Spectral Bands Research Articles

Acquisition of Bathymetry for Inland Shallow and Ultra-Shallow Water Bodies Using PlanetScope Satellite Imagery

This study is structured to address the problem of mapping the bottom of shallow and ultra-shallow inland water bodies using high-resolution satellite imagery. These environments, with their diverse distribution of optically relevant components, pose a challenge to traditional mapping methods. The study was conducted on several research issues, each focusing on a specific aspect of the SDB, related to the selection of spectral bands and regression models, regression models creation, evaluation of the influence of the number and spatial distribution of reference soundings, and assessment of the quality of the bathymetric surface, with a focus on microtopography. The study utilized basic empirical techniques, incorporating high-precision reference data acquired via an unmanned surface vessel (USV) integrated with a single-beam echosounder (SBES), and Global Navigation Satellite System (GNSS) receiver measurements. The performed investigation allowed the optimization of a methodology for bathymetry acquisition of such areas by identifying the impact of individual processing components. The first results indicated the usefulness of the proposed approach, which can be confirmed by the values of the obtained RMS errors of elaborated bathymetric surfaces in the range of up to several centimeters in some study cases. The work also points to the problematic nature of this type of study, which can contribute to further research into the application of remote sensing techniques for bathymetry, especially during acquisition in optically complex waters.

Potential of land degradation index for soil salinity mapping in irrigated agricultural land in a semi-arid region using Landsat-OLI and Sentinel-MSI data.

Irrigated agricultural lands in arid and semi-arid regions are prone to soil degradation. Remote sensing technology has proven useful for mapping and monitoring the extent of this issue. To accurately discern soil salinity, it is essential to choose appropriate spectral wavelengths. This study evaluated the potential of the land degradation index (LDI) using the visible and near infrared (VNIR) and the short wavelength infrared (SWIR) spectral bands compared to that of soil salinity indices by integrating only the VNIR wavelengths. Landsat-OLI and Sentinel-MSI data, acquired 2weeks apart, were rigorously preprocessed and used. This research was conducted over irrigated agricultural land in Morocco, which is well known for its semi-arid climate and moderately saline soil. Furthermore, a field soil survey was conducted and 42 samples with variable electrical conductivity (EC) were collected for index calibration and validation of the results. The results showed that the visual analysis of the derived maps based on the examined indices exhibited a clear spatial pattern of gradual soil salinity changes extending from the elevated upstream plateau to the downstream of the plain, which limits agricultural activities in the southwestern sector of the study area. The results of this study show that LDI is effective in identifying soil salinity, as indicated by a coefficient of determination (R2) of 0.75 when using Sentinel-MSI and 0.72 with Landsat-OLI. The R2 value of 0.89 and root mean square error (RMSE) of 0.87 dS/m for soil salinity maps generated from LDI with Sentinel-MSI demonstrate high accuracy. In contrast, the R2 value of 0.83 and RMSE of 1.24 dS/m for maps produced from Landsat-OLI indicate lower accuracy. These findings indicate that high-resolution Sentinel-MSI data significantly improved the prediction of salinity-affected soils. Furthermore, this study highlights the benefits of using VNIR and SWIR bands for precise soil salinity mapping.

Improved method for cropland extraction of seasonal crops from multi-sensor satellite data

ABSTRACT Monitoring agricultural land over vast geographical areas presents challenges due to the absence of accurate, comprehensive and precise data, which has become a complex process that is difficult to do in terms of both timespans and consistency. Hence, this study presents an improved approach for the identification of agricultural land by utilizing the capabilities of Sentinel-1 and Sentinel-2 satellites with a variety of vegetation and non-vegetation indices and machine learning algorithms. The Multispectral Correlation Mapper (MCM) and Random Forest (RF) algorithms are adopted to train different agricultural lands, crop types and sowing and cultivation seasons. The 45-bands mega-file data cube (MFDC) fusion for each season incorporates essential indices and features derived from the Sentinel-1 and Sentinel-2 datasets for both seasons, i.e. Rabi (winter-spring season) and Kharif (summer-autumn season). The proposed method demonstrated resilience when applied to satellite datasets while effectively reducing the impact of non-agricultural elements such as shrubs, grass, bare soil and orchards. The results demonstrate a notable ability to differentiate between the Rabi and Kharif seasons, resulting in a high level of precision in measuring the extent of cultivated land during the Rabi and Kharif seasons with an area of 626,947 acres and 590,858 acres, respectively. The total land area, ascertained from the observation of the comprehensive cropping pattern and agricultural modifications during the entire year (June 2021–May 2022) is 635,655 acres. The validation exercise shows the higher accuracy of this method for cropland, with an overall accuracy of 98.8%, kappa of 0.97, user accuracy of 98.69% and producer accuracy of 99.13%. Additionally, it was spatially compared with the ESRI, ESA and MODIS cropland layers and government statistical data. Furthermore, the research investigates the temporal dynamics of agricultural growth phases using spectral bands and indices. This approach improves the accuracy of precise cropland identification and provides useful insights into crop phenology.

