Search

The medium-resolution spectral imager (MERSI) onboard China’s new generation of polar-orbiting meteorological satellite series FengYun-3 (FY-3) is providing global observations of the Earth’s environment. One of the important characteristics of the MERSI sensor is that it has four bands at 250-m spatial resolution. However, very few high-level land surface and atmospheric products have been generated from MERSI data. This article presents an effective optimization method to estimate the continuous temporal distribution of multiple land surface and atmospheric variables at a 250-m spatial resolution from time series FY-3B MERSI top-of-atmosphere (TOA) observations. The MODIS cloud detection method was applied to the MERSI TOA reflectance to determine the clear observations. Then, a physics-based BRDF correction was adopt to reduce the topographic effects using the slope angle and aspect angle of the slope. Finally, a coupled land surface-atmosphere radiative transfer (RT) model and the shuffled complex evolution (SCE) optimization algorithm were used to estimate a suit of variables, including the leaf area index (LAI), the aerosol optical depth (AOD), the cloud optical thickness (COT), the cloud effective particle radius (CER), the land surface reflectance, the shortwave and visible albedo, the incident shortwave radiation (ISR), the incident photosynthetically active radiation (PAR), the fraction of absorbed PAR (FAPAR), the surface broadband emissivity (BBE), and the TOA shortwave albedo. The method was applied to the data from the 18 SURFRAD, the America FluxNet (AmeriFlux), and the Images and Beijing Normal University net (BNUnet) sites. All the estimated variables were also compared with Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Land Surface Satellite (GLASS) products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR were also validated using ground measurements from the selected sites. The results show that our method can simultaneously estimate multiple temporally continuous variables in both clear-and cloudy-sky conditions, with an accuracy comparable to those of MODIS and GLASS products. The estimated LAI, PAR, FAPAR, shortwave albedo, and ISR are consistent with the field measurements, with coefficients of determination ($R^{2}$) of 0.692, 0.783, 0.788, 0.559, and 0.833, and root mean square errors (RMSEs) of 0.427, 56.681 W/m2, 0.092, 0.084, and 115.305 W/m2, respectively. The proposed algorithm can effectively estimate the instantaneous atmospheric and land surface variables directly from satellite observations without cumbersome atmospheric corrections. Moreover, it can potentially be applied to multiple satellite observations. [ABSTRACT FROM AUTHOR]

Since a new generation of geostationary satellite data has incredibly high temporal, spatial, and spectral resolutions, new methodologies are now needed to take advantage of both the temporal and spectral signatures of them for accurate estimation of Earth’s environmental variables. This article describes a novel optimization method to estimate a suite of 11 physically consistent land surface and atmospheric variables under all-sky conditions from the geostationary advanced Himawari imager (AHI) top-of-atmosphere (TOA) observations. This method is based on a coupled soil, snow, vegetation, and atmospheric radiative transfer (RT) model from 0.28 to 14 μm. The inversion algorithm consists of three major steps. First, the “clearest” observations at each moment during a temporal window were determined and then the essential variables that characterize surface RT models, such as leaf area index (LAI), leaf chlorophyll concentration, and soil parameters were estimated. Second, the atmospheric variables, including aerosol optical depth (AOD) under clear-sky conditions, and cloud optical thickness (COT) and cloud effective particle radius (CER) under cloudy-sky conditions, were inverted given surface reflectance calculated by the surface RT models. Finally, the inverted atmospheric and land surface variables were fed into the coupled RT model to calculate the remaining set of variables, including spectral directional reflectance, surface broadband albedo, thermal emissivity, incident shortwave radiation (ISR), photosynthetically active radiation (PAR), fraction of absorbed PAR by green vegetation (FAPAR), and TOA shortwave albedo. The retrieved variables were validated using in-situ measurements from Ozflux network sites and compared with the other existing satellite products. Intercomparisons demonstrate that the AHI-retrieved atmospheric variables (AOD, CER, and COT) and surface variables (surface reflectance, LAI, FAPAR, PAR, and surface emissivity) are well correlated with the corresponding JAXA released AHI, NASA Moderate Resolution Imaging Spectroradiometer (MODIS) and Clouds and the Earth’s Radiant Energy System (CERES), and the Global LAnd Surface Satellite (GLASS) products. Direct validation using in-situ measurements indicates that the retrieved ISR achieves higher accuracy than the CERES ISR product (with R2 values of 0.95 and 0.89, and root-mean-square error (RMSE) of 20.3 and 29.6 Wm2 for the AHI-retrieved and CERES daily ISR, respectively). Validation also shows that the estimated daily surface albedo has an accuracy comparable to the MODIS daily albedo product (RMSE = 0.03). Both the direct validation and product comparisons have demonstrated that this proposed inversion framework works very well for the AHI data. Unlike other algorithms that are usually used for estimating an individual parameter and rely heavily on a separate atmospheric correction, this inversion framework can effectively estimate a group of atmospheric and land surface variables and be easily applied to other similar multispectral geostationary satellite data. A comprehensive sensitivity and validation study is still needed to quantify the uncertainties of the retrieval variables. [ABSTRACT FROM AUTHOR]

