NIR Bands Research Articles

Methodology for Removing Striping Artifacts Encountered in Planet SuperDove Ocean-Color Products

The Planet SuperDove sensors produce eight-band, three-meter resolution images covering the blue, green, red, red-edge, and NIR spectral bands. Variations in spectral response in the data used to perform atmospheric correction combined with low signal-to-noise over ocean waters can lead to visible striping artifacts in the downstream ocean-color products. It was determined that the striping artifacts could be removed from these products by filtering the top of the atmosphere radiance in the red and NIR bands prior to selecting the aerosol models, without sacrificing high-resolution features in the imagery. This paper examines an approach that applies this filtering to the respective bands as a preprocessing step. The outcome and performance of this filtering technique are examined to assess the success of removing the striping effect in atmospherically corrected Planet SuperDove data.

Detection of changes in water salinity based on patrol monitoring and remote sensing methods

A significant number of rivers heavily influenced by anthropogenic pressures are not monitored (or monitored infrequently). For this reason, there is a need to develop modern methods allowing for the ongoing observation of water quality parameters, of which salinity is a key one. As a result of patrol monitoring information on changes in conductivity in the longitudinal profile of the Oder were obtained. The aim of this study is to correlate these results with Sentinel – 2 satellite imagery (VIS + NIR bands) to verify the hypothesis that remote sensing methods can be used to detect salinity changes in inland flowing waters. For this purpose data acquired with the multi-parameter probe during field expeditions, remote sensing methods and Geographical Information Systems tools (such as inter-bands algebra, pixel value extraction) and statistical methods were used. We concluded that the best spectra for salinity detection is Green as well as Salinity Index (SI) mostly showed statistically significant correlations. Remote sensing can be successfully used to detect changes in the salinity of flowing waters.

Improving Forest Above-Ground Biomass Estimation Accuracy Using Multi-Source Remote Sensing and Optimized Least Absolute Shrinkage and Selection Operator Variable Selection Method

Estimation of forest above-ground biomass (AGB) using multi-source remote sensing data is an important method to improve the accuracy of the estimate. However, selecting remote sensing factors that can effectively improve the accuracy of forest AGB estimation from a large amount of data is a challenge when the sample size is small. In this regard, the Least Absolute Shrinkage and Selection Operator (Lasso) has advantages for extensive redundant variables but still has some drawbacks. To address this, the study introduces two Least Absolute Shrinkage and Selection Operator Lasso-based variable selection methods: Least Absolute Shrinkage and Selection Operator Genetic Algorithm (Lasso-GA) and Variance Inflation Factor Least Absolute Shrinkage and Selection Operator (VIF-Lasso). Sentinel 2, Sentinel 1, Landsat 8 OLI, ALOS-2 PALSAR-2, Light Detection and Ranging, and Digital Elevation Model (DEM) data were used in this study. In order to explore the variable selection capabilities of Lasso-GA and VIF-Lasso for remote sensing estimation of forest AGB. It compares Lasso-GA and VIF-Lasso with Boruta, Random Forest Importance Selection, Pearson Correlation, and Lasso for selecting remote sensing factors. Additionally, it employs eight machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Bayesian Regression Neural Network (BRNN), Elastic Net (EN), K-Nearest Neighbors (KNN), Extremely Randomized Trees (ETR), and Stochastic Gradient Boosting (SGBoost)—to estimate forest AGB in Wuyi Village, Zhenyuan County. The results showed that the optimized Lasso variable selection could improve the accuracy of forest biomass estimation. The VIF-Lasso method results in a BRNN model with an R2 of 0.75 and an RMSE of 16.48 Mg/ha. The Lasso-GA method results in an ETR model with an R2 of 0.73 and an RMSE of 16.70 Mg/ha. Compared to the optimal SGBoost model with the Lasso variable selection method (R2 of 0.69, RMSE of 18.63 Mg/ha), the VIF-Lasso method improves R2 by 0.06 and reduces RMSE by 2.15 Mg/ha, while the Lasso-GA method improves R2 by 0.04 and reduces RMSE by 1.93 Mg/ha. From another perspective, they also demonstrated that the RX sample count and sensitivity provided by LiDAR, as well as the Horizontal Transmit, Vertical Receive provided by Microwave Radar, along with the feature variables (Mean, Contrast, and Correlation) calculated from the Green, Red, and NIR bands of optical remote sensing in 7 × 7 and 5 × 5 windows, play an important role in forest AGB estimation. Therefore, the optimized Lasso variable selection method shows strong potential for forest AGB estimation using multi-source remote sensing data.

