Results for “sensor validation” — Page 12

NASA NTRS2001

Validation of Carbon Flux and Related Products for SIMBIOS: The CARIACO Continental Margin Time Series and the Orinoco River Plume

Between 1997 and 2000, this Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) investigation collected bio-optical measurements in the Southeastern Caribbean Sea and the tropical western Atlantic to help understand the color of coastal and continental shelf waters. Specifically, bio-optical data were collected to complement an oceanographic time series maintained within the Cariaco Basin, a site affected by seasonal coastal upwelling. Bio-optical data were also collected within the plume of the Orinoco River during seasonal extremes in discharge. This program focused on providing data to the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) and SIMBIOS Projects for validating SeaWiFS products. The data are unique in that they provide a substantial number of observations on repeated seasonal cycles for the SeaWIFS Bio-Optical Archive and Storage System (SeaBASS) bio-optical database. An important aspect of this SIMBIOS investigation was a focus on proper interpretation of ocean color remote sensing data from coastal and continental shelf environments. With this goal in mind, ocean color satellite data from a variety and locations and from different satellite sensors were examined to understand spatial and temporal variability in pigment concentrations, and also to conduct an in-depth study of current atmospheric correction and bio-optical algorithms.

Muller-Karger, Frank↗

NASA NTRS2018

Sensor Data Quality and Angular Rate Down-Selection Algorithms on SLS EM-1

The NASA Space Launch System Block 1 launch vehicle is equipped with an Inertial Navigation System (INS) and multiple Rate Gyro Assemblies (RGA) that are used in the Guidance, Navigation, and Control (GN&C) algorithms. The INS provides the inertial position, velocity, and attitude of the vehicle along with both angular rate and specific force measurements. Additionally, multiple sets of co-located rate gyros supply angular rate data. The collection of angular rate data, taken along the launch vehicle, is used to separate out vehicle motion from flexible body dynamics. Since the system architecture uses redundant sensors, the capability was developed to evaluate the health (or validity) of the independent measurements. A suite of Sensor Data Quality (SDQ) algorithms is responsible for assessing the angular rate data from the redundant sensors. When failures are detected, SDQ will take the appropriate action and disqualify or remove faulted sensors from forward processing. Additionally, the SDQ algorithms contain logic for down-selecting the angular rate data used by the GNC software from the set of healthy measurements. This paper explores the trades and analyses that were performed in selecting a set of robust fault-detection algorithms included in the GN&C flight software. These trades included both an assessment of hardware-provided health and status data as well as an evaluation of different algorithms based on time-to-detection, type of failures detected, and probability of detecting false positives. We then provide an overview of the algorithms used for both fault-detection and measurement down selection. We next discuss the role of trajectory design, flexible-body models, and vehicle response to off-nominal conditions in setting the detection thresholds. Lastly, we present lessons learned from software integration and hardware-in-the-loop testing.

Park, Thomas↗

NASA NTRS2018

Sensor Data Quality and Angular Rate Down-Selection Algorithms on SLS EM-1

The NASA Space Launch System Block 1 launch vehicle is equipped with an Inertial Navigation System (INS) and multiple Rate Gyro Assemblies (RGA) that are used in the Guidance, Navigation, and Control (GN&C) algorithms. The INS provides the inertial position, velocity, and attitude of the vehicle along with both angular rate and specific force measurements. Additionally, multiple sets of co-located rate gyros supply angular rate data. The collection of angular rate data, taken along the launch vehicle, is used to separate out vehicle motion from flexible body dynamics. Since the system architecture uses redundant sensors, the capability was developed to evaluate the health (or validity) of the independent measurements. A suite of Sensor Data Quality (SDQ) algorithms is responsible for assessing the angular rate data from the redundant sensors. When failures are detected, SDQ will take the appropriate action and disqualify or remove faulted sensors from forward processing. Additionally, the SDQ algorithms contain logic for down-selecting the angular rate data used by the GN&C software from the set of healthy measurements. This paper provides an overview of the algorithms used for both fault-detection and measurement down selection.

