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Nemani, Ramakrishna R.

Publications and source records attributed to Nemani, Ramakrishna R..

At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Earth Observations from a New Generation of 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 widely used polar orbiting sensors such as EOS/MODIS. More importantly, they provide observations at 1-5-15 minute intervals, instead of twice a day from MODIS, offering unprecedented opportunities for monitoring large parts of the Earth. In addition to serving the needs of weather forecasting, these observations offer new and exciting opportunities in managing solar power, fighting wildfires, and tracking air pollution. Creation of actionable information in near real-time from these data streams is a challenge that is best addressed through collaborative efforts among the industry, academia and government agencies.

Nemani, Ramakrishna R.↗
NASA NTRS2019

Surface Reflectance Product from Geostationary Satellite

We have generated provisional Himawari-8 AHI surface reflectance (SR) product for land and vegetation monitoring. The Himawari-8 AHI surface reflectance product is part of our GeoNEX land products, which integrate level 2 and higher remote sensing data from a set of geostationary satellite sensors (i.e. GOES-16, -17 ABI, Himawari-8 AHI, FY4-A AGRI, and MTG-I). Adapted Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm is used to process time series Himawari-8 AHI observations. Himawari-8 AHI SR provides gridded and tiled land SR in 1-km resolution with high frequency (every 10 minutes during daylight time). There are three subdatasets: 1) retrieved atmospheric properties (e.g. column water vapor at 0.86 m, aerosol optical depth at 0.47m and 0.51m); 2) spectral (AHI bands 1-6) surface reflectance, kernels of RTLS BRDF model; 3)spectral BRDF kernel weights, and extensive quality assurance flags. The evaluation results show that Himawari-8 AHI data yield much more valid pixels in a single day in the characterization of land surface, when compare to NASA flagship satellite MODIS Terra/Aqua. This observation frequency and resolution of geostationary data should allow for using continuous ecosystem monitoring in diurnal studies at continental scale. Initial evaluations indicate a stable Himawari-8 AHI land SR product.

Li, Shuang↗
NASA NTRS2019

Generating Accurate and Consistent Top-Of-Atmosphere Reflectance Products from the New Generation Geostationary Satellite Sensors

GeoNEX is a collaborative project by scientists from NASA, NOAA, JAXA, and other organizations around the world with the purpose of generating a suite of Earth-monitoring products using data streams from the latest geostationary (GEO) sensors including the GOES-16/17 ABI and the Himawari-8/9 AHI. An accurate and consistent top-of-atmosphere (TOA) reflectance product, in particular the bidirectional reflectance factor (BRF), is the starting point in the scientific processing chain. We describe the main considerations and corresponding algorithms in generating the GeoNEX TOA BRF product. First, a special advantage of geostationary data streams is their high temporal resolution (~10 minutes per full-disk scan), providing a key source of information for many downstream products. To fully utilize this high temporal frequency demands a high georegistration accuracy for every acquired image. Our analysis shows that there can be substantial georegistration uncertainties in both GOES and Himawari L1b data which we addressed by implementing a phase-based correction algorithm to remove residual errors. Second, geostationary sensors have distinct illumination-view geometry features in that the solar angle changes for every pixel. Therefore, to accurately derive a BRF requires a solar position algorithm and the estimation of the pixel-wise acquisition time within an uncertainty of 10 seconds. Third, we discuss the measures we adopted to check and correct residual radiometric calibration issues of individual sensors to enable time-series analysis as well as the cross calibration between different satellite sensors (including those from low-Earth orbit). Finally, we also explain the rationale for the choice of the global grid/tile system of the GeoNEX TOA BRF product.

Wang, Weile↗
NASA NTRS2019

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.

Optical Flow↗
NASA NTRS2019

GeoNEX: A Cloud Gateway for Near Real-time Processing of Geostationary Satellite Products

The emergence of a new generation of geostationary satellite sensors provides land andatmosphere monitoring capabilities similar to MODIS and VIIRS with far greater temporal resolution (5-15 minutes). However, processing such large volume, highly dynamic datasets requires computing capabilities that (1) better support data access and knowledge discovery for scientists; (2) provide resources to enable real-time processing for emergency response (wildfire, smoke, dust, etc.); and (3) provide reliable and scalable services for the broader user community. This paper presents an implementation of GeoNEX (Geostationary NASA-NOAA Earth Exchange) services that integrate scientific algorithms with Amazon Web Services (AWS) to provide near realtime monitoring (~5 minute latency) capability in a hybrid cloud-computing environment. It offers a user-friendly, manageable and extendable interface and benefits from the scalability provided by Amazon Web Services. Four use cases are presented to illustrate how to (1) search and access geostationary data; (2) configure computing infrastructure to enable near real-time processing; (3) disseminate and utilize research results, visualizations, and animations to concurrent users; and (4) use a Jupyter Notebook-like interface for data exploration and rapid prototyping. As an example of (3), the Wildfire Automated Biomass Burning Algorithm (WF_ABBA) was implemented on GOES-16 and -17 data to produce an active fire map every 5 minutes over the conterminous US. Details of the implementation strategies, architectures, and challenges of the use cases are discussed.

