The rapid advancement of remote sensing (RS) technology has posed increasing demands for secure and efficient processing of multispectral data. However, conventional joint image encryption and compression schemes, originally developed for natural images, are not well suited to the specific requirements of multispectral RS scenarios, such as managing interband redundancy, capturing spatial texture variation, and preserving spectral consistency for downstream applications. To address these challenges, we propose a joint encryption and compression algorithm for multispectral images (JECA-MS), the first joint encryption and compression framework specifically designed for multispectral RS images with support for ciphertext domain recompression. The JECA-MS incorporates four key innovations: 1) an adaptive two-size texture block decision (TBD) strategy that classifies image regions into strong and weak texture blocks (WTBs), reducing data volume in weak-texture areas by up to fourfold; 2) a modified 3-D discrete cosine transform (3D-MDCT) that enhances spatial–spectral decorrelation, particularly in homogeneous regions such as clouds and water; 3) a ciphertext domain recompression mechanism that enables flexible adjustment of compression ratios (CRs) without decryption; and 4) a dedicated JECA-MS coding format (JECA-MS-CF) for efficient data encapsulation and compatibility with RS data structures. Extensive experiments show that the JECA-MS achieves 55% and 36% improvements in CRs for water and cloud images, while reducing encoding and decoding time by 39% and 68%, compared to state-of-the-art methods. Security evaluation shows that the JECA-MS can resist statistical attacks, achieve a tradeoff between lightweight encryption and compression performance. This work offers a flexible solution for secure and efficient RS data management.

Improving hyperspectral image (HSI) semantic segmentation by exploiting complementary information from supplementary modalities (termed X-modality) is promising but challenging due to significant differences in imaging sensors, image content, and resolution. Existing methods often underutilize the unique spatial–spectral features of HSIs by processing them uniformly with X-modality data. In addition, current cross-modality fusion strategies often suffer from limited intermodal interaction or significantly increased model complexity. To address these limitations, we propose CoMiX, an asymmetric encoder-decoder architecture with deformable convolutions (DCNs) for HSI-X semantic segmentation. CoMiX includes an encoder with two parallel, interacting backbones and a lightweight all-multilayer perceptron (ALL-MLP) decoder. The encoder consists of four stages, each incorporating 2D DCN blocks for the X-modality to accommodate geometric variations and 3D DCN blocks for HSIs to adaptively capture spatial-spectral features. Each stage also incorporates a Cross-Modality Feature enhancement and eXchange (CMFeX) module and a feature fusion module (FFM). CMFeX exploits spatial-spectral correlations across modalities to recalibrate and enhance modality-specific and modality-shared features, while adaptively exchanging complementary information. Its outputs are subsequently fused in the FFM and propagated to the next stage for further learning. Finally, the ALL-MLP decoder aggregates the fused features from all stages to produce the final predictions. Extensive experiments demonstrate that CoMiX achieves state-of-the-art performance and generalizes well to various multimodal datasets. The CoMiX code will be released soon.

Large-area medium-resolution land cover classification is a key information for monitoring land use, human activity influence on ecological environments, etc. However, the existing land cover classification products lack insights of the utilization of multi-source data and regional characteristics, thus facing accuracy shackles. In this work, we supplemented SAR and DEM data into classification procedure, and gathered a comprehensive set of features, including global ecological zones (GEZs) announced by FAO as an indicator. With a multi-source land cover point label dataset for Mekong basin (LanCoMe) with a revised classification system, a random forest model was trained to produce Mekong land cover classification mappings. The model achieves validation accuracy of 91.3%, and the product is well correlated with other published global land cover products. The result also indicates that GEZs can be a prior feature for large-area land cover tasks.

Hyperspectral image (HSI) classification has recently reached its performance bottleneck. Multimodal data fusion is emerging as a promising approach to overcome this bottleneck by providing rich complementary information from the supplementary modality (X-modality). However, achieving comprehensive cross-modal interaction and fusion that can be generalized across different sensing modalities is challenging due to the disparity in imaging sensors, resolution, and content of different modalities. In this study, we propose a local-to-global cross-modal attention-aware fusion (LoGoCAF) framework for HSI-X segmentation. LoGoCAF adopts a two-branch semantic segmentation architecture to learn information from HSI and X modalities. The pipeline of LoGoCAF consists of a local-to-global encoder and a lightweight all multilayer perceptron (ALL-MLP) decoder. In the encoder, convolutions are used to encode local and high-resolution fine details in shallow layers, while transformers are used to integrate global and low-resolution coarse features in deeper layers. The ALL-MLP decoder aggregates information from the encoder for feature fusion and prediction. In particular, two cross-modality modules, the feature enhancement module (FEM) and the feature interaction and fusion module (FIFM), are introduced in each encoder stage. The FEM is used to enhance complementary information by combining information from the other modality across direction-aware, position-sensitive, and channel-wise dimensions. With the enhanced features, the FIFM is designed to promote cross-modality information interaction and fusion for the final semantic prediction. Extensive experiments demonstrate that our LoGoCAF achieves superior performance and generalizes well on various multimodal datasets. Code is available athttps://github.com/xumzhang.

