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In this work we discuss various selected mission concepts addressing Venus evolution through time. More specifically, we address investigations and payload instrument concepts supporting scientific goals and open questions presented in the companion articles of this volume. Also included are their related investigations (observations & modeling) and discussion of which measurements and future data products are needed to better constrain Venus’ atmosphere, climate, surface, interior and habitability evolution through time. A new fleet of Venus missions has been selected, and new mission concepts will continue to be considered for future selections. Missions under development include radar-equipped ESA-led EnVision M5 orbiter mission (European Space Agency 2021 ), NASA-JPL’s VERITAS orbiter mission (Smrekar et al. 2022a ), NASA-GSFC’s DAVINCI entry probe/flyby mission (Garvin et al. 2022a ). The data acquired with the VERITAS, DAVINCI, and EnVision from the end of this decade will fundamentally improve our understanding of the planet’s long term history, current activity and evolutionary path. We further describe future mission concepts and measurements beyond the current framework of selected missions, as well as the synergies between these mission concepts, ground-based and space-based observatories and facilities, laboratory measurements, and future algorithmic or modeling activities that pave the way for the development of a Venus program that extends into the 2040s (Wilson et al. 2022 ).

Sustained observations are required to determine the marine plastic debris mass balance and to support effective policy for planning remedial action. However, observations currently remain scarce at the global scale. A satellite remote sensing system could make a substantial contribution to tackling this problem. Here, we make initial steps towards the potential design of such a remote sensing system by: (1) identifying the properties of marine plastic debris amenable to remote sensing methods and (2) highlighting the oceanic processes relevant to scientific questions about marine plastic debris. Remote sensing approaches are reviewed and matched to the optical properties of marine plastic debris and the relevant spatio-temporal scales of observation to identify challenges and opportunities in the field. Finally, steps needed to develop marine plastic debris detection by remote sensing platforms are proposed in terms of fundamental science as well as linkages to ongoing planning for satellite systems with similar observation requirements.

Among the essential tools to address global environmental information requirements are the Earth-Observing (EO) satellites with free and open data access. This paper reviews those EO satellites from international space programs that already, or will in the next decade or so, provide essential data of importance to the environmental sciences that describe Earth’s status. We summarize factors distinguishing those pioneering satellites placed in space over the past half century, and their links to modern ones, and the changing priorities for spaceborne instruments and platforms. We illustrate the broad sweep of instrument technologies useful for observing different aspects of the physio-biological aspects of the Earth’s surface, spanning wavelengths from the UV-A at 380 nanometers to microwave and radar out to 1 m. We provide a background on the technical specifications of each mission and its primary instrument(s), the types of data collected, and examples of applications that illustrate these observations. We provide websites for additional mission details of each instrument, the history or context behind their measurements, and additional details about their instrument design, specifications, and measurements.

Global water environment monitoring urgently requires remote sensing data with high temporal resolution and wide spatial coverage. However, current space-borne ocean color spectrometers still face a significant trade-off among spatial resolution, swath width, and system compactness, which limits the large-scale deployment of satellite constellations. To address this challenge, this study developed a lightweight high-resolution spectral imager (LHRSI) with a total mass of less than 25 kg and power consumption below 80 W. The visible (VIS) camera adopts an interleaved dual-field-of-view and detectors splicing fusion design, while the shortwave infrared (SWIR) camera employs a transmission-type focal plane with staggered detector arrays. Through the field-of-view (FOV) optical design, the instrument achieves swath widths of 207.33 km for the VIS bands and 187.8 km for the SWIR bands at an orbital altitude of 500 km, while maintaining spatial resolutions of 12 m and 24 m, respectively. On-orbit imaging results demonstrate that the spectrometer achieves excellent performance in both spatial resolution and swath width. In addition, preliminary analysis using index-based indicators illustrates LHRSI’s potential for observing chlorophyll-related features in water bodies. This research not only provides a high-performance, miniaturized spectrometer solution but also lays an engineering foundation for developing low-cost, high-revisit global ocean and water environment monitoring constellations.

