Enhanced algorithm for estimating snow depth from geostationary satellites

Today most operational Earth observing satellites reside in low Earth orbits (LEO) at less than 1,000 km altitude, and in geostationary Earth orbits (GEO) at ~35,800 km altitude. These orbits have been the venues of choice for observations, albeit for very different reasons. LEO provides high spatial resolution with low temporal resolution while GEO provides for low spatial resolution, but high temporal resolution. NOAA utilizes both venues for their environmental satellites. The NOAA Polar-orbiting Operational Environmental Satellites (POES) reside in LEO Sun synchronous orbits at approximately 830 km in altitude, as do the Defense Meteorological Satellite Program (DMSP) satellites of the Department of Defense. In the near future the POES and DMSP satellites will be merged into a new satellite system referred to as the National Polar-orbiting Operational Environmental Satellite System (NPOESS). The NOAA Geostationary Operational Environmental Satellite (GOES) system, as the name specifies, resides at the other preferred observational venue of GEO. The Jet Propulsion Laboratory (JPL), under contract to NOAA, has been studying the characteristics of medium Earth orbits (MEO), at altitudes between 1000 and 35,800 km, as an observation venue to answer the question as to whether MEO might capture the attributes of the two traditional venues. This on-going study initially focused on determining the optimal altitude for MEO observations, through numerous trade studies involving altitude, instrument complexity, coverage, radiation environment, data temporality, revisit time, data rates, downlink requirements and other parameters including cost and launch complexity. Once the optimal altitude of 10,400 km had been determined the study proceeded to explore single through multiple MEO satellite constellation performance capabilities using two instrument types, a visible through infrared (IR) imager and IR sounder as the satellites’ payload. The MEO performance capabilities were compared to comparable LEO and GEO satellite constellation capabilities. This portion of the study concluded that indeed for global coverage a constellation of satellites operating in the MEO venue could capture the attributes of those operating in the LEO and GEO venues. Three 8-satellite constellations configurations - Walker, ICO, and Equatorial-Polar (EP) - then were studied to develop more constellation coverage statistics including robustness to individual satellite failure. That study phase concluded that the EP constellation was superior to both the ICO and Walker configurations. The study is presently examining if, and to what extent, the equatorial portion of the EP constellation might provide substantive supplemental data to that collected by the NPOESS and GOES satellite constellations.

Snow depth or snow water equivalent in mountainous region is crucial for hydrology, water resources management, meteorological and climate research. Remote sensing can be used for snow depth monitoring in regional scale or global scale. However, the spaceborne remote sensing of snow depth in mountain is challenging because of the sensor sensitivity and spatial resolution problems. Recently, time series Sentinel-1 is used for snow depth retrieval in mountains, which shows encouraging accuracy. In this study, an algorithm to estimate the snow depth in mountainous regions using optical and passive microwave remote sensing observations is proposed, which can be applied in periods before and after the launch of Sentinel-1. The optical and passive microwave remote sensing observations and the Sentinel-1 derived snow depth in 2016-2021 are used to train the snow depth retrieval algorithm using the Extreme gradient boosting (XGBoost) machine learning algorithm. Validations are performed using cross validation method and independent <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> snow depth data. The cross validation shows correlation coefficient of 0.81 and mean absolute error (MAE) of 0.17m. The correlation coefficient and MAE of predicted snow depth and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> snow depth in 2002-2016 are 0.61 and 0.33 m, respectively, which shows significant higher accuracy compared with AMSR-E/AMSR2 snow depth products. The site dependence of the machine learning method is also discussed. The machine learning based snow depth retrieval presented in this study can be applied to mountains globally to the optical and passive microwave remote sensing era.