Fine particulate matter, specifically PM2.5, has raised increasing public and governmental concerns over the past decade for its threats to the environment and public health. For large-scale PM2.5 estimation, spatial neighborhood information is frequently ignored when modeling the spatiotemporal heterogeneity of the PM2.5-aerosol optical depth (AOD) relationship. In this regard, applying convolutional neural networks (CNNs) to extract the spatial neighborhood characteristic has great potential; therefore, this article establishes a spatial neighborhood deep neural network (SNDNN) model to predict PM2.5 concentrations across China. In addition to the backward propagation neural network (BPNN) model for extracting spatiotemporal features, the model integrates a CNN model to achieve spatial neighborhood data mining within a$3 \times 3$km2 window. The cross-validation (CV) results show that the daily model achieves high accuracy and stability from 2016 to 2020. The coefficient of determination ($R^{2}$) value reached 0.92 in 2018, with a root mean square error (RMSE) of$9.39 \mu \text{g}/\text{m}^{3}$and a mean absolute error (MAE) of$6.09 \mu \text{g}/\text{m}^{3}$. Further, a monthly SNDNN model is established to predict seasonal and annual PM2.5 concentrations with greater accuracy and wider spatial coverage. The study results demonstrate the superiority of introducing spatial neighborhood information to PM2.5 estimation and indicate that the SNDNN can provide a practical reference for spatial neighborhood feature extraction.

Transformer is a powerful tool for capturing long-range dependencies and has shown impressive performance in hyperspectral image (HSI) classification. However, such power comes with a heavy memory footprint and huge computation burden. In this paper, we propose two types of lightweight self-attention modules (a channel lightweight multi-head self-attention module and a position lightweight multi-head self-attention module) to reduce both memory and computation while associating each pixel or channel with global information. Moreover, we discover that transformers are ineffective in explicitly extracting local and multi-scale features due to the fixed input size and tend to overfit when dealing with a small number of training samples. Therefore, a lightweight transformer (LiT) network, built with the proposed lightweight self-attention modules, is presented. LiT adopts convolutional blocks to explicitly extract local information in early layers and employs transformers to capture long-range dependencies in deep layers. Furthermore, we design a controlled multi-class stratified sampling strategy to generate appropriately sized input data, ensure balanced sampling, and reduce the overlap of feature extraction regions between training and test samples. With appropriate training data, convolutional tokenization, and lightweight transformers, LiT mitigates overfitting and enjoys both high computational efficiency and good performance. Experimental results on several HSI datasets verify the effectiveness of our design.

Poor atmospheric conditions including persistent clouds and seasonal snow could cause data gaps in satellite land surface observation and remote sensing products, especially in the mountainous region with seasonal snow. A modified missing information reconstruction method is proposed here based on the neighborhood similar pixel interpolator (NSPI) iterative method to reconstruct spatially continuous MCD43A4 product over Nepal. To solve this limitation of NSPI in predicting the persistent missing pixels in a time series, additional spatial information from MOD09GA products is used as auxiliary data. Compared with the original NSPI iterative method, the proposed modified NSPI iterative method has performed better with a higher Correlation Coefficient (CC) and lower RMSE (CC>0.90, RMSE<0.04). This indicates that the proposed method can obtain better performance in reconstructing the persistent missing information of MCD43A4 products in a long time series.

This paper presents an attention based lightweight network (ALN) for hyperspectral image (HSI) classification, aiming to jointly optimize classification performance and parameter efficiency. It employs a light spectral feature extraction module to learn the spectral information of the input data, and then uses the proposed spatial self-attention module to aggregate the spatial information related to the center pixel. Therefore, it can extract more informative spectral-spatial features related to the classified pixels. Extensive experiments show that the proposed ALN significantly outperforms the state-of-the-art methods. The codes of this work will be available at https://github.com/xmzhang2018.