Accurate and continuous monitoring of crop growth is vital for the development of precision agriculture. Unmanned aerial vehicle (UAV) and satellite platforms have considerable complementarity in high spatial resolution (centimeter-scale) and fixed revisit cycle. It is meaningful to optimize the cross-platform synergy for agricultural applications. Considering the characteristics of UAV and satellite platforms, a spatio-temporal fusion (STF) framework of UAV and satellite imagery is developed. It includes registration, radiometric normalization, preliminary fusion, and reflectance reconstruction. The proposed STF framework significantly improves the fusion accuracy with both better quantitative metrics and visualized results compared with four existing STF methods with different fusion strategies. Especially for the prediction of object boundary and spatial texture, the absolute values of Robert’s edge (EDGE) and local binary pattern (LBP) decreased by a maximum of more than 0.25 and 0.10, respectively, compared with the spatial and temporal adaptive reflectance fusion model (STARFM). Moreover, the STF framework enhances the temporal resolution to daily, although the satellite imagery is discontinuous. Further, its application potential for winter wheat growth monitoring is explored. The daily synthetic imagery with UAV spatial resolution describes the seasonal dynamics of winter wheat well. The synthetic Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2) are consistent with the observations. However, the error in NDVI and EVI2 at boundary changes is relatively large, which needs further exploration. This research provides an STF framework to generate very dense and high-spatial-resolution remote sensing data at a low cost. It not only contributes to precision agriculture applications, but also is valuable for land-surface dynamic monitoring.

This aim of this study was to find effective spectral bands for the early detection of oral cancer. The spectral images in different bands were acquired using a self-made portable light-emitting diode (LED)-induced autofluorescence multispectral imager equipped with 365 and 405 nm excitation LEDs, emission filters with center wavelengths of 470, 505, 525, 532, 550, 595, 632, 635, and 695 nm, and a color image sensor. The spectral images of 218 healthy points in 62 healthy participants and 218 tumor points in 62 patients were collected in the ex vivo trials at China Medical University Hospital. These ex vivo trials were similar to in vivo because the spectral images of anatomical specimens were immediately acquired after the on-site tumor resection. The spectral images associated with red, blue, and green filters correlated with and without nine emission filters were quantized by four computing method, including summated intensity, the highest number of the intensity level, entropy, and fractional dimension. The combination of four computing methods, two excitation light sources with two intensities, and 30 spectral bands in three experiments formed 264 classifiers. The quantized data in each classifier was divided into two groups: one was the training group optimizing the threshold of the quantized data, and the other was validating group tested under this optimized threshold. The sensitivity, specificity, and accuracy of each classifier were derived from these tests. To identify the influential spectral bands based on the area under the region and the testing results, a single-layer network learning process was used. This was compared to conventional rules-based approaches to show its superior and faster performance. Consequently, four emission filters with the center wavelengths of 470, 505, 532, and 550 nm were selected by an AI-based method and verified using a rule-based approach. The sensitivities of six classifiers using these emission filters were more significant than 90%. The average sensitivity of these was about 96.15%, the average specificity was approximately 69.55%, and the average accuracy was about 82.85%.

Inland waters consist of multiple concentrations of constituents, and solving the interference problem of chlorophyll-a and colored dissolved organic matter (CDOM) can help to accurately invert total suspended matter concentration (Ctsm). In this study, according to the characteristics of the Multispectral Imager for Inshore (MII) equipped with the first Sustainable Development Goals Science Satellite (SDGSAT-1), an iterative inversion model was established based on the iterative analysis of multiple linear regression to estimate Ctsm. The Hydrolight radiative transfer model was used to simulate the radiative transfer process of Lake Taihu, and it analyzed the effect of three component concentrations on remote sensing reflectance. The characteristic band combinations B6/3 and B6/5 for multiple linear regression were determined using the correlation of the three component concentrations with different bands and band combinations. By combining the two multiple linear regression models, a complete closed iterative inversion model for solving Ctsm was formed, which was successfully verified by using the modeling data (R2 = 0.97, RMSE = 4.89 g/m3, MAPE = 11.48%) and the SDGSAT-1 MII image verification data (R2 = 0.87, RMSE = 3.92 g/m3, MAPE = 8.13%). And it was compared with iterative inversion models constructed based on other combinations of feature bands and other published models. Remote sensing monitoring Ctsm was carried out using SDGSAT-1 MII images of Lake Taihu in 2022–2023. This study can serve as a technical reference for the SDGSAT-1 satellite in terms of remote sensing monitoring of Ctsm, as well as monitoring and improving the water environment.