Autonomous 3-D exploration with unmanned aerial vehicles (UAVs) is increasingly prevalent for environment monitoring without human intervention. In this article, we present a systematic solution toward efficient UAV exploration in 3-D environments. Innovatively, a road map is incrementally built and maintained along with the exploration process, which explicitly exhibits the topological structure of the 3-D environment. By simplifying the environment, the road map can efficiently provide the information gain and the cost-to-go for a candidate region to be explored, which are two quantities for next-best-view (NBV) evaluation, thus prompting the efficiency for NBV determination. In addition, with reference to the global plan queried on the road map, we propose a local planner based on the potential field method that drives the robot to the information-rich area during the navigation process, which further improves the exploration efficiency. The proposed framework and its composed modules are verified in various 3-D environments, which exhibit their distinctive features in NBV selection and better performance in improving the exploration efficiency than other methods. [ABSTRACT FROM AUTHOR]

We present a data-driven approach that colorizes 3D furniture models and indoor scenes by leveraging indoor images on the internet. Our approach is able to colorize the furniture automatically according to an example image. The core is to learn image-guided mesh segmentation to segment the model into different parts according to the image object. Given an indoor scene, the system supports colorization-by-example, and has the ability to recommend the colorization scheme that is consistent with a user-desired color theme. The latter is realized by formulating the problem as a Markov random field model that imposes user input as an additional constraint. Our system is able to imitate the colorization results for those scenes containing the same type of objects, but with spatially varied patterns. We contribute to the community a hierarchically organized image-model database with correspondences between each image and the corresponding model at the part-level. Our experiments and a user study show that our system produces perceptually convincing results comparable to those generated by interior designers. [ABSTRACT FROM AUTHOR]

Traditional methods for estimating land-surface parameters from remotely sensed data generally focus on a single parameter with a specific spectral region, resulting in physical and spatiotemporal inconsistencies in current satellite products. We recently proposed a unified inversion scheme to estimate a suite of parameters simultaneously from both visible and near-infrared and thermal-infrared MODIS data. In this letter, we implemented this scheme to estimate six time-series parameters [leaf area index, fraction of absorbed photosynthetically active radiation, surface albedo, land-surface emissivity, land-surface temperature (LST), and upwelling longwave radiation (LWUP)] from the Visible Infrared Imaging Radiometer Suite (VIIRS) data. Several components of these schemes are refined, including the incorporation of a snow bidirectional reflectance distribution function model, determination of the best band combination, and better estimation of the snow-covered surface emissivity by accounting for the snow-cover fraction. Validation using the measurements at 12 sites of SURFRAD, CarboEuropeIP, and FLUXNET, and intercomparisons with MODIS and Global Land-Surface Satellite products, are carried out: the retrieved albedo, LST, and LWUP achieved accuracies ( R^2 ) of 0.77, 0.96, and 0.95, root mean square errors of 0.06, 2.9 K, and 18.3 W/m2, and biases of 0.01, 0.09 K, and −0.08 W/m2, respectively. The retrieved parameters can achieve comparable or higher accuracy than existing products, which indicates that the unified algorithm can be applied effectively to the VIIRS data with high physical and temporal consistency and accuracy. [ABSTRACT FROM PUBLISHER]