The Dual-Role of Benzothiadiazole Fluorophores for Enabling Electrofluorochromic and Electrochromic Devices.

Three unsymmetric fluorophores differing in their flanking electron acceptor (-NO2, -CN, -CHO) were investigated for their electrofluorochromism and electrochromism. The emission yield of the -NO2 substituted fluorophore in the solid state was ca. 9-fold less than its -CN and -CHO counterparts. Visible color changes of the fluorophores were observed with an applied potential. The intensity of the near-infrared absorption at ca. 1500 nm formed upon electrochemical oxidation was contingent on the electron-withdrawing group: 2-fold more intense with the -CN substitution than its -H counterpart. The coloration efficiency was upwards of 655 cm2 C-1 and the bleaching kinetics (tb; 22 s) of the NIR band decreased according to -NO2 > -CN > - CHO ≈ -H. The electrochemically generated states could be reversibly formed during 120 min of switching the applied potential. The contrast ratio of the radical cation and the NIR absorption was near unity and 60-82%, respectively. The photoemission intensity of the fluorophores could also be modulated with applied potentials. The collective electrochemically mediated color switching and emission intensity modulation along with their solid-state emission demonstrate that benzothiadiazole fluorophores can play a dual role in chromic devices both as the color switching and photoemission intensity modulating material.

Defoliation Categorization in Soybean with Machine Learning Algorithms and UAV Multispectral Data

Traditional disease severity monitoring is subjective and inefficient. This study employs a Parrot multispectral sensor mounted on an unmanned aerial vehicle (UAV) to apply machine learning algorithms, such as random forest, for categorizing defoliation levels in R7-stage soybean plants. This research assesses the effectiveness of vegetation indices, spectral bands, and relative vegetation cover as input parameters, demonstrating that machine learning approaches combined with multispectral imagery can provide a more accurate and efficient assessment of Asian soybean rust in commercial soybean fields. The random forest algorithm exhibited satisfactory classification performance when compared to recent studies, achieving accuracy, precision, recall, F1-score, specificity, and AUC values of 0.94, 0.92, 0.92, 0.92, 0.97, and 0.97, respectively. The input variables identified as most important for the classification model were the WDRVI and MPRI indices, the red-edge and NIR bands, and relative vegetation cover, with the highest Gini importance index.

Calcination-assisted hydrothermal crystallization and comparative up-/down-conversion luminescence of RE3+ (RE=Ho, Er and Tm)-doped YbNbO4 and Yb3NbO7

The RE3+ doped YbNbO4 and Yb3NbO7 nanophosphors (RE=Ho, Er and Tm) were synthesized via a calcination-assisted hydrothermal procedure. YbNbO4 intensified almost the whole up-conversion (UC)/down-conversion (DC) emissions of RE3+ by ∼2 times when compared with Yb3NbO7 components, indicating that a lower symmetry environment in the former case is more favorable for photoluminescence. UC luminescence in each case was dominated by a green band at ∼539 nm for Ho3+ and a NIR band at ∼803 nm for Tm3+, while doping YbNbO4 and Yb3NbO7 with Er3+ separately yielded the slightly stronger green 556 nm and red 658 nm emissions. The UC/DC luminescence mechanisms were elaborated via Yb3+ → RE3+ forward/backward energy transfer and cross relaxation.

Accurate Estimation of Gross Primary Production of Paddy Rice Cropland with UAV Imagery-Driven Leaf Biochemical Model

Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy (Vcmax,025), was quantified using various spatial information representation methods, including mean (μref) and standard deviation (σref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of Vcmax,025 and GPP estimation. CNN methods achieved the best Vcmax,025 estimation, with an R of 0.94, an RMSE of 19.44 μmol m−2 s−1, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 μmol m−2 s−1, and an MdAPE of 23%. The μref-GLCM texture features and μref-LBPH joint-driven models also gave promising results. However, σref contributed less to Vcmax,025 estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The μref-LBPH joint-driven model mainly prioritized reflectance information. The μref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle.

