Radiometric Resolution Research Articles

Measurement of Total Dissolved Solids and Total Suspended Solids in Water Systems: A Review of the Issues, Conventional, and Remote Sensing Techniques

This study provides a comprehensive review of the efforts utilized in the measurement of water quality parameters (WQPs) with a focus on total dissolved solids (TDS) and total suspended solids (TSS). The current method used in the measurement of TDS and TSS includes conventional field and gravimetric approaches. These methods are limited due to the associated cost and labor, and limited spatial coverages. Remote Sensing (RS) applications have, however, been used over the past few decades as an alternative to overcome these limitations. Although they also present underlying atmospheric interferences in images, radiometric and spectral resolution issues. Studies of these WQPs with RS, therefore, require the knowledge and utilization of the best mechanisms. The use of RS for retrieval of TDS, TSS, and their forms has been explored in many studies using images from airborne sensors onboard unmanned aerial vehicles (UAVs) and satellite sensors such as those onboard the Landsat, Sentinel-2, Aqua, and Terra platforms. The images and their spectral properties serve as inputs for deep learning analysis and statistical, and machine learning models. Methods used to retrieve these WQP measurements are dependent on the optical properties of the inland water bodies. While TSS is an optically active parameter, TDS is optically inactive with a low signal–noise ratio. The detection of TDS in the visible, near-infrared, and infrared bands is due to some process that (usually) co-occurs with changes in the TDS that is affecting a WQP that is optically active. This study revealed significant improvements in incorporating RS and conventional approaches in estimating WQPs. The findings reveal that improved spatiotemporal resolution has the potential to effectively detect changes in the WQPs. For effective monitoring of TDS and TSS using RS, we recommend employing atmospheric correction mechanisms to reduce image atmospheric interference, exploration of the fusion of optical and microwave bands, high-resolution hyperspectral images, utilization of ML and deep learning models, calibration and validation using observed data measured from conventional methods. Further studies could focus on the development of new technology and sensors using UAVs and satellite images to produce real-time in situ monitoring of TDS and TSS. The findings presented in this review aid in consolidating understanding and advancement of TDS and TSS measurements in a single repository thereby offering stakeholders, researchers, decision-makers, and regulatory bodies a go-to information resource to enhance their monitoring efforts and mitigation of water quality impairments.

Multi-modal image matching to colorize a SLAM based point cloud with arbitrary data from a thermal camera

Thermal mapping of buildings can be one approach to assess the insulation, which is important in regard to upgrade buildings to increase energy efficiency and for climate change adaptation. Personal laser scanning (PLS) is a fast and flexible option that has become increasingly popular to efficiently map building facades. However, some measurement systems do not include sufficient colorization of the point cloud. In order to detect, map and reference any damages to building facades, it is of great interest to transfer images from RGB and thermal infrared (TIR) cameras to the point cloud. This study aims to answer the research question if a flexible tool can be developed, which enable such measurements with high spatial resolution and flexibility. Therefore, an image-to-geometry registration approach for rendered point clouds is combined with a deep learning (DL)-based image feature matcher to estimate the camera pose of arbitrary images in relation to the geometry, i.e. the point cloud, to map color information. We developed a research design for multi-modal image matching to investigate the alignment of RGB and TIR camera images to a PLS point cloud with intensity information using calibrated and un-calibrated images. The accuracies of the estimated pose parameters reveal the best performance of the registration for pre-calibrated, i.e. undistorted, RGB camera images. The alignment of un-calibrated RGB and TIR camera images to a point cloud is possible if sufficient and well-distributed 2D-3D feature matches between image and point cloud are available. Our workflow enables the colorization of point clouds with high accuracy using images with very different radiometric characteristics and image resolutions. Only a rough approximation of the camera pose is required and hence the approach reliefs strict sensor synchronization requirements.

