Wavelength-Dependent Seeing Systematically Changes the Normalized Slope of Telescopic Reflectance Spectra of Mercury

Telescopic observations of Mercury consistently report systematic variations of the normalized spectral slope of visible-to-near-infrared reflectance spectra. This effect was previously assumed to be a photometric property of the regolith, but it is not yet fully understood. After the MESSENGER mission, detailed global spectral maps of Mercury are available that better constrain Mercury’s photometry. So far, wavelength-dependent seeing has not been considered in the context of telescopic observations of Mercury. This study investigates the effect of wavelength-dependent seeing on systematic variations of Mercury’s normalized spectral reflectance slope. Therefore, we simulate the disk of Mercury for an idealized scenario, as seen by four different telescopic campaigns using the Hapke and the Kaasalainen–Shkuratov photometric model, the MDIS global mosaic, and a simple wavelength-dependent seeing model. The simulation results are compared with the observations of previous telescopic studies. We find that wavelength-dependent seeing affects the normalized spectral slope in several ways. The normalized slopes are enhanced near the limb, decrease toward the rim of the seeing disk, and even become negative. The decrease of the normalized spectral slope is consistent with previous observations. However, previous studies have associated the spectral slope variations with photometric effects that correlate with the emission angle. Our study suggests that wavelength-dependent seeing may cause these systematic variations. The combined reflectance and seeing model can also account for slope variations between different measurement campaigns. We report no qualitative differences between results based on the Hapke model or the Kaasalainen–Shkuratov model.

Estimation of Cultivated Land Quality Based on Soil Hyperspectral Data

Efficient monitoring of cultivated land quality (CLQ) plays a significant role in cultivated land protection. Soil spectral data can reflect the state of cultivated land. However, most studies have used crop spectral information to estimate CLQ, and there is little research on using soil spectral data for this purpose. In this study, soil hyperspectral data were utilized for the first time to evaluate CLQ. We obtained the optimal spectral variables from dry soil spectral data using a gradient boosting decision tree (GBDT) algorithm combined with the variance inflation factor (VIF). Two estimation algorithms (partial least-squares regression (PLSR) and back-propagation neural network (BPNN)) with 10-fold cross-validation were employed to develop the relationship model between the optimal spectral variables and CLQ. The optimal algorithms were determined by the degree of fit (determination coefficient, R2). In order to estimate CLQ at the regional scale, HuanJing-1A Hyperspectral Imager (HJ-1A HSI) data were transformed into dry soil spectral data using the linkage model of original soil spectral reflectance to dry soil spectral reflectance. This study was conducted in the Guangdong Province, China and the Conghua district within the same province. The results showed the following: (1) the optimal spectral variables selected from the dry soil spectral variables were 478 nm, 502 nm, 614 nm, 872 nm, 966 nm, 1007 nm, and 1796 nm. (2) The BPNN was the optimal model, with an R2(C) of 0.71 and a normalized root mean square error (NRMSE) of 12.20%. (3) The results showed the R2 of the regional-scale CLQ estimation based on the proposed method was 0.05 higher, and the NRMSE was 0.92% lower than that of the CLQ map obtained using the traditional method. Additionally, the NRMSE of the regional-scale CLQ estimation base on dry soil spectral variables from HJ-1A HSI data was 2.00% lower than that of the model base on the original HJ-1A HSI data.

Temporal analysis on spectral reflectance of clove vegetation based on landsat 8 imagery

This study aims to analyze temporally the spectral reflectance of clove vegetation using Landsat 8 multitemporal imagery data in Buleleng district, Bali. The analysis method uses the conversion of raw data from Landsat 8 images to the spectral reflectance value at the Top of Atmosphere (TOA). This conversion scales back the pixel values ??of the Landsat 8 image in the visible spectrum, namely bands 2, 3, 4 and infrared bands 5, 6, and 7 into percentage units. The temporal analysis technique is carried out by grouping the time series of Landsat 8 image data for 1 period, in 2015, into 4 quarterly groups based on the acquisition time, namely Quarter I (January, February, March), Quarter II (April, May, June), Quarter III (July, August, September) and Quarter IV (October, November, December). The results showed that the graph pattern of the average percentage of spectral reflectance in each quarter was the same and in the infrared spectrum was greater than the visible spectrum. The average value of the largest spectral reflectance was found in the second Quarter which was acquired by band 5 of 28.143%, while the smallest in the first Quarter which was acquired by band 2 was 2.503%.

Spectral Reflectance-Based Mangrove Species Mapping from WorldView-2 Imagery of Karimunjawa and Kemujan Island, Central Java Province, Indonesia

Mangrove mapping at the species level enables the creation of a detailed inventory of mangrove forest biodiversity and supports coastal ecosystem management. The Karimunjawa National Park in Central Java Province is one of Indonesia’s mangrove habitats with high biodiversity, namely, 44 species representing 25 true mangroves and 19 mangrove associates. This study aims to (1) classify and group mangrove species by their spectral reflectance characteristics, (2) map mangrove species by applying their spectral reflectance to WorldView-2 satellite imagery with the spectral angle mapper (SAM), spectral information divergence (SID), and spectral feature fitting (SFF) algorithms, and (3) assess the accuracy of the produced mangrove species mapping of the Karimunjawa and Kemujan Islands. The collected field data included (1) mangrove species identification, (2) coordinate locations of targeted mangrove species, and (3) the spectral reflectance of mangrove species measured with a field spectrometer. Dendrogram analysis was conducted with the Ward linkage method to classify mangrove species based on the distance between the closest clusters of spectral reflectance patterns. The dendrogram showed that the 24 mangrove species found in the field could be grouped into four levels. They consisted of two, four, and five species groups for Levels 1 to 3, respectively, and individual species for Level 4. The mapping results indicated that the SID algorithm had the highest overall accuracy (OA) at 49.72%, 22.60%, and 15.20% for Levels 1 to 3, respectively, while SFF produced the most accurate results for individual species mapping (Level 4) with an OA of 5.08%. The results suggest that the greater the number of classes to be mapped, the lower the mapping accuracy. The results can be used to model the spatial distribution of mangrove species or the composition of mangrove forests and update databases related to coastal management.