Park, Thomas↗

NASA NTRSDate not supplied

Optimal Sensor Mobility Design for Target Tracking with Distributed Sensing, Communication and Computing Infrastructure

The paper presents an airborne target tracking approach with data fusion from distributed stationary and mobile sensor network transmitted through a wireless/wired communication network and using a distributed computing infrastructure. To obtain high quality measurements, sensor flight platforms' positions and orientations are optimized with respect to the available target's position and velocity estimates, and a formation control strategy is applied to derive desired trajectories for the flight platforms. As sensors move, the communication network topology switching is determined using the worst-case approach to handle uncertainties in the target's and sensors' positions, which alters communication delays for sensors' data transmission to computing centers. An optimal data migration algorithm is periodically applied to determine these communication delays for each sensor and the optimal location with minimum end-to-end latency for the target tracking algorithm execution, which is based on adaptive information fusion from multi-modal mobile and stationary sensors inside an Extended Kalman Filter framework. The approach is validated in a desktop simulation environment using synthetic sensor data generated for a simulated target's flight.

Distributed sensing↗

NASA NTRS1988

DMSP-SSM/1 NASA algorithm validation using primarily LANDSAT and secondarily DMSP and/or AVHRR visible and thermal infrared satellite imagery

The approach to the DMSP SSMI (Defense Meteorological Satellite Program; Special Sensor Microwave Imager) sea-ice validation effort is to demonstrate a quantitative relationship between the SSMI-derived sea ice parameters and those same parameters derived from other data sets including visible and infrared satellite imagery, aerial photographic and high-resolution microwave aircraft imagery. The question to be addressed is to what accuracy (relative to these other observations) can the following ice parameters be determined: (1) position of the ice boundary; (2) total sea ice concentration; and (3) multiyear sea ice concentration. Specific tasks include: (1) a study of the interrelationship of surface information content and sensor spatial and spectral resolution in order to establish relationships between ice surface features and the manner in which they are expressed in the satellite observations; and (2) apply these relationships to map the sea ice features which can be used to evaluate NASA's proposed SSM/1 sea ice algorithms. Other key points to be addressed include the accuracy to which these parameters can be determined in different regions (marginal ice zone such as Bering Sea, Arctic ocean, such as Beaufort Sea); the accuracy of these parameters for different seasons; the accuracy of the algorithms weather filter under different weather conditions; and the effectiveness of the 85.5 GHz channels to locate the ice edge.

Steffen, K.↗

NASA NTRS2005

Meeting the Challenges of Exploration Systems: Health Management Technologies for Aerospace Systems With Emphasis on Propulsion

The constraints of future Exploration Missions will require unique Integrated System Health Management (ISHM) capabilities throughout the mission. An ambitious launch schedule, human-rating requirements, long quiescent periods, limited human access for repair or replacement, and long communication delays all require an ISHM system that can span distinct yet interdependent vehicle subsystems, anticipate failure states, provide autonomous remediation, and support the Exploration Mission from beginning to end. NASA Glenn Research Center has developed and applied health management system technologies to aerospace propulsion systems for almost two decades. Lessons learned from past activities help define the approach to proper ISHM development: sensor selection- identifies sensor sets required for accurate health assessment; data qualification and validation-ensures the integrity of measurement data from sensor to data system; fault detection and isolation-uses measurements in a component/subsystem context to detect faults and identify their point of origin; information fusion and diagnostic decision criteria-aligns data from similar and disparate sources in time and use that data to perform higher-level system diagnosis; and verification and validation-uses data, real or simulated, to provide variable exposure to the diagnostic system for faults that may only manifest themselves in actual implementation, as well as faults that are detectable via hardware testing. This presentation describes a framework for developing health management systems and highlights the health management research activities performed by the Controls and Dynamics Branch at the NASA Glenn Research Center. It illustrates how those activities contribute to the development of solutions for Integrated System Health Management.