Comparing the seasonal relationship of land surface temperature with vegetation indices and other land surface indices

ABSTRACT Land surface temperature (LST) is largely influenced by various biophysical properties of land surface materials, especially in urban areas where multiple spectral bands directly impact LST. The present study compares the seasonal relationship of LST with six vegetation indices, one built-up index, one water index and one soil index in Imphal City, India, using Landsat 8 satellite images from the summer and winter of 2021. The study found that these nine indices exhibit different responses to changes in LST in urban landscapes. Pearson’s correlation coefficient, scatter plot graphs, box plot graphs, and cross-section drawings are used to present the relationships between LST and the indices. The study discovered that all six vegetation indices show a moderate negative relationship with LST in both summer and winter. Additionally, the water index demonstrates a weak negative relation in summer and a moderate negative relation in winter. The built-up index shows a neutral relation in summer and a weak positive relation in winter, while the soil index forms a weak positive relation in summer and a moderate positive relation in winter. Further analysis presents that higher values of vegetation indices correspond to lower LST, while built-up and bare surfaces generate high LST.

Prediction of secondary metabolites in maize under different nitrogen inputs by hyperspectral sensing and machine learning

Flavonoids are compounds resulting from secondary plant metabolism that provide benefits to human health by food. This study aimed to accuracy of predicting flavonoids in maize plants subjected to different nitrogen rates using hyperspectral reflectance and machine learning (ML) algorithms. The experiment was carried out in randomized blocks in a 4 × 5 factorial design (four N inputs: 0; 30; 60 and 120 % of the recommended N input; and five readings of the reflectance spectra in maize leaves from different vegetative stages: V6, V8, V10, V12 and V14, in four replications, totaling 80 treatments. N rates were applied in the V4 and V8 phenological stages, using urea as the N source. For hyperspectral analysis, four leaves from each treatment were collected and analyzed using a spectroradiometer (FieldSpec 4 HRes, Analytical Spectral Devices), capturing the spectrum in the 350 to 2500 nm range. Subsequently, the leaf samples used in the reflectance readings were dried, ground and subjected to isoflavone quantification, analyzed by ultra-performance liquid chromatography in three repetitions, quantifying daidzein 1 (D1), daidzein 2 (D2), genistein 1 (G1), genistein 2 (G2), and total isoflavones. Data obtained was subjected to machine learning analysis, testing two data set input configurations: wavelengths (WL) and calculated spectral bands (B), and D1, D2, G1, G2 and total isoflavones as output variables. The ML algorithms tested were artificial neural networks (ANN), REPTree (DT), M5P decision tree (M5P), ZeroR (R), Random Forest (RF) and support vector machine (SVM), evaluated according to their performance by the correlation coefficient (r) and mean absolute error (MAE). The results show that the SVM algorithm had the highest accuracy in predicting the variables D1, D2, G1, G2 and total isoflavones, outperforming the other algorithms when WL was used as input in dataset.

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.

Spatial Feature Enhancement and Attention-Guided Bidirectional Sequential Spectral Feature Extraction for Hyperspectral Image Classification

Hyperspectral images have the characteristics of high spectral resolution and low spatial resolution, which will make the extracted features insufficient and lack detailed information about ground objects, thus affecting the accuracy of classification. The numerous spectral bands of hyperspectral images contain rich spectral features but also bring issues of noise and redundancy. To improve the spatial resolution and fully extract spatial and spectral features, this article proposes an improved feature enhancement and extraction model (IFEE) using spatial feature enhancement and attention-guided bidirectional sequential spectral feature extraction for hyperspectral image classification. The adaptive guided filtering is introduced to highlight details and edge features in hyperspectral images. Then, an image enhancement module composed of two-dimensional convolutional neural networks is used to improve the resolution of the image after adaptive guidance filtering and provide a high-resolution image with key features emphasized for the subsequent feature extraction module. The proposed spectral attention mechanism helps to extract more representative spectral features, emphasizing useful information while suppressing the interference of noise. Experimental results show that our method outperforms other comparative methods even with very few training samples.

