Resolution Of Bands Research Articles

A method for high-concentration agarose gel preparation and its application in high-resolution separation of low-molecular-weight nucleic acids and proteins

Separation of nucleic acids and proteins using gels has always been a crucial part of molecular biology research. For low-molecular-weight nucleic acids and proteins, low- and medium-concentration agarose gels cannot achieve the high resolution as polyacrylamide gels. We found that 6 %–14 % high-concentration agarose gels (HAGs) could be easily dissolved in an autoclave and the vertical gel cast can be effortlessly filled using an easy-made plastic box. Coupled with the improved buffer condition, HAG electrophoresis resulted in a good resolution of DNA and protein bands. With conventional TBE buffer plus 0.2 % NaCl, DNA fragments that differ by 2–5-bp within the 50–200-bp size range can be resolved on 6 %–8 % HAGs. By using TBE without NaCl, DNA fragments that differ by 2-bp or 2-nt within the 10–100-bp size range can be well resolved on >8 % HAGs. Using a buffer system comprising 1 M Tris-Cl for gel preparation, 0.2 M Tris-Cl/0.2 % SDS as upper tank buffer, and 0.2 M Tris-Cl as the lower tank buffer, HAGs achieved good molecular weight separation of total bacterial and plant proteins in the 10–200 kDa range. In conclusion, we developed a method for HAG preparation and electrophoresis of low-molecular-weight nucleic acids and proteins.

Prediction of the concentration of cadmium in agricultural soil in the Czech Republic using legacy data, preferential sampling, Sentinel-2, Landsat-8, and ensemble models

The current study assesses and predicts cadmium (Cd) concentration in agricultural soil using two Cd datasets, namely legacy data (LD) and preferential sampling-legacy data (PS-LD), along with four streams of auxiliary datasets extracted from Sentinel-2 (S2) and Landsat-8 (L8) bands. The study was divided into two contexts: Cd prediction in agricultural soil using LD, ensemble models, 10 and 20 m spatial resolution of S2 and L8 (context 1), and Cd prediction in agricultural soil using PS-LD, ensemble models and 10 and 20 m spatial resolution of S2 and L8 (context 2). In context 1, ensemble 1, L8 with PS-LD was the cumulative optimal approach that predicted Cd in agricultural soil with a higher R2 value of 0.76, root mean square error (RMSE) of 0.66, mean absolute error (MAE) of 0.35, and median absolute error (MdAE) of 0.13. However, with R2 = 0.78, RMSE = 0.63, MAE = 0.34, and MdAE = 0.15, ensemble 1, S2 of PS-LD was the best prediction approach in predicting Cd concentration in agricultural soil in context 2. Overall, the predictions from both contexts indicated that ensemble 1 of S2 combined with PS-LD was the most appropriate and best model for Cd prediction in agricultural soil. The modeling approaches' uncertainty in both contexts was assessed using ensemble-sequential gaussian simulation (EnSGS), which revealed that the degree of uncertainty propagated in the study area was within 5% in both contexts. The combination of the PS dataset and the LD along with ensemble models and the remote sensing dataset, produced promising results. Nonetheless, the results demonstrated that the 20 m spatial resolution band dataset used in the prediction of Cd in agricultural soil outperformed the 10 m spatial resolution. When PS is combined with LD, an appropriate modeling approach, and a well-correlated remote sensing dataset are used, good results are obtained.

Detection of Spectral Reflective Changes for Temporal Resolution of Land Cover (LC) for Two Different Seasons in central Iraq

