Evaluating GOES-16 ABI surface brightness temperature observation biases over the central Sierra Nevada of California

Pestana, Steven; Lundquist, Jessica D.

doi:10.1016/j.rse.2022.113221

Cited by 8 publications

(14 citation statements)

References 63 publications

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“…Our evaluation results were somewhat impacted by the geolocation errors of the DSR products [102]. However, our primary conclusions persist because of the relatively homogeneous values of the clear-sky DSR products (Fig.…”

Section: Additional Issues and Future Studymentioning

confidence: 64%

“…In addition to the accuracies of the DSR products being dependent, aspect-dependent, and time-dependent (Sections IV.C and IV.D), the result of our study indicates that additional issues may be introduced into DSR products when topographic effects are ignored, and our results highlight the necessity of integrating topographic consideration into DSR generation. For future updates of the DSR products, we recommend incorporating two key points relating to topography: (i) the suntarget-sensor geometry changes in complex terrain, so the impact of the topography on satellite observations should be considered [86,102]; and (ii) the mountains typically experience high cloud dynamics [104], so integrating topographic consideration under cloudy sky conditions is necessary (Fig. D1 in Appendix D and Ma, et al [37]).…”

Section: Additional Issues and Future Studymentioning

confidence: 99%

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Evaluating Topographic Effects on Kilometer-Scale Satellite Downward Shortwave Radiation Products: A Case Study in Mid-Latitude Mountains

Ma,

He,

Aguilar

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Downward shortwave radiation (DSR) is critical to many surface processes, and many satellite-derived DSR products have been released. Few studies have validated DSR over mountains where it is highly heterogeneous and so the shortwave flux measured at ground stations does not match kilometer-scale DSR products. To tackle this challenge, we used a high spatial resolution (30 m) daily DSR over Sierra Nevada, Spain for 2008-2015, and a mountainous radiative transfer model to explore how topographic effects impacted the performances of DSR products. Four widely-used satellite products were selected as proxies for our evaluation: (i) MCD18A1 V6.1 (with a spatial resolution of 1 km); (ii) MSG DSR (~ 3.3 km); (iii) GLASS DSR V42 (0.05°); and(iv) BESS DSR (0.05°). There are three main findings under clear skies. Firstly, the product accuracies were slope-dependent, decreasing by 59.8-134.6% with slope ≥ 25° compared to areas with slope < 10°. Secondly, the product accuracies were aspectdependent, exhibiting a higher degree of overestimation (i.e., average of 27.6 W/m² ) on the north side and underestimation (i.e., average of -1.3 W/m² ) on the south side. Thirdly, and finally, the product accuracies were time-dependent, exhibiting seasonal variations and pronounced overestimation in summer (i.e., 8.8 to 18.2 W/m² ). Moreover, the impact of topography decreased with increasing cloud cover. Our findings can be applied to various mountainous areas due to the same mechanism of how topography influences the DSR estimation. This study corroborates the substantial uncertainties of the current DSR products in mountains and the necessity of incorporating topographic information into DSR estimations.

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Section: Additional Issues and Future Studymentioning

confidence: 64%

Section: Additional Issues and Future Studymentioning

confidence: 99%

Evaluating Topographic Effects on Kilometer-Scale Satellite Downward Shortwave Radiation Products: A Case Study in Mid-Latitude Mountains

Ma,

He,

Aguilar

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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“…Code is available on Google Colab at: https://colab.research.google.com/drive/1lgyPhYVXr4MffWnN7m-5Bo3Lt5f4f49D?usp=sharing Data are currently available at https://portals.edirepository.org/nis/mapbrowse?packageid=edi.1420.1 Figure 2: A schematic showing how off-nadir view angles can impact the projected locations of elevated surface targets 37 . Figure 3: Ameriflux and NEON, Inc. eddy covariance sites with available GOES-16 L2 bidirectional reflectance factor (BRF) products as black dots and those without available data as red dots, produced in Google Earth Engine 44 .…”

Section: Code Availabilitymentioning

confidence: 99%

“…Figure1: GOES-R ABI pixel outlines and areas (in km/km 2 ) at increasing VZA northeast of nadir (in degrees). Figure2:A schematic showing how off-nadir view angles can impact the projected locations of elevated surface targets37 . Figure3: Ameriflux and NEON, Inc. eddy covariance sites with available GOES-16 L2 bidirectional reflectance factor (BRF) products as black dots and those without available data as red dots, produced in Google Earth Engine44 .…”