Design and on-orbit performance evaluation of Ethiopian earth observation satellite multispectral optical imaging payload

The Ethiopian Earth observation micro-satellite was successfully launched into a sun-synchronous orbit equipped with an optical multispectral imaging payload system. This optical multispectral imaging payload camera weighs 1.4 kg with four spectral bands: RGB and NIR, each having a unique spectral range. Furthermore, it has a field of view of 7.4°×0.3°, a relative F-number of 9.8°, and a spectral range of 0.45∼0.9μm. The present article covers the design and on-orbit performance analysis of the ETRSS-1 electro-optical imaging payload system, which is based on the COTS compact and unobscured off-axis three-mirror anastigmatic system for high-resolution imaging. Additionally, selecting an appropriate imaging sensor, optomechanical architecture, and performance characteristics for optical instruments is investigated to evaluate spatial, spectral, and radiometric requirements and the payload’s on-orbit performance and capability.

Electro-Clinical Features and Functional Connectivity Analysis in SYN1-Related Epilepsy.

There is currently scarce data on the electroclinical characteristics of epilepsy associated with synapsin 1 (SYN1) pathogenic variations. We examined clinical and electro-encephalographic (EEG) features in patients with epilepsy and SYN1 variants, with the aim of identifying a distinctive electroclinical pattern. In this retrospective multicenter study, we collected and reviewed demographic, genetic, and epilepsy data of 19 male patients with SYN1 variants. Specifically, we analyzed interictal EEG data for all patients, and electro-clinical data from 10 epileptic seizures in 5 patients, using prolonged video-EEG monitoring recordings. Inter-ictal EEG functional connectivity parameters and frequency spectrum of the 10 patients over 12 years of age, were computed and compared with those of 56 age- and sex-matched controls. The main electroclinical features of epilepsy in patients with SYN1 were (1) EEG background and organization mainly normal; (2) interictal abnormalities are often rare or not visible on EEG; (3) more than 60% of patients had reflex seizures (cutaneous contact with water and defecation being the main triggers) isolated or associated with spontaneous seizures; (4) electro-clinical semiology of seizures was mainly temporal or temporo-insulo/perisylvian with a notable autonomic component; and (5) ictal EEG showed a characteristic rhythmic theta/delta activity predominating in temporo-perisylvian regions at the beginning of most seizures. Comparing patients with SYN1 to healthy subjects, we observed a shift to lower frequency bands in power spectrum of interictal EEG and an increased connectivity in both temporal regions. A distinct epilepsy syndrome emerges in patients with SYN1, with a rather characteristic clinical and EEG pattern suggesting predominant temporo-insular involvement. ANN NEUROL 2024.

Superhydrophobic Composite Coatings Can Achieve Durability and Efficient Radiative Cooling of Energy-Saving Buildings.

Passive daytime radiative cooling (PDRC) technology has received a great deal of attention in the field of energy efficiency and environmental protection as a sustainable technology and a large-scale and promising solution to mitigate the environmental impact of global warming. In this study, we prepared PDRC material by combining FEP with modified Al2O3 particles and using the method of spray combined with phase separation. The synergistic effect of the formed surface micronanostructures, combined with the molecular vibration of FEP and the phonon polarization resonance of Al2O3, further improves the optical performance of the PDRC coating. The PDRC coating has an average reflectivity of 0.96 in the solar spectral band (0.3-2.5 μm) and an average emissivity of 0.963 in the atmospheric window band ((8-13 μm). In addition, the PDRC coating had good hydrophobicity, and its water contact angle (WAC) reached 159.3°. Under direct sunlight conditions, PDRC materials have a good temperature drop (4.9 °C) compared to ambient temperatures and radiative cooling power (81.2 W/m2). The prepared coating maintains superhydrophobicity and excellent cooling performance when soaked in solutions of different pH values and UV radiation, which was of great significance for sustainable applications. Our work provides a form of long-term cooling that can be effectively implemented in green and energy-efficient buildings.