GeoNEX↗
NASA NTRS2019

Deep Learning Emulation of Atmospheric Correction for Geostationary Sensors

New generation geostationary satellites make reflectance observations available at a continental scale with unprecedented spatiotemporal resolution and spectral range. Generating Earth monitoring products from these observations requires retrieval of the basic parameter, surface reflectance (SR), by atmospheric correction (AC). Algorithms for atmospheric correction, including Multi-Angle Implementation of Atmospheric Correction (MAIAC), are adapted for each sensor and are too computationally complex to be run in real time, relying instead on look-up tables with precomputed values. Machine learning methods, including convolutional neural networks, have demonstrated performance in learning complex, nonlinear mappings and extracting insight from high-dimensional remote sensing data. In this work, we present a deep learning emulator of MAIAC to retrieve both SR and cloud products. Using this adaptation of deep learning-based emulation to remote sensing, we demonstrate stable SR retrieval over a variety of land covers and viewing conditions and accurate cloud detection. Further, a comparison of computation time suggests emulation as a compelling alternative for expensive physical simulation, especially for applications benefited by near-real time data, such as agricultural management and disaster response.

Duffy, Kate↗
NASA NTRS2019

GEONEX Data Products: Geostationary Satellite Derived Land Surface and Atmospheric Public Data Products

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 will begin to produce provisional data products to be consumed by external collaborators and the academic community. In order to better inform and introduce the GEONEX products to the science community, the provisional products shall be distributed from the NAS data portal, located at data.nas.nasa.gov, and simple webpage at www.nasa.gov/geonex has been deployed, which describes any algorithms used in deriving the products, user manuals and data file information. We will also update the status of the data processing, on the website and provide links to the latest datasets, and use a geonex mailing list, using lists.nasa.gov.

Wang, Weile↗
NASA NTRS2019

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.

Earth↗
NASA NTRS2019

Earth Observations from a New Generation of 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 widely used polar orbiting sensors such as EOS/MODIS. More importantly, they provide observations at 1-5-15 minute intervals, instead of twice a day from MODIS, offering unprecedented opportunities for monitoring large parts of the Earth. In addition to serving the needs of weather forecasting, these observations offer new and exciting opportunities in managing solar power, fighting wildfires, and tracking air pollution. Creation of actionable information in near real-time from these data streams is a challenge that is best addressed through collaborative efforts among the industry, academia and government agencies.

Nemani, Ramakrishna R.↗
NASA NTRS2019

Geonex: Land Surface Monitoring from a New Generation of Geostationary Sensors

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.

Nemani, Ramakrishna R.↗
NASA NTRS2018

NASA Earth eXchange (NEX) App Store

NASA Earth Exchange (NEX), and her public cloud version OpenNEX, have become platforms supporting scientific collaboration, knowledge sharing and research for the entire Earth science community. To date, a number of custom tools and capabilities have been integrated into the platforms. However, such integration has to undergo a case-by-case manual process thus lacks scalability. This timely project builds an App Store onto OpenNEX as a building block. Climate data analytics tools/programs can be easily uploaded, shared, organized, searched, and recommended like photos and videos on the YouTube. The foundation of our App Store is a provenance server, which not only records metadata but also execution history of climate data analytics apps including the input data and parameters, output data and products, who runs the app for which purpose, and how apps may be chained into workflows. Researchers can thus understand, reproduce, and repurpose existing apps and workflows. Machine learning approaches are applied to mine provenance to provide recommend-as-you-go services for Earth scientists, such as to recommend suitable apps and workflow snippets. A browser-based workflow tool is also provided for researchers to explore the provenance server and design value-added workflows. Scalability, sustainability, extensibility, usability, adaptability, security and privacy are considered in the App Store.

eXchange↗
NASA NTRS2018

GEONEX: Challenges in Producing MODIS-Like Land Products from a New Generation of Geostationary Sensors

The new generation geostationary (GEO) remote sensors (GOES-R ABI, Himawari AHI, and FY4 AGRI) provide high frequency (5-15 minute) observations spatially/spectrally similar to MODIS/VIIRS for land monitoring. These new features of GEO satellite sensors make producing MODIS like land products for terrestrial monitoring possible. The NASA Earth Exchange (NEX) team developed the GEONEX pipeline that is containerized, deployable on NASA Pleiades supercomputer as well as public cloud platforms (e.g. AWS). The processing pipeline is designed to take Himawari Standard Data (HSD) and GOES-16 L1b to generate surface reflectance (SR) and other high-level land remote sensing products. In order to produce low-Earth-orbiting (LEO) remote sensing compatible land products, inter-comparison between Himawari AHI and MODIS Terra/Aqua has been conducted in this research work. Comparisons of TOA reflectance and surface reflectance between AHI and Terra/Aqua are presented. Ray-Matching method was used to locate the co-located pixels, where GEO and LEO sensors look at the land target with similar Viewing Zenith Angle (VZA) and Viewing Azimuth Angle (VAA) simultaneously. Here, we address challenges associated with the selection of qualified pixels of similar solar illumination condition and atmosphere path. We used strict criterion to constrain the pixel selection: the time difference between GEO and LEO observations is less than +-2.5 mins, the cosine of VZA difference is less than 1%, and the VAA difference is less than 10 deg. We also discuss the strong radiometric consistency that the new generation GEO sensors along with the popular LEO sensors would benefit the environmental remote sensing community.