Determining nearshore bathymetry by traditional surveying methods is a challenging task as it involves huge costs and efforts. Most of the coastal shallow-water zones worldwide either remain unmapped or not updated. Bathymetry estimations from optical satellite imageries have been increasingly implemented as an alternative tool for traditional bathymetry surveys. In this study, we examine the usefulness of freely available, five-day revisit and relatively high-resolution Multi Spectral Instruments (MSI) on-board Sentinel-2A and 2B twin satellites. A process workflow has been developed which automatically incorporates a robust atmospheric correction through ACOLITE software and multi-scene compositing of several scenes to improve the reliability and no data gaps. Two study sites in India are explored owing to their variability in submarine morphology. High-resolution bathymetry maps are generated through a log-ratio transform model calibrated with minimal in situ data from the jet ski soundings. The satellite-derived bathymetry obtained has an overall bias of −0.01 and 0.02 m, and root mean square error of 1.09 and 0.93 m respectively, at two study sites up to 15 m depth. The consistency in bathymetry retrieval indicates a potential for automated application for the benefit of operational and scientific studies. These high-resolution maps capture small-scale nearshore features like sandbars and rip channels, which are of prime importance for coastal and beach managers.

A cloud property retrieved from multispectral imagers having spectral channels in the shortwave infrared (SWIR) and/or midwave infrared (MWIR) is the effective particle radius (CER), a radiatively relevant weighting of the cloud particle size distribution. The physical basis of the CER retrieval is the dependence of SWIR/MWIR cloud reflectance on the cloud particle single scattering absorption, which in turn depends on the complex index of refraction of bulk liquid water (or ice) in addition to the cloud particle size. There is a general consistency in the choice of the liquid water index of refraction by the cloud remote sensing community, largely due to the few available independent datasets and compilations. Here we examine the sensitivity of CER retrievals to the available laboratory index of refraction datasets in the SWIR and MWIR using the retrieval software package that produces NASA’s standard MODIS/VIIRS continuity cloud products. The sensitivity study incorporates two laboratory index of refraction datasets that include measurements at supercooled water temperatures, one in the SWIR [Kou et al., 1993] and one in the MWIR [Wagner et al., 2005]. Neither has been broadly utilized in the cloud remote sensing community. It is shown that these two new datasets can significantly change CER retrievals (e.g., 1-2 μm) relative to common datasets used by the community. Further, index of refraction data for a 265 K water temperature results in more consistent retrievals between the two spectrally distinct 2.2 μm atmospheric window channels on MODIS and VIIRS. As a result, the 265 K values from the SWIR and MWIR index of refraction datasets were adopted for use in the production version of the continuity cloud product. The results indicate the need to better understand temperature-dependent bulk water absorption and uncertainties in these spectral regions.

Remote sensing images are inevitably produced with radiometric artifacts due to the photo-response non-uniformity of charge-coupled device (CCD) sensors. In situations where time constraints demand the prompt acquisition of imaging products, integrating an onboard radiometric correction system becomes essential. This paper advocates for a hardware–firmware co-design approach to achieve radiometric correction within the payload front-end electronics (FEE), leveraging the capabilities of field programmable gate array circuits (FPGA). The selection of an appropriate CCD sensor and optical device is guided by a thorough payload mission analysis, ensuring compliance with the specifications derived from Alsat-1B, the Algerian microsatellite launched in September 2016. Simulation results demonstrate that the designed FPGA firmware effectively controls the CCD sensor and configures its settings to achieve real-time radiometric correction of the acquired pixels in accordance with the mission requirements. To ensure efficient utilization during imaging operations, a hardware solution for onboard storage and in-orbit update of the radiometric coefficients has been considered for the radiometric correction system.