Allethrin and tetramethrin molecular adsorption on novel phosphoaluminane nanosheet based on first-principles investigation

Allethrin and tetramethrin are both active ingredients in the production of insect spray. Being very toxic in nature, the inhalation of allethrin and tetramethrin causes various side effects in human beings. For the detection of allethrin and tetramethrin, the effectiveness of the base material phosphoaluminane is studied in the current work with the help of the density functional theory (DFT) calculations. The stability of phosphoaluminane is ensured with the support of formation energy and phonon band spectrum. The electronic properties of phosphoaluminane are systematically investigated using band structure and projected density of states (PDOS) maps. The obtained band gap value of phosphoaluminane is 1.716 eV, which confirms that the base material has semiconducting properties. The adsorption behaviour of target molecules allethrin and tetramethrin on phosphoaluminane are explored with prominent parameters including adsorption energy, Mulliken population analysis, and relative band gap changes. The adsorption energy range is recorded to be in the scope of −0.237 eV to −0.597 eV. From the results, it is clear that both allethrin and tetramethrin are physisorbed on phosphoaluminane sheets. Hence, novel phosphoaluminane nanosheets can be utilised as a sensing material for allethrin and tetramethrin.

ChromaFlash: Snapshot Hyperspectral Imaging Using Rolling Shutter Cameras

Hyperspectral imaging captures scene information across narrow, contiguous bands of the electromagnetic spectrum. Despite its proven utility in industrial and biomedical applications, its ubiquity has been limited by bulky form factors, slow capture times, and prohibitive costs. In this work, we propose a generalized approach to snapshot hyperspectral imaging that only requires a standard rolling shutter camera and wavelength-adjustable lighting. The crux of this approach entails using the rolling shutter as a spatiotemporal mask, varying incoming light quicker than the camera's frame rate in order for the captured image to contain rows of pixels illuminated at different wavelengths. An image reconstruction pipeline then converts this coded image into a complete hyperspectral image using sparse optimization. We demonstrate the feasibility of this approach by deploying a low-cost system called ChromaFlash, which uses a smartphone's camera for image acquisition and a series of LEDs to change the scene's illumination. We evaluated ChromaFlash through simulations on two public hyperspectral datasets and assessed its spatial and spectral accuracy across various system parameters. We also tested the real-world performance of our prototype by capturing diverse scenes under varied ambient lighting conditions. In both experiments, ChromaFlash outperformed state-of-the-art techniques that use deep learning to convert RGB images into hyperspectral ones, achieving snapshot performance not demonstrated by prior attempts at accessible hyperspectral imaging.

Electromagnetic interference shielding behavior of flexible PVA composite made using betel nut husk biocarbon and steel microwire in E, F, I, and J band spectrum

AbstractThis study investigates the electromagnetic interference (EMI) shielding behavior of a flexible polyvinyl alcohol (PVA) composite fabricated using betel nut husk biocarbon and steel microwire in the E, F, I, and J band spectrum. The incorporation of steel microwire into the PVA based composite is novel approach since it provides flexibility along with good mechanical strength. The fabrication process involves the solution casting method, and the composite is characterized according to American society of testing and materials (ASTM) standards. The research focuses on analyzing dielectric behavior, EMI shielding effectiveness, and mechanical properties. Results indicates that, the composite with 5 vol.% of biocarbon and 5 vol.% of micro metal wire, exhibits exceptional relative permittivity of 7.6, 6.9, 6.3, and 1.5 for E, F, I, and J frequency bands and same composition delivers exceptional electromagnetic shielding with total shielding values of 17.7 dB, 25.5 dB, 31.7 dB, and 38.6 dB for E, F, I, and J frequency bands. In mechanical characteristics, composite with 5 vol.% of biocarbon and 5 vol.% of micro metal wire, exhibits high tensile strength of 73 MPa with lower elongation percentage of 109.2% and shore‐D hardness of 40, respectively. Thus, the inclusion of betel nut husk biocarbon and steel microwire into the PVA matrix enhanced the overall EMI shielding properties along with mechanical properties. These flexible EMI shielding composites could be used in applications such as defense, telecommunication, drones, and electronic gadget making segments.Highlights Flexible polyvinyl alcohol (PVA) composite is made with steel microwire and betel nut husk biocarbon. Biocarbon is derived from betel nut husk for first time and used as EM wave absorber. Composites were made via simple solution casting method for high flexibility index. Composite contains 5 vol.% biocarbon and 5 vol.% micro metal wire, shows high relative permittivity 7.6 for E frequency bands. The composites have high tensile strength of 73 MPa with a low elongation percentage of 109.2% and a shore‐D hardness of 40.

Vertical Barrier Heterostructures for Reliable and High-Performance Self-Powered Infrared Detection.

