Multispectral Observations Research Articles

Making full use of hyperspectral data for gross primary productivity estimation with multivariate regression: Mechanistic insights from observations and process-based simulations

Statistical gross primary productivity (GPP) estimation from remote sensing observations has mostly been attempted on the basis of multispectral observations. To make full use of the information contained in vegetation spectra, however, hyperspectral observations should be used in combination with appropriate multivariate methods. Nevertheless, only very few previous studies attempted to estimate GPP directly from hyperspectral observations and did so on the basis of reflectance, with only a limited number of temporally discontinuous observations. In this study, we used long-term, continuous, half-hourly hyperspectral observations covering the visible and near-infrared spectral range to estimate GPP directly from upwelling irradiance using partial least square (PLS) regression in a rice paddy. To gain a better understanding of processes underlying the PLS estimation, we used extensive complementary field observations to run process-based simulations using the SCOPE model. We then applied PLS regression to the simulated hyperspectral data in the same way as for the observations and disentangled contributions related to relevant physiological processes, namely sun-induced chlorophyll fluorescence (SIF) and xanthophyll cycle-related spectral changes (XC). We found that upwelling hyperspectral irradiance in the visible and near-infrared spectral range predicted GPP better than reflectance. Furthermore, PLS-based GPP estimates outperformed both far-red SIF and widely used vegetation index-based methods. However, the most relevant information for the observation-based PLS-models was not clearly related to XC or SIF as the near-infrared spectral range showed comparable performance. Also, the simple average of upwelling irradiance over the 850–900 nm range outperformed the other non-multivariate approaches, including far-red SIF. These results held for the evaluation in terms of the seasonal variation of GPP, while there was apparently a small contribution of SIF and XC for the diurnal variation. The simulation-based analysis showed that SIF and XC contributed useful information to both GPP and photosynthetic light use efficiency (LUE) estimates at both seasonal and diurnal time scales. The strongest unique contribution from either SIF or XC, however, was to the diurnal variation of GPP and XC showed considerably better performance than SIF. We did not find improvements when combining the spectral regions of XC (500–570 nm) and SIF (650–800 nm) to estimate GPP. While SIF showed improvements when combined with the remaining spectral information excluding XC, this was not the case for XC. Our approach combines the strengths of process-based modeling with multivariate statistical analysis to improve our understanding of the usable information content in vegetation spectra and is highly relevant for further developing suitable methods for GPP estimation at large scales.

A multilayer cloud detection algorithm for the Suomi-NPP Visible Infrared Imager Radiometer Suite (VIIRS)

A new multilayer (ML) cloud detection algorithm based on three shortwave infrared (SWIR) and two longwave infrared (LWIR) channels is developed and applied to the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Suomi-NPP satellite. The algorithm identifies ML clouds, i.e., ice clouds overlying water clouds, based on satellite multispectral observations in the 1.38, 1.6, 2.25, 8.5, and 11 μm channels. We perform synthetic radiative transfer simulations to understand the sensitivities of the aforementioned channels on ML and single-layer (SL) clouds. Active CALIOP observations are used to evaluate the algorithm. Compared with the collocated CALIOP results, the algorithm can determine SL and ML clouds correctly with success rates of approximately 80% and 60%, respectively, and has similar performance to that of the current MODIS operational ML cloud detection algorithm. The misclassification of ML clouds as SL clouds is primarily caused by thin ice clouds that are practically undetectable using LWIR tests. Furthermore, the algorithm is extended to analyze data from radiometers onboard the geostationary Himawari-8 and FengYun-4A satellites, and results similar to those of VIIRS are obtained.