Analysis of Changes in Vegetation Index Through Long-term Monitoring of Petroglyphs of Cheonjeon-ri, Ulju

In this study, vegetation index, the vegetation index calculated based on hyperspectral images was used to monitor Petroglyphs of Cheonjeon-ri, Ulju from 2014 to 2020. To select suitable the vegetation index for monitoring, indoor analysis was performed, and considering the sensitivity to biocontamination, Normalized Difference Vegetation Index (NDVI) and Triangular Vegetation Index (TVI) were selected. As a result of monitoring using the selected vegetation index, NDVI increased from 2014 to 2018 and then decreased in 2020, after preservation treatment. On the other hand, TVI was difficult to confirm the tendency during the monitoring. This difference was due to the variation in spectral reflectance according to the photographing conditions by year. Therefore NDVI is less sensitive to spectral reflectance deviation than TVI, so it can be used for monitoring. In order for TVI to be used, however, in-depth study is needed.

Estimation of Cotton Leaf Area Index (LAI) Based on Spectral Transformation and Vegetation Index

Unmanned aerial vehicles (UAV) has been increasingly applied to crop growth monitoring due to their advantages, such as their rapid and repetitive capture ability, high resolution, and low cost. LAI is an important parameter for evaluating crop canopy structure and growth without damage. Accurate monitoring of cotton LAI has guiding significance for nutritional diagnosis and the accurate fertilization of cotton. This study aimed to obtain hyperspectral images of the cotton canopy using a UAV carrying a hyperspectral sensor and to extract effective information to achieve cotton LAI monitoring. In this study, cotton field experiments with different nitrogen application levels and canopy spectral images of cotton at different growth stages were obtained using a UAV carrying hyperspectral sensors. Hyperspectral reflectance can directly reflect the characteristics of vegetation, and vegetation indices (VIs) can quantitatively describe the growth status of plants through the difference between vegetation in different band ranges and soil backgrounds. In this study, canopy spectral reflectance was extracted in order to reduce noise interference, separate overlapping samples, and highlight spectral features to perform spectral transformation; characteristic band screening was carried out; and VIs were constructed using a correlation coefficient matrix. Combined with canopy spectral reflectance and VIs, multiple stepwise regression (MSR) and extreme learning machine (ELM) were used to construct an LAI monitoring model of cotton during the whole growth period. The results show that, after spectral noise reduction, the bands screened by the successive projections algorithm (SPA) are too concentrated, while the sensitive bands screened by the shuffled frog leaping algorithm (SFLA) are evenly distributed. Secondly, the calculation of VIs after spectral noise reduction can improve the correlation between vegetation indices and LAI. The DVI (540,525) correlation was the largest after standard normal variable transformation (SNV) pretreatment, with a correlation coefficient of −0.7591. Thirdly, cotton LAI monitoring can be realized only based on spectral reflectance or VIs, and the ELM model constructed by calculating vegetation indices after SNV transformation had the best effect, with verification set R2 = 0.7408, RMSE = 1.5231, and rRMSE = 24.33%, Lastly, the ELM model based on SNV-SFLA-SNV-VIs had the best performance, with validation set R2 = 0.9066, RMSE = 0.9590, and rRMSE = 15.72%. The study results show that the UAV equipped with a hyperspectral sensor has broad prospects in the detection of crop growth index, and it can provide a theoretical basis for precise cotton field management and variable fertilization.

Successional and Intraspecific Variations in Leaf Traits, Spectral Reflectance Indices and Herbivory in a Brazilian Tropical Dry Forest

Leaf traits are good indicators of ecosystem functioning and can affect herbivory and leaf reflectance patterns, allowing a better understanding of changes in environmental conditions, such those observed during forest natural regeneration. The aim of this study was to evaluate the intraspecific variation in leaf traits and their influence on the pattern of herbivory and leaf reflectance in three species distributed along a successional gradient (early, intermediate and late stages) in a tropical dry forest (TDF) in northern Minas Gerais, Brazil. We sampled individuals of the following abundant tree species that occurred in multiple successional stages: Cenostigma pluviosum, Handroanthus ochraceus, and Tabebuia reticulata. We collected 10 leaves from each tree to determine the contents of chlorophyll a, b, and total, carotenoids and water, as well as the percentage of leaf area removed by herbivores and leaf specific mass (LSM). We also measured five spectral reflectance indices (Normalized Difference Vegetation Index-NDVI, Simple Ratio-SR, modified Normalized Difference-nND, modified SR-mSR and Water Index-WI) using a portable spectrometer. Our results showed intraspecific differences in most leaf traits along the successional gradient, suggesting that local adaptation may play an important role in plant community assembly. However, herbivory only differed for H. ochraceus in early and intermediate stages, but it was not affected by the leaf traits considered here. Spectral reflectance indices also differed among successional stage for all species together and for each species separately, except for T. reticulata in intermediate and late stages. Thus, leaf spectral signatures may be an important tool to the remote detection of different successional stages in TDFs, with implications for forest management.