The purpose of the study is the city of Baghdad, the capital of Iraq, was chosen to study the spectral reflection of the land cover and to determine the changes taking place in the areas of the main features of the city using the temporal resolution of multispectral bands of the satellite Landsat 5 and 8 for MSS and OLI sensors respectively belonging to NASA and for the period 1999-2021, and calculating the increase and decrease in the basic features of Baghdad .The main conclusions of the study were, This study from 1999 to 2021 and in two different seasons: the Spring of the growing season and Summer the dry season. When using the supervised classification method to determine the differences, the results showed remarkable changes. Where he was in 1999 Normalized Difference Vegetation Index (NDVI) 925km2 and Normalized Difference Water Index (NDWI) 75.3km2 In the case of an increase during the growth period, while the values decreased during the period of dry to (NDVI) 390.8km2 and (NDWI) 51.9km2.As for Soil Adjusted Vegetation Index (SAVI) 1692.9km2 and Normalized Difference Built up Index (NDBI) 782.1km2 we notice a decrease in the growth period, while the values increase during the dry period to (SAVI) 2239.1km2 and (NDBI) 1495.7km2. In 2021 (NDVI) 242.7km2 (NDWI) 83.4km2 in the case of an increase during the growth period, while the values decreased during the period of dry to (NDVI) 122.2km2 and (NDWI) 73.2km2 .As for (SAVI) 3016.3km2 (NDBI) 1263.3km2 we notice a decrease in the growth period, while the values increase during the dry period to (SAVI) 3702.3km2 and (NDBI) 1882.2km2. Where most of the changes in LC were due to human activities, and the most prominent changes in LC were due to urban expansion on agricultural lands continuously in all years that resulted from land degradation.

Relevance of UAV and sentinel-2 data fusion for estimating topsoil organic carbon after forest fire

The evaluation at detailed spatial scale of soil status after severe fires may provide useful information on the recovery of burned forest ecosystems. Here, we aimed to assess the potential of combining multispectral imagery at different spectral and spatial resolutions to estimate soil indicators of burn severity. The study was conducted in a burned area located at the northwest of the Iberian Peninsula (Spain). One month after fire, we measured soil burn severity in the field using an adapted protocol of the Composite Burn Index (CBI). Then, we performed soil sampling to analyze three soil properties potentially indicatives of fire-induced changes: mean weight diameter (MWD), soil moisture content (SMC) and soil organic carbon (SOC). Additionally, we collected post-fire imagery from the Sentinel-2A MSI satellite sensor (10–20 m of spatial resolution), as well as from a Parrot Sequoia camera on board an unmanned aerial vehicle (UAV; 0.50 m of spatial resolution). A Gram-Schmidt (GS) image sharpening technique was used to increase the spatial resolution of Sentinel-2 bands and to fuse these data with UAV information. The performance of soil parameters as indicators of soil burn severity was determined trough a machine learning decision tree, and the relationship between soil indicators and reflectance values (UAV, Sentinel-2 and fused UAV-Sentinel-2 images) was analyzed by means of support vector machine (SVM) regression models. All the considered soil parameters decreased their value with burn severity, but soil moisture content, and, to a lesser extent, soil organic carbon discriminated at best among soil burn severity classes (accuracy = 91.18 %; Kappa = 0.82). The performance of reflectance values derived from the fused UAV-Sentinel-2 image to monitor the effects of wildfire on soil characteristics was outstanding, particularly for the case of soil organic carbon content (R2 = 0.52; RPD = 1.47). This study highlights the advantages of combining satellite and UAV images to produce spatially and spectrally enhanced images, which may be relevant for estimating main impacts on soil properties in burned forest areas where emergency actions need to be applied.

Forest landscape restoration: Spectral behavior and diversity of tropical tree cover classes