Global navigation satellite system (GNSS) multipath reflectometry (MR) has emerged as a new technique that uses signals of opportunity broadcast by GNSS satellites and tracked by ground-based receivers to retrieve environmental variables such as snow depth. The technique is based on the simultaneous reception of direct or line-of-sight (LOS) transmissions and corresponding coherent surface reflections (non-LOS). Until recently, snow depth retrieval algorithms only used legacy and modernized GPS signals. Using multiple GNSS constellations for reflectometry would improve GNSS-MR applications by providing more observations from more satellites and independent signals (carrier frequencies and code modulations). We assess GPS and GLONASS for combined multi-GNSS-MR using simulations as well as field measurements. Synthetic observations for different signals indicated a lack of detectable interfrequency and intercode biases in GNSS-MR snow depth retrievals. Received signals from a GNSS station continuously operating in France for a two-winter period are used for experimental snow depth retrieval. We perform an internal validation of various GNSS signals against the proven GPS-L2-C signal, which was validated externally against in situ snow depth in previous studies. GLONASS observations required a more complex handling to account for topography because of its particular ground track repeatability. Signal intercomparison show an average correlation of 0.922 between different GPS snow depths and GPS-L2-CL, while GLONASS snow depth retrievals have an average correlation that exceeds 0.981. In terms of precision and accuracy, legacy GPS signals are worse, while GLONASS signals and modernized GPS signals are of comparable quality. Finally, we show how an optimal multi-GNSS combined daily snow depth time series can be formed employing variance factors with a ~59%-90% precision improvement compared to individual signal snow depth retrievals, resulting in snow depth retrieval with uncertainty of 1.3 cm. The developed combination strategy can also be applied for the European Galileo and the Chines BeiDou navigation systems.

Snow depth retrieval by using robust estimation algorithm to perform multi-SNR and multi-system fusion in GNSS-IR

Abstract. Lidar is an effective tool to measure snow depth over key watersheds across the United States. Lidar-derived snow depth observations from airborne platforms have demonstrated centimeter-level accuracy at high spatial resolution. However, ground-based and airborne lidar surveys are costly and limited in space and time. In recent years, there has been an emerging interest in using spaceborne lidar to estimate snow depth. Preliminary results from spaceborne lidar altimeters such as the NASA Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) can provide routine snow depth retrievals over watersheds, though further research on accuracy, coverage, and operational potential is needed. In this review, we outline the current status of research using spaceborne lidar to derive snow depth. We focus on the currently operational ICESat-2 mission, with a summary of snow observations gathered from previous studies. An example snow depth retrieval using ICESat-2 is also given over the Alaskan tundra. We also outline best practices for spaceborne lidar snow depth retrieval, based on findings from recent studies. We conclude with a discussion of ongoing challenges for spaceborne lidar, with suggestions for future studies and requirements for future mission concepts.

For more than four decades, geostationary weather satellites have been used successfully by NOAA's National Weather Service for daily weather forecasting and for tracking storm events. At the same time, polar orbit systems, such as NOAA/AVHRR, SeaWIFS and Landsat TM, have provided valuable information about slowly varying features and processes in the open ocean and on land. However, coastal and estuarine applications of remote sensing require significantly finer temporal, spatial, and spectral resolution than open ocean studies. Current polar orbit satellites lack the temporal resolution, while present geostationary orbit satellites have limited spatial and spectral resolution. Geostationary satellites can provide images of clouds, ocean and land surfaces very frequently, since they are positioned above a selected point on the equator and continuously view the same one third of the Earth. However, in order to have the same angular velocity as the Earth, they must be located in an orbit 23,000 miles above the equator, which limits their spatial resolution. NOAA, with support from NASA and the Navy, is developing GOES-R, a geostationary satellite. GOES-R will include a Hyperspectral Environmental Suite for Coastal Waters (HES-CW) and atmospheric sounding sensors which should provide the resolution levels required for coastal and estuarine observations. To achieve this, full use will be made of the fact that a geostationary platform offers a very high sampling frequency and long integration times, allowing for high signal-to-noise (S/N) ratios. Thus, useful trade-offs can be made between S/N, number of spectral bands, and sampling frequency, even though the spatial resolution is constrained to 150-300 m at the equator by system design considerations, such as mirror size, stability and pointing of the spacecraft. The objective of this paper is to illustrate coastal and estuarine resolution requirements for typical coastal features and processes such as: 1) chlorophyll concentration and coastal productivity, 2) harmful algal blooms and eutrophication, 3) current measurement by feature tracking, 4) oil slick drift and capture by estuarine fronts, 5) dynamic behavior of turbidity plumes and ocean-dumped waste, 6) propagation of internal waves on the continental shelf, 7) ice cover for coastal shipping, 8) coastal flooding and other catastrophic events, and 9) coastal ecosystem and wetland habitat changes. These resolution requirements are then compared to current GOES-R specifications.

