About half of all optical observations collected via spaceborne satellites are affected by haze or clouds. Consequently, cloud coverage affects the remote sensing practitioner's capabilities of a continuous and seamless monitoring of our planet. This work addresses the challenge of optical satellite image reconstruction and cloud removal by proposing a novel multi-modal and multi-temporal data set called SEN12MS-CR-TS. We propose two models highlighting the benefits and use cases of SEN12MS-CR-TS: First, a multi-modal multi-temporal 3D-Convolution Neural Network that predicts a cloud-free image from a sequence of cloudy optical and radar images. Second, a sequence-to-sequence translation model that predicts a cloud-free time series from a cloud-covered time series. Both approaches are evaluated experimentally, with their respective models trained and tested on SEN12MS-CR-TS. The conducted experiments highlight the contribution of our data set to the remote sensing community as well as the benefits of multi-modal and multitemporal information to reconstruct noisy information. Our data set is available at https://patrickTUM.github.io/cloud removal.

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Viral diseases have seriously restricted the healthy development of aquaculture, and decapod iridescent virus 1 (DIV1) has led to heavy losses in the global shrimp aquaculture industry. Due to the lack of effective treatment, early detection and regular monitoring are the most effective ways to avoid infection with DIV1. In this study, a novel real-time quantitative recombinase polymerase amplification (qRPA) assay and its instrument-free visualization improvement were described for the rapid detection of DIV1. Optimum primer pairs, suitable reaction temperatures, and probe concentrations of a DIV1-qRPA assay were screened to determine optimal reaction conditions. Then, its ability to detect DIV1 was evaluated and compared with real-time quantitative polymerase chain reactions (qPCRs). The sensitivity tests demonstrated that the limit of detection (LOD) of the DIV1-qRPA assay was 1.0 copies μL−1. Additionally, the presentation of the detection results was improved with SYBR Green Ⅰ, and the LOD of the DIV1-RPA-SYBR Green Ⅰ assay was 1.0 × 103 copies μL−1. Both the DIV1-qRPA and DIV1-RPA-SYBR Green Ⅰ assays could be performed at 42 °C within 20 min and without cross-reactivity with the following: white spot syndrome virus (WSSV), Vibrio parahaemolyticus associated with acute hepatopancreatic necrosis disease (VpAHPND ), Enterocytozoon hepatopenaei (EHP), and infectious hypodermal and hematopoietic necrosis virus (IHHNV). In conclusion, this approach yields rapid, straightforward, and simple DIV1 diagnoses, making it potentially valuable as a reliable tool for the detection and prevention of DIV1, especially where there is a paucity of laboratory equipment.