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confidence: 99%

GOES-R land surface products at Western Hemisphere eddy covariance tower locations

Losos¹,

Hoffman²,

Stoy³

2023

Preprint

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The terrestrial carbon cycle varies dynamically over short periods that can be difficult to observe. Geostationary (“weather”) satellites like the Geostationary Environmental Operational Satellite - R Series (GOES-R) deliver near-hemispheric imagery at a ten-minute cadence, and its Advanced Baseline Imager (ABI) measures visible and near-infrared spectral bands that can be used to estimate land surface properties and carbon dioxide flux. GOES-R data are designed for real-time dissemination and are difficult to link with eddy covariance time series of land-atmosphere carbon dioxide exchange. We compiled time series of GOES-R land surface attributes including visible and near-infrared reflectances, land surface temperature, and downwelling shortwave radiation (DSR) at 314 ABI fixed grid pixels containing eddy covariance towers. We demonstrate how to best combine satellite and in-situ datasets, and show how ABI attributes useful for carbon cycle science vary across space and time. By connecting observation networks that infer rapid changes to the carbon cycle, we can gain a richer understanding of the processes that control it.

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“…The Geostationary Operational Environmental Satellites (GOES)-R, which is operated by the National Oceanic and Atmospheric Administration (NOAA), can provide global diurnal observations [22]. The LST product retrieved from GOES-16 Advanced Baseline Imager (ABI) can provide 10 km, hourly LST over North and South America, and 2 km, hourly data covering the contiguous US (CONUS) and Mexico [23]. However, due to frequent cloud cover, there always exists a large amount of data loss in the ABI LST product.…”

mentioning

confidence: 99%

Generation of 100-m, Hourly Land Surface Temperature Based on Spatio-Temporal Fusion

Tang,

Wang,

Tong

et al. 2024

IEEE Trans. Geosci. Remote Sensing

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Landsat surface temperature (LST) is an important physical quantity for global climate change monitoring. Over the past decades, several LST products have been produced by satellite thermal infrared (TIR) bands or land surface models (LSMs). Recent research has increased the spatio-temporal resolution of LST products to 2 km, hourly based on Geostationary Operational Environmental Satellites (GOES)-R Advanced Baseline Imager (ABI) LST data. The spatial resolution of 2 km, however, is insufficient for monitoring at the regional scale. This paper investigates the feasibility of applying spatio-temporal fusion to generate reliable 100 m, hourly LST data based on fusion of the newly released 2 km, hourly GOES-16 ABI LST and 100 m Landsat LST data. The most accurate fusion method was identified through a comparison between several popular methods. Furthermore, a comprehensive comparison was performed between fusion (with Landsat LST) involving satellite-derived LST (i.e., GOES) and model-derived LSMs (i.e., European Centre for Medium-range Weather Forecasts (ECMWF) Reanalysis v.5 (ERA5)-Land). The spatial and temporal adaptive reflectance fusion model (STARFM) method was demonstrated to be an appropriate method to generate 100 m, hourly data, which produced an average root mean square error (RMSE) of 2.640 K, mean absolute error (MAE) of 2.159 K and average coefficient of determination (R 2 ) of 0.982 referring to the in situ time-series. Furthermore, inheriting the advantages of direct observation, and the fusion of Landsat and GOES for the generation of 100 m, hourly LST produced greater accuracy compared to the fusion of Landsat and ERA5-Land LST in the experiments. The generated 100 m, hourly LST can provide important diurnal data with fine spatial resolution for various monitoring applications.  Index Terms-Land surface temperature (LST); Landsat; GOES; ERA5; spatio-temporal fusion. I. INTRODUCTION Land surface temperature (LST), as an important parameter in the energy exchange between the land surface and atmosphere, has been researched extensively in recent years [1]-[3]. LST is central to many applications including mapping the urban heat island effect [4], [5], forest fire monitoring [6], [7] and drought Manuscript

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Evaluating GOES-16 ABI surface brightness temperature observation biases over the central Sierra Nevada of California

Cited by 8 publications

References 63 publications

Evaluating Topographic Effects on Kilometer-Scale Satellite Downward Shortwave Radiation Products: A Case Study in Mid-Latitude Mountains

Evaluating Topographic Effects on Kilometer-Scale Satellite Downward Shortwave Radiation Products: A Case Study in Mid-Latitude Mountains

GOES-R land surface products at Western Hemisphere eddy covariance tower locations

Generation of 100-m, Hourly Land Surface Temperature Based on Spatio-Temporal Fusion

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