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Adjustment of Sentinel-2 Multi-Spectral Instrument (MSI) Red-Edge Band Reflectance to Nadir BRDF Adjusted Reflectance (NBAR) and Quantification of Red-Edge Band BRDF Effects
摘要： Optical wavelength satellite data have directional re?ectance effects over non-Lambertian surfaces, described by the bidirectional re?ectance distribution function (BRDF). The Sentinel-2 multi-spectral instrument (MSI) acquires data over a 20.6? ?eld of view that have been shown to have non-negligible BRDF effects in the visible, near-infrared, and short wave infrared bands. MSI red-edge BRDF effects have not been investigated. In this study, they are quanti?ed by an examination of 6.6 million (January 2016) and 10.7 million (April 2016) pairs of forward and back scatter re?ectance observations extracted over approximately 20? × 10? of southern Africa. Non-negligible MSI red-edge BRDF effects up to 0.08 (re?ectance units) across the 290 km wide MSI swath are documented. A recently published MODIS BRDF parameter c-factor approach to adjust MSI visible, near-infrared, and short wave infrared re?ectance to nadir BRDF-adjusted re?ectance (NBAR) is adapted for application to the MSI red-edge bands. The red-edge band BRDF parameters needed to implement the algorithm are provided. The parameters are derived by a linear wavelength interpolation of ?xed global MODIS red and NIR BRDF model parameters. The ef?cacy of the interpolation is investigated using POLDER red, red-edge, and NIR BRDF model parameters, and is shown to be appropriate for the c-factor NBAR generation approach. After adjustment to NBAR, red-edge MSI BRDF effects were reduced for the January data (acquired close to the solar principal where BRDF effects are maximal) and the April data (acquired close to the orthogonal plane) for all the MSI red-edge bands.
关键词： Landsat,NBAR,POLDER,bidirectional re?ectance distribution function (BRDF),Africa,Sentinel-2,red-edge,MODIS
更新于2025-09-04 15:30:14
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Investigation on Perceptron Learning for Water Region Estimation Using Large-Scale Multispectral Images
摘要： Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth’s surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches are not sufficient to produce a globally adaptable water classification. The aim of this study is to develop a formula with automatically derived tuning parameters using perceptron neural networks for water/non-water region estimation, which we call the Perceptron-Derived Water Formula (PDWF), using Landsat-8 images. Water/non-water region estimates derived from PDWF were compared with three different approaches—Modified Normalized Difference Water Index (MNDWI), Automatic Water Extraction Index (AWEI), and Deep Convolutional Neural Network—using various case studies. Our proposed method outperforms all three approaches, showing a significant improvement in water/non-water region estimation. PDWF performance is consistently better even in cases of challenging conditions such as low reflectance due to hill shadows, building-shadows, and dark soils. Moreover, our study implemented a sunglint correction to adapt water/non-water region estimation over sunglint-affected pixels.
关键词： surface water bodies,Landsat-8,MNDWI,deep neural network,perceptron neural network,AWEI,PDWF
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Automatic Mapping of Irrigated Areas in Mediteranean Context Using Landsat 8 Time Series Images and Random Forest Algorithm
摘要： Groundwater withdrawals by farmers, in Morocco, are very numerous and informal. Therefore, the need for information on the location of irrigated areas is becoming increasingly important. Our main objective, in this study, is to evaluate the use of high-resolution Landsat 8 (L8) time series images and Random forest (RF) method to produce a land cover map with a sufficient precision to monitor the extension of irrigated areas. In the first part of this study, four parameters were evaluated: Number of trees, min split samples, max features and max depth. The results proves that the last parameter is the most important and has more impact on the oob score, which can reach 91%. The second part of this study was devoted to reduce furthermore the number of features taken as input in the classification process. This was done through feature reduction then selection. The computational time was highly reduced and the best level of classification accuracy was reached by using only Landsat 8 (L8) time series images, statistics on the temporal spectral indices (NDVI, MNDWI) and Range texture.
关键词： Random forest,NDVI,Landsat 8,Irrigated areas,Feature selection,time series,MNDWI,tuning,Range texture
更新于2025-09-04 15:30:14
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Testing a Modified PCA-Based Sharpening Approach for Image Fusion
摘要： Image data sharpening is a challenging field of remote sensing science, which has become more relevant as high spatial-resolution satellites and superspectral sensors have emerged. Although the spectral property is crucial for mineral mapping, spatial resolution is also important as it allows targeted minerals/rocks to be identified/interpreted in a spatial context. Therefore, improving the spatial context, while keeping the spectral property provided by the superspectral sensor, would bring great benefits for geological/mineralogical mapping especially in arid environments. In this paper, a new concept was tested using superspectral data (ASTER) and high spatial-resolution panchromatic data (WorldView-2) for image fusion. A modified Principal Component Analysis (PCA)-based sharpening method, which implements a histogram matching workflow that takes into account the real distribution of values, was employed to test whether the substitution of Principal Components (PC1–PC4) can bring a fused image which is spectrally more accurate. The new approach was compared to those most widely used—PCA sharpening and Gram–Schmidt sharpening (GS), both available in ENVI software (Version 5.2 and lower) as well as to the standard approach—sharpening Landsat 8 multispectral bands (MUL) using its own panchromatic (PAN) band. The visual assessment and the spectral quality indicators proved that the spectral performance of the proposed sharpening approach employing PC1 and PC2 improve the performance of the PCA algorithm, moreover, comparable or better results are achieved compared to the GS method. It was shown that, when using the PC1, the visible-near infrared (VNIR) part of the spectrum was preserved better, however, if the PC2 was used, the short-wave infrared (SWIR) part was preserved better. Furthermore, this approach improved the output spectral quality when fusing image data from different sensors (e.g., ASTER and WorldView-2) while keeping the proper albedo scaling when substituting the second PC.