Characterizing a New England Saltmarsh with NASA G-LiHT Airborne Lidar

Airborne lidar can observe saltmarshes on a regional scale, targeting phenological and tidal states to provide the information to more effectively utilize frequent multispectral satellite observations to monitor change. Airborne lidar observations from NASA Goddard Lidar Hyperspectral and Thermal (G-LiHT) of a well-studied region of saltmarsh (Plum Island, Massachusetts, United States) were acquired in multiple years (2014, 2015 and 2016). These airborne lidar data provide characterizations of important saltmarsh components, as well as specifications for effective surveys. The invasive Phragmites australis was observed to increase in extent from 8374 m2 in 2014, to 8882 m2 in 2015 (+6.1%), and again to 13,819 m2 in 2016 (+55.6%). Validation with terrestrial lidar supported this increase, but suggested the total extent was still underestimated. Estimates of Spartina alterniflora extent from airborne lidar were within 7% of those from terrestrial lidar, but overestimation of height of Spartina alterniflora was found to occur at the edges of creeks (+83.9%). Capturing algae was found to require observations within ±15° of nadir, and capturing creek structure required observations within ±10° of nadir. In addition, 90.33% of creeks and ditches were successfully captured in the airborne lidar data (8206.3 m out of 9084.3 m found in aerial imagery).

Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure

The Sentinel-2 satellite mission, developed by the European Space Agency (ESA) for the Copernicus program of the European Union, provides repetitive multi-spectral observations of all Earth land surfaces at a high resolution. The Level 2A product is a basic product requested by many Sentinel-2 users: it provides surface reflectance after atmospheric correction, with a cloud and cloud shadow mask. The cloud/shadow mask is a key element to enable an automatic processing of Sentinel-2 data, and therefore, its performances must be accurately validated. To validate the Sentinel-2 operational Level 2A cloud mask, a software program named Active Learning Cloud Detection (ALCD) was developed, to produce reference cloud masks. Active learning methods allow reducing the number of necessary training samples by iteratively selecting them where the confidence of the classifier is low in the previous iterations. The ALCD method was designed to minimize human operator time thanks to a manually-supervised active learning method. The trained classifier uses a combination of spectral and multi-temporal information as input features and produces fully-classified images. The ALCD method was validated using visual criteria, consistency checks, and compared to another manually-generated cloud masks, with an overall accuracy above 98%. ALCD was used to create 32 reference cloud masks, on 10 different sites, with different seasons and cloud cover types. These masks were used to validate the cloud and shadow masks produced by three Sentinel-2 Level 2A processors: MAJA, used by the French Space Agency (CNES) to deliver Level 2A products, Sen2Cor, used by the European Space Agency (ESA), and FMask, used by the United States Geological Survey (USGS). The results show that MAJA and FMask perform similarly, with an overall accuracy around 90% (91% for MAJA, 90% for FMask), while Sen2Cor’s overall accuracy is 84%. The reference cloud masks, as well as the ALCD software used to generate them are made available to the Sentinel-2 user community.

Development and Ground Evaluation of Ground-Target Tracking Control of Microsatellite RISESAT

Space Robotics Laboratory (SRL) of Tohoku University is currently developing a 50-kg-class Earth observation satellite "RISESAT". In this paper, we propose a new ground-target tracking control algorithm and attitude control system for RISESAT. The primal mission of RISESAT is multi-spectral observation of the Earth surface using a 5 m GSD High-Precision-Telescope (HPT) with liquid crystal tunable filters. During an observation, the HPT takes a dozen images of a ground target by changing observation bands. General Earth observation satellites using push-broom cameras require high attitude stability to obtain continuous images. However, RISESAT has to track a ground target with an accuracy of 0.1° and 0.008°/s attitude stability because imaging and wavelength switching take few seconds in total. Therefore, we propose a new ground-target tracking algorithm which can keep orientation of ground-target image to a constant direction which satellite operator requires. Evaluations of the algorithms were carried out by hardware-in-the-loop simulator "MEVIμS" which is a satellite system verification environment developed by SRL. We confirm that the algorithm satisfies the mission requirements under the influence of sensor noise and computation time limits.