Most methods for generating global land surface products from satellite data are parameter specific and do not use multiple temporal observations, which often results in spatial and temporal discontinuity and physical inconsistency among different products. This paper proposes a data assimilation (DA) scheme to simultaneously estimate five land surface parameters from Moderate Resolution Imaging Spectroradiometer (MODIS) top-of-atmosphere (TOA) time series reflectance data under clear and cloudy conditions. A coupled land surface–atmosphere radiative transfer model is developed to simulate TOA reflectance, and an ensemble Kalman filter technique is used to retrieve the most influential surface parameters of the coupled model, such as leaf area index, by combining predictions from dynamic models and the MODIS TOA reflectance data whether under clear or cloudy conditions. Then, the retrieved surface parameters are input to the coupled model to calculate four other parameters: 1) land surface reflectance; 2) incident photosynthetically active radiation (PAR); 3) land surface albedo; and 4) the fraction of absorbed PAR (FAPAR). The estimated parameters are compared with those of the corresponding MODIS, the Global LAnd Surface Satellite, and the Geoland2/BioPar version 1 (GEOV1) products. Validation of the estimated parameters against ground measurements from several sites with different vegetation types demonstrates that this method can estimate temporally complete land surface parameter profiles from MODIS TOA time series reflectance data, with accuracy comparable to that of existing satellite products over the selected sites. The retrieved leaf area index profiles are smoother than the existing satellite products, and unlike the MOD09GA product, the retrieved surface reflectance values do not have the high peak values influenced by clouds. The use of the coupled land surface–atmosphere model and the DA technique ensures physical connections between the land surface parameters and makes it possible to calculate radiation-related parameters for clear and cloudy atmospheric conditions, which is an improvement for FAPAR retrieval compared with the MODIS and GEOV1 products. The retrieved FAPAR and PAR values can reveal the significant differences in them under clear and cloudy atmospheric conditions. [ABSTRACT FROM PUBLISHER]

Leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FAPAR), and surface broadband albedo are three routinely generated land-surface parameters from satellite observations, which have been widely used in land-surface modeling and environmental monitoring. Currently, most global land products are retrieved separately from individual satellite data. Many issues, such as data gaps, spatial and temporal inconsistencies, and insufficient accuracy under certain conditions resulting from the inadequacies of single-sensor observations, have made the incorporation of multiple sensors a reasonable solution. In this paper, an approach to simultaneous estimation of LAI, broadband albedo, and FAPAR from multiple-satellite sensors is further refined. The method, improved from that proposed in an earlier study using Moderate Resolution Imaging Spectroradiometer (MODIS) data, consists of several steps. First, a coupled dynamic and radiative-transfer model based on MODIS, SPOT/VEGETATION, and Multiangle Imaging SpectroRadiometer data was developed to retrieve LAI values and use them to construct a time-evolving dynamic model. Second, an iteration process with predefined exit criteria was developed to obtain consistent gap-filled LAI estimates. Third, a spectral albedo based on the retrieved LAI values was simulated using a radiative-transfer model and then converted to a broadband albedo using empirical methods. Snow-covered pixels identified by normalized difference snow index thresholds were adjusted to the weighted average of the underlying albedo and the maximum snow albedo. Finally, the FAPAR of green vegetation was calculated as a combination of the albedo at the top of the canopy, the soil albedo, and the transmittance of the PAR down to the background. Validation of retrieved LAI, albedo, and FAPAR values obtained from multiple-satellite data over ten study sites has demonstrated that the proposed method can produce more accurate products than presently distributed global products. [ABSTRACT FROM PUBLISHER]

Two wideband antennas sharing a common aperture are presented. One antenna consists of an electrically loaded rectangle monopole, an electrically loaded inverse L-shape monopole, and a lowpass matching network between the two monopoles. It covers 30-600 MHz (VSWR<3) with band-rejection characteristic in 86-110 MHz. The other one is a planar open-sleeve monopole, which covers 820-1200 MHz with VSWR<2. These two antennas are integrated in an aperture with size of only 420 x 200 mm2. Both antennas are planar, which are promising candidates for either vehicular or airborne applications. [ABSTRACT FROM AUTHOR]