An automated system has been developed for monitoring snow cover over South America. The system uses observations in the visible, middle infrared and infrared spectral bands from the Imager instrument onboard Geostationary Operational Environmental Satellite (GOES). Maps of snow cover are generated on a daily basis at a spatial resolution of 4 km. In the Letter, we present the results of snow cover monitoring during an 18-month period extending from May 2000 to November 2001. Seasonal changes of the snow-covered area are analysed. The extent of wintertime snow cover in South America was found to be lower than reported in earlier studies.

Arctic sea ice and snow affect the energy balance of the global climate system through the radiation budget. Accurate determination of the snow cover over Arctic sea ice is significant for the retrieval of the sea ice thickness (SIT). In this study, we developed a new snow depth retrieval method over Arctic sea ice with a long short-term memory (LSTM) deep learning algorithm based on Operation IceBridge (OIB) snow depth data and brightness temperature data of AMSR-2 passive microwave radiometers. We compared climatology products (modified W99 and AWI), altimeter products (Kwok) and microwave radiometer products (Bremen, Neural Network and LSTM). The climatology products and altimeter products are completely independent of the OIB data used for training, while microwave radiometer products are not completely independent of the OIB data. We also compared the SITs retrieved from the above different snow depth products based on Cryosat-2 radar altimeter data. First, the snow depth spatial patterns for all products are in broad agreement, but the temporal evolution patterns are distinct. Snow products of microwave radiometers, such as Bremen, Neural Network and LSTM snow depth products, show thicker snow in early winter with respect to the climatology snow depth products and the altimeter snow depth product, especially in the multiyear ice (MYI) region. In addition, the differences in all snow depth products are relatively large in the early winter and relatively small in spring. Compared with the OIB and IceBird observation data (April 2019), the snow depth retrieved by the LSTM algorithm is better than that retrieved by the other algorithms in terms of accuracy, with a correlation of 0.55 (0.90), a root mean square error (RMSE) of 0.06 m (0.05 m) and a mean absolute error (MAE) of 0.05 m (0.04 m). The spatial pattern and seasonal variation of the SITs retrieved from different snow depths are basically consistent. The total sea ice decreases first and then thickens as the seasons change. Compared with the OIB SIT in April 2019, the SIT retrieved by the LSTM snow depth is superior to that retrieved by the other SIT products in terms of accuracy, with the highest correlation of 0.46, the lowest RMSE of 0.59 m and the lowest MAE of 0.44 m. In general, it is promising to retrieve Arctic snow depth using the LSTM algorithm, but the retrieval of snow depth over MYI still needs to be verified with more measured data, especially in early winter.