Homomorphic filtering in digital multichannel image processing

Purpose. The purpose of this article is to develop a preprocessing method for digital multispectral remote sensing images obtained through optical and infrared means in the electromagnetic spectrum. The method aims to ensure invariance with respect to positional formation conditions that determine spatial and radiometric resolution. By implementing homomorphic filtering in this method, we can significantly increase the informative value of processed imagery. Methodology. The problem solving, including the development of the spatial and radiometric resolution increase ways for multispectral geospatial data are based on the methods of brightness spatial distribution fusion, methods of data dimension reduction, de-correlation techniques and geometric correction of image spatial distributions. Findings. The method of preprocessing digital remote sensing data has been developed, which is a component of the methodology for identifying geometric shapes (GS) of objects in multi-channel aerospace images, allowing for a significant improvement in their recognition efficiency when noise is present. Originality. The method of preprocessing photogrammetric scenes using homomorphic filtering to enhance their informational significance is proposed. The method ensures invariance to positional conditions of fixation, improves the accuracy of further recognition, eliminates the drawbacks of known methods associated with the existence of parametric uncertainty dependence, the features of fixation of species information, low values of information indices of synthesized images, and computational process peculiarities. Practical value. Practical value is consists in improving of identification accuracy of objects GS in digital geospatial data, in significant increasing of raster multispectral images information value and in rising of automated image processing efficiency. The use of the method can greatly enhance the value and usefulness of multispectral photogrammetric images in a wide range of applications, from environmental monitoring to urban planning.

ABWI AIRBORNE BINOCULAR WHISKBROOM IMAGER: CAMERA PRINCIPLES AND THE WORKFLOW

Abstract. The common imaging methods of airborne cameras are linear array pushbroom and area-array interval exposure, and the sensors or lens of them cannot be rotated. With the development of oblique photogrammetry and UAV mapping, there are new advances in airborne whiskbroom sensors. Because of their unique imaging method, they can obtain the side texture information of features more efficiently. ABWI is a new generation of airborne sweeping sensors with the dual-view area-array whiskbroom imaging method. It has a maximum sweeping field of view of 120° and can also acquire spectral information in four bands (RGB and NIR) at the radiometric resolution of 12 bits. Additionally, the camera system is equipped with a laser ranger to assist with other measurement purposes. This paper introduces the camera structure, imaging principle and corresponding data processing workflow of ABWI, and summarizes its advantages and new application scenarios.

Improved burned area mapping using monotemporal Landsat-9 imagery and convolutional shift-transformer

Satellite imagery, specifically Landsat, have been widely used for mapping and monitoring wildfire burned areas. The new Landsat-9 satellite – with higher radiometric resolution compared to its predecessors, and improved temporal resolution when combined with Landsat-8 (∼8 days) – enables a wide range of applications, particularly burned area mapping (BAM). We propose a novel deep learning BAM model that leverages the strengths of the convolutional layers for deep feature generation from Landsat-9 imagery and shift-transformer block for burned area classification. The performance of the model is evaluated in five large fire case studies across the globe. BAM results are also compared with two state-of-the-art models, namely residual convolutional neural network and vision transformer. The proposed convolutional shift-transformer (CST) outperforms other models with an F1-score of greater than 96% across the case studies. Furthermore, CST only requires a single post-fire image that reduces computational costs compared to traditional models that use bi-temporal images.

Deep Learning Models for the Classification of Crops in Aerial Imagery: A Review

In recent years, the use of remote sensing data obtained from satellite or unmanned aerial vehicle (UAV) imagery has grown in popularity for crop classification tasks such as yield prediction, soil classification or crop mapping. The ready availability of information, with improved temporal, radiometric, and spatial resolution, has resulted in the accumulation of vast amounts of data. Meeting the demands of analysing this data requires innovative solutions, and artificial intelligence techniques offer the necessary support. This systematic review aims to evaluate the effectiveness of deep learning techniques for crop classification using remote sensing data from aerial imagery. The reviewed papers focus on a variety of deep learning architectures, including convolutional neural networks (CNNs), long short-term memory networks, transformers, and hybrid CNN-recurrent neural network models, and incorporate techniques such as data augmentation, transfer learning, and multimodal fusion to improve model performance. The review analyses the use of these techniques to boost crop classification accuracy by developing new deep learning architectures or by combining various types of remote sensing data. Additionally, it assesses the impact of factors like spatial and spectral resolution, image annotation, and sample quality on crop classification. Ensembling models or integrating multiple data sources tends to enhance the classification accuracy of deep learning models. Satellite imagery is the most commonly used data source due to its accessibility and typically free availability. The study highlights the requirement for large amounts of training data and the incorporation of non-crop classes to enhance accuracy and provide valuable insights into the current state of deep learning models and datasets for crop classification tasks.