Melcher, Kevin J.↗

NASA NTRS1998

An Operational Wake Vortex Sensor Using Pulsed Coherent Lidar

NASA and FAA initiated a program in 1994 to develop methods of setting spacings for landing aircraft by incorporating information on the real-time behavior of aircraft wake vortices. The current wake separation standards were developed in the 1970's when there was relatively light airport traffic and a logical break point by which to categorize aircraft. Today's continuum of aircraft sizes and increased airport packing densities have created a need for re-evaluation of wake separation standards. The goals of this effort are to ensure that separation standards are adequate for safety and to reduce aircraft spacing for higher airport capacity. Of particular interest are the different requirements for landing under visual flight conditions and instrument flight conditions. Over the years, greater spacings have been established for instrument flight than are allowed for visual flight conditions. Preliminary studies indicate that the airline industry would save considerable money and incur fewer passenger delays if a dynamic spacing system could reduce separations at major hubs during inclement weather to the levels routinely achieved under visual flight conditions. The sensor described herein may become part of this dynamic spacing system known as the "Aircraft VOrtex Spacing System" (AVOSS) that will interface with a future air traffic control system. AVOSS will use vortex behavioral models and short-term weather prediction models in order to predict vortex behavior sufficiently into the future to allow dynamic separation standards to be generated. The wake vortex sensor will periodically provide data to validate AVOSS predictions. Feasibility of measuring wake vortices using a lidar was first demonstrated using a continuous wave (CW) system from NASA Marshall Space Flight Sensor and tested at the Volpe National Transportation Systems Center's wake vortex test site at JFK International Airport. Other applications of CW lidar for wake vortex measurement have been made more recently, including a system developed by the MIT Lincoln Laboratory. This lidar has been used for detailed measurements of wake vortex velocities in support of wake vortex model validation. The first measurements of wake vortices using a pulsed, lidar were made by Coherent Technologies, Inc. (CTI) using a 2 micron solid-state, flashlamp-pumped system operating at 5 Hz. This system was first deployed at Denver's Stapleton Airport. Pulsed lidar has been selected as the baseline technology for an operational sensor due to its longer range capability.

Barker, Ben C., Jr.↗

NASA NTRS2004

Comparisons of Arctic In-Situ Snow and Ice Data with Airborne Passive Microwave Measurements

As part of the AMSR-E sea ice validation campaign in March 2003, aircraft flights over the Arctic sea ice were coordinated with ground measurements of snow and sea ice properties. The surface-based measurements were in the vicinity of Barrow, AK, and at a Navy ice camp located in the Beaufort Sea. The NASA P-3 aircraft was equipped with the NOAA ETL PSR microwave radiometer that has the same frequencies as the AMSR-E sensor. The goal was to validate the standard AMSR-E products ice temperature and snow depth on sea ice. Ground measurements are the only way to validate these parameters. The higher spatial resolution of the PSR instrument (between 30 and 500 m, depending on altitude) enables a better comparison between ground measurements and microwave data because of the expected smaller spatial variability. Maps of PSR data can then be used for further down-scaling to AMSR-E pixel areas. Initial results show a good qualitative agreement between the in-situ snow depths and the PSR data. Detailed studies are underway and latest results will be presented.

