEOS) is leading the CEOS Analysis Ready Data for Land (CARD4L) initiative. The rationale behind this initiative is to enable users to access products that have been already processed to a certain level. This will allow users to carry out immediate analysis on the data and to address a variety of applications without the extra time and cost that is associated with the data pre-processing steps. The aim of the proposed paper is to give an overview on the work already carried out by the CEOS community on the CARD4L framework, to introduce the processes and steps that Earth Observation (EO) data providers should undertake to have a CARD4L level product as well as the planned steps that CEOS LSIVC will be undertaking to accomplish the CARD4L initiative.

The Australian Government spends well over half a billion dollars every year in programs designed to protect, enhance or measure the environment. This diverse range of programs covers a breadth of activities such as driving changes in land management practices to protect the Great Barrier Reef, undertaking surveys of agricultural productivity, and the management of millions of dollars of environmental water that is used to protect and improve critical wetlands in the Murray-Darling Basin.

For many land monitoring applications using remote sensing, lack of data is no longer an issue, as it may have been in the past. Programs, such as Copernicus by the European Commission and the Landsat Missions by the United States Geological Survey, have adopted systematic acquisition strategies, and distribute vast amounts of satellite data under open licenses. In parallel, storage and computing capability have evolved to make it cost-effective and practical to process and analyze these data at various scales. Data architecture solutions, such as the Open Data Cube (ODC) and the Copernicus Data and Information Access Services (DIAS), are providing frameworks that make [scientific] analysis much simpler and straightforward. However, enabling non-expert users without the expertise and/or computation resources to pre-process and store low-level data products in order to exploit these capabilities, has proven more challenging. The Committee on Earth Observation Satellites (CEOS1) is working to address this challenge through the CEOS Analysis Ready Data for Land (CARD4L) initiative [1]. CARD4 L is foreseen to enable users to access satellite data products that are `ready to use' for a wide range of land applications. Moreover, CARD4L aims to enable non-expert users access to products that have been processed `far enough' to be suitable for immediate analysis for a range of applications, while ensuring they are not too specific to only be used for particular topics or areas. CARD4L will be an important enabler of the Open Data Cube (ODC) initiative [2]. Through CARD4L, users will be able to easily locate products that are suitable for ingestion into Data Cubes [3], and will have confidence that these different CARD4 L products will limit as far as possible barriers to interoperability.

An appropriate resolution of the Digital Elevation Model (DEM) data with sufficient quality of the gradient field is critical for effective correction of remotely sensed data over mountainous areas. Conversely, using performance of terrain illumination correction and scale-based analysis, such as filter bank analysis, the quality of DEM data can be evaluated. In this study, TanDEM-X Intermediate DEM (IDEM) data at 12 m resolution and the 1-arc second Shuttle Radar Topography Mission (SRTM) data were used independently to evaluate the relative effectiveness of the terrain illumination correction for Landsat 8 optical data over Tasmania. Results from the terrain illumination correction and filter bank analysis show that IDEM 12 m data can resolve finer details of terrain shading than the SRTM based DEM and deliver better results in areas with detail-rich terrain. However, since the data available for this study is an intermediate product, spikes and other noise artefacts were prevalent, especially over areas covered by water. Operational use of the IDEM would require the removal of such noise artefacts.

Monitoring of Australia's land and water using remote sensing is being advanced through new policy and coordination at the national level, and by strategic science and collaborative projects, including investments in national research infrastructure, that are allowing robust calibration, large scale processing and time-series analysis of data in high performance computing environments. The benefits of these developments will be wide-spread, but will first be seen in large scale projects, such as the National Flood Risk Information Project, which will improve knowledge of flooding through analysis of the complete Landsat archive. Together, these developments position Australia for both improved and sustained land monitoring capabilities.

The intra- and inter-annual variability of surface bidirectional reflectance distribution function (BRDF) in Australia has been analyzed using 11 years (2001-2011) of MODIS BRDF data. A statistic called here Root Mean Square (RMS) was used as a BRDF shape indicator to represent the overall BRDF shape and an Australian vegetation structure map was used to separate the different BRDF shape patterns by structure. The results show that the intra-annual variation of BRDF shape is stronger than the inter-annual variation although it is not clear yet whether the variation is related more to climate patterns or to vegetation structure (height and cover) or landcover class. However, BRDF shape patterns have strong similarity with vegetation structure classes. There is strong correlation between RMS and the Normalized Difference Vegetation Index (NDVI) at annual scale within structural classes indicating good relationship between BRDF and annual changes in cover within the classes.

Understanding how land cover responds to natural and anthropogenic drivers is critical as increasing population, climate fluctuations and competing land uses place increased pressure on both natural and food/fibre production systems. The Dynamic Land Cover Dataset (DLCD) Version 2 is a series of biennial land cover maps that uses the ISO 19144-2 land cover classification scheme. The Moderate Resolution Image Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) time series [1] are used to characterize greenness dynamics observed at 250-metre scale. These greenness dynamics are used to generate a series of 9 land cover maps. Version 2 of the DLCD provides a series of land cover maps updated on an annual basis to enable resource managers, decision makers and biophysical modelers to track the change in land cover on a systematic basis.

The National Dynamic Land Cover Dataset (DLCD) classifies Australian land cover into 34 categories, which conform to 2007 International Standards Organisation (ISO) Land Cover Standard (19144-2). The DLCD has been developed by Geoscience Australia and the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES), aiming to provide nationally consistent land cover information to federal and state governments and general public. This paper describes the modeling procedure to generate the DLCD, including machine learning methodologies and time series analysis techniques involved in the process.

Precise georeferencing is one of the prerequisites for orthoimage generation from high-resolution satellite imagery. This requires the availability of a small number of GCPs that have to be visible in each scene. In this paper, it is shown how the number of GCPs required for the precise georeferencing of Advanced Land Observation Satellite (ALOS) imagery can be reduced by up to 90% using a generic pushbroom sensor model and strip adjustment while still achieving an accuracy of better than 1 pixel. A fully automatic work flow requiring an existing digital orthophoto and a digital elevation model (DEM) is also presented. Using an orthophoto mosaic generated from Landsat-7 panchromatic imagery for automatic GCP measurement, pixel-level accuracy can be achieved for images from the Advanced Visible and Near Infrared Radiometer type 2 (AVNIR-2) instrument. For images from the Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM), the accuracy of the automated procedure is only about 2 pixels due to the poor resolution of the orthoimage, compared with PRISM.