ESTIMATION OF SURFACE SNOW WETNESS USING SENTINEL-2 MULTISPECTRAL DATA

Abstract. Snow cover characterization and estimation of snow geophysical parameters is a significant area of research in water resource management and surface hydrological processes. With advances in spaceborne remote sensing, much progress has been achieved in the qualitative and quantitative characterization of snow geophysical parameters. However, most of the methods available in the literature are based on the microwave backscatter response of snow. These methods are mostly based on the remote sensing data available from active microwave sensors. Moreover, in alpine terrains, such as in the Himalayas, due to the geometrical distortions, the missing data is significant in the active microwave remote sensing data. In this paper, we present a methodology utilizing the multispectral observations of Sentinel-2 satellite for the estimation of surface snow wetness. The proposed approach is based on the popular triangle method which is significantly utilized for the assessment of soil moisture. In this case, we develop a triangular feature space using the near infrared (NIR) reflectance and the normalized differenced snow index (NDSI). Based on the assumption that the NIR reflectance is linearly related to the liquid water content in the snow, we derive a physical relationship for the estimation of snow wetness. The modeled estimates of snow wetness from the proposed approach were compared with in-situ measurements of surface snow wetness. A high correlation determined by the coefficient of determination of 0.94 and an error of 0.535 was observed between the proposed estimates of snow wetness and in-situ measurements.

Exploiting the centimeter resolution of UAV multispectral imagery to improve remote-sensing estimates of canopy structure and biochemistry in sugar beet crops

The recent emergence of unmanned aerial vehicles (UAV) has opened a new horizon in vegetation remote sensing, especially for agricultural applications. However, the benefits of UAV centimeter-scale imagery are still unclear compared to coarser resolution data acquired from satellites or aircrafts. This study aims (i) to propose novel methods for retrieving canopy variables from UAV multispectral observations, and (ii) to investigate to what extent the use of such centimeter-scale imagery makes it possible to improve the estimation of leaf and canopy variables in sugar beet crops (Beta vulgaris L.). Five important structural and biochemical plant traits are considered: green fraction (GF), green area index (GAI), leaf chlorophyll content (Cab), as well as canopy chlorophyll (CCC) and nitrogen (CNC) contents.Based on a comprehensive data set encompassing a large variability in canopy structure and biochemistry, the results obtained for every targeted trait demonstrate the superiority of centimeter-resolution methods over two standard remote-sensing approaches (i.e., vegetation indices and PROSAIL inversion) applied to average canopy reflectances. Two variables (denoted GFGREENPIX and VICAB) extracted from the images are shown to play a major role in these performances. GFGREENPIX is the GF estimate obtained by thresholding the Visible Atmospherically Resistant Index (VARI) image, and is shown to be an accurate and robust (e.g., against variable illumination conditions) proxy of the structure of sugar beet canopies, i.e., GF and GAI. VICAB is the mNDblue index value averaged over the darkest green pixels, and provides critical information on Cab. When exploited within uni- or multivariate empirical models, these two variables improve the GF, GAI, Cab, CCC and CNC estimates obtained with standard approaches, with gains in estimation accuracy of 24, 8, 26, 37 and 8%, respectively. For example, the best CCC estimates (R2 = 0.90) are obtained by multiplying Cab and GAI estimates respectively derived from VICAB and a log-transformed version of GFGREENPIX, log(1-GFGREENPIX).The GFGREENPIX and VICAB variables, which are only accessible from centimeter-scale imagery, contributes to a better identification of the effects of canopy structure and leaf biochemistry, whose influences may be confounded when considering coarser resolution observations. Such results emphasize the strong benefits of centimeter-scale UAV imagery over satellite or airborne remote sensing, and demonstrate the relevance of low-cost multispectral cameras to retrieve a number of plant traits, e.g., for agricultural applications.

Remote Predictive Mapping of the Tunnunik Impact Structure in the Canadian Arctic using Multispectral and Polarimetric SAR Data Fusion

The 28-km diameter Tunnunik impact structure in northern Victoria Island, Arctic Canada, was mapped using ASTER, Landsat 8, RADARSAT-2 polarimetric synthetic aperture radar (SAR), and Quickbird data. Multispectral analysis was accomplished through band ratios, MNF transform, and spectral matching algorithms, from which 4 distinct spectral units were defined. Polarimetric SAR decompositions also showed different scattering mechanisms for these 4 units indicating different surface roughness properties. These multispectral and polarimetric SAR observations were combined with detailed surface textures and morphological features as visible in very high-resolution Quickbird imagery (61 cm/pixel). Remote sensing parameters and their thresholds for characterizing each unit were implemented into a decision-tree algorithm and a remote predictive geological map was produced. Subsequent field and follow-up laboratory investigations enabled the ground-truthing of these predictions. The geological units were defined as follows: (i) (smooth) fluvioglacial deposits, (ii) (moderately rough) chert-bearing dolostone, (iii) (rough) dolostone, and (iv) (rough) dolostone covered by silicified surfaces. The rough surfaces characterized by multiple scattering in the polarimetric SAR decomposition correspond to the occurrences of weathered carbonate rocks, which are relatively resistant to weathering and form blocky surfaces. This shows that SAR-derived surface roughness properties can greatly contribute to defining geological units by combining with lithological mapping.