Snow depth is a general input variable in many models of agriculture, hydrology, climate, and ecology. However, there are some uncertainties in the retrieval of snow depth by remote sensing. Errors occurred in snow depth evaluation under the D-InSAR methods will affect the accuracy of snow depth inversion to a certain extent. This study proposes a scheme to estimate spatial snow depth that combines remote sensing with site observation. On the one hand, this scheme adopts the Sentinel-1 C-band of the European Space Agency (ESA), making use of the two-pass method of differential interferometry for inversion of spatial snow depth. On the other hand, the 3DVAR (three dimensional variational) fusion algorithm is used to integrate actual snow depth data of virtual stations and real-world observation stations into the snow depth inversion results. Thus, the accuracy of snow inversion will be improved. This scheme is applied in the study area of Bayanbulak Basin, which is located in the central hinterland of Tianshan Mountains in Xinjiang, China. Observation data from stations in different altitudes are selected to test the fusion method. According to the results, most of the obtained snow depth values using interferometry are lower than the observed ones. However, after the fusion using the 3DVAR algorithm, the snow depth accuracy is slightly higher than it was in the inversion results (R2 = 0.31 vs. R2 = 0.50, RMSE = 2.51 cm vs. RMSE = 1.96 cm; R2 = 0.27 vs. R2 = 0.46, RMSE = 4.04 cm vs. RMSE = 3.65 cm). When compared with the inversion results, the relative error (RE) improved by 6.97% and 3.59%, respectively. This study shows that the scheme can effectively improve the accuracy of regional snow depth estimation. Therefore, its future application is of great potential.

International comparison of satellite winds — An update

For operational weather forecasting and nowcasting, a refresh rate (RR) of 10 min and a better-than-4-km footprint size for Infrared (IR) bands is desired. Such imaging capability is only available from geostationary orbit (GEO) satellites, such as ABI onboard geostationary operational environmental satellites (GOES)-16/-17, but mainly limited to the tropics and mid-latitudes (referred to as GEO-like imaging capability). For high latitudes such as the Alaskan region, the IR footprint size from ABI/GOES-17 is worse than 6 km, limiting the application over the region. Tundra satellites, with a nonzero inclination angle and a nonzero eccentricity, have longer dwell times near the apogee than the perigee and can be used to monitor the high latitudes and polar regions. This study investigates Tundra satellites’ imaging capability by assuming an ABI-like instrument with the same IR spatial resolution of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$56~\mu $ </tex-math></inline-formula> rad onboard. For regional applications, a constellation of two Tundra satellites may provide GEO-like imaging capability for a large domain. This useful domain is further improved when combined with a GEO, i.e., two <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Tundra + GOES-17$ </tex-math></inline-formula> . For global applications, a constellation of three Tundra satellites may provide GEO-like imaging capability for high latitudes (improved capability over GEO) and polar regions (unprecedented capability) in both hemispheres. The additional capability from Tundra satellites in tropics and mid-latitudes makes the global space-based meteorological observing system more robust and resilient. While a constellation of three <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$Tundra + 3$ </tex-math></inline-formula> GEOs may provide global GEO-like imaging capability, having more than three GEOs may be desired by agencies/countries, allowing for improved spatial resolutions over their sub-point locations.

This article provides a description of a land surface temperature (LST) data set generated (and provided in near-real-time or offline) based on infrared data from sensors onboard different geostationary (GEO) satellites: Meteosat Second Generation (MSG), Geostationary Operational Environmental Satellite (GOES), and Multifunction Transport Satellite (MTSAT). Given the different characteristics of the imagers onboard each GEO platform, different algorithmic methodologies for the retrieval of LST are presented and implemented – namely the Generalized Split-Window (GSW) algorithm and the Dual Algorithm (DA) in its mono- and dual-channel forms – using semi-empirical functions that relate LST to top-of-atmosphere brightness temperatures in infrared window channels. The assumptions and physics underlying each methodology, as well as the uncertainties of LST estimates, are discussed. The formulations are trained using a data set of radiative transfer simulations for a wide range of atmospheric and surface conditions. The performance of each algorithm is then assessed by comparing its output against an independent set of simulations, suggesting that product uncertainties range from 2°C (for GSW and the two-channel algorithm) to 4°C (for the one-channel algorithm). Finally, LST retrievals from different GEO satellites are merged into a single field. In overlapping areas, the average discrepancies between LST products derived from GOES and from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard MSG are within 1°C during night-time, but may reach 3°C during daytime. Over those areas, the merged LST field is obtained as a weighted average of available LST retrievals for the same time slot, taking into account the respective error bar.