Remote sensing of turbid coastal and estuarine waters with VIIRS I (375 m) and M (750 m) bands

ABSTRACT The Visible Infrared Imaging Radiometer Suite (VIIRS) is a visible, near and shortwave, to thermal infrared multispectral scanning instrument operational on three polar orbiting satellites, Suomi-NPP, JPSS-1, and JPSS-2. In the present paper, the processing of VIIRS using ACOLITE is introduced, using the Dark Spectrum Fitting (DSF) algorithm for processing of the visible to shortwave infrared bands. ACOLITE now includes support for processing both the imaging (I) and moderate (M) resolution bands at 375 m and 750 m spatial resolution, respectively. In most conditions encountered in the present study, the SWIR bands (either I or M) are automatically selected by the DSF for performing the aerosol correction. The processing is evaluated for turbid water remote sensing via autonomous hyperspectral radiometry from four sites across coastal and estuarine waters: two sites in Belgium and one each in France and Argentina. Through analysis of hundreds of matchups between the satellite and in situ measurements, a generally good performance is found for both I and M bands, especially for bands with the largest water signal, i.e. bands between 490 and 670 nm, where on average relative differences of 10–15% were found. Reflectance biases are generally less than 0.01, with a negative sign in the green and red bands and a positive sign in the blue and NIR bands. Similar matchup results are found for the I and M red and NIR bands, with a slightly higher scatter for the NIR bands. An additional comparison with OCSSW/l2gen processing of the M band data is performed for various configurations. Overall, DSF performance is better in the visible bands, whereas l2gen outputs are more closely aligned with the in situ measurements in the NIR. On average, negative biases are found for all l2gen configurations, up to −0.02 in the blue bands. Using either the SWIR1 + 2 or SWIR1 + 3 bands for the aerosol correction gives the best performance for l2gen processing. For the three VIIRS instruments separately, the average spectral differences with in situ measurements are comparable, with the most important deviation occurring at the Suomi-NPP shortest blue bands, where DSF processing gives a larger positive bias, up to nearly 0.02. For these bands, results from l2gen correspond more closely across the three instruments – although with significant negative biases for all three sensors up to −0.02 – presumably due to the use of system vicarious calibration gains in that processor. An operational network of autonomous hyperspectral instruments provides validation data for any overpassing optical imaging satellite in its commissioning or operational phase and eliminates the need for spectral interpolation or band shifting. In the case of VIIRS specifically, the hyperspectral instruments provide adequate data for the validation of the 20, 40 and 80 nm wide bands. With three operational wide-swath instruments, which provide largely interoperable data, a high frequency of observations is available, especially for study areas at higher latitudes. The novel exploitation of the I bands is now possible, thanks to the free and open source availability of ACOLITE. The advantage of the higher resolution I band data, combined with multiple VIIRS overpasses per day, is demonstrated for mapping turbidity in nearshore regions with high spatial variabilty and for detecting under-resolved floating algae.

TreeSeg—A Toolbox for Fully Automated Tree Crown Segmentation Based on High-Resolution Multispectral UAV Data

Single-tree segmentation on multispectral UAV images shows significant potential for effective forest management such as automating forest inventories or detecting damage and diseases when using an additional classifier. We propose an automated workflow for segmentation on high-resolution data and provide our trained models in a Toolbox for ArcGIS Pro on our GitHub repository for other researchers. The database used for this study consists of multispectral UAV data (RGB, NIR and red edge bands) of a forest area in Germany consisting of a mix of tree species consisting of five deciduous trees and three conifer tree species in the matured closed canopy stage at approximately 90 years. Information of NIR and Red Edge bands are evaluated for tree segmentation using different vegetation indices (VIs) in comparison to only using RGB information. We trained Faster R-CNN, Mask R-CNN, TensorMask and SAM in several experiments and evaluated model performance on different data combinations. All models with the exception of SAM show good performance on our test data with the Faster R-CNN model trained on the red and green bands and the Normalized Difference Red Edge Index (NDRE) achieving best results with an F1-Score of 83.5% and an Intersection over Union of 65.3% on highly detailed labels. All models are provided in our TreeSeg toolbox and allow the user to apply the pre-trained models on new data.