Remote sensing of convective clouds using multi-spectral observations and examining their variability over India

Convective clouds are sources of heavy precipitation triggering flood related disasters. They are the drivers of atmospheric circulation and fundamental part of hydrological cycle. Changes in convective clouds affect atmospheric circulation and hydrological cycle. Long term reliable record of convective clouds is very useful to understand and study the hydrological cycle. Present research focuses on remote sensing of convective clouds using multispectral measurements at thermal Near Infrared channels (near 11 and 12 µm) and water vapor absorption channels (near 6.7 µm) from Meteosat First Generation (MFG) observations. Observed convective clouds are consistent with increased ice cloud water path and integrated water vapor and decreased incoming shortwave flux over Indian monsoon region. Convective clouds obtained from the present study are validated against space based radar observations from Precipitation Radar (PR) Onboard Tropical Rainfall Measuring Mission (TRMM). It is reported that present technique shows a correlation coefficient (CC) of 0.73 and Standard error of estimate of 2.96% in detecting seasonal convective clouds over India and nearby regions. 19 years record of reliable convective clouds has been generated using multispectral observations over India and nearby regions. It is reported that convective clouds show coherent variation with regional temperature over India. Impact of regional warming on convective clouds has been investigated. Present study reports that there is an increase of about 41.56% ± 11.43% in convective clouds for a unit degree increase in regional temperature over India. Results reported in this study highlight the importance of mitigation and adaptation actions against flood like disasters caused by increased convective clouds over Indian region.

The retrieval of ice cloud parameters from multi-spectral satellite observations of reflectance using a modified XBAER algorithm

Retrieval of ice cloud properties from passive remote satellite instruments using multispectral observations of the upwelling radiance at the top of atmosphere is challenging. This requires an accurate separation of the surface spectral reflectance below the cloud and that coming from the ice cloud particle. In this paper, a weighting-function based method is described, which retrieves ice cloud optical thickness (COT) and ice cloud crystal effective radius (CER). The retrieval algorithm belongs to the newly developed eXtensible Bremen Aerosol Retrieval (XBAER) algorithm family, and is called XBAER-ice. The XBAER-ice retrieval uses a surface reflectance parameterization minimizing the impact of surface scattering on the retrieval of optically thin ice cloud properties. A fractal model is used to describe the ice cloud optical properties. The comparison of COT and CER between XBAER-ice and latest Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products shows good agreement. We note that ice cloud property retrievals are particularly sensitive to assumptions about the ice particle shape. The comparison between satellite derived COTs using both the MODIS C6 product and XBAER-ice algorithm agree well with Atmospheric Radiation Measurement (ARM) in-situ measurements (with correlation coefficient above 0.9) whereas CERs show poorer agreement (~0.6). The global spatial distribution patterns and values of COT from ice clouds retrieved by using the MODIS C6 and XBAER-ice algorithms show a high degree of consistency. The XBAER-ice derived ice cloud CER values are systematically smaller than the MODIS C6 product, but both products have similar spatial distribution patterns. The XBAER-ice offers a potential alternative algorithm for the synergistic operational retrieval of ice cloud properties from the observations of Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) onboard Sentinel-3.