With their fascinating properties, emerging two-dimensional (2D) materials offer innovative ways to prepare high-performance infrared (IR) detectors. However, the current performance of 2D IR photodetectors is still below the requirements for practical application owing to the severe interfacial recombination, sharply raised contact resistance, and deteriorated metal conductivity at nanoscale. Here, we introduce a vertical barrier heterojunction with a structure of PtSe2/GaAs that combines the excellent optoelectronic properties of transition metal sulfides with topological semi-metals, which allows for an adjustable bandgap and high carrier mobility. The heterojunction was fabricated using the wet transfer method. The heterostructures show significant rectification behaviors and photovoltaic effects, which allow it to operate as a self-driven photodetector at zero bias. The photoresponse parameters at 850 nm with zero bias voltage are 67.2 mA W-1, 6.7 × 1012 Jones, 9.8%, 3.8 × 105, 164 μs, and 198 μs for the responsivity, specific detectivity, external quantum efficiency, Ilight/Idark ratio, rise time, and fall time, respectively. Moreover, the heterojunction is highly sensitive to a wide spectral band from ultraviolet to near-infrared (360-1550 nm). At the same time, this heterostructure demonstrates significant potential for applications in IR polarized light detection and room-temperature high-resolution IR imaging. The excellent properties of the heterojunction make it well-suited for high-performance, self-powered IR detection.

Assessment of Atmospheric Correction Algorithms for Correcting Sunglint Effects in Sentinel-2 MSI Imagery: A Case Study in Clean Lakes

The Sentinel-2 Multi-Spectral Instrument (MSI) is characterized by short revisit times (5 days), red-edge spectral bands (665 nm and 705 nm), and a high spatial resolution (10 m), making it highly suitable for monitoring water quality in both inland and coastal waters. Unlike SeaWiFS, which can adjust its viewing angles to minimize sunglint, the Sentinel-2 MSI operates with fixed near-nadir angles, which makes it more susceptible to sunglint. Additionally, the complex optical properties of water pose challenges in accurately determining its water-leaving reflectance. Therefore, we compared the effectiveness of six atmospheric correction (AC) algorithms (POLYMER, MUMM, DSF, C2RCC, BP, and GRS) in correcting sunglint using two typical lakes in Xinjiang, China, as examples. The results indicated that POLYMER achieved the highest overall evaluation score (1.61), followed by MUMM (1.21), while BP exhibited the lowest performance (0.62). Specifically, POLYMER showed robust performance at the 665 nm band with RMSE = 0.0012 sr−1, R2 = 0.74, and MAPE = 30.68%, as well as at the 705 nm band with RMSE = 0.0014 sr−1, R2 = 0.42, and MAPE = 38.44%. At the 443, 490, and 560 nm bands, MUMM showed better performance (RMSE ≤ 0.0026 sr−1, R2 ≥ 0.86, MAPE ≤ 28.20%). In terms of band ratios, POLYMER exhibited the highest accuracy (RMSE ≤ 0.093 and MAPE ≤ 22.2%), particularly for the ratio Rrs(490)/Rrs(560) (R2 = 0.71). In general, POLYMER is the best choice for the sunglint correction of Xinjiang’s clean lakes. This study assessed the capability of different AC algorithms for sunglint correction and enhanced the monitoring capability of MSI data in clean waters.

The Development of a Novel Nitrate Portable Measurement System Based on a UV Paired Diode-Photodiode.

Nitrates can cause severe ecological imbalances in aquatic ecosystems, with considerable consequences for human health. Therefore, monitoring this inorganic form of nitrogen is essential for any water quality management structure. This research was conducted to develop a novel Nitrate Portable Measurement System (NPMS) to monitor nitrate concentrations in water samples. NPMS is a reagent-free ultraviolet system developed using low-cost electronic components. Its operation principle is based on the Beer-Lambert law for measuring nitrate concentrations in water samples through light absorption in the spectral range of 295-315 nm. The system is equipped with a ready-to-use ultraviolet sensor, light emission diode (LED), op-amp, microcontroller, liquid crystal display, quartz cuvette, temperature sensor, and battery. All the components are assembled in a 3D-printed enclosure box, which allows a very compact self-contained equipment with high portability, enabling field and near-real-time measurements. The proposed methodology and the developed instrument were used to analyze multiple nitrate standard solutions. The performance was evaluated in comparison to the Nicolet Evolution 300, a classical UV-Vis spectrophotometer. The results demonstrate a strong correlation between the retrieved measurements by both instruments within the investigated spectral band and for concentrations above 5 mg NO3-/L.

