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1. Effect of Satellite Spatial Resolution on Submesoscale Variance in Ocean Color and Temperature

   https://doi.org/10.1029/2024GL114266  

   Carberry, Luke; Siegel, David_A; Nidzieko, Nicholas_J (March 2025, Geophysical Research Letters)

   Abstract The capability of moderate‐spatial‐resolution satellites to accurately resolve submesoscale variations in surface tracers remains an open question, one with relevance to observing physical‐biological interactions in the surface ocean. In this study, we address this question by comparing the variance of two tracers, chlorophyll concentration (Chl) and sea surface temperature (SST), resolved by two satellites—MODIS Aqua, with a resolution of 1.5 km, and Landsat 8/9, with a resolution of 30 m. We quantify tracer variance resolved by both satellites on the submesoscale using spatial variance spectral slopes. We find that MODIS measures significantly higher variance compared to Landsat, in both Chl and SST. This is because, despite higher signal‐to‐noise ratio for MODIS per pixel, Landsat signal‐to‐noise ratio increases considerably when aggregating pixels. Furthermore, by comparing Chl to SST variance for each satellite we find Landsat to be better match to theory for resolving submesoscale physical‐biological interactions. 

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2. Land Surface Temperature Derivation under All Sky Conditions through Integrating AMSR-E/AMSR-2 and MODIS/GOES Observations

   https://doi.org/10.3390/rs11141704  

   Sun, Donglian; Li, Yu; Zhan, Xiwu; Houser, Paul; Yang, Chaowei; Chiu, Long; Yang, Ruixin (July 2019, Remote Sensing)

   Land surface temperature (LST) is an important input to the Atmosphere–Land Exchange Inverse (ALEXI) model to derive the Evaporative Stress Index (ESI) for drought monitoring. Currently, LST inputs to the ALEXI model come from the Geostationary Operational Environmental Satellite (GOES) and Moderate Resolution Imaging Spectroradiometer (MODIS) products, but clouds affect them. While passive microwave (e.g., AMSR-E and AMSR-2) sensors can penetrate non-rainy clouds and observe the Earth’s surface, but usually with a coarse spatial resolution, how to utilize multiple instruments’ advantages is an important methodology in remote sensing. In this study, we developed a new five-channel algorithm to derive LST from the microwave AMSR-E and AMSR-2 measurements and calibrate to the MODIS and GOES LST products. A machine learning method is implemented to further improve its performance. The MODIS and GOES LST products still show better performance than the AMSR-E and AMSR-2 LSTs when evaluated against the ground observations. Therefore, microwave LSTs are only used to fill the gaps due to clouds in the MODIS and GOES LST products. A gap filling method is further applied to fill the remaining gaps in the merged LSTs and downscale to the same spatial resolution as the MODIS and GOES products. With the daily integrated LST at the same spatial resolution as the MODIS and GOES products and available under nearly all sky conditions, the drought index, like the ESI, can be updated on daily basis. The initial implementation results demonstrate that the daily drought map can catch the fast changes of drought conditions and capture the signals of flash drought, and make flash drought monitoring become possible. It is expected that a drought map that is available on daily basis will benefit future drought monitoring. 

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3. 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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4. Leveraging spatial textures, through machine learning, to identify aerosols and distinct cloud types from multispectral observations

   https://doi.org/10.5194/amt-13-5459-2020  

   Marais, Willem J.; Holz, Robert E.; Reid, Jeffrey S.; Willett, Rebecca M. (January 2020, Atmospheric Measurement Techniques)

   null (Ed.)

   Abstract. Current cloud and aerosol identification methods for multispectral radiometers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS), employ multichannel spectral tests on individual pixels (i.e., fields of view). The use of the spatial information in cloud and aerosol algorithms has been primarily through statistical parameters such as nonuniformity tests of surrounding pixels with cloud classification provided by the multispectral microphysical retrievals such as phase and cloud top height. With these methodologies there is uncertainty in identifying optically thick aerosols, since aerosols and clouds have similar spectral properties in coarse-spectral-resolution measurements. Furthermore, identifying clouds regimes (e.g., stratiform, cumuliform) from just spectral measurements is difficult, since low-altitude cloud regimes have similar spectral properties. Recent advances in computer vision using deep neural networks provide a new opportunity to better leverage the coherent spatial information in multispectral imagery. Using a combination of machine learning techniques combined with a new methodology to create the necessary training data, we demonstrate improvements in the discrimination between cloud and severe aerosols and an expanded capability to classify cloud types. The labeled training dataset was created from an adapted NASA Worldview platform that provides an efficient user interface to assemble a human-labeled database of cloud and aerosol types. The convolutional neural network (CNN) labeling accuracy of aerosols and cloud types was quantified using independent Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and MODIS cloud and aerosol products. By harnessing CNNs with a unique labeled dataset, we demonstrate the improvement of the identification of aerosols and distinct cloud types from MODIS and VIIRS images compared to a per-pixel spectral and standard deviation thresholding method. The paper concludes with case studies that compare the CNN methodology results with the MODIS cloud and aerosol products. 

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5. Analyzing Changing Thermal Features at Puyehue-Cordon Caulle, Chile Using Satellite, Field, and Drone Observations

   Andrea Gomez-Patron, Diego Aron (January 2022, AGU Fall Meeting, Chicago, IL & Online Everywhere, 12-16 December 2022)

   The Puyehue-Cordon Caulle (PCC) volcanic complex, Chile, hosts numerous thermal features, including a ~0.8 km3 laccolith formed during the 2011-2012 eruption. Laccoliths are large intrusions that form between country rock layers that have been rarely observed during the process of formation. We use medium-spatial resolution (90 m/pixel) satellite data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), to identify changes at the laccolith and other thermal features within PCC between 2000 and 2022. Previous studies have analyzed thermal behavior using MODIS images, which have low spatial resolution but high temporal resolution, and Landsat images, which have medium spatial resolution, but were only examined during the eruption (2011-2012). Prior research using ASTER data have only recorded the maximum temperature at PCC, while this study analyzes all of the individual thermal features and records both temperature and area for each feature identified in all 41 cloud-free, nighttime ASTER images available over the last 22 years. We focus on changes to seven features observed by satellite with temperatures at least 2 K above background (Trahuilco, Las Sopas, Los Venados, Los Baños/El Azufral, Puyehue, Laccolith, and a new unnamned feature). We create time series for each feature in order to: (1) evaluate temporal changes in area and temperature, (2) detect significant deviations from standard seasonality in non-eruptive periods, and (3) test for statistically significant precursors to the 2011 eruption. We identify both seasonal temperature variation and a general subtle increase in temperature over time at the laccolith. Furthermore, we find growth in the area of the laccolith with temperatures above background since 2016 including two periods of sudden increase in area between 11/2017 - 9/2018 and in mid 2020. We compare the ASTER observations with higher spatial resolution observations of fissures, craters, and fumaroles identified from field observations, drone thermal and optical imagery and high spatial resolution (~1 m/pixel) satellite SAR and optical data. We interpret the thermal changes at the laccolith to be related to fractures and craters in the laccolith exposing hot regions. 

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