Markus, T.↗

NASA NTRS2018

Validation of MODIS 3 km Land Aerosol Optical Depth from NASA's EOS Terra and Aqua Missions

In addition to the standard resolution product (10km), the MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6 (C006) data release included a higher resolution (3km). Other than accommodations for the two different resolutions, the 10 and 3km Dark Target (DT) algorithms are basically the same. In this study, we perform global validation of the higher-resolution aerosol optical depth (AOD) over global land by comparing against AErosol RObotic NETwork (AERONET) measurements. The MODIS-AERONET collocated data sets consist of 161410 high-confidence AOD pairs from 2000 to 2015 for Terra MODIS and 2003 to 2015 for Aqua MODIS. We find that 62.5 and 68.4% of AODs retrieved from Terra MODIS and Aqua MODIS, respectively, fall within previously published expected error bounds of +/-(0.05+0.2 × AOD), with a high correlation (R = 0.87). The scatter is not random, but exhibits a mean positive bias of ∼ 0.06 for Terra and ∼ 0.03 for Aqua. These biases for the 3km product are approximately 0.03 larger than the biases found in similar validations of the 10km product. The validation results for the 3km product did not have a relationship to aerosol loading (i.e., true AOD), but did exhibit dependence on quality flags, region, viewing geometry, and aerosol spatial variability. Time series of global MODIS-AERONET differences show that validation is not static, but has changed over the course of both sensors' lifetimes, with Terra MODIS showing more change over time. The likely cause of the change of validation over time is sensor degradation, but changes in the distribution of AERONET stations and differences in the global aerosol system itself could be contributing to the temporal variability of validation.

Gupta, Pawan↗

NASA NTRS2014

Absolute Calibration of Optical Satellite Sensors Using Libya 4 Pseudo Invariant Calibration Site

The objective of this paper is to report the improvements in an empirical absolute calibration model developed at South Dakota State University using Libya 4 (+28.55 deg, +23.39 deg) pseudo invariant calibration site (PICS). The approach was based on use of the Terra MODIS as the radiometer to develop an absolute calibration model for the spectral channels covered by this instrument from visible to shortwave infrared. Earth Observing One (EO-1) Hyperion, with a spectral resolution of 10 nm, was used to extend the model to cover visible and near-infrared regions. A simple Bidirectional Reflectance Distribution function (BRDF) model was generated using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) observations over Libya 4 and the resulting model was validated with nadir data acquired from satellite sensors such as Aqua MODIS and Landsat 7 (L7) Enhanced Thematic Mapper (ETM+). The improvements in the absolute calibration model to account for the BRDF due to off-nadir measurements and annual variations in the atmosphere are summarized. BRDF models due to off-nadir viewing angles have been derived using the measurements from EO-1 Hyperion. In addition to L7 ETM+, measurements from other sensors such as Aqua MODIS, UK-2 Disaster Monitoring Constellation (DMC), ENVISAT Medium Resolution Imaging Spectrometer (MERIS) and Operational Land Imager (OLI) onboard Landsat 8 (L8), which was launched in February 2013, were employed to validate the model. These satellite sensors differ in terms of the width of their spectral bandpasses, overpass time, off-nadir-viewing capabilities, spatial resolution and temporal revisit time, etc. The results demonstrate that the proposed empirical calibration model has accuracy of the order of 3% with an uncertainty of about 2% for the sensors used in the study.

radiometric calibration↗

NASA NTRSDate not supplied

Reconstruction of Thermal Protection System Aeroheating using a Green’s Function Approach