Prediction of soil texture using remote sensing data. A systematic review

Soil particle size fractions play a critical role in determining soil health attributes, including soil aeration, water infiltration and retention capacity, nutrients, and organic matter dynamics. Traditional soil mapping methods rely predominantly on ground-based surveys and laboratory analysis which are reported to be time-consuming and expensive. To address these challenges, there has been a global shift towards digital soil mapping (DSM) techniques that utilize remote sensing data. This review, conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guideline, aims to provide a comprehensive synthesis of the current state of soil texture prediction using remote sensing data. In particular, the review extract and synthesizes the satellite images used, identify the derived environmental covariates and their relative importance, and assesses the prediction models/algorithms used in the prediction of soil texture. Synthesis and analysis of 70 articles show that clay content is the most predicted of the three soil particle fractions accounting for 37% of the reviewed studies predominantly from topsoil layer (74.29%). Sentinel 2 and Landsat 8 are reported as the most frequently used satellite images. Among the covariates derived from these images, NDVI (80.4%) and SAVI (60.8%) are by far the most derived band ratios (indices). Red (37.3%), NIR (35.3%), Green (33.3%), Blue (33.3%), and SW2 (29.4%) bands were the five most incorporated as covariates for soil texture prediction amongst individual satellite bands. Regarding the DSM algorithms, Random Forest (RF) appeared in most reviewed articles followed by Support Vector Machines (SVM), and Quantile Regression Forest (QRF). The comparative model performance analysis showed that RF and Artificial neural network (ANN) had a good trade-off across validation metrics indicating their best performance in the prediction of both clay, sand, and silt. The RF performance showed a decreasing trend with increasing depth interval for clay and sand prediction and inconsistent for silt prediction.

Comprehensive assessment of matcha qualities and visualization of constituents using hyperspectral imaging technology

Matcha, made from different tea leaves as raw material, exhibits diverse aromas and flavors. Therefore, there is an urgent need for a rapid, non-destructive method to assess the quality of matcha to ensure that these different characteristics are accurately assessed without compromising the integrity of the product. In this study, hyperspectral imaging technology (HSI) combined with machine learning methods enabled the first visual in situ assessment of matcha quality. The physicochemical contents of matcha were determined chemically. Qualitative and quantitative detection models for different types and grades were developed using HSI (containing Vis-NIR and NIR band). The results showed that hyperspectral data in the Vis-NIR were better than in the NIR band. The accuracy of XGBoost in modelling the classification of matcha grades reached 98.10 %. After feature selection using the random forest (RF) method, partial least squares regression (PLSR) was built to predicted the quality of matcha, which showed high prediction accuracy (test set Rp2 > 0.95). The model uses HSI to visually visualize spatial variations in constitutions (catechins, free amino acids, caffeine, soluble proteins, and soluble sugars) to show compositional differences between different types of matcha, providing a rapid non-destructive method for comprehensive assessment of matcha quality.

Interlayer engineered bilayered V₂O₅ NPs as saturable absorbers for infrared ultrashort pulse generation

Interlayer engineering offers a promising approach to modifying the structure of layered vanadium-based oxides, enhancing their saturable absorption effect and making them a desirable choice for developing infrared wavelength-range photonics devices. This experimental work synthesizes interlayer vanadium pentoxide (V2O5) using hydrothermal techniques to produce an excellent saturable absorber (SA). The interlayer engineering strategy enhances strong saturable absorption and self-defocusing effects in V2O5-based SA, enabling the generation of ultrashort pulses in the infrared region. Their superior nonlinear optical characteristics are demonstrated by a modulation depth of 13.9 % and saturation intensity of 0.42 GW cm−1, respectively. By utilizing a transmission coupling fiber-based V2O5-SA inside an Er-doped fiber laser cavity, a passively Q-switched (PQS) laser with a narrow pulse width of 2.78 μs and signal-to-noise ratio (SNR) ∼50 dB is realized for the first time. Furthermore, we explore a V2O5 NPs-SA long-term stability and high damage threshold. The results not only offer additional proof of the capable potential of interlayer V2O5 as a photo-bleaching absorber for realizing pulsed lasers in the NIR band but also cover the method for advancing their probable applications in optoelectronic devices.