Fusion of Remotely Sensed Images using Wavelet Transforms and Decorrelated Multispectral Channels

The article is devoted to the development of mathematical models based on combining images obtained by remote sensing means with different spatial and radiometric resolutions. An analysis of modern means of remote sensing, which form images that are fixed under the same positional conditions of projection, in different spectral ranges of radiation, was carried out. Images formed in a wide spectral range and have a higher linear resolution than images formed in narrower ranges, but the latter contain spectral information. An applied model of combining images captured in different spectral ranges using the pyramidal wavelet transform has been developed. The optimal model of decorrelation of spectral channels of multispectral images based on signal entropy was determined.

Harmonization of Meteosat First and Second Generation Datasets for Fog and Low Stratus Studies

Operational weather satellites, dating back to 1970s, currently provide the best basis for climatological investigations, such as an analysis of changes in the cloud cover. Because clouds are highly dynamic in time, temporally high-resolution data from the geostationary orbit are preferred in order to take variations in the diurnal cycles into account. For such studies, a consistent dataset in space and time is mandatory, but not yet available. Ground-based point measurements of various cloud parameters, such as ceiling, visibility, and cloud type are often sparsely spread and inconsistent, making it difficult to derive reliable spatio-temporal information over large areas. The Meteosat program has generally provided suitable data from over Europe since 1977, but different spatial, spectral, and radiometric resolution of the instruments of the individual satellites, including early-years calibration uncertainties, makes harmonization necessary to finally derive a time series applicable to any kind of climatological study. In this study, a machine learning-based approach has been employed to generate a long-term consistent dataset with high spatio-temporal resolution and extensive coverage over Europe by the harmonization of Meteosat First Generation (MFG) and Meteosat Second Generation (MSG) satellite datasets (1991–2020). A random forest (RF) regressor is trained on the overlap period (2004–2006), where datasets of both satellite generation (MFG and MSG) are available to predict MFG Water Vapour (WV) and Infrared (IR) channels brightness temperature (BT) values based on MSG channels. The aim of the study is to synthesize MFG MVIRI data from MSG SEVIRI to generate a consistent MFG time series. The results indicate a good match of MFG synthesized data with the original MFG data with a mean absolute error of 0.7 K for the WV model and 1.6 K for the IR model, and an out-of-bag (OOB) R² score of 0.98 for both the models. Based on the trained models, the MFG scenes are synthesized from the MSG scenes for the years from 2006 to 2020. The long-term homogeneity of the generated time series is analyzed. The harmonized dataset will be applied to generate a continuous time series on fog and low stratus (FLS) occurrence for a climatological time scale of 30 years.

Radiometric and Polarimetric Quality Validation of Gaofen-3 over a Five-Year Operation Period

GaoFen-3 was the first Chinese civilian C-band synthetic aperture radar (SAR) satellite, launched in August 2016. The need for monitoring the satellite’s image quality has been boosted by its widespread applications in various fields. The efficient and scientific assessment of the system’s radiometric and polarimetric performance has been essential in its more than five years of service. The authors collected 90 images of the Inner Mongolia calibration site, 888 images of the Amazon rainforest, and 39,929 images of the Chinese mainland from 2017 to 2021. This was achieved whilst covering the leading imaging modes, such as the spotlight mode, stripmap mode, ultra-fine mode, wave imaging mode, etc. In this study, we derive a framework that incorporates the man-made corner reflectors (CRs) in Mongolia, the traditional Amazon rainforest datasets, and even the long-strip data in the Chinese mainland (known as CRAS) for the purposes of GaoFen-3 radiometric quality analysis and polarimetric validation over its five years of operation. Polarimetric calibration without recourse to the CRs is utilized to measure the polarimetric distortions regardless of the region, and thus requires a higher calibration accuracy for the GaoFen-3 polarimetric monitoring task. Consequently, the modified Quegan method is developed by relaxing the target azimuth symmetry constraint with the Amazon forest datasets. The experiments based on the CRAS demonstrate that the main radiometric characteristics could reach the international level, with an estimated noise-equivalent sigma zero of approximately −30 dB, a radiometric resolution that is better than 2.9 dB, and a single-imagery relative radiation accuracy that is better than 0.51 dB. For polarimetric validation, the modified Quegan method was utilized to measure the crosstalk for quad-pol products to ensure that it was than −40 dB. Meanwhile, non-negligible channel imbalance errors were found in the QPSII and WAV modes, and they were effectively well-calibrated with strip estimators to satisfy the system design.