EFFECT OF THE TRAINING SET CONFIGURATION ON SENTINEL-2-BASED URBAN LOCAL CLIMATE ZONE CLASSIFICATION

Abstract. As any supervised classification procedure, also Local Climate Zone (LCZ) mapping requires reliable reference data. These are usually created manually and inevitably include label noise, caused by the complexity of the LCZ class scheme as well as variations in cultural and physical environmental factors. This study aims at evaluating the impact of the training set configuration, i.e. training sample number and imbalance, on the performance of Canonical Correlation Forests (CCFs) for a classification of the 11 urban LCZ classes. Experiments are carried out based on globally available Sentinel-2 imagery. Besides multi-spectral observations, different index measures extracted from the images as well as the Global Urban Footprint (GUF) and Open Street Map (OSM) layers are fed into the CCFs classifier. The results show that different LCZs favor different configurations in terms of training sample number and balance. Based on the findings, majority voting of different predictions from different configurations is proposed and performed. This way, a significant accuracy improvement can be achieved.

Benthic coverage and bottom topography of coral reef environment over Pirotan reef, Gulf of Kachchh region, India

Remote sensing measurements in coral reef environments commonly confront the problem of overlying atmosphere and modification of spectral signal due to water column over the bottom substrates. In order to correct these problems, hyperspectral observations offer an advantage over multispectral observations. Airborne hyperspectral remote sensing data from Airborne Visible Infrared Imaging Spectrometer- Next Generation (AVIRIS-NG) sensor was acquired during low tidal condition on 14 February 2016 at Pirotan reef, Gulf of Kachchh region, India. The objective of this study is to map benthic coverage and bottom topography over Pirotan reef. The methodology involved atmospheric correction, simultaneous retrieval of water parameters, bathymetry, water column correction and mapping. Atmospheric correction was performed by removing path radiance and aerosol contribution and dividing by atmospheric transmittance and incoming solar irradiance to obtain remote sensing reflectance. Model derived error minimization technique was used for simultaneous retrieval of water parameters and bathymetry. Derived water parameters were used to account for water column attenuation and retrieve concomitant true bottom signature.

Универсальный многоспектральный прицельно-наблюдательный прибор ккак средство совершенствования СУО объектов БТВТ

Универсальный многоспектральный прицельно-наблюдательный прибор ккак средство совершенствования СУО объектов БТВТ

Three-Dimensional Geographically Weighted Inverse Regression (3GWR) Model for Satellite Derived Bathymetry Using Sentinel-2 Observations

ABSTRACTCurrent trends of development of satellite derived bathymetry (SDB) models rely on applying calibration techniques including analytical approaches, neuro-fuzzy systems, regression optimization and others. In most of the cases, the SDB models are calibrated and verified for test sites, that provide favorable conditions for the remote derivation of bathymetry such as high water clarity, homogenous bottom type, low amount of sediment in the water and other factors. In this paper, a novel 3-dimensional geographical weighted regression (3GWR) SDB technique is presented, it binds together methods already presented in other studies, the geographically weighted local regression (GWR) model, with depth dependent inverse optimization. The proposed SDB model was calibrated and verified on a relatively difficult test site of the South Baltic near-shore areas with the use of multispectral observations acquired by a recently launched Sentinel-2 satellite observation system. By conducted experiments, it was shown that the proposed SDB model is capable of obtaining satisfactory results of RMSE ranging from 0.88 to 1.23[m] depending on the observation and can derive bathymetry for depths up to 12m. It was also shown, that the proposed approach may be used operationally, for instance, in the continuous assessment of temporal bathymetry changes, for areas important in the context of ensuring local maritime safety.

Visible to near-infrared MSL/Mastcam multispectral imaging: Initial results from select high-interest science targets within Gale Crater, Mars

