In 2015 and 2016, the AfriSAR campaign was carried out as a collaborative effort among international space and National Park agencies (ESA, NASA, ONERA, DLR, ANPN and AGEOS) in support of the upcoming ESA BIOMASS, NASA-ISRO Synthetic Aperture Radar (NISAR) and NASA Global Ecosystem Dynamics Initiative (GEDI) missions. The NASA contribution to the campaign was conducted in 2016 with the NASA LVIS (Land Vegetation and Ice Sensor) Lidar, the NASA L-band UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar). A central motivation for the AfriSAR deployment was the common AGBD estimation requirement for the three future spaceborne missions, the lack of sufficient airborne and ground calibration data covering the full range of ABGD in tropical forest systems, and the intercomparison and fusion of the technologies. During the campaign, over 7000 km2 of waveform Lidar data from LVIS and 30000 km2 of UAVSAR data were collected over 10 key sites and transects. In addition, field measurements of forest structure and biomass were collected in sixteen 1 hectare sized plots. The campaign produced gridded Lidar canopy structure products, gridded aboveground biomass and associated uncertainties, Lidar based vegetation canopy cover profile products, Polarimetric Interferometric SAR and Tomographic SAR products and field measurements. Our results showcase the types of data products and scientific results expected from the spaceborne Lidar and SAR missions; we also expect that the AfriSAR campaign data will facilitate further analysis and use of waveform Lidar and multiple baseline polarimetric SAR datasets for carbon cycle, biodiversity, water resources and more applications by the greater scientific community.

The dynamic distribution and change of terrestrial water, manifested in wetlands that support a wide range of vegetation types and ecosystems, and also in floodplains that are prone to inundations, are very important to the understanding of our changing climate and our ability to mitigate risk. At present all of the existing measurement techniques have serious limitations in the ability to observe terrestrial water bodies globally and at the spatial and temporal scales required to fully capture their dynamics. In the last few years a number of studies in the community of Global Navigation Satellite Systems (GNSS) Reflectometry have been focusing on reflections over wetlands and inundated areas [Zuffada et al, 2016; Nghiem et al, 2017; Chew et al., 2018; Morris et al., 2019], particularly since data analysis of the CYclone GNSS (CYGNSS) mission began showing the ability to resolve small-scale land features such as rivers and bodies of water even partially obstructed by vegetation. CYGNSS is a constellation of 8 microsatellites, that provides significantly increased temporal sampling and revisit rate in the tropical latitudinal band as compared to traditional monolithic instruments, thus enabling a new observing strategy for capturing dynamic water events. In [Zuffada et al., 2017; Nghiem et al., 2017], based on reflected signal characteristics such as peaked (limited spread in delay and doppler) and symmetric shape, and very high reflected peak power, it was hypothesized that over wetlands there are strong coherent specular reflections in the collection area of the signal, originating from (even small) areas of standing water, resulting in the measurements’ magnified sensitivity to water because of its high electric permittivity compared to dry land and/or vegetation. Plots of peak power, corresponding to CYGNSS measurements’ specular points, aggregated over a period of time, and displayed over large regions with complex hydrology such as the Amazon basin clearly showed the potential of CYGNSS to map surface hydrology of intricate scenes at the continental level. At the regional scale, availability of in-situ and other correlative data have led to introduce thresholds in peak power values that differentiate between two binary states, i.e. dry and wet associated with inundations [Chew et al., 2018; Morris et al., 2019]. The limitations of the assumptions used to map wetlands have been analyzed in [Loria et al., 2019 (in preparation)] and shown to be traceable to the complex nature of the scattering from inhomogeneous scenes where the local water topology, surface topography and local meteorology can affect the mix of coherent and incoherent scattering, thus producing highly variable peak power that confounds the measurements. This communication summarizes our best understanding of the retrieval accuracy of GNSS reflectometry for dynamic monitoring of wetlands and inundations. It is based on analysis of actual CYGNSS constellation data acquired over terrestrial water bodiesand discusses the limitations in estimating surface water globally when inhomogeneities at the small scales are at play. Comparisons and cross-validations have been performed with measurements from ALOS-2 and Sentinel-1 data. References Chew, C., Reager, J.T. and Small, E., 2018. CYGNSS data map flood inundation during the 2017 Atlantic hurricane season. Scientific Reports (Nature Publisher Group), 8, pp.1-8. Loria, E. et al. Analysis of Wetland Extent Retrieval Accuracy Using CYGNSS Data. In preparation, 2019. Morris, M., C. Chew, J.T. Reager, R. Shah, C. Zuffada. A Novel Approach to Monitoring Wetland Dynamics using CYGNSS: Everglades Case Study. Remote Sensing of Environment, in press. 2019. Nghiem SV, Zuffada C, Shah R, Chew C, Lowe ST, Mannucci AJ, Cardellach E, Brakenridge GR, Geller G, Rosenqvist A. Wetland monitoring with Global Navigation Satellite System reflectometry. Earth and Space Science. 2017 Jan 1;4(1):16-39. Zuffada, C., Chew, C., Nghiem, S.V., Shah, R., Podest, E., Bloom, A.A., Koning, A., Small, E., Schimel, D., Reager, J.T. and Mannucci, A., 2016, August. Advancing Wetlands Mapping and Monitoring with GNSS Reflectometry. In Living Planet Symposium (Vol. 740, p. 83). Zuffada, C., Chew, C. and Nghiem, S.V., 2017. GNSS-R algorithms for wetlands observations. In IGARSS 2017-2017 IEEE International Geoscience and Remote Sensing Symposium (pp. 1126 – 1129), Fort Worth, TX.

The ESA-NASA Multi-mission Algorithm and Analysis Platform (MAAP) is a platform designed to meet the need of the international scientific community to collaborate on the generation and analysis of increasingly massive datasets from space-based, airborne, and field observations for aboveground terrestrial carbon dynamics. The MAAP is an open-source, cloud-based platform that distinguishes itself from other science platforms by being agnostic to any science disciplines and allows scientists to write, develop, and execute their own algorithms in shared workspaces that are tailored to their specific area of research. We present an overview of the capabilities of the Pilot implementation of MAAP. Users can explore and visualize ESA and NASA data that is ingested in the MAAP data catalog, develop and test algorithms to generate new datasets and act upon existing ones, launch matured algorithms as large-scale processing jobs, and analyze the results. This allows scientists to follow the entire scientific process within the platform, from the conception of a hypothesis to the creation of scientific results in a version-controlled and reproducible environment. Users can document their process, collaborate with other users, and share algorithms and datasets. We will conclude with an outlook on the features that the next phase of development will bring to the platform.