Improving Water Leaving Reflectance Retrievals from ABI and AHI Data Acquired Over Case 2 Waters from Present Geostationary Weather Satellite Platforms

The use of multispectral geostationary satellites to study aquatic ecosystems improves the temporal frequency of observations and mitigates cloud obstruction, but no operational capability presently exists for the coastal and inland waters of the United States. The Advanced Baseline Imager (ABI) on the current iteration of the Geostationary Operational Environmental Satellites, termed the R Series (GOES-R), however, provides sub-hourly imagery and the opportunity to overcome this deficit and to leverage a large repository of existing GOES-R aquatic observations. The fulfillment of this opportunity is assessed herein using a spectrally simplified, two-channel aquatic algorithm consistent with ABI wave bands to estimate the diffuse attenuation coefficient for photosynthetically available radiation, K d ( P A R ) . First, an in situ ABI dataset was synthesized using a globally representative dataset of above- and in-water radiometric data products. Values of K d ( P A R ) were estimated by fitting the ratio of the shortest and longest visible wave bands from the in situ ABI dataset to coincident, in situ K d ( P A R ) data products. The algorithm was evaluated based on an iterative cross-validation analysis in which 80% of the dataset was randomly partitioned for fitting and the remaining 20% was used for validation. The iteration producing the median coefficient of determination ( R 2 ) value (0.88) resulted in a root mean square difference of 0.319 m − 1 , or 8.5% of the range in the validation dataset. Second, coincident mid-day images of central and southern California from ABI and from the Moderate Resolution Imaging Spectroradiometer (MODIS) were compared using Google Earth Engine (GEE). GEE default ABI reflectance values were adjusted based on a near infrared signal. Matchups between the ABI and MODIS imagery indicated similar spatial variability ( R 2 = 0.60 ) between ABI adjusted blue-to-red reflectance ratio values and MODIS default diffuse attenuation coefficient for spectral downward irradiance at 490 nm, K d ( 490 ) , values. This work demonstrates that if an operational capability to provide ABI aquatic data products was realized, the spectral configuration of ABI would potentially support a sub-hourly, visible aquatic data product that is applicable to water-mass tracing and physical oceanography research.

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A Novel Method for Weather Nowcasting Based on Spatial Complex Fuzzy Inference With Multiple Band Input Data

Tuan

Giang

Thong³

et al. 2023

JCC

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The prediction of weather changes, such as rainfall, clouds, floods, and storms, is critical in weather forecasting. There are several sources of input data for this purpose, including radar and observational data, but satellite remote sensing images are the most commonly used due to their ease of collection. In this paper, we present a novel method for weather nowcasting based on Mamdani complex fuzzy inference with multiple band input data. The proposed approach splits the process into two parts: the first part converts the multiple band satellite images into real and imaginary parts to facilitate the rule process, and the second part uses the Spatial CFIS+ algorithm to generate the predicted weather state, taking into account factors such as cloud, wind, and temperature. The use of MapReduce helps to speed up the algorithm's performance. Our experimental results show that this new method outperforms other relevant methods and demonstrates improved prediction accuracy.

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Improving Water Leaving Reflectance Retrievals from ABI and AHI Data Acquired Over Case 2 Waters from Present Geostationary Weather Satellite Platforms

Cited by 4 publications

References 28 publications

Retrieval of oceanic chlorophyll concentration from GOES-R Advanced Baseline Imager using deep learning

Retrieval of oceanic chlorophyll concentration from GOES-R Advanced Baseline Imager using deep learning

Spectrally simplified approach for leveraging legacy geostationary oceanic observations

A Novel Method for Weather Nowcasting Based on Spatial Complex Fuzzy Inference With Multiple Band Input Data

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