Cloud properties are critical to our understanding of weather and climate variability, but their estimation from satellite imagers is a nontrivial task. In this work, we aim to improve cloud detection, which is the most fundamental cloud property. We use a neural network applied to Visible Infrared Imaging Radiometer Suite (VIIRS) measurements to determine whether an imager pixel is cloudy or cloud-free. The neural network is trained and evaluated using 4 years (2016–2019) of coincident measurements between VIIRS and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). We successfully address the lack of sun glint in the collocation dataset with a simple semi-supervised learning approach. The results of the neural network are then compared with two operational cloud masks: the Continuity MODIS-VIIRS Cloud Mask (MVCM) and the NOAA Enterprise Cloud Mask (ECM). We find that the neural network outperforms both operational cloud masks in most conditions examined with a few exceptions. The largest improvements we observe occur during the night over snow- or ice-covered surfaces in the high latitudes. In our analysis, we show that this improvement is not solely due to differences in optical-depth-based definitions of a cloud between each mask. We also analyze the differences in true-positive rate between day–night and land–water scenes as a function of optical depth. Such differences are a contributor to spatial artifacts in cloud masking, and we find that the neural network is the most consistent in cloud detection with respect to optical depth across these conditions. A regional analysis over Greenland illustrates the impact of such differences and shows that they can result in mean cloud fractions with very different spatial and temporal characteristics.

Inland waters are warming at highly variable rates that often differ from regional air temperature trends. This variable warming is partially attributable to an individual lake’s geographical and morphological characteristics. In very large lakes, significant intralake variability in long-term warming trends has also been observed. In light of this intralake and interlake heterogeneity of lake surface water temperature (LSWT) and LSWT trends, we revisit the 1.1 km Advanced Very High Resolution Radiometer (AVHRR) record for the Laurentian Great Lakes. In this work, we have assembled a long-term (1986–2016) and high-spatial-resolution (0.018°) daily LSWT dataset using AVHRR record. Subtracting an empirically-determined mean diurnal cycle mitigates the effects of varying observation times. Adjustments in the georegistration of the images are made to reduce the impact of AVHRR navigational errors on the earlier platforms. Both the original daily composites, and a gap-filled product using locally weighted interpolation methods will be made available to support fine-scale physical and environmental research in the region.

Superresolution is the general task of artificially increasing the spatial resolution of an image. The recent surge in machine learning (ML) research has yielded many promising ML-based approaches for performing single-image superresolution including applications to satellite remote sensing. We develop a convolutional neural network (CNN) to superresolve the 1- and 2-km bands on the GOES-R series Advanced Baseline Imager (ABI) to a common high resolution of 0.5 km. Access to 0.5-km imagery from ABI band 2 enables the CNN to realistically sharpen lower-resolution bands without significant blurring. We first train the CNN on a proxy task, which allows us to only use ABI imagery, namely, degrading the resolution of ABI bands and training the CNN to restore the original imagery. Comparisons at reduced resolution and at full resolution with Landsat-8/Landsat-9 observations illustrate that the CNN produces images with realistic high-frequency detail that is not present in a bicubic interpolation baseline. Estimating all ABI bands at 0.5-km resolution allows for more easily combining information across bands without reconciling differences in spatial resolution. However, more analysis is needed to determine impacts on derived products or multispectral imagery that use superresolved bands. This approach is extensible to other remote sensing instruments that have bands with different spatial resolutions and requires only a small amount of data and knowledge of each channel’s modulation transfer function. Significance Statement: Satellite remote sensing instruments often have bands with different spatial resolutions. This work shows that we can artificially increase the resolution of some lower-resolution bands by taking advantage of the texture of higher-resolution bands on the GOES-16 ABI instrument using a convolutional neural network. This may help reconcile differences in spatial resolution when combining information across bands, but future analysis is needed to precisely determine impacts on derived products that might use superresolved bands.

Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can present a challenge for pipeline development. Here, we describe a machine learning approach to quantify Puccinia sorghi incidence on maize leaves utilizing U-Net convolutional neural network models. We analyzed several U-Net models with increasing amounts of training image data, either randomly chosen from a large data pool or randomly chosen from a subset of disease time course data. As the training dataset size increases, the models perform better, but the rate of performance decreases. Additionally, the use of a diverse training dataset can improve model performance and reduce the amount of annotated training data required for satisfactory performance. Models with as few as 48 whole-leaf training images are able to replicate the ground truth results within our testing dataset. The final model utilizing our entire training dataset performs similarly to our ground truth data, with an intersection over union value of 0.5002 and an F1 score of 0.6669. This work illustrates the capacity of U-Nets to accurately answer real-world plant pathology questions related to quantification and estimation of plant disease symptoms. Copyright (c) 2024 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Cloud optical property retrievals from passive satellite imagers tend to be most accurate during the daytime due to the availability of visible and near‐infrared solar reflectance. Infrared (IR) channels have a relative lack of spectral sensitivity to optically thick clouds and are heavily influenced by cloud‐top temperature making retrievals of cloud optical depth, cloud effective radius, and cloud water path more difficult at night. We examine whether the use of spatial context — information about the local structure and organization of cloud features — can help overcome these limitations of IR channels and provide more accurate estimates of nighttime cloud optical properties. We trained several neural networks to emulate the Advanced Baseline Imager (ABI) NOAA Daytime Cloud Optical and Microphysical Properties (DCOMP) algorithm using only IR channels. We then compared the neural networks to the NOAA operational daytime and nighttime products, and the Nighttime Lunar Cloud Optical and Microphysical Properties (NLCOMP) algorithm, which utilizes the low‐light visible band on VIIRS. These comparisons show that the use of spatial context can improve estimates of nighttime cloud optical properties. The primary model we trained, U‐NetCOMP, can reasonably match DCOMP during the day and significantly reduces artifacts associated with day/night terminator. We also find that U‐NetCOMP estimates align more closely with NLCOMP at night compared to the nighttime NOAA operational products for ABI. Lastly, we perform a comparison with ground‐based instruments and find that U‐NetCOMP improves upon the nighttime operational product with some exceptions for thin cirrus clouds over cold surfaces.