2D Gr/WSe2/MoTe2 vertical heterojunction for self-powered photodiode with ultrafast response and high sensitivity

Two-dimensional materials without lattice constraints can be combined into form heterojunctions via van der Waals forces, providing opportunities for the future development of novel high-performance photodetectors. In non-vertical heterojunction devices with long carrier transport channels, SRH recombination due to defect and interface states in the heterojunction and Langevin recombination due to Coulomb interactions induce large amounts of photogenerated carrier recombination, leading to low quantum efficiencies of the heterojunction. At the same time, defect and interface trap states, as well as the long channel of the non-vertical heterojunction device, lead to a slow response speed of the device. In this work, a 2D WSe2/MoTe2 vertical heterojunction photodiode with a transparent graphene top electrode has been designed to simultaneously achieve high sensitivity and fast response speed of photodetectors. Benefiting from the vertical device structure, high-quality interface and low contact resistance, the photogenerated electron-hole pairs can be efficiently separated and transported. The photodiode exhibits remarkable rectification characteristics with a rectification ratio as high as 1.4 × 104, and provides a broadband and self-powered photodetection from the visible to NIR bands (405–1064 nm) with a maximum responsivity of 0.33 A/W at 785 nm. In particular, the photodiode achieves an ultra-fast rise/fall time of 6.49/6.22 μs at 0 V and a further reduction of the response time to 1.68/1.2 μs at a reverse bias of −1 V. This ultra-fast response allows the photodiode to detect switching signals with a cutoff frequency of more than 70 kHz and 200 kHz at 0 V and −1 V, respectively. This work opens up new opportunities for the development of integrated 2D photodetectors with low power consumption, high sensitivity, and high speed.

Construction and Application of Dynamic Threshold Model for Agricultural Drought Grades Based on Near-Infrared and Short-Wave Infrared Bands for Spring Maize

Maize (Zea mays L.) is one of the most important grain crops in the world. Drought caused by climate change in recent years may greatly threaten water supply and crop production, even if the drought only lasts for a few days or weeks. Therefore, effective daily drought monitoring for maize is crucial for ensuring food security. A pivotal challenge in current related research may be the selection of data collection and the methodologies in the construction of these indices. Therefore, orthorectified reflectance in the short-wave infrared (SWIR) band, which is highly sensitive to variations in vegetation water content, was daily obtained from the MODIS MCD43A4 product. Normalized Difference Water Index (NDWI) calculated using the NIR and SWIR bands and days after planting (DAP) were normalized to obtain the Vegetation Water Index (VWI) and normalized days after planting (NDAP), respectively. The daily dynamic threshold model for different agricultural drought grades was constructed based on the VWI and NDAP with double-logistic fitting functions during the maize growing season, and its specific threshold was determined with historical drought records. Verification results indicated that the VWI had a good effect on the daily agricultural drought monitoring of spring maize in the “Golden Maize Belt” in northeast China. Drought grades produced by the VWI were completely consistent with historical records for 84.6% of the validation records, and 96.2% of the validation records differed by only one grade level or less. The VWI can not only daily identify the occurrence and development process of drought, but also well reflect the impact of drought on the yield of maize. Moreover, the VWI could be used to monitor the spatial evolution of drought processes at both regional and precise pixel scales. These results contribute to providing theoretical guidance for the daily dynamic monitoring and evaluation of spring maize drought in the “Golden Maize Belt” of China.

Mid‐Infrared Single‐Photon Compressive Spectroscopy

AbstractSensitive mid‐infrared (MIR) spectroscopy plays an indispensable role in various photon‐starved conditions. However, the detection sensitivity of conventional MIR spectrometers is severely limited by excessive noises of the involved infrared sensors, especially for multi‐pixel arrays in parallel spectral acquisition. Here, an ultra‐sensitive MIR single‐pixel spectrometer is devised and implemented, which relies on high‐fidelity spectral upconversion and wavelength‐encoding compressive measurement. Specifically, a MIR nanophotonic supercontinuum from 3.1 to 3.9 µm is nonlinearly converted to the NIR band via synchronous chirped‐pulse pumping, which facilitates both the precise spectral mapping and sensitive upconversion detection. The upconverted signal is then spatially dispersed onto a programmable digital micromirror device, before being registered by a single‐element silicon detector. Consequently, the spectral information can be deciphered from the correlation between encoded patterns and recorded measurements, which results in a spectral resolution of 0.5 under an illumination flux down to 0.01 photons nm–1 pulse–1. Moreover, faithful reconstructions at sub‐Nyquist sampling rates are demonstrated using the compressive sensing algorithm, which leads to a 95% reduction in data acquisition time. The presented single‐pixel computational spectrometer features wavelength multiplexing, high throughput, and efficient sampling, which thus paves a new way for sensitive and fast spectroscopic analysis at the single‐photon level.