Forest landscape restoration (FLR) commitments have been established in the past years to restore over 200 million hectares, as part of the global Bonn Challenge goal, mostly through the implementation of several different restorative practices in degraded lands, ranging from commercial tree monocultures to restoration plantings. The potential of such contrasting restorative practices to support biodiversity conservation and ecosystem services provision vary over space and time, making the monitoring of FLR programs an emerging challenge. Remote sensing techniques, together with innovative technologies for data acquisition, treatment, and analysis have proven to be strategic for planning and monitoring FLR, yet there are still important unresolved questions. Here, we evaluated the potential of multispectral orbital images of the high spatial (5 m) and spectral (12 bands) resolution VENμS microsatellite, joint project of the Israeli Space Agency and CNES, to classify the spectral behavior and diversity of six tree cover classes (savanna woodlands, old- and second-growth semi-deciduous forests, young restoration plantings, and eucalyptus and pine tree monocultures) commonly found in FLR programs in tropical regions. We assessed how these tree cover classes located in a study landscape in southeastern Brazil differ according to their spectral response (winter and summer bands, and vegetation indices), canopy variability (textural features), seasonal behavior (delta layers - difference between summer and winter vegetation indexes), and spectral diversity, and used these attributes as variables to the model. We used the Random Forest algorithm to generate the models and evaluate how the tree cover classes differ in the classification and how the variables supported the model. We achieved high values of global accuracy (91.9%) and “F1 score” (above 0.8) for all tree cover classes, in which second-growth forest presented the lowest accuracy. The textural layers, delta layers, and the spectral diversity layers were the most important attributes to discriminate among tree cover classes. We demonstrate here the potential of using VENμS or similar sensor images together with different image processing and machine learning algorithms to monitor FLR programs, allowing further remote sensing approaches and in-deep field assessments to advance evaluation of FLR benefits for nature and people. We demonstrated how the fusion of all these types of data and innovative approaches to data processing, can result in novel ways to assess restoration performance and open new avenues to upscale monitoring, bridging the gap between FLR expectations and achieved goals.

Super-resolution of Sentinel-2 images using Wasserstein GAN

ABSTRACT The Sentinel-2 satellites deliver 13 band multi-spectral imagery with bands having 10 m, 20 m or 60 m spatial resolution. The low-resolution bands can be upsampled to match the high resolution bands to extract valuable information at higher spatial resolution. This paper presents a Wasserstein Generative Adversarial Network (WGAN) based approach named as DSen2-WGAN to super-resolve the low-resolution (i.e., 20 m and 60 m) bands of Sentinel-2 images to a spatial resolution of 10 m. A proposed generator is trained in an adversarial manner using the min-max game to super-resolve the low-resolution bands with the guidance of available high-resolution bands in an image. The performance evaluated using metrics such as Signal Reconstruction Error (SRE) and Root Mean Squared Error (RMSE) shows the effectiveness of the proposed approach as compared to the state-of-the-art method, DSen2 as the DSen2-WGAN reduced RMSE by 14.68% and 7%, while SRE improved by almost 4% and 1.6% for 6 and 2 super-resolution. Lastly, for further evaluation, we have used trained DSen2-WGAN model to super-resolve the bands of EuroSAT dataset, a satellite image classification dataset based on Sentinel-2 images. The per band classification accuracy of low-resolution bands shows significant improvement after super-resolution using our proposed approach.

Remote detection of marine debris using Sentinel-2 imagery: A cautious note on spectral interpretations.

Remote detection of marine debris (also called marine litter) has received increased attention in the past decade, with the Multispectral Instruments (MSI) onboard the Sentinel-2A and Sentinel-2B satellites being the most used sensors. However, because of their mixed band resolutions and small sub-pixel coverage of debris within a pixel (e.g., <10%), caution is required when interpreting the spectral shapes of MSI pixels. Otherwise, the spectrally distorted shapes may be misused as spectral endmembers (signatures) or interpreted as from certain types of floating matters. Here, using simulations and MSI data, I show the origin of the spectral distortions and emphasize why both pixel averaging and pixel subtraction are critical in algorithm design and spectral interpretation for the purpose of remote detection of marine debris using Sentinel-2 MSI sensors.

Coexistence of ground and excited state intramolecular proton transfer of the Schiff base 2-((E)-(naphthalene-3-ylimino)-methyl)phenol: A combined experimental and computational study

Prototropic behavior of the Schiff base compound, 2-((E)-(naphthalene-3-ylimino)-methyl)phenol (NMP), has been explored by photophysical/photochemical means and reported in this paper in a comprehensive manner. The study, comprising of absorption, steady state and time resolved fluorescence, 1H NMR, together with quantum chemical calculations, establishes that the probe undergoes intramolecular proton transfer (IPT) process in both ground and lowest excited singlet state. Resolution of the low energy absorption band of NMP in different solvents and observation of two different emission bands upon excitation at two different positions within the low energy absorption band indicates co-existence of two distinct species of the probe in the ground state, implying occurrence of the ground state intramolecular proton transfer (GSIPT) process. Large downshifted 1H NMR peak at δ > 13 ppm suggests a strong intramolecular H-bonding in the ground state and goes in favor of the GSIPT process. Excited state intramolecular proton transfer (ESIPT) of the probe has been confirmed by the existence of the large Stokes shifted emission band of the tautomer along with the normal (enol) emission band, especially in non-polar media. Observation of the single isoemissive point in the time resolved area normalized emission spectra (TRANES) in nonpolar solvents confirms existence of two distinct species (enol (N*) and tautomer (T*)) in the photoexcited state. Quantum chemical calculations using DFT/TDDFT have been performed to provide support to the experimental findings.

