Geonex: A NASA-NOAA Collaboration for Producing Land Surface Products from Geostationary Sensors Using Cloud Computing

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement.Initial analyses of various products from ABI/AHI sensors suggest that they are comparable to those from MODIS in representing the spatio-temporal dynamics of land conditions. Cloud computing offers a variety of options for deploying the GEONEX pipeline including choice CPUs, storage media, and automation. We estimate the cost of deploying GEONEX to be $400 - 750 a month for processing data (every 30 minutes) and producing products over the conterminous US. For products such as Fire, latency can be as little as 10 minutes from the time of data acquisition

Earth Observations from Geostationary Satellites

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GeoNEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GeoNEX pipeline has been deployed on Amazon Web Services cloud platform and it currently leverages near-realtime geostationary data hosted in AWS public datasets under a NOAA-AWS agreement. Initial analyses of various products from ABI/AHI sensors suggest that they are comparable to those from MODIS in representing the spatio-temporal dynamics of land conditions. Cloud computing offers a variety of options for deploying the GeoNEX pipeline including choice CPUs, storage media, and automation. By making the GEONEX pipeline available on the cloud, we hope to engage a broad community of Earth scientists from around the world in utilizing this new source of data for Earth monitoring

Evaluation of Decision Trees for Cloud Detection from AVHRR Data

Automated cloud detection and tracking is an important step in assessing changes in radiation budgets associated with global climate change via remote sensing. Data products based on satellite imagery are available to the scientific community for studying trends in the Earth's atmosphere. The data products include pixel-based cloud masks that assign cloud-cover classifications to pixels. Many cloud-mask algorithms have the form of decision trees. The decision trees employ sequential tests that scientists designed based on empirical astrophysics studies and simulations. Limitations of existing cloud masks restrict our ability to accurately track changes in cloud patterns over time. In a previous study we compared automatically learned decision trees to cloud masks included in Advanced Very High Resolution Radiometer (AVHRR) data products from the year 2000. In this paper we report the replication of the study for five-year data, and for a gold standard based on surface observations performed by scientists at weather stations in the British Islands. For our sample data, the accuracy of automatically learned decision trees was greater than the accuracy of the cloud masks p < 0.001

Transfer Learning to Generate True Color Images from GOES-16

Along with scientific applications, Geostationary imagery is often used to learn about weather patterns through true color visualizations. NOAA/NASA's GOES-R series of satellites uses the advanced baseline imager with 16-bands which, unlike previous generations, does not include the green wavelength (500-565 nm) and hence cannot directly generate true color images. However, Himawari, Japan's geostationary satellite, uses a similar 16-band advanced Himawari imager that does include a green band (but missing cirrus). In this work, we show how transfer learning with convolutional neural networks can be applied across satellites to generate "virtual sensors". We apply this approach to generate a green band for GOES-16 and present near true color images

Optical Flow for Intermediate Frame Interpolation of Multispectral Geostationary Satellite Data

Applications in areas such as weather tracking and modeling, ecosystem monitoring, wildfire detection, and land-cover change are heavily dependent on spatial and temporal resolutions of satellite observations. However, there are typically trade-offs between spatial and temporal resolutions in dataset selection. For instance, geostationary weather tracking satellites are designed to take snapshots many times throughout the day but sensor hardware limits data collection. In this work we tackle this limitation, developing a method for temporal upsampling of multi-spectral satellite imagery using optical flow video interpolation deep convolutional neural networks. The presented model, extends Super SloMo (SSM) from single optical flow estimates to multichannel where flows are computed per band. We apply this technique on 8 multi-spectral bands of NOAA/NASA's GOES-16 mesoscale dataset to temporally enhance full disk hemispheric snapshots from 15 minutes to 1 minute. Through extensive experimentation, we show SSM vastly outperforms the linear interpolation baseline and that multichannel optical flows improves performance on GOES-16. A visual analysis of optical flow vectors clearly identifies hurricanes and large-scale atmospheric dynamics. Furthermore, we discuss challenges and open questions related to optical flow and temporal interpolation of multispectral geostationary satellite imagery

Estimation of Regional Surface Resistance to Evapotranspiration from NDVI and Thermal-IR AVHRR Data

Infrared surface temperatures from satellite sensors have been used to infer evaporation and soil moisture distribution over large areas. However, surface energy partitioning to latent versus sensible heat changes with surface vegetation cover and water availability. We tested a hypothesis that the relationship between surface temperature and canopy density is sensitive to seasonal changes in canopy resistance of conifer forests. Surface temperature (Ts) and canopy density were computed for a 20 × 25 km forested region in Montana, from the NOAA/AVHRR for 8 days during the summer of 1985. A forest ecosystem model, FOREST-BGC, simulated canopy resistance (Rc) for the same period. For all eight days. surface temperatures had high association with canopy density, measured as Normalized Difference Vegetation Index (NDVI) (R2 = 0.73 − 0.91), implying that latent heat exchange is the major cause of spatial variations in surface radiant temperatures. The slope of Ts and NDVI, σ, was sensitive to changes in canopy resistance on two contrasting days of canopy activity. The trajectory of σ followed seasonal changes in canopy resistance simulated by the model. The relationship found between σ and Rc (R2 = 0.92), was nonlinear, expected because Rc values beyond 20 s cm−1 do not influence energy partitioning significantly. The slope of Ts and NDVI, σ, could provide a useful parameterization of surface resistance in regional evapotranspiration research

LAND COVER CHARACTERIZATION USING MULTITEMPORAL RED, NEAR-IR, AND THERMAL-IR DATA FROM NOAA/AVHRR

A simple land cover classification scheme is proposed based on energy absorption and exchange properties of various land cover types, observable from remote sensing. Seasonal trajectories of the Normalized Difference Vegetation Index (NDVI) and surface temperature (Ts), routinely available from NOAA/AVHRR (National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer), are used to characterize different land cover types into four groups: water limited (shrub, grass), energy limited (wetlands, boreal forests, snow, ice, and water), atmospherically coupled (aerodynamically rough canopies, forests), and atmospherically decoupled (aerodynamically smooth canopies, crops). Further separation is achieved using growing-season average NDVI for shrub and grass, seasonal NDVI amplitude for deciduous vs. evergreen, and near-infrared (NIR) reflectance for broadleaf vs. needleleaf vegetation. The methodology using threshold-based rules is completely remote sensing based; classification rules are simple and easily modifiable. A first test of this logic over the continental United States, when compared with existing maps, showed that the methodology adequately captures the spatial distribution of various land cover types. The logic is also useful for monitoring seasonal dynamics of land cover, evapotranspiration, and disturbances due to fire, floods, insects/disease, and other anthropogenic processes. Future improvements needed to deal with mixed landscapes and global implementation details are discussed