Deep Convolutional Compressed Sensing-Based Adaptive 3D Reconstruction of Sparse LiDAR Data: A Case Study for Forests

LiDAR point clouds are characterized by high geometric and radiometric resolution and are therefore of great use for large-scale forest analysis. Although the analysis of 3D geometries and shapes has improved at different resolutions, processing large-scale 3D LiDAR point clouds is difficult due to their enormous volume. From the perspective of using LiDAR point clouds for forests, the challenge lies in learning local and global features, as the number of points in a typical 3D LiDAR point cloud is in the range of millions. In this research, we present a novel end-to-end deep learning framework called ADCoSNet, capable of adaptively reconstructing 3D LiDAR point clouds from a few sparse measurements. ADCoSNet uses empirical mode decomposition (EMD), a data-driven signal processing approach with Deep Learning, to decompose input signals into intrinsic mode functions (IMFs). These IMFs capture hierarchical implicit features in the form of decreasing spatial frequency. This research proposes using the last IMF (least varying component), also known as the Residual function, as a statistical prior for capturing local features, followed by fusing with the hierarchical convolutional features from the deep compressive sensing (CS) network. The central idea is that the Residue approximately represents the overall forest structure considering it is relatively homogenous due to the presence of vegetation. ADCoSNet utilizes this last IMF for generating sparse representation based on a set of CS measurement ratios. The research presents extensive experiments for reconstructing 3D LiDAR point clouds with high fidelity for various CS measurement ratios. Our approach achieves a maximum peak signal-to-noise ratio (PSNR) of 48.96 dB (approx. 8 dB better than reconstruction without data-dependent transforms) with reconstruction root mean square error (RMSE) of 7.21. It is envisaged that the proposed framework finds high potential as an end-to-end learning framework for generating adaptive and sparse representations to capture geometrical features for the 3D reconstruction of forests.

Exploration of convolutional neural network architectures for large region map automation

Deep learning semantic segmentation algorithms have provided improved frameworks for the automated production of Land-Use and Land-Cover (LULC) maps, which significantly increases the frequency of map generation as well as consistency of production quality. In this research, a total of 28 different model variations were examined to improve the accuracy of LULC maps. The experiments were carried out using Landsat 5/7 or Landsat 8 satellite images with the North American Land Change Monitoring System labels. The performance of various CNNs and extension combinations were assessed, where VGGNet with an output stride of 4, and modified U-Net architecture provided the best results. Additional expanded analysis of the generated LULC maps was also provided. Using a deep neural network, this work achieved 92.4% accuracy for 13 LULC classes within southern Manitoba representing a 15.8% improvement over published results for the NALCMS. Based on the large regions of interest, higher radiometric resolution of Landsat 8 data resulted in better overall accuracies (88.04%) compare to Landsat 5/7 (80.66%) for 16 LULC classes. This represents an 11.44% and 4.06% increase in overall accuracy compared to previously published NALCMS results, including larger land area and higher number of LULC classes incorporated into the models compared to other published LULC map automation methods.

Editorial for the Special Issue “Latest Development in 3D Mapping Using Modern Remote Sensing Technologies”

Recent advances in remote sensing technologies have provided the research community with unprecedented geospatial data characterized by high geometric, radiometric, spectral, and temporal resolution [...]

On the radiometric resolution of SAR signals

In accordance with the provisions of the theory of statistical decisions, an expression for the error probability of discriminating the intensities of two signals as a function of the radiometric resolution of a synthetic aperture radar is obtained. The result of observing fluctuations at the input of the processing channel over a finite interval is represented by a sequence of complex samples that are the sum of samples of uncorrelated normal noise and signal samples. This sequence is considered as a normal complex random vector with a specified normalized correlation matrix, while the processing algorithm is assumed to be given. The proposed approach is compared with known methods for determining the radiometric resolution: the heuristic method and the differential radio contrast method. The advantages of the proposed method are demonstrated.

Achievable parameters of the X-band synthetic aperture radar based on small satellite

Space-based synthetic aperture radar systems are actively used for remote sensing of the Earth and have significant capabilities. Advances in microelectronics have made it possible to create and launch into orbit compact radars based on small satellites. Analysis of the capabilities of such radars is an actual problem. In this paper the issues of simulation and estimation of the antenna area, noise equivalent sigma zero, spatial and radiometric resolution of space-based synthetic aperture radars (SAR) operating on the basis of small spacecraft are considered. The necessary mathematical apparatus is given and the above values are calculated for the X-band SAR operating in strip mode at maximum resolution. The influence of the main technical parameters of space SAR on dimensions of antenna, on sensitivity, spatial and radiometric resolution is analyzed.