Combining Landsat 8 and Sentinel-2 Data in Google Earth Engine to Derive Higher Resolution Land Surface Temperature Maps in Urban Environment

Thermal infrared (TIR) satellite imagery collected by multispectral scanners is important to map land surface temperature on a global scale. However, the TIR spectral bands are typically available in coarser spatial resolution than other multispectral bands of shorter wavelengths. Therefore, the spatial resolution of the derived land surface temperature (LST) is limited to around 100 m. This constrains the applications of such thermal satellite sensors in which finer detail of LST spatial pattern is relevant, especially in an urban environment where the land cover structure is complex. Among the missions deployed on the Earth’s orbit, NASA’s TIRS sensor onboard Landsat 8 and Landsat 9, and ASTER onboard Terra provide the highest spatial resolution of the thermal band. On the other hand, ESA’s Sentinel-2 multispectral imagery is collected at a higher spatial resolution of 10 m with a 5-day temporal resolution, but scanning in the TIR band is not available. This study makes use of the known relationship between LST and land cover metrics, such as the normalized difference vegetation index (NDVI), built-up index (NDBI), and water index (NDWI). We define a multiple linear regression model based on the spectral indices and LST derived from Landsat 8 data to inform the same model in which the equivalent spectral indices derived from Sentinel-2 are used to predict LST at 10 m resolution. Results of this approach are demonstrated in a case study for Košice city, Slovakia, where the multiple linear model based on Landsat 8 data achieved an R2 of 0.642. The correlation between the observed Landsat 8 LST and predicted LST from Sentinel-2 aggregated to the same resolution as the observed LST was high (r = 0.91). Despite the imperfections of the downscaling model, the derived LST at 10 m resolution provides a better perception of the LST field that can be easily associated with land cover features present in urban environment. The LST downscaling approach was implemented into Google Earth Engine. It provides a user-friendly online application that can be used for any city or urban region for generating a more realistic spatial pattern of LST than can be directly observed by contemporary Earth observation satellites. The tool aids in urban decision making and planning on how to mitigate overheating of cities to improve the life quality of their citizens.

First-principles Calculations to Investigate Emerging Double Perovskites K2NaMoX6 (X=Cl, I) Compounds for Magnetic and Optoelectronic Applications

In the present work, the structural, electronic, optical and mechanical properties of lead free halide double perovskites (HDPs) K2NaMoX6 (X = I, Cl) are addressed by using density functional theory (DFT). The optimized structural parameters validate the stability of the magnetic states of K2NaMoX6 (X = I, Cl) compounds with magnetic moment of 3.0006 and 3.00001 μB, respectively. The stability of cubic phase of both compounds is justified by tolerance factor. Spin resolved band structure (BS) and density of states (DOS) demonstrate that K2NaMoCl6 and K2NaMoI6 are direct-gap semiconducting materials. Moreover, the computed elastic properties reveal the ductile and anisotropic nature of both compounds. The optical features such as, the absorption coefficient (α(ω)), real ϵ1(ω) and imaginary parts ϵ2(ω) of the dielectric function, optical conductivity (σ(ω)), refractive index (n(ω)), extinction coefficient (k(ω)) and reflectivity (R(ω)) are also computed. In optoelectronic devices, these HDPs have intriguing uses due to the excessive α(ω) and small reflection in the visible-UV span.

