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1. Efficient and Flexible Aggregation and Distribution of MODIS Atmospheric Products Based on Climate Analytics as a Service Framework

   https://doi.org/10.3390/rs13173541  

   Zheng, Jianyu; Huang, Xin; Sangondimath, Supriya; Wang, Jianwu; Zhang, Zhibo (September 2021, Remote Sensing)

   null (Ed.)

   MODIS (Moderate Resolution Imaging Spectroradiometer) is a key instrument onboard NASA’s Terra (launched in 1999) and Aqua (launched in 2002) satellite missions as part of the more extensive Earth Observation System (EOS). By measuring the reflection and emission by the Earth-Atmosphere system in 36 spectral bands from the visible to thermal infrared with near-daily global coverage and high-spatial-resolution (250 m ~ 1 km at nadir), MODIS is playing a vital role in developing validated, global, interactive Earth system models. MODIS products are processed into three levels, i.e., Level-1 (L1), Level-2 (L2) and Level-3 (L3). To shift the current static and “one-size-fits-all” data provision method of MODIS products, in this paper, we propose a service-oriented flexible and efficient MODIS aggregation framework. Using this framework, users only need to get aggregated MODIS L3 data based on their unique requirements and the aggregation can run in parallel to achieve a speedup. The experiments show that our aggregation results are almost identical to the current MODIS L3 products and our parallel execution with 8 computing nodes can work 88.63 times faster than a serial code execution on a single node. 

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2. Validation of Space-Based Albedo Products from Upscaled Tower-Based Measurements Over Heterogeneous and Homogeneous Landscapes

   https://doi.org/10.3390/rs12050833  

   Song, Rui; Muller, Jan-Peter; Kharbouche, Said; Yin, Feng; Woodgate, William; Kitchen, Mark; Roland, Marilyn; Arriga, Nicola; Meyer, Wayne; Koerber, Georgia; et al (March 2020, Remote Sensing)

   null (Ed.)

   Surface albedo is a fundamental radiative parameter as it controls the Earth’s energy budget and directly affects the Earth’s climate. Satellite observations have long been used to capture the temporal and spatial variations of surface albedo because of their continuous global coverage. However, space-based albedo products are often affected by errors in the atmospheric correction, multi-angular bi-directional reflectance distribution function (BRDF) modelling, as well as spectral conversions. To validate space-based albedo products, an in situ tower albedometer is often used to provide continuous “ground truth” measurements of surface albedo over an extended area. Since space-based albedo and tower-measured albedo are produced at different spatial scales, they can be directly compared only for specific homogeneous land surfaces. However, most land surfaces are inherently heterogeneous with surface properties that vary over a wide range of spatial scales. In this work, tower-measured albedo products, including both directional hemispherical reflectance (DHR) and bi-hemispherical reflectance (BHR), are upscaled to coarse satellite spatial resolutions using a new method. This strategy uses high-resolution satellite derived surface albedos to fill the gaps between the albedometer’s field-of-view (FoV) and coarse satellite scales. The high-resolution surface albedo is generated from a combination of surface reflectance retrieved from high-resolution Earth Observation (HR-EO) data and moderate resolution imaging spectroradiometer (MODIS) BRDF climatology over a larger area. We implemented a recently developed atmospheric correction method, the Sensor Invariant Atmospheric Correction (SIAC), to retrieve surface reflectance from HR-EO (e.g., Sentinel-2 and Landsat-8) top-of-atmosphere (TOA) reflectance measurements. This SIAC processing provides an estimated uncertainty for the retrieved surface spectral reflectance at the HR-EO pixel level and shows excellent agreement with the standard Landsat 8 Surface Reflectance Code (LaSRC) in retrieving Landsat-8 surface reflectance. Atmospheric correction of Sentinel-2 data is vastly improved by SIAC when compared against the use of in situ AErosol RObotic NETwork (AERONET) data. Based on this, we can trace the uncertainty of tower-measured albedo during its propagation through high-resolution EO measurements up to coarse satellite scales. These upscaled albedo products can then be compared with space-based albedo products over heterogeneous land surfaces. In this study, both tower-measured albedo and upscaled albedo products are examined at Ground Based Observation for Validation (GbOV) stations (https://land.copernicus.eu/global/gbov/), and used to compare with satellite observations, including Copernicus Global Land Service (CGLS) based on ProbaV and VEGETATION 2 data, MODIS and multi-angle imaging spectroradiometer (MISR). 

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3. A simple cloud-filling approach for remote sensing water cover assessments

   https://doi.org/10.5194/hess-25-2373-2021  

   Mullen, Connor; Penny, Gopal; Müller, Marc F. (January 2021, Hydrology and Earth System Sciences)

   null (Ed.)