Li, Shuang↗
NASA NTRS2018

Land Surface Reflectances from Geostationary Sensors

GEONEX is a processing pipeline that produces a suite of satellite land surface products using data streams from the latest geostationary (GEO) sensors including the GOES016/ABI and the Himawari-8/AHI. The suite, created collaboratively by scientists from NASA and NOAA, includes top-of-atmosphere (TOA) reflectances, land surface reflectances (LSRs), vegetation indices, LAI/fPAR, and other downstream products. As a key component of the GEONEX product processing, we have adapted the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to produce LSRs from the TOA data. Because the algorithm depends on building "stacks" of images, we first run internal geo-registration checks to ensure geo-spatial accuracy and consistency of the input (L1B) data before transferring them from the geostationary projection into a tile system in geographic grids. Scan-time is inferred from metadata and applied to calculate the sun-sensor angles for each grid cell. The MAIAC algorithm is run to detect clouds/shadows, estimate aerosol optical thickness (AOT), perform atmospheric corrections, and generate LSRs. We have processed 18-months (from 2016/04 onward) of AHI data over East Asia and Oceania at a 10-minute time step and 10-months (from 2018/01 onward) of ABI data over North and South Americas at a 15-minute time step. As a verification measure, we compare the GEONEX (AHI/ABI) surface reflectances with the standard MODIS products (MOD09GA) and the MODIS MAIAC products over pixels that have similar sun-view geometries. The results indicate general linear relationships between GEONEX and corresponding MODIS LSRs. In particular, the RMSEs between GEONEX and MOD09 data are comparable to those between MOD09 and MODIS MAIAC products, suggesting that the uncertainties of GEONEX LSRs fall into an acceptable range. However, direct comparisons of LSRs over pixels with different sun-view angles are not as straightforward and require more modeling efforts to correct the directional effects. Evaluation of such angular influences on the downstream products (e.g., vegetation indices) is also under investigation.

Geostationary satellite; Remote Sensing; Atmospher↗
NASA NTRS2018

Multi-Sensor Integration of Vegetation Index Products for Long-Term Monitoring of Vegetation Dynamics: A Case Study from MODIS to VIIRS

Spectral vegetation index (VI) time series data from moderate resolution sensors, such as Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), have widely been used to identify "hot spot" areas of vegetation changes and to characterize long-term trends of vegetation changes. The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor series of the Joint Polar Satellite System (JPSS) program is slated to continue the highly calibrated data stream initiated with MODIS. The first VIIRS sensor has operationally been acquiring Earth-reflected radiation since March 2012. The second VIIRS sensor (NOAA-20) was launched in November 2017, which is to continue the measurements made by the first VIIRS sensor. In this study, we examined an integrated use of MODIS and VIIRS VI time series data on capturing vegetation dynamics in the Asia-Pacific region using their overlapped period of observations (2013-2017). Three VIs, the "top-of-canopy (TOC)" normalized difference vegetation index (NDVI), TOC enhanced vegetation index (EVI), and TOC two-band enhanced vegetation index (EVI2), were investigated. For all the three VIs, VIIRS VIs were systematically higher than the MODIS counterparts due mainly to their spectral bandpass differences. However, both VIIRS and MODIS VIs showed the comparable spatial patterns in their temporal variations. Empirical spectral corrections allowed to merge the two data streams, which slightly improved the temporal resolution of the VI temporal profiles. These results suggest the suitability of VIIRS data to extend and merge into the MODIS VI record for long-term vegetation dynamics studies.

MODIS↗
NASA NTRS2018

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.

Geostationary↗
NASA NTRS2018

GEONEX: Webpage to Display NASA-NOAA Collaboration of Producing Land Surface Products from Geostationary Sensors

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.In order to better introduce the GEONEX products to the science community, we set up a simple webpage (www.geonex.org) to describe the background and the motivation of the project, the algorithms used in deriving the products, and user manuals to the data files. We will also update the status of the data processing, in particular the near-real-time products, on the website and provide links (in text or json files) to the latest datasets.

geostationary↗
NASA NTRS2017

H31G-1596: DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remote sensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud shadow mask from geostationary satellite data is critical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds,which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classify cloudshadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoderdecoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multispectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

GOES-1↗