Super-resolving and composing building dataset using a momentum spatial-channel attention residual feature aggregation network

Model generalizability is crucial in the deployment of deep learning (DL) techniques. When trained on specific datasets, generalizability problems arise across many applications of DL including building extractions. Apart from regularizing the training process, collecting data with distinctive characteristics or distributions can be a promising solution. Over the past decade, several open building datasets have been released. However, in practice, a single dataset cannot overcome the generalization error. By unifying the spatial resolution and spectral bands of different datasets, those datasets could be integrated to relieve the generalization error in building footprint extraction. In this work, we focused on the difference in the spatial resolution between different building datasets. We first examined state-of-the-art super-resolution methods and proposed our own method based on Residual Feature Aggregation Network (RFANet), which we named Momentum and Spatial-Channel Attention RFANet (MSCA-RFANet). We then benchmarked our MSCA-RFANet in a comparative study; our new method achieved higher performance on spatial resolution enhancement. Specifically, in the four times spatial resolution enhancement on the SWOOP 2010 Dataset, our MSCA-RFANet result’s peak signal-to-noise ratio (PSNR) of 30.72 dB exceeded that of RFANet (30.66 dB). Likewise, we achieved a lower mean squared error (MSE) of 36.64 compared to RFANet’s 36.94. With detailed benchmarks against Second-order Attention Network (SAN) and Residual Channel Attention Network (RCAN), we confirmed the superior performance of our method in enhancing the spatial resolution of high-spatial-resolution images. Then, we explored the impact of super-resolution resolution and data composition on building footprint extraction. Our building footprint extraction experiments demonstrated the positive impact of super-resolution and data composition. These promising results showed that our method is suitable to integrating existing public building dataset to overcome generalization error in DL-based building footprint extraction.

Identification of Proteases and Protease Inhibitors in Seeds of the Recalcitrant Forest Tree Species Quercus ilex.

Proteases and protease inhibitors have been identified in the recalcitrant species Quercus ilex using in silico and wet methods, with focus on those present in seeds during germination. In silico analyses showed that the Q. ilex transcriptome database contained 2,240 and 97 transcripts annotated as proteases and protease inhibitors, respectively. They belonged to the different families according to MEROPS,1 being the serine and metallo ones the most represented. The data were compared with those previously reported for other Quercus species, including Q. suber, Q. lobata, and Q. robur. Changes in proteases and protease inhibitors alongside seed germination in cotyledon and embryo axis tissues were assessed using proteomics and in vitro and in gel activity assays. Shotgun (LC–MSMS) analysis of embryo axes and cotyledons in nonviable (NV), mature (T1) and germinated (T3) seeds allowed the identification of 177 proteases and 12 protease inhibitors, mostly represented by serine and metallo types. Total protease activity, as determined by in vitro assays using azocasein as substrate, was higher in cotyledons than in embryo axes. There were not differences in activity among cotyledon samples, while embryo axis peaked at germinated T4 stage. Gel assays revealed the presence of protease activities in at least 10 resolved bands, in the Mr range of 60–260 kDa, being some of them common to cotyledons and embryo axes in either nonviable, mature, and germinated seeds. Bands showing quantitative or qualitative changes upon germination were observed in embryo axes but not in cotyledons at Mr values of 60–140 kDa. Proteomics shotgun analysis of the 10 bands with protease activity supported the results obtained in the overall proteome analysis, with 227 proteases and 3 protease inhibitors identified mostly represented by the serine, cysteine, and metallo families. The combined use of shotgun proteomics and protease activity measurements allowed the identification of tissue-specific (e.g., cysteine protease inhibitors in embryo axes of mature acorns) and stage-specific proteins (e.g., those associated with mobilization of storage proteins accumulated in T3 stage). Those proteins showing differences between nonviable and viable seeds could be related to viability, and those variables between mature and germinated could be associated with the germination process. These differences are observed mostly in embryo axes but not in cotyledons. Among them, those implicated in mobilization of reserve proteins, such as the cathepsin H cysteine protease and Clp proteases, and also the large number of subunits of the CNS and 26S proteasome complex differentially identified in embryos of the several stages suggests that protein degradation via CNS/26S plays a major role early in germination. Conversely, aspartic proteases such as nepenthesins were exclusively identified in NV seeds, so their presence could be used as indicator of nonviability.