Inverse heat transfer (IHT) techniques are often used to reconstruct the surface heating conditions on spacecraft thermal protection systems (TPS) during atmospheric entry. Current IHT techniques for entry spacecraft applications, however, demand substantial computational resources, and are impractical for analyses such as uncertainty quantification and real-time health monitoring. In this paper, a Green’s function sensor fusion approach is used to reconstruct the TPS surface aeroheating conditions on experimental spaceflight and ground test systems from collocated temperature and heat flux sensors embedded in the TPS. The algorithm leverages Green’s functions to model the heat conduction within the spacecraft TPS and stabilizes the recovery of the surface heating condition using the direct heat flux sensor measurement. The algorithm is validated using arc-jet ground test data and applied to the reconstruction of the Mars 2020 backshell heating during Martian atmospheric entry. The performance of the algorithm is benchmarked against a current state-of-the-art IHT framework, FIAT_Opt. The Green’s function-based reconstruction algorithm recovers the net hot-wall heat flux absorbed by the TPS and the incident heat flux from the atmospheric entry environment in close agreement with FIAT_Opt. Notably, computation of the surface heating condition is completed in three orders of magnitude less time with the Green’s function sensor fusion approach using a consumer-grade PC, versus with FIAT_Opt running on a high performance computer cluster. The efficiency of the algorithm is leveraged to compute the uncertainty contributions of input parameters to the total uncertainty in reconstructed Mars 2020 backshell heating for the full atmospheric entry heat pulse. The sensitivity analysis uncovers that, at different times throughout the entry heat pulse, uncertainties in the TPS specific heat, thermal conductivity, and emissivity are all dominant drivers of the reconstruction uncertainty. These results demonstrate Green’s functions and sensor-fusion techniques as promising IHT approaches to reconstruct atmospheric entry environments from TPS-embedded measurements, and highlight how these techniques may give access to post-flight analyses previously hindered by the prohibitive cost of current methods.

Kenneth McAfee↗

NASA NTRSDate not supplied

Reconstruction of Thermal Protection System Aeroheating using a Green’s Function Approach

Inverse heat transfer (IHT) techniques are often used to reconstruct the surface heating conditions on spacecraft thermal protection systems (TPS) during atmospheric entry. Current IHT techniques for entry spacecraft applications, however, demand substantial computational resources, and are impractical for analyses such as uncertainty quantification and real-time health monitoring. In this paper, a Green’s function sensor fusion approach is used to reconstruct the TPS surface aeroheating conditions on experimental spaceflight and ground test systems from collocated temperature and heat flux sensors embedded in the TPS. The algorithm leverages Green’s functions to model the heat conduction within the spacecraft TPS and stabilizes the recovery of the surface heating condition using the direct heat flux sensor measurement. The algorithm is validated using arc-jet ground test data and applied to the reconstruction of the Mars 2020 backshell heating during Martian atmospheric entry. The performance of the algorithm is benchmarked against a current state-of-the-art IHT framework, FIAT_Opt. The Green’s function-based reconstruction algorithm recovers the net hot-wall heat flux absorbed by the TPS and the incident heat flux from the atmospheric entry environment in close agreement with FIAT_Opt. Notably, computation of the surface heating condition is completed in three orders of magnitude less time with the Green’s function sensor fusion approach using a consumer-grade PC, versus with FIAT_Opt running on a high performance computer cluster. The efficiency of the algorithm is leveraged to compute the uncertainty contributions of input parameters to the total uncertainty in reconstructed Mars 2020 backshell heating for the full atmospheric entry heat pulse. The sensitivity analysis uncovers that, at different times throughout the entry heat pulse, uncertainties in the TPS specific heat, thermal conductivity, and emissivity are all dominant drivers of the reconstruction uncertainty. These results demonstrate Green’s functions and sensor-fusion techniques as promising IHT approaches to reconstruct atmospheric entry environments from TPS-embedded measurements, and highlight how these techniques may give access to post-flight analyses previously hindered by the prohibitive cost of current methods.