关键词： Image fusion,ASTER,empirical line,PCA,WorldView-2,sharpening,Landsat 8,histogram matching
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A FFT-Based Approach to Explore Periodicity of Vines/Soil Properties in Vineyard from Time Series of Satellite-Derived Spectral Indices
摘要： From literature, NDVI proved to be correlated to vigour and midday stem water potential of vines; NDWI to soil water content. It is thus expected that significant periodicities can be found looking at spectral indices time series. This can be useful to better interpret vines behaviour and, possibly, relate intra-vineyard macroscopic differences to this type of information. To preliminarily test this hypothesis 25 Landsat 8 OLI images, Level-2 Data Products, were processed to compute NDVI and NDWI time series. An interpolation step aimed at generating daily estimates of indices in the explored period (May 2013-February 2016) was performed. Successively vegetation effects were removed from NDWI to emphasize soil signal. Finally a FFT analysis of both NDVI and NDWI interpolated time series was achieved to explore their periodicity, giving a first agronomic interpretation of results.
关键词： NDVI,volumetric water content,water potential,Landsat 8 OLI,NDWI,FFT
更新于2025-09-04 15:30:14
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New Insights for Detecting and Deriving Thermal Properties of Lava Flow Using Infrared Satellite during 2014–2015 Effusive Eruption at Holuhraun, Iceland
摘要： A new lava ?eld was formed at Holuhraun in the Icelandic Highlands, north of Vatnaj?kull glacier, in 2014–2015. It was the largest effusive eruption in Iceland for 230 years, with an estimated lava bulk volume of ~1.44 km3 covering an area of ~84 km2. Satellite-based remote sensing is commonly used as preliminary assessment of large scale eruptions since it is relatively ef?cient for collecting and processing the data. Landsat-8 infrared datasets were used in this study, and we used dual-band technique to determine the subpixel temperature (Th) of the lava. We developed a new spectral index called the thermal eruption index (TEI) based on the shortwave infrared (SWIR) and thermal infrared (TIR) bands allowing us to differentiate thermal domain within the lava ?ow ?eld. Lava surface roughness effects are accounted by using the Hurst coef?cient (H) for deriving the radiant ?ux (Φ rad) and the crust thickness (?h). Here, we compare the results derived from satellite images with ?eld measurements. The result from 2 December 2014 shows that a temperature estimate (1096 ?C; occupying area of 3.05 m2) from a lava breakout has a close correspondence with a thermal camera measurement (1047 ?C; occupying area of 4.52 m2). We also found that the crust thickness estimate in the lava channel during 6 September 2014 (~3.4–7.7 m) compares closely with the lava height measurement from the ?eld (~2.6–6.6 m); meanwhile, the total radiant ?ux peak is underestimated (~8 GW) compared to other studies (~25 GW), although the trend shows good agreement with both ?eld observation and other studies. This study provides new insights for monitoring future effusive eruption using infrared satellite images.
关键词： TEI,radiant ?ux,SWIR,crust thickness,TIR,dual-band,Landsat-8,effusive eruption,Hurst coef?cient
更新于2025-09-04 15:30:14
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Classification of Rice Heavy Metal Stress Levels Based on Phenological Characteristics Using Remote Sensing Time-Series Images and Data Mining Algorithms
摘要： Heavy metal pollution in crops leads to phenological changes, which can be monitored by remote sensing technology. The present study aims to develop a method for accurately evaluating heavy metal stress in rice based on remote sensing phenology. First, the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) was applied to blend Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat to generate a time series of fusion images at 30 m resolution, and then the vegetation indices (VIs) related to greenness and moisture content of the rice canopy were calculated to create the time-series of VIs. Second, phenological metrics were extracted from the time-series data of VIs, and a feature selection scheme was designed to acquire an optimal phenological metric subset. Finally, an ensemble model with optimal phenological metrics as classification features was built using random forest (RF) and gradient boosting (GB) classifiers, and the classification of stress levels was implemented. The results demonstrated that the overall accuracy of discrimination for different stress levels is greater than 98%. This study suggests that fusion images can be utilized to detect heavy metal stress in rice, and the proposed method may be applicable to classify stress levels.
关键词： ensemble model,feature selection,time-series,MODIS and Landsat,remote sensing phenology,heavy metal stress
更新于2025-09-04 15:30:14