Estimation of Canopy Structure of Field Crops Using Sentinel-2 Bands with Vegetation Indices and Machine Learning Algorithms

Leaf angle distribution (LAD), or the leaf mean tilt angle (MTA) capturing its central value, is used to quantify the direction of the leaf surface in a canopy and is one of the most important canopy structuraltraits. Combined with the other important structure parameter, leaf area index (LAI), LAD determines the light interception of a crop canopy. However, unlike LAI, only few studies have addressed the direct retrieval of LAD or MTA from remote sensing data. Recently, it has been shown that the red edge is a key spectral region where the effect of leaf angle on crop spectral reflectance can be separated from that of other structural variables. The Multispectral imager (MSI) onboard the Sentinel-2 (S2) satellite has two specially designed red-edge channels in this spectral region and thus can potentially be used for large-scale mapping of MTA at high spatial and temporal resolutions. Unfortunately, no field data on leaf angles at the scale of S2 pixel are available. Therefore, we simulated 5000 observations of different crops using the PROSAIL canopy reflectance model. Further, we used the MTA and LAI data of six crop species growing in 162 experimental plots in Finland and simulated their reflectance signal in S2 bands by resampling AISA airborne imaging spectroscopy data. Four common machine learning regression algorithms (random forest, support vector machine, multilayer perceptron network and partial least squares regression) were examined for retrieving canopy structure parameters, including leaf angle, from the simulated reflectances. Further, we analyzed the utility of 12 vegetation indices (VIs) well known to be sensitive to canopy structure for canopy structure estimation. Six of the studied indices used information from the visible part of the spectrum and the near infrared (NIR) while another six were selected to also utilize the red edge bands specific to S2. We found that S2 band 6 in the red edge had a strong correlation with MTA (R2 = 0.79 in model simulation and R2 = 0.87 in field measurements) but a low correlation with LAI (R2 = 0.07 in model simulation and R2= 0.06 in field measurements). Of the six red edge-based VIs, four (NDVIRE, CIRE, WDRVIRE and MSRRE) depended less on MTA than the visible NIR-based VIs and thus could be useful for estimating LAI for any LAD. The other two red edge-based VIs, IRECI and S2REP, had stronger correlations with MTA (R2 = 0.67 and 0.52, respectively) than LAI (R2 = 0.24 and 0.19, respectively). Additionally, MTA was accurately estimated (RMSE = 1.1–2.4° in model simulations and RMSE = 2.2–3.9° in field measurements) using the four 10 m spatial resolution bands with the RF, SVM and MLP algorithms, without information in the red edge. These promising results indicate the capability of S2 in accurately mapping the MTA of field crops on a large scale.

Hyperspectral denoising based on the principal component low-rank tensor decomposition

Abstract Due to the characteristics of hyperspectral images (HSIs), such as their high spectral resolution and multiple continuous narrow bands, HSI technology has become widely used in fields such as target recognition, environmental detection, and agroforestry detection. HSIs are subject, for various reasons, to noise in the processes of data acquisition and transmission. Therefore, the denoising of HSIs is very necessary and important. In this article, according to the characteristics of HSIs, an HSI denoising model combining principal component analysis (PCA) and CANDECOMP/PARAFAC decomposition (CP decomposition) is proposed, which is called PCA-TensorDecomp. First, we use PCA to reduce the dimension of HSI signals by obtaining the first K principal components and get the principal composite components. The low-rank part corresponding to the first K principal components is considered the characteristic signal. Then, low-rank CP decomposition is carried out, to denoise the first principal components and the remaining minor components, the secondary composite components, which contain a large amount of noise. Finally, the inverse PCA is then used to restore the HSIs denoised, such that the effect of comprehensive denoising is achieved. To test the effectiveness of the improved algorithm introduced in this article, we compare it with several methods on simulated and real hyperspectral data. The results of the analysis herein indicate that the proposed algorithm possesses a good denoising effect.