Kenneth McAfee↗

NASA NTRS2004

Results of CoSMIR Under-Flights of the SSMIS

The first copy of the SSMIS (Special Sensor Microwave/Imager/Sounder) was launched on board the DMSP (Defense Meteorological Satellite Project) F-16 satellite in October 2003. During March-April 2004, six 5-hour SSMIS under-flights were conducted with the CoSMIR on board the NASA ER-2 aircraft over the coastal region of California. CoSMIR has nine channels at the frequencies of 50.3, 52.8, 53.6, 91.665 (V and H polarization), 150, 183.3+/-1, 183.3+/-3, and 183.3+/-6.6 GHz. All except the two 91.665 GHz channels are horizontally polarized. The instrument was carefully calibrated with LN2 target in the laboratory before the flights. Three of the aircraft flights passed over Lakes Pyramid and Tahoe that could be used to validate the in-flight sensor calibration. Immediately after these flights, an inter-comparison of the calibrated SSMIS and CoSMIR brightness temperatures (T(sub b)) followed. The results showed that, for channels at frequencies > or equal to 91.665 GHz, the SSMIS and CoSMIR T(sub b) values tracked each other very well; for some channels there were some bias with magnitude generally less than 3-4 K (SSMIS values were higher). For the three 50-54 GHz channels, the SSMIS T(sub b) values were higher and frequency-dependent. For the least opaque channel at 50.3 GHz, the SSMIS T(sub b)'s over the ocean surface were higher than those of CoSMIR by more than 20 K under the clear-sky conditions. The most plausible explanation for this to happen is to assume that the 50-54 GHx channels of the SSMIS are vertically polarized. This assumption appears to be consistent with independent radiative transfer calculations. Attempts to estimate vertically polarized radiometric responses for 50-54 GHz channels of the SSMIS based on the CoSMIR observations are not plausible and results not reliable because of the highly variable ocean surface conditions (e.g., wind-induced emissivity changes). A conversion of the CoSMIR 50-54 GHz channels from horizontal to vertical polarization, and a subsequent repetition of the SSMIS under-flights are the right approach for the calibration/validation of the 50-54 GHz channels of the SSMIS. Details of the SSMIS-CoSMIR inter-comparison will be presented.

Wang, J. R.↗

NASA NTRS2001

SIMBIOS Project Data Processing and Analysis Results

The Sensor Intercomparison and Merger for Biological and Interdisciplinary Oceanic Studies (SIMBIOS) Project is concerned with ocean color satellite sensor data intercomparison and merger for biological and interdisciplinary studies of the global oceans. Imagery from different ocean color sensors can now be processed by a single software package using the same algorithms, adjusted by different sensor spectral characteristics, and the same ancillary meteorological and environmental data. This enables cross-comparison and validation of the data derived from satellite sensors and, consequently, creates continuity in ocean color information on both the temporal and spatial scale. The next step in this process is the integration of in situ ocean and atmospheric parameters to enable cross-validation and further refinement of the ocean color methodology. The SIMBIOS Project Office accomplishments during 2000 year are summarized under satellite data processing, data product validation, SeaWiFS Bio-Optical Archive and Storage System (SeaBASS) database, supporting services, sun photometers and calibration activities, and calibration round robins. These accomplishments are described.

Ainsworth, Ewa↗

NASA NTRS1991

Aircraft active and passive microwave validation of sea ice concentration from the Defense Meteorological Satellite Program special sensor microwave imager

Results are presented of a series of coordinate special sensor microwave imager (SSM/I) underflights that were carried out during March 1988 with NASA and Navy aircraft over portions of the Bering, Beaufort, and Chukchi seas. NASA DC-8 AMMR data from Bering Sea ice edge crossings were used to verify that the ice edge location, defined as the position of the initial ice bands encountered by the aircraft, corresponds to an SSM/I ice concentration of 15 percent. Direct comparison of SSM/I and aircraft ice concentrations for regions having at least 80 percent aircraft coverage reveals that the SSM/I total ice concentration is lower on average by 2.4 +/-2.4 percent. For multiyear ice, NASA and Navy flights across the Beaufort and Chukchi seas show that the SSM/I algorithm correctly maps the large-scale distribution of multiyear ice: the zone of first-year ice off the Alaskan coast, the large areas of mixed first-year and multiyear ice, and the region of predominantly multiyear ice north of the Canadian archipelago.