Crop yield estimation using different remote sensing data: literature review

The climatic conditions and many other environmental factors have an impact on the crop growth stage and then on crop yield. The evaluation of seasonal crop production requires simultaneously monitoring crop yield conditions and early evaluation of significantly reduced production caused by unexpected disasters. Early detection of stunted crop growth can help prevent a disaster or help plan to prevent its occurrence or spread. On the other side, Farmlands cover a wide area of the planet’s surface, so the use of advanced technologies (for example, remote sensing) is very important to minimize the cost of monitoring, and eliminate the wasting of natural resources. Nowadays, the different satellite types such as Landsat, Sentinel, MODIS, and, Spot lead to variations in remote sensing data in spatial, temporal, radiometric, and spectral resolution. This is done to obtain many indices to utilize in crop management and the environmental effect. This review paper presents many studies to clarify three main tasks. Firstly, present the importance of using different types of remote sensing data depending on the reason for use. Secondly, the most famous indices have been used in many studies for different purposes to obtain accurate crop management, such as Normalized Difference Vegetation Index (NDVI), and Land Surface Temperature (LST). Lastly, conclude the review by presenting the potential of the various remote sensing data and the importance of many indices that are helpful for crop and environmental factors monitoring.

Impacts of Agricultural Activities on Land Degradation along the Bomboré River in Burkina Faso

Land along the Bomboré River in the rural commune of Mogtédo in Burkina Faso is experiencing degradation. The explanatory causes of this degradation constitute the subject of this study. To do this, a survey was conducted among agricultural producers deployed along the watercourse. Soil profiles were described and samples were taken to analyze pH, soil organic carbon, soil organic matter, total nitrogen, and texture. The RUSLE model approach based on landstat8 OLI/TIRS and SRTM satellite images dated December 17, 2021 with fairly good radiometric, spatial, and spectral resolution was used to calculate the land loss rate. In terms of results, the potentially irrigable areas that spread out on both sides of the banks of the river cover 209.23 ha with a perimeter of 6.16 km. The number of irrigators is 26 producers and they grow 17.92 ha of vegetables. Soil analyzes indicate the presence of a moderate acid on the vertisol with a pH between 5.57 and 5.86. On the depth 0 - 30 cm of the horizon, the color of the horizons ranges from 5YR4/2 on the talweg and on the right bank to 7.5YR3/2 on the left bank and presents no risk of salinity because the electrical conductivity measured is less than 1dS/cm. The diagnosis of hydromechanical equipment shows that producers use 46 motor pumps for irrigation, of which 15 motor pumps run on gasoline and 31 motor pumps on butane gas with a ratio of 1.7 motor pumps per producer. The number of Polyvinyl Chloride (PVC) pipes used by producers in combination with a motor pump gives an average of 44 per farmer. In terms of mineral fertilization, the gross doses used by producers are 415.53 kg/ha of NPK and 201.55 kg/ha of urea, while the quantities of phytosanitary products are 3.99 l/ha of pesticides and 1.42 l/ha of herbicides. Agricultural activities emit about 222,436.66 kgCO2eq into the atmosphere, whose emissions from motor pumps represent 84.52% of these total emissions. The land loss estimate gives an average rate of 2.30 t/ha/year of land loss. This loss is due to the effects of poor agricultural practices, water erosion, and the drainage channels and gullies created by the anarchic installation of dwellings around the edges of the river. This study calls for more monitoring actions to sustainably safeguard the soil and water resources of this river which contribute to the survival of more than 73,214 inhabitants.

Suspended Particulate Matter Analysis of Pre and During Covid Lockdown Using Google Earth Engine Cloud Computing: A Case Study of Ukai Reservoir.

Presence of suspended particulate matter (SPM) in a waterbody or a river can be caused by multiple parameters such as other pollutants by the discharge of poorly maintained sewage, siltation, sedimentation, flood and even bacteria. In this study, remote sensing techniques were used to understand the effects of pandemic-induced lockdown on the SPM concentration in the lower Tapi reservoir or Ukai reservoir. The estimation was done using Landsat-8 OLI (Operational Land Imager) having radiometric resolution (12-bit) and a spatial resolution of 30m. The Google Earth Engine (GEE) cloud computing platform was used in this study to generate the products. The GEE is a semi-automated workflow system using a robust approach designed for scientific analysis and visualization of geospatial datasets. An algorithm was deployed, and a time-series (2013-2020) analysis was done for the study area. It was found that the average mean value of SPM in Tapi River during 2020 is lowest than the last seven years at the same time.