Developing and evaluating the feasibility of a new spatiotemporal fusion framework to improve remote sensing reflectance and dynamic LAI monitoring

Multi-sensor fusion provides an effective way for applications requiring remote sensing data with high spatiotemporal resolution. Especially for agricultural areas with complex planting structures and rapid changes in crop phenology, more detailed and dense time-series remote sensing data are necessary. The Sentinel-2 Multispectral Imager (S2-MSI) sensor with high spatial resolution (10–60 m) and temporal resolution (5–10 days) plays a key role in spatiotemporal fusion. But the inconsistent spatial resolution of the various bands hinders its potential application at 10 m resolution, and the multiple available fine images it provides are not fully utilized for spatiotemporal fusion. It is worth exploring how to maximize the spatial and temporal resolution of S2-MSI images to help improve the effect of spatiotemporal fusion and the dynamic monitoring of rapid crop growth. In this research, a new spatiotemporal fusion (STF) framework is developed to fuse the S2-MSI image (10 m) enhanced by Super-Resolution for multispectral Multiresolution Estimation (SupReME) algorithm and MODIS image (460 m) with a large spatial ratio (46). The proposed fusion method in the new STF framework combines the existing STF methods with Consistent Adjustment of the Climatology to Actual Observations (CACAO) algorithm, abbreviated as CA-STF. The accuracy of the fused reflectance and its capability for dynamic LAI monitoring were tested in Daman Superstation of Heihe watershed. The results indicate that: (1) the new STF framework is competent to fuse multi-source images with a ratio of 46 and outperforms the existing STF methods for both near-real-time and post-growth applications; (2) the proposed CA-STF method improves the fusion accuracy and spatial details even if only two S2-MSI images are available, especially for post-growth applications; (3) the vegetation indices (VIs) calculated from the fused images by the new STF framework provide a better correlation with LAINet measurements and improve dynamic LAI monitoring in accuracy and spatial details. This study proposes a framework to maximize the spatial and temporal resolution of S2-MSI images for spatiotemporal fusion. The synthetic daily time-series images with a high resolution of 10 m will have great potential for monitoring the dynamic changes of the land surface.

Higher resolution protein band visualisation via improvement of colloidal CBB-G staining by gel fixation

BackgroundGel staining is a crucial step that allows the visualisation of proteins separated through SDS-PAGE. Colloidal Coomassie Brilliant Blue-G (CBB-G) staining is among the commonly used visualisation methods due to several factors such as compatibility with mass spectrometry (MS) analysis, sensitivity, reproducibility, and simplicity of the staining process. However, the standard colloidal CBB-G staining has a drawback: the resolution of protein bands is compromised because of diffusion of proteins during the washing step.ResultsA modification to an established colloidal CBB-G staining method, which greatly increases the resolution of protein bands, is described. The addition of a fixation step, which prevents the diffusion of proteins during the washing step, is shown to increase protein band resolution.ConclusionThe fixation step is fast, flexible, and also retains all the advantages of the standard colloidal CBB-G staining methods. As there are no drawbacks, incorporating this fixation step into the standard colloidal CBB-G staining is an easy way to improve protein visualisation in SDS-PAGE.Graphical

Wideband, high spectral resolution UV convex grating imaging spectrometer based on an Offner structure.

Ultraviolet is an effective electromagnetic spectrum in material detection, which has wide application prospects in aerospace and environmental monitoring. A conventional imaging spectrometer has a narrow UV band and low spectral resolution. To solve this problem, a convex grating imaging spectrometer based on an Offner structure with F#2.5 and a 13 mm long slit was designed and developed. The working wavelength ranges from 200 to 433 nm, and the spectral resolution is greater than 0.5 nm. A hyperspectral data cube with both high spatial and spectral resolutions of external scenes can be obtained by the push-broom imaging mode. Fine Fraunhofer lines can be distinguished in the spectrum. The ultraviolet hyperspectral imager can be used for marine oil spills, trace gas monitoring, and other applications that require high signal-to-noise ratios, wide bands, and high spectral resolutions.