The Mastcam CCD cameras on the Mars Science Laboratory Curiosity Rover each use an 8-position filter wheel in acquiring up to 1600 × 1200 pixel images. The filter set includes a broadband near-infrared cutoff filter for RGB Bayer imaging on each camera and 12 narrow-band geology filters distributed between the two cameras, spanning the wavelength range 445–1013 nm. This wavelength region includes the relatively broad charge-transfer and crystal-field absorption bands that are most commonly due to the presence of iron-bearing minerals. To identify such spectral features, sequences of images taken with identical pointings through different filters have been calibrated to relative reflectance using pre-flight calibration coefficients and in-flight measurements of an onboard calibration target. Within the first 1000 sols of the mission, Mastcam observed a spectrally diverse set of materials displaying absorption features consistent with the presence of iron-bearing silicate, iron oxide, and iron sulfate minerals. Dust-coated surfaces as well as soils possess a strong positive reflectance slope in the visible, consistent with the presence of nanophase iron oxides, which have long been considered the dominant visible-wavelength pigmenting agent in weathered martian surface materials. Fresh surfaces, such as tailings produced by the drill tool and the interiors of rocks broken by the rover wheels, are grayer in visible wavelengths than their reddish, dust-coated surfaces but possess reflectance spectra that vary considerably between sites. To understand the mineralogical basis of observed Mastcam reflectance spectra, we focus on a subset of the multispectral data set for which additional constraints on the composition of surface materials are available from other rover instruments, with an emphasis on sample sites for which detailed mineralogy is provided by the results of CheMin X-ray diffraction analyses. We also discuss the results of coordinated observations with the ChemCam instrument, whose passive mode of operation is capable of acquiring reflectance spectra over wavelengths that considerably overlap the range spanned by the Mastcam filter set (Johnson et al. 2016). Materials that show a distinct 430 nm band in ChemCam data also are observed to have a strong near-infrared absorption band in Mastcam spectral data, consistent with the presence of a ferric sulfate mineral. Long-distance Mastcam observations targeted toward the flanks of the Gale crater central mound are in agreement with both ChemCam spectra and orbital results, and in particular exhibit the spectral features of a crystalline hematite layer identified in MRO/CRISM data. Variations observed in Mastcam multi-filter images acquired to date have shown that multispectral observations can discriminate between compositionally different materials within Gale Crater and are in qualitative agreement with mineralogies from measured samples and orbital data.

A daytime VIIRS RGB pseudo composite for snow detection

RGB pseudo composite imagery is a robust and fast processing technique for satellite observations to provide near real-time synthesized weather information to forecasters. The technique consists of superposing three images resulting from a linear combination of multi-spectral observations and corresponding them to red, green and blue. A new algorithm for the detection of snow during daytime has recently been developed for the Suomi-NPP VIIRS instruments. The algorithm uses five VIIRS bands including that centered at 2.25μm to enhance snow detection and that centered at 1.24μm to highlight the temporal variability of the snow cover. Furthermore, this paper shows the capability of the algorithm to detect snow under thin cloud conditions. This product is already provided in near real-time to forecasters of the French national weather service, Météo-France.

HS remote sensing image restoration using fusion with MS images by EM algorithm

Remote sensing images are widely used for different areas from mineral exploration to agricultural applications and poor quality of hyperspectral (HS) images will directly have adverse effect on these applications. In this study, a method is proposed to restore degraded HS images. To achieve this aim, another multispectral (MS) observation of the same scene is supposed to be available and restoration is fulfilled by fusion of HS images and MS images. The proposed method gains maximum a posteriori estimation and is based on expectation maximisation algorithm. Deblurring and denoising are performed separately. Deblurring is done in spatial domain via non-overlapping blocks, whereas denoising is implemented in wavelet domain. To represent the coefficients in wavelet domain, instead of multinormal model, Gaussian scale mixture is exploited. The proposed method is validated on airborne visible/infrared imaging spectrometer (AVIRIS) and HS digital imagery collection experiment (HYDICE) databases and experimental results signify that the proposed method outperforms state-of-the-art techniques cited in the literature and signal-to-noise ratio is improved as much as 15.71 dB for Moffett database and 16.26 dB for HYDICE database.

Multi-Staged NDVI Dependent Snow-Free Land-Surface Shortwave Albedo Narrowband-to-Broadband (NTB) Coefficients and Their Sensitivity Analysis