   Abstract. The empirical attribution of hydrologic change presents a unique data availability challenge in terms of establishing baseline prior conditions, as one cannot go back in time to retrospectively collect the necessary data. Although global remote sensing data can alleviate this challenge, most satellite missions are too recent to capture changes that happened long ago enough to provide sufficient observations for adequate statistical inference. In that context, the 4 decades of continuous global high-resolution monitoring enabled by the Landsat missions are an unrivaled source of information. However, constructing a time series of land cover observation across Landsat missions remains a significant challenge because cloud masking and inconsistent image quality complicate the automatized interpretation of optical imagery. Focusing on the monitoring of lake water extent, we present an automatized gap-filling approach to infer the class (wet or dry) of pixels masked by clouds or sensing errors. The classification outcome of unmasked pixels is compiled across images taken on different dates to estimate the inundation frequency of each pixel, based on the assumption that different pixels are masked at different times. The inundation frequency is then used to infer the inundation status of masked pixels on individual images through supervised classification. Applied to a variety of global lakes with substantial long term or seasonal fluctuations, the approach successfully captured water extent variations obtained from in situ gauges (where applicable), or from other Landsat missions during overlapping time periods. Although sensitive to classification errors in the input imagery, the gap-filling algorithm is straightforward to implement on Google's Earth Engine platform and stands as a scalable approach to reliably monitor, and ultimately attribute, historical changes in water bodies. 

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4. High-Resolution Nearshore Sea Surface Temperature from Calibrated Landsat Brightness Data

   https://doi.org/10.3390/rs16234477  

   Speiser, William H; Largier, John L (December 2024, Remote Sensing)

   Understanding and monitoring nearshore environments is essential, given that these fine-scaled ecosystems are integral to human well-being. While satellites offer an opportunity to gain synchronous and spatially extensive data of coastal areas, off-the-shelf calibrated satellite sea surface temperature (SST) measurements have only been available at coarse resolutions of 1 km or larger. In this study, we develop a novel methodology to create a simple linear equation to calibrate fine-scale Landsat thermal infrared radiation brightness temperatures (calibrated for land sensing) to derive SST at a resolution of 100 m. The constants of this equation are derived from correlations of coincident MODIS SST and Landsat data, which we filter to find optimal pairs. Validation against in situ sensor data at varying distances from the shore in Northern California shows that our SST estimates are more accurate than prior off-the-shelf Landsat data calibrated for land surfaces. These fine-scale SST estimates also demonstrate superior accuracy compared with coincident MODIS SST estimates. The root mean square error for our minimally filtered dataset (n = 557 images) ranges from 0.76 to 1.20 °C with correlation coefficients from r = 0.73 to 0.92, and for our optimal dataset (n = 229 images), the error is from 0.62 to 0.98 °C with correlations from r = 0.83 to 0.92. Potential error sources related to stratification and seasonality are examined and we conclude that Landsat data represent skin temperatures with an error between 0.62 and 0.73 °C. We discuss the utility of our methodology for enhancing coastal monitoring efforts and capturing previously unseen spatial complexity. Testing the calibration methodology on Landsat images before and after the temporal bounds of accurate MODIS SST measurements shows successful calibration with lower errors than the off-the-shelf, land-calibrated Landsat product, extending the applicability of our approach. This new approach for obtaining high-resolution SST data in nearshore waters may be applied to other upwelling regions globally, contributing to improved coastal monitoring, management, and research. 

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5. Global tropical precipitation relationships to free tropospheric water vapor using Radio Occultations

   https://doi.org/10.1175/JAS-D-21-0052.1  

   Padullés, Ramon; Kuo, Yi-Hung; Neelin, J. David; Turk, F. Joseph; Ao, Chi O.; De la Torre Juárez, Manuel (February 2022, Journal of the Atmospheric Sciences)

   Abstract The transition to deep convection and associated precipitation is often studied in relationship to the associated column water vapor owing to the wide availability of these data from various ground or satellite-based products. Based on radiosonde and ground-based Global Navigation Satellite System (GNSS) data examined at limited locations and model comparison studies, water vapor at different vertical levels is conjectured to have different relationships to convective intensity. Here, the relationship between precipitation and water vapor in different free tropospheric layers is investigated using globally distributed GNSS radio occultation (RO) temperature and moisture profiles collocated with GPM IMERG precipitation across the tropical latitudes. A key feature of the RO measurement is its ability to directly sense in and near regions of heavy precipitation and clouds. Sharp pickups (i.e. sudden increases) of conditionally averaged precipitation as a function of water vapor in different tropospheric layers are noted for a variety of tropical ocean and land regions. The layer-integrated water vapor value at which this pickup occurs has a dependence on temperature that is more complex than constant RH, with larger subsaturation at warmer temperatures. These relationships of precipitation to its thermodynamic environment for different layers can provide a baseline for comparison with climate model simulations of the convective onset. Furthermore, vertical profiles before, during, and after convection are consistent with the hypothesis that the lower troposphere plays a causal role in the onset of convection, while the upper troposphere is moistened by de-trainment from convection. 

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