The Identification of Irrigated Crop Types Using Support Vector Machine, Random Forest and Maximum Likelihood Classification Methods with Sentinel-2 Data in 2018: Tashkent Province, Uzbekistan

Accurately mapping land use and land cover including agricultural use and the state of crops at various stages is important to address specific agro-ecological challenges, to implement sustainable agricultural practices, and monitor crops periodically. This study aims to provide a timely and accurate main irrigated crop types mapping at 10m resolution for Tashkent province based on multi-temporal Sentinel-2 data acquired for the growing season in 2018. This paper shows the potential use of multitemporal Sentinel-2 satellite data to derive an up-to-date irrigated crop types classification map of the study area. As single-date satellite imagery does not allow proper cropland classification, multitemporal and high-resolution Sentinel-2 data was used to capture small cropland fields and specific crop types for the vegetation period (April to October 2018). NDVI monthly profiles of crop types as well as additional 10 m resolution bands 2 and 3 were used as input data to perform and assess three classification algorithms: Support Vector Machine (SVM), Random Forest (RF), and Maximum Likelihood Classification (MLC). Accuracy assessment results showed that SVM showed the highest Overall Accuracy (OA) and Kappa Accuracy (KA). KA of classified images for SVM were 0.90 and 0.89 for the RF algorithm. Both performed well with close values. But MLC showed a lower result of KA 0.60. The paper also compares the area of derived irrigated cropland area with data from the State Committee for Statistics of Uzbekistan for selected crop types. Values for the crops "cotton" and "wheat" derived by SVM and RF methods show a high correlation with the provided statistical data. Based on the results, the SVM classification method is recommended for further mapping and monitoring of irrigated crop types in the region when Sentinel-2 data is used.

Solar park detection from publicly available satellite imagery

ABSTRACT The rapid increase in large-scale photovoltaic installations, or solar parks, causes a need to monitor their amount and allocation, and assess their impacts. While their spectral signature suggests that solar parks can be identified among other land covers, this detection is challenged by their low occurrence. Here, we develop an object-based random forest (RF) classification approach, using publicly available satellite imagery, which has the advantage of requiring relatively little training data and being easily extendable to large spatial extents and new areas. First, we segmented Sentinel-2 imagery into homogenous objects using a Simple Non-Iterative Clustering algorithm in Google Earth Engine. Thereafter, we calculated for each object the mean, standard deviation, and median for all 10- and 20-meter resolution bands of Sentinel-1 and Sentinel-2. These features are subsequently used to train and validate a range of RF models to select the most promising model setup. The training datasets consisted of subsampled presence/absence data, oversampled presence/absence data, and multiple land-cover categories. The best-performing model used an oversampled dataset trained on all 10- and 20- meter resolution spectral bands and the radar backscatter properties of one period. Independent test results show an overall classification accuracy of 99.97% (Kappa: 0.90). For this result, the producer accuracy was 85.86% for solar park objects and 99.999% for non-solar park objects. The user accuracy was 92.39% for solar park objects and 99.999% for non-solar park objects. These high classification accuracies indicate that our approach is suitable for transfer learning and is able to detect solar parks in new study areas.

Examples and Results of Aerial Photogrammetry in Archeology with UAV: Geometric Documentation, High Resolution Multispectral Analysis, Models and 3D Printing

The use of unmanned aerial vehicles (UAVs, also known as drones or RPA) in archaeology has expanded significantly over the last twenty years. Improvements in terms of the reliability, size, and manageability of these aircraft have been largely complemented by the high resolution and spectral bands provided by the sensors of the different cameras that can be incorporated into their structure. If we add to this the functionalities and improvements that photogrammetry programs have been experiencing in recent years, we can conclude that there has been a qualitative leap in the possibilities, not only of geometric documentation and in the presentation of the archaeological data, but in the incorporation of non-intrusive high-resolution analytics. The work that we present here gives a sample of the possibilities of both geometric documentation, creation of 3D models, their subsequent printing with different materials, and techniques to finally show a series of analytics from images with NGB (Nir + Green + Blue), Red Edge, and Thermographic cameras applied to various archaeological sites in which our team has been working since 2013, such as Clunia (Peñalba de Castro, Burgos), Puig Rom (Roses), Vilanera (L’Escala, Girona), and Cosa (Ansedonia, Italy). All of them correspond to different chronological periods as well as to varied geographical and morphological environments, which will lead us to propose the search for adequate solutions for each of the environments. In the discussions, we will propose the lines of research to be followed in a project of these characteristics, as well as some results that can already be viewed.