Development and application of a new sensitivity analysis model for the remote sensing retrieval of heavy metals in water

This work proposes a new sensitivity analysis model, referred to as the D-δ(ε) model, for the remote sensing retrieval of heavy metals in bodies of water. By defining the reflectance ratio function (δ(ε)), we deduce the mathematical relationships between the heavy metal concentration sequences (Di) that can be effectively used for remote sensing retrievals and the radiometric resolution (ε) of the remote sensing instrument. Then, as a function of wavelength, we obtain the curve of the lower limit of the heavy metal concentrations in water that can be retrieved by remote sensing. To demonstrate the advantages of this model, we take two compounds, copper sulphate (CuSO4) and cadmium sulphide (CdS), as examples to discuss the remote sensing sensitivity of different wavelengths when retrievals are performed using the Chinese HJ-1A's hyperspectral imager (HSI). The results showed that the lowest detectable concentration of CuSO4 in the wavelength range of 460.04–496 nm (corresponding to bands 1–17 of the HSI image) can be below 0.15 mg/L, while the concentration of CdS can be lower than 0.001 mg/L in the separate ranges of 460.04–493.59 nm (bands 1–16) and 526.885–594.79 nm (bands 29–51). This model clearly demonstrates the mathematical relationship obeyed by "D-ε". Additionally, this model can not only calculate the retrieval concentration sequences at any observation wavelength but also intuitively provide the curve of the lower concentration limit for heavy metal retrievals. This work provides a theoretical basis for the selection of the most sensitive bands for remote sensing retrieval using hyperspectral images in the future.

Assessment of Atmospheric Correction Processors and Spectral Bands for Satellite-Derived Bathymetry Using Sentinel-2 Data in the Middle Adriatic

Satellite-derived bathymetry (SDB) based on multispectral satellite images (MSI) from the satellite’s optical sensors is a recent technique for surveying shallow waters. Sentinel-2 satellite mission with an open access policy and high spatial, radiometric, and temporal resolution of MSI-s started a new era in the mapping of coastal bathymetry. More than 90 percent of the electromagnetic (EM) signal received by satellites is due to the atmospheric path of the EM signal. While Sentinel-2 MSI Level 1C provides top-of-atmosphere reflectance, Level 2A provides bottom-of-atmosphere reflectance. The European Space Agency applies the Sen2Cor algorithm for atmospheric correction (AC) to model the atmospheric path of the signal and reduce the MSI reflectance from L1C to L2A over the land area. This research evaluated the performance of different image-based AC processors, namely: Sen2Cor, Acolite, C2RCC, and iCOR for SDB modelling. The empirical log band ratio algorithm was applied to a time series of Sentinel-2 MSI in the middle Adriatic. All AC processors outperformed Sentinel-L2A MSI for SDB. Acolite and iCOR demonstrated accurate performance with a correlation coefficient higher than 90 percent and the RMSE under 2 m for depths up to 20 m. C2RCC produced more robust bathymetry models and was able to retrieve the depth information from more scenes than any other correction. Furthermore, a switch model combining different spectral bands improved mapping in shallow waters, demonstrating the potential of SDB technology for the effective mapping of shallow waters.

Multi-spectral multi-image super-resolution of Sentinel-2 with radiometric consistency losses and its effect on building delineation

High resolution remote sensing imagery is used in a broad range of tasks, including detection and classification of objects. High-resolution imagery is however expensive to obtain, while lower resolution imagery is often freely available and can be used for a range of social good applications. To that end, we curate a multi-spectral multi-image dataset for super-resolution of satellite images. We use PlanetScope imagery from the SpaceNet-7 challenge as the high resolution reference and multiple Sentinel-2 revisits of the same location as the low-resolution imagery. We present the first results of applying multi-image super-resolution (MISR) to multi-spectral remote sensing imagery. We, additionally, introduce a radiometric-consistency module into the MISR model to preserve the high radiometric resolution and quality of the Sentinel-2 sensor. We show that MISR is superior to single-image super-resolution (SISR) and other baselines on a range of image fidelity metrics. Furthermore, we present the first assessment of the utility of multi-image super-resolution on a semantic and instance segmentation – common remote sensing tasks – showing that utilizing multiple images results in better performance in these downstream tasks, but MISR pre-processing is non-essential.