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.

Normalized Difference Red-NIR-SWIR: A new Sentinel-2 three-band spectral index for mapping freshly-opened swiddens in the tropics

Swidden agriculture is undergoing a rapid but overlooked transition and transformation in the tropics, complicating global carbon budgeting and sustainable livelihood assessment of swiddeners. Remotely sensed algorithms for accurately detecting swiddening practices have been slowly developed to generate annual updates on their dynamics. This is primarily because, using medium spatial resolution imagery (≥30 m), it is challenging to identify the exact boundary of swidden patches. Spectral-based approaches have by far dominated the detection and mapping of swidden agriculture, but the potential of Sentinel-2 has not been examined. To reconstruct annual information of swidden agriculture, a new Sentinel-2 three-band spectral index, i.e., the Normalized Difference Red, Near-infrared (NIR), and Shortwave-infrared (SWIR), or NDRII, has been developed to map freshly-opened swiddens in tropical regions. As Red (visible), NIR, and SWIR spectral band combinations (i.e., the VNIR-SWIR spectroscopy) are sensitive to vegetation-moisture variations and thermal anomalies caused by slash and burn in tropical uplands during the dry season, NDRII delineates exact patches of freshly opened swiddens. The latest 20-m map facilitates probing into the landscape patterns of newly opened swiddens and underlines their prevalence in Laos for the first time. Established within the VNIR-SWIR spectroscopy of Sentinel-2 and Landsat-8 Operational Land Imager, the NDRII algorithm contributes to reconstructing historical datasets of tropical swiddens via integrating state-of-the-art approaches that use temporally stacked observations with available VNIR/SWIR satellite imagery and further understanding the dynamics of landscape pattern and disturbance due to rapid transition and transformation.

High SNR eSWIR Image Sensor Applied in Advanced Hyperspectral Imager Aboard China’s GaoFen-5 Satellite and ZY-1 Satellite

This paper presents an extended short-wave infrared (eSWIR) image sensor, a large format infrared focal plane array (IRFPA), applied in advanced hyperspectral imager (AHSI) aboard China’s three GaoFen-5 satellites and two more followed ZY-1 satellites. Hyperspectral imaging has been emerged as a very important application in Earth observation instruments, for the spectral detection provides the spectrum information of ground objects except for the spatial imaging of the objects. AHSI has a 60-km swath width and a 30-m spatial resolution, and has high signal-to-noise ratio (SNR) in spectrum of interest. Large format infrared focal plane arrays which are ordinarily used in staring infrared imaging have some special performance requirements when applied in hyperspectral imaging, such as higher quantum efficiency, lower dark current, lower noise and selectable current conversion gain to match the different radiation flux of different subdivided spectral bands. We developed a format of 2048×512 HgCdTe/Si IRFPA sensor (equivalent 2012×256 after pixel overlapping and binning) in which 2012 pixels meet the demands of 60km swath width and 256 pixels line meet the 200 spectral bands. The sensor has 8-level gains selectable by spectral bands, and array dark current densities on an order of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-10</sup> A/cm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , quantum efficiency exceeding 80%, and the operability of 99.5% at operating temperature of around 110K. The SNR of this FPA achieved 150 when illuminated under 5×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sup> photons/pixel.

Accuracy of Bathymetric Depth Change Maps Using Multi-Temporal Images and Machine Learning

Most work to date on satellite-derived bathymetry (SDB) depth change estimates water depth at individual times t1 and t2 using two separate models and then differences the model estimates. An alternative approach is explored in this study: a multi-temporal Sentinel-2 image is created by “stacking” the bands of the times t1 and t2 images, geographically coincident reference data for times t1 and t2 allow for “true” depth change to be calculated for the pixels of the multi-temporal image, and this information is used to fit a single model that estimates depth change directly rather than indirectly as in the model-differencing approach. The multi-temporal image approach reduced the depth change RMSE by about 30%. The machine learning modelling method (categorical boosting) outperformed linear regression. Overfitting of models was limited even for the CatBoost models having the maximum number of variables examined. The visible Sentinel-2 spectral bands contributed most to the model predictions. Though the multi-temporal stacked image approach produced clearly superior depth change estimates compared to the conventional approach, it is limited only to those areas for which geographically coincident multi-temporal reference/“true” depth data exist.