The United States is embarking on the implementation of an Integrated Ocean Observing System (IOOS), as part of a broader global environmental observation effort, in order to increase the understanding of the ocean environment to derive societal benefits. These benefits will be achieved by making timely, accurate observations of the environment leading to quality predictions and forecasts of the environment and the consequences on the public. To this end, IOOS will consist of an observing subsystem, a modeling and analysis subsystem, and a data management and communications subsystem that ties it all together. The observing subsystem will provide sustained in situ and remotely-sensed measurements of the coastal and global ocean. Initially, IOOS will derive observations from existing operational and pre-operational programs and subsequently incorporate observations from novel programs as they attain operational status. The Geostationary Operational Environmental Satellite (GOES) program, and its follow-on, GOES-R, will play a key role in IOOS. The IOOS Development Plan calls for the GOES program to support the coastal and global components of the observing subsystem. Coordination of technology and applications across the various international geostationary satellite programs (GOES, MTSAT, METEOSAT, INSAT) will achieve a truly global observation system. In the near-term, GOES will be able to provide global and coastal zone sea surface temperature, sea ice, and heat flux observations that will impact some of the IOOS societal goals. As GOES-R comes online in the next decade, the planned sensor suite will permit the measurement of a variety of new core parameters including ocean color, optical properties, surface currents, and possibly bathymetry. Observing those new parameters in a frequency, spatial extent, and spatial resolution never before achieved will open up a new horizon of potential impacts on the societal benefits of IOOS

A proposed instrument for deployment on next-generation Geostationary Operational Environmental Satellite (GOES) platforms is the Geostationary Synthetic Thinned Aperture Radiometer (GeoSTAR). A high-resolution full-Earth disk model has been developed to aid in the design of the instrument and to characterize sensor performance. A number of ancillary geophysical data fields are used as inputs into a radiative-transfer model that also accounts for the propagation and viewing geometries from a geostationary Earth orbit (GEO). The model produces high-resolution (10 km times 10 km) simulated full-Earth disk microwave images from GEO. The model is used as a tool to examine several critical aspects of GeoSTAR performance and design. Differential image processing is assessed as a means of mitigating the effects of the Gibbs phenomenon; its performance is found to be excellent, even with nonideal a priori information. The spatial resolution and precision of images generated at 50 GHz are evaluated. The magnitude of the highest spatial-frequency components sampled by GeoSTAR is found to be well above its minimum detectable signal. However, the differential image processing removes most of the high-frequency content, which is due to static high-contrast boundaries in the scene. Most of the residual high-frequency content lies at or below the instrument noise floor.

The goal of this study is to understand the pattern of snow distribution over mountain ranges and the capability of L-band Synthetic Aperture Radar (SAR) data to retrieve snow depth. Ground-based snow records and Airborne Lidar and SAR data collected as part of NASA's snow expedition over Mores Creek Summit in 2021 were employed for this study. The preliminary result shows that co-polarization particularly VV has better coherence and thus most optimal for snow monitoring. The impact of large temporal baseline, vegetation and elevation on coherence were analyzed. Result shows that decorrelation increases with vegetation and temporal separation as expected but decreases with elevation. A good agreement exists between lidar snow depth and snow depth recorded by SNOTEL. Snow depth retrieved from the UAVSAR data captured snow accumulation and melt pattern between the satellite acquisition dates as confirmed by the snow depth record at SNOTEL study site. Atmospheric correction of the phase change is required to improve the accuracy of InSAR techniques for snow depth estimation. This study will contribute to existing efforts in the snow science community to understand the capability of future satellite missions such as NISAR, a U.S-Indian satellite that is planned to operate on L-band.