Narrowband-to-broadband conversion is a critical procedure for mapping land-surface broadband albedo using multi-spectral narrowband remote-sensing observations. Due to the significant difference in optical characteristics between soil and vegetation, NTB conversion is influenced by the variation in vegetation coverage on different surface types. To reduce this influence, this paper applies an approach that couples NTB coefficient with the NDVI. Multi-staged NDVI dependent NTB coefficient look-up tables (LUT) for Moderate Resolution Imaging Spectroradiometer (MODIS), Polarization and Directionality of Earth’s Reflectance (POLDER) and Advanced Very High Resolution Radiometer (AVHRR) were calculated using 6000 spectra samples collected from two typical spectral databases. Sensitivity analysis shows that NTB conversion is affected more by the NDVI for sensors with fewer band numbers, such as POLDER and AVHRR. Analysis of the validation results based on simulations, in situ measurements and global albedo products indicates that by using the multi-staged NDVI dependent NTB method, the conversion accuracies of these two sensors could be improved by 2%–13% on different NDVI classes compared with the general method. This improvement could be as high as 15%, on average, and 35% on dense vegetative surface compared with the global broadband albedo product of POLDER. This paper shows that it is necessary to consider surface reflectance characteristics associated with the NDVI on albedo-NTB conversion for remote sensors with fewer than five bands.

The Model Inversion of Leaf Area Index of Vegetation by Means of Electromagenetic Wave Radiative Transfer Model

This paper puts forward a novel approach for model inversion of leaf area index (LAI) of vegetation based on the integrated arithmetic of data assimilation and genetic-particle swarm algorithm (DAGS). The article expounds the design principle of electromagenetic wave radiative transfer model (ERTM) for vegetation canopies. On this basis, this study constructs the inversion model of LAI based on DAGS. Furthermore, this experiment realizes the model inversion of LAI with the aid of Remote Sensing (RS) multi-spectral data and biophysical component data of vegetation canopies, which are provided by the multispectral RS observation data set (MOD15A2). The bullet points of the text are summarized as follows. (1) The contribution proposes DAGS for LAI inversion. (2) The article discusses ERTM model for electromagenetic wave radiative transfer mechanism of vegetation canopies. (3) This text achieves LAI inversion with the help of RS multi-spectral data and biophysical component data of vegetation canopies supplied by MOD15A2. The experimental results demonstrate the validity and reliability of the model inversion of LAIby making use of DAGS. The proposed algorithm exploits a novel algorithmic pathway for the model inversion of LAI by means of RS multi-spectral data and biophysical component data of vegetation canopies.

VNIR multispectral observations of aqueous alteration materials by the Pancams on the Spirit and Opportunity Mars Exploration Rovers

Multispectral visible and near infrared (VNIR) observations from the Mars Exploration Rover Pancam multispectral stereo camera systems are consistent with materials having been subjected to various aqueous processes. Ferric oxides in the form of hematite in the Burns and Grasberg formations of Meridiani Planum have been well characterized by Opportunity on the basis of strong 535 and 864 nm absorptions and positive 754–1009 nm and 934–1009 nm slopes. On the rim of Noachian-aged Endeavour crater, Opportunity has observed light-toned veins with high Ca and S, as determined by the rover’s Alpha Particle X-ray Spectrometer (APXS), and a negative 934–1009 nm slope in VNIR spectra extracted from Pancam data, indicative of a 1000 nm H2O overtone absorption. Together these observations indicate that the veins are composed of gypsum. Rocks overturned by Opportunity on the Murray Ridge portion of the Endeavour crater rim display dark- and light-toned coatings. The dark-toned coatings have a red, featureless slope that is consistent with the slope observed in laboratory spectra of high-valence manganese oxide minerals. Potential Mn oxide coatings may also be associated with some exposures of the Grasberg formation. APXS results for high Mg and S in the light-toned coatings of the Murray Ridge overturned rocks and a negative 934–1009 nm slope are consistent with hydrated Mg-sulfates. Opportunity has also observed spectral features in rocks that are consistent with orbital observations of Fe-smectites, as well as Al-smectites and possible hydrated silica in light-toned fracture-fill materials. The Spirit rover observed sulfate-rich light-toned soils exposed by the rover’s wheels. Several of these soil observations contained spectral features, such as a broad absorption centered near 800 nm, consistent with ferric sulfate minerals, a finding confirmed by the rover’s Mossbauer spectrometer. Spirit also excavated light-toned Si-rich soils. These soils have a flat near-infrared spectrum with a drop in reflectance from 934–1009 nm that is consistent with free water contained in voids or adsorbed onto the surface of the silica.