An efficient and generalisable approach for mapping paddy rice fields based on their unique spectra during the transplanting period leveraging the CIE colour space

As one of the most important staple foods globally, rice sustains nearly half of the world's population. Accurate and timely paddy rice mapping is, thus, essential for rice-related policy-making to ensure food security in the context of anthropogenic, environmental and climate changes. However, paddy rice mapping remains a challenging task since it usually has similar spectral characteristics to other land covers. In this research, for the first time, an entirely new approach, called RiceTColour, was proposed for mapping rice fields within the Commission Internationale de l'Eclairage (CIE) colour space based on their unique spectra during the rice transplanting period as observed in remotely sensed imagery. We demonstrate that transplanted rice fields, representing a mixture of soil, water and rice seedlings, consistently exhibit relatively low spectral values in both SWIR and NIR bands across various geographical locations, leading to their unique dark green colours in the false-colour image composed of SWIR, NIR and Red bands. Based upon this, we transformed these three spectral bands into the CIE colour space where paddy rice was found to be readily and completely separated from the other land covers. Straightforward, but specific classification criteria were established within the CIE colour space to differentiate paddy rice from the other land covers. The proposed RiceTColour, thus, represents a new approach for paddy rice identification, that is mapping paddy rice using the CIE colour space based upon the previous underexplored remotely sensed spectra of paddy fields during the transplanting season. The effectiveness of the proposed method was investigated over five rice-planting regions distributed across different geographical regions, characterised by different climates, rice cropping intensities, irrigation schemes and cultural practices. Specifically, the mapping criteria established in a training site (S1) were directly generalised to the other four sites (S2 to S5) for paddy rice mapping. Experimental results demonstrated that the RiceTColour method consistently achieved the most accurate and balanced classifications across all five sites compared with four benchmark comparators: a SAR-based method, an index-based method and two supervised classifier-based methods. In particular, the RiceTColour method performed relatively stable, producing an overall accuracy exceeding 95% in the training site (S1) as well as the four generalised sites (S2 to S5), which is an encouraging result. Such efficient yet stable rice mapping results across various rice-planting regions suggest a very strong generalisation capability of the proposed RiceTColour method. In consideration of the relatively large planting area of paddy rice fields globally, the proposed parameter-free, efficient, and generalisable RiceTColour method, thus, holds great potential for widespread application in various rice-planting areas worldwide.

Variations in solar irradiance caused by solar cycle affect the temporal stability of ETM+ (LS7) ToA reflectance values at pseudo-invariant calibration sites (PICS)

ABSTRACT The aim of this study is to quantify the impact of the effect of long-term solar cycle variations in solar irradiance on the accuracy and temporal stability of ToA reflectance values from Enhanced Thematic Mapper Plus (ETM+/L7) sensor images. We used 1382 ETM+ scenes corresponding to five Pseudo-Invariant Calibration Sites (PICS), obtained between 2003 and 2020. Solar irradiance data from the Spectral Irradiance Monitor (SIM/SORCE) were used to calculate daily values of mean exoatmospheric solar irradiance (ESUN), implemented in the radiometric correction of ToA reflectance. Enhanced temporal stability coefficients and variance tests were calculated to evaluate the stability of the time series obtained. The results indicate that the use of daily SIM/SORCE solar spectra has a significant impact (alpha <0.001) on radiometric correction and the accuracy of ToA reflectance values. In radiometric calibration, the percentage of top of atmosphere temporal stability that can be recovered based on 5 Sahara PICS ETM+/L7 scenes can exceed 0.75%, 0.50%, 0.39%, 0.45%, 0.30% and 0.25% in the blue, green, red, NIR, SWIR-1 and SWIR-2 spectral bands, respectively, and is not considered statistically significant (alpha >0.05). We concluded that considering the effects of solar cycles during the radiometric calibration of multispectral sensors contributes to improving the long-term stability of ToA reflectance products.