In this study, a temporal shape–based fusion method using a spatially and temporally moving window is proposed to incorporate time lag of fine and coarse resolution observations, and to fully utilize target fine resolution pixel and similar coarse resolution pixels in the process. This method provides high accuracy fused images with Pearson’s r of ~0.95, root mean square error of ~0.04, and bias of ~0.01 for commonly used fine spatial resolution satellites, including Landsat 7 and 8, Sentinel 2, and Gaofen 1, over different heterogeneous regions, such as urban, mountain, forest, and savanna regions. The fused fine resolution Enhanced Vegetation Index (EVI) time series using different fine spatial resolution satellites data as input are all highly correlated with the PhenoCam monitored green chromatic coordinate, with no temporal lag. Compared with commonly used data fusion method, this method provides equivalent and slightly higher accuracy because both neighboring similar pixels and the annual temporal variation are fully considered. This temporal shape–based fusion method does not require each input fine resolution image to be cloud-free; therefore, it can be used at a large spatial scale without further preprocessing and generates continuous datasets over a long-time range with only one input preparation process. The factors that could affect the method accuracy are the cloud detection accuracy of fine resolution data and the temporal continuity of the coarse resolution data. The method may also be used to produce spatially and temporally continuous surface reflectance and other surface reflectance derived indices.

Synthetic aperture radar (SAR) can provide stable data source for earth observation due to its advantages of all day and night, all-weather, and strong penetration. SAR image classification as a fundamental procedure has been proved its great value in plenty of remote sensing applications. Conventional classification algorithms mainly rely on hand-designed features, which are susceptible to widespread coherent speckle noise and geometric distortion in high-resolution SAR images. Inspired by the recent impressive success in data mining and deep learning, a greedy hierarchical convolutional neural network (GHCNN) is developed. It aims at obtaining optimized feature representation, relieving the effect of speckle noise, and promoting the local pattern recognition of geometric distortion in single-polarized SAR image classification. First, a series of convolutional autoencoders (CAEs) is trained in the greedy layer-wise unsupervised strategy. This step provides an unbiased regularizer anda prioridistribution derived from large volumes of unlabeled SAR patches. Then, to optimize multiple parameter subspaces globally, several CAEs are coupled together to form a deeper hierarchical structure in a stacked and unsupervised fashion. Afterward, a convolutional network with identical topology inherits the pretrained weights. After supervised finetuning, it realizes class prediction. Synchronously, t-distributed stochastic neighbor embedding (t-SNE) algorithm is applied to monitor the efficiency of feature representation during the training period. Experimental results demonstrate that the proposed method has competitive advantages over involved contrast methods.

Aerosol distributions may change at fine spatial scales in urban areas due to building and transport infrastructure and human population density variations. The recent availability of Sentinel-2 satellite data provides the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. In this study, a novel high-spatial-resolution AOD retrieval algorithm for bright unban surfaces was developed based on the Sentinel-2 images. The surface reflectance for AOD retrieval was estimated from the image that has the minimal aerosol contamination in a temporal window. Validation of the Sentinel-2 AOD retrievals was conducted against four Aerosol Robotic Network (AERONET) sites located in Beijing. The results show that the Sentinel-2 AOD retrievals are highly consistent with the AERONET AOD measurements ( R=0.9424), with 85.56% of them falling within the Expected Error (EE). The mean absolute error (MAE) and the root mean square error (RMSE) are 0.0688 and 0.0882, respectively. These results suggest that our high-resolution AOD retrieval algorithm is robust and useful to retrieve high-resolution AOD over bright urban surfaces based on Sentinel-2 images.

For the measurement of LAI (Leaf Area Index) by DHP (Digital Hemispherical Photography) method, imprecise segmentation is the key error source. In this paper, to our knowledge, a deep learning algorithm is used for the first time to segment upward hemispherical image of vegetation. Pix2pix, a general mapping learning model, was improved in our study to make it more suitable for processing segmentation problem. Thousands of images collected in the field were labeled to train the model, and the conventional methods based on pattern recognition, such as the Otsu and HSV, were compared. The result shows that the improved pix2pix algorithm significantly improved the accuracy of the segmentation, which reached to 0.9834. Furthermore, this model has a good performance in processing pictures of complex environments, and the segmentation of edge details has also been optimized. Those results show that the method has great potential to improve the LAI measurement accuracy.

With the continuous development of optical lens and imaging chip technology, the fisheye camera method has been widely studied in the world because of its characteristics of TRAC instrument and LAI-2200C coronal analyzer. To obtain the critical ecological parameter of the leaf area index at low cost, a synchronous LAI-2200C leaf area index hemispheric image acquisition system was proposed in this paper. The system consists of an embedded platform, a low-cost image sensor, and a fisheye lens, fixed to the LAI-2200C optical sensor detector, triggered by the LAI-2200C synchronous of hemispheric vegetation image. This study used the system to measure the coronary photos of the tall shrubs in the Chengdu area at different times. It used an image processing algorithm to analyze and obtain LAI. The results show that there is a significant linear correlation between LAI and LAI-2200C measurements obtained by the Fisheye Camera Method (DH-P) (R2= 0.814), the average square root error is 0.278.The test results show that the system can effectively collect the image of the vegetation canopy can be low-cost, and obtain a leaf area index results with minor error.