Cavalieri, D. J.↗

NASA NTRS2003

Assessment, Validation, and Refinement of the Atmospheric Correction Algorithm for the Ocean Color Sensors

The primary focus of this proposed research is for the atmospheric correction algorithm evaluation and development and satellite sensor calibration and characterization. It is well known that the atmospheric correction, which removes more than 90% of sensor-measured signals contributed from atmosphere in the visible, is the key procedure in the ocean color remote sensing (Gordon and Wang, 1994). The accuracy and effectiveness of the atmospheric correction directly affect the remotely retrieved ocean bio-optical products. On the other hand, for ocean color remote sensing, in order to obtain the required accuracy in the derived water-leaving signals from satellite measurements, an on-orbit vicarious calibration of the whole system, i.e., sensor and algorithms, is necessary. In addition, it is important to address issues of (i) cross-calibration of two or more sensors and (ii) in-orbit vicarious calibration of the sensor-atmosphere system. The goal of these researches is to develop methods for meaningful comparison and possible merging of data products from multiple ocean color missions. In the past year, much efforts have been on (a) understanding and correcting the artifacts appeared in the SeaWiFS-derived ocean and atmospheric produces; (b) developing an efficient method in generating the SeaWiFS aerosol lookup tables, (c) evaluating the effects of calibration error in the near-infrared (NIR) band to the atmospheric correction of the ocean color remote sensors, (d) comparing the aerosol correction algorithm using the singlescattering epsilon (the current SeaWiFS algorithm) vs. the multiple-scattering epsilon method, and (e) continuing on activities for the International Ocean-Color Coordinating Group (IOCCG) atmospheric correction working group. In this report, I will briefly present and discuss these and some other research activities.

Wang, Menghua↗

NASA NTRS1997

Wake Vortex Radar System Development: Overview

The objectives of the work are to: (1) Investigate microwave and millimeter wave sensors to locate, track, quantify, and observe the wake vortex hazard; (2) Develop and evaluate system concepts and designs using sensor system models and employing a theoretical reflectivity model for the wake vortex; (3) Test the validity of the theoretical model; (4) Acquire sensor systems and conduct field testing to evaluate; and to (5) Refine a system for field testing as a wake vortex sensor.

Neece, Robert T.↗

NASA NTRS2022

Estimating groundwater use and demand in arid Kenya through assimilation of satellite data and in-situ sensors with machine learning toward drought early action

Groundwater is an important source of water for people, livestock, and agriculture during drought in the Horn of Africa. In this work, areas of high groundwater use and demand in drought-prone Kenya were identified and forecasted prior to the dry season. Estimates of groundwater use were extended from a sentinel network of 69 in-situ sensored mechanical boreholes to the region with satellite data and a machine learning model. The sensors contributed 756 site-month observations from June 2017 to September 2021 for model building and validation at a density of approximately one sensor per 3700 sq.km. An ensemble of 19 parameterized algorithms was informed by features including satellite-derived precipitation, surface water availability, vegetation indices, hydrologic land surface modeling, and site characteristics to dichotomize high groundwater pump utilization. Three operational definitions of high demand on groundwater infrastructure were considered: 1) mechanical runtime of pumps greater than a quarter of a day (6+ hr) and daily per capita volume extractions indicative of 2) domestic water needs (35+ L), and 3) intermediate needs including livestock (75+ L). Gridded interpolation of localized groundwater use and demand was provided from 2017 to 2020 and forecasted for the 2021 dry season, June–September 2021. Cross-validated skill for contemporary estimates of daily pump runtime and daily volume extraction to meet domestic and intermediate water needs was 68%, 69%, and 75%, respectively. Forecasts were externally validated with an accuracy of at least 56%, 70%, or 72% for each groundwater use definition. The groundwater maps are accessible to stakeholders including the Kenya National Drought Management Authority (NDMA) and the Famine Early Warning Systems Network (FEWS NET). These maps represent the first operational spatially-explicit sub-seasonal to seasonal (S2S) estimates of groundwater use and demand in the literature. Knowledge of historical and forecasted groundwater use is anticipated to improve decision-making and resource allocation for a range of early warning early action applications.

Katie Fankhauser↗