Early Radiometric Assessment of NOAA-21 Visible Infrared Imaging Radiometer Suite Reflective Solar Bands Using Vicarious Techniques

The VIIRS instrument on the JPSS-2 (renamed NOAA-21 upon reaching orbit) spacecraft was launched in November 2022, making it the third sensor in the VIIRS series, following those onboard the SNPP and NOAA-20 spacecrafts, which are operating nominally. As a multi-disciplinary instrument, the VIIRS provides the worldwide user community with high-quality imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans. This study provides an early assessment of the calibration stability and radiometric consistency of the NOAA-21 VIIRS RSBs using the latest NASA SIPS C2 L1B products. Vicarious approaches are employed, relying on reflectance data obtained from the Libya-4 desert and Dome C sites, deep convective clouds, and simultaneous nadir overpasses, as well as intercomparison with the first two VIIRS instruments using MODIS as a transfer radiometer. The impact of existing band spectral differences on sensor-to-sensor comparison is corrected using scene-specific a priori hyperspectral observations from the SCIAMACHY sensor onboard the ENVISAT platform. The results indicate that the overall radiometric performance of the newly launched NOAA-21 VIIRS is quantitatively comparable to the NOAA-20 for the VIS and NIR bands. For some SWIR bands, the measured reflectances are lower by more than 2%. An upward adjustment of 6.1% in the gain of band M11 (2.25 µm), based on lunar intercomparison results, generates more consistent results with the NOAA-20 VIIRS.

Comparative Analysis between Remote Sensing Burned Area Products in Brazil: A Case Study in an Environmentally Unstable Watershed

Forest fires can profoundly impact the hydrological response of river basins, modifying vegetation characteristics and soil infiltration. This results in a significant increase in surface flow and channel runoff. In response to these effects, many researchers from different areas of earth sciences are committed to determining emergency measures to rehabilitate river basins, intending to restore their functions and minimize damage to soil resources. This study aims to analyze the mapping detection capacity of burned areas in a river basin in Brazil based on images acquired by AMAZÔNIA-1/WFI and the AQ1KM product. The effectiveness of the AMAZÔNIA-1 satellite in this regard is evaluated, given the importance of the subject and the relatively recent introduction of the satellite. The AQ1KM data were used to analyze statistical trends and spatial patterns in the area burned from 2003 to 2023. The U-Net architecture was used for training and classification of the burned area in AMAZÔNIA-1 images. An increasing trend in burned area was observed through the Mann–Kendall test map and Sen’s slope, with the months of the second semester showing a greater occurrence of burned areas. The NIR band was found to be the most sensitive spectral resource for detecting burned areas. The AMAZÔNIA-1 satellite demonstrated superior performance in estimating thematic accuracy, with a correlation of above 0.7 achieved in regression analyses using a 10 km grid cell resolution. The findings of this study have significant implications for the application of Brazilian remote sensing products in ecology, water resources, and river basin management and monitoring applications.

Clothing color effect as a target of the smallest scale climate change adaptation.

The purpose of this study is to understand a physical mechanism to determine the surface temperature of clothes in calm and fine conditions of outdoors. We observed surface temperatures of polo shirts of the same material and design but different colors. The shirts were placed in unshaded and well-ventilated outdoor, open spaces on sunny summer days. The maximum difference between dark green or black and white was more than 15°C during calm, fine weather and was greatest when the solar radiation was strong. If the transmission of solar radiation energy through a shirt is ignored to calculate the absorption by the shirt, the difference in solar radiation absorption due to different colors is as much as 24% in the maximum, and if considered, we concluded that an absorption difference of 34% led to a temperature difference of 15℃. When we compared the brightness of the colors, we found that the albedo of both the visible and NIR bands explained why the red and green colors were so different with respect to the surface temperatures we observed. The reflection in the NIR bands was also an important determinant of the surface temperature. An additional experiment using masks showed that the temperature difference between white and black was almost eliminated at a wind speed of ~ 3m/s. The color of clothing is therefore a target for small-scale adaptation to climate change.