The popular enhanced deep blue (DB) algorithm, though performs well in aerosol retrieval over the entire land surfaces, limited by the low temporal resolution and insufficient satellite-derived products of most high resolution satellite sensors, is hardly to be applied to urban areas using high spatial resolution satellite imageries. In this paper, we developed a simplified deep blue algorithm to retrieve 30m spatial resolution aerosol optical thickness (AOT) over urban surfaces using Landsat 8 OLI measurements. With a few atmospheric correction surface reflectance-apparent reflectance pairs, robust relationships between visible (0.65μm and 0.48μm) and 2.2μm reflectance can be constructed by this algorithm. Difficulties described above are overcame, and high consistency with ground-based AERONET measurements are achieved over Beijing, with correlation coefficient (R2) ~0.951, root mean square error (RMSE) ~0.005, mean absolute error (MAE) ~0.05 and 82.61% retrievals fall within the expected error (EE) envelop. This study demonstrates that the simplified deep blue algorithm has advantages in retrieval high spatial resolution AOTs over urban areas.

Leaf area index (LAI) is the basic factor to understand canopy productivity, soil water evaporation, total transpiration loss, and soil temperature. On the basis of analyzing the merits and demerits of various LAI measurement methods, this paper affirms the development prospect and application value of the hemispherical image method. The paper focuses on the inversion theory of LAI and the extraction of canopy porosity. The essence of the hemispherical image method is further elaborated: after the canopy porosity is obtained by image processing, LAI is retrieved based on Lambert-Beer law. In this paper, four tall arbor forests of Chengdu city are selected as research objects to explore the method of obtaining LAI by hemispherical images and compare with LAI-2200C Plant Canopy Analyzer. The results show that LAI measurement based on the hemispherical image is feasible and credible.

A novel multisensor synergy method, using data from Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua as well as Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership, is presented to retrieve optical atmosphere-surface properties. By adopting three-sensor observations’ synergy, the proposed method can grasp the multitemporal characteristics of aerosol optical depth (AOD) and the multidirectional characteristics of surface reflectance. In addition, the bidirectional reflectance distribution function (BRDF) can be derived at daily scale by adopting the novel shape function constrained BRDF retrieval (SFCBR) method. The 550-nm AOD retrieval result of the proposed method is validated by AErosol RObotic NETwork (AERONET) measurement at Beijing, XiangHe, Noto, and Gwangju_GIST sites with$R^{2}$equaling to 0.78, 0.75, 0.70, and 0.75, respectively. Compared with MODIS/VIIRS AOD official product, the proposed method shows higher coverage rate (especially in AERONET Beijing site with approximately 50% increase) with comparative accuracy. The expected error of the retrieved AOD from the proposed method is estimated as$\Delta \tau = \pm 0.05 \pm 0.24\tau $. The correlation coefficients of BRDF-derived albedo time series between the proposed method and MODIS BRDF/Albedo product can reach up to 0.85 with an obvious improvement in temporal resolution by adopting an SFCBR method. The average relative differences of BRDF shape function between the retrieval result and MODIS BRDF/Albedo product in all directions equal to 0.026, 0.036, 0.037, and 0.016 in AERONET Beijing, XiangHe, Noto, and Gwangju_GIST sites, respectively.

This paper, we propose a method that exploit total generalized variation (TGV) and Shearlet transform for image restoring. The TGV adaptive regularize different image regions at different levels and the Shearlet transform can efficiently represent image anisotropic features such as edges, curves. A new image restoration model combining TGV and Shearlet transform is proposed for image restoration. The proposed model is solved by splitting variables and applying the alternating direction method of multiplier (ADMM). Experimental results show that the proposed algorithm can effectively restore image and improve the quality.

The high-spatial-resolution aerosol retrieval algorithm using Chinese High-Resolution Earth Observation Satellite I (GF-1) wide-field images is developed, which retrieves the aerosol optical depth (AOD) over China for studying the impact of aerosol on climatic and environmental change. The algorithm is based on the red/blue surface reflectance correlations and the lookup table method. To reduce the enormous relative error caused by the constant surface reflectance relationship in the retrieval algorithm, the correlation is parameterized as a function of low, medium, and high values of normalized difference vegetation index (NDVI). Three linear relationships are simulated using MODIS BRDF-adjusted reflectance products (MCD43A4), and MODIS NDVI products are used to ascertain the value of NDVI. By applying the present algorithm to GF-1 images, two different aerosol cases of clear and turbid are analyzed to test the algorithm. Compared with the 10-km MODIS aerosol properties productions, the GF-1 retrieved AOD by our algorithm revealed a significant correlation coefficient with MODIS Dark Target AOD (R = 0.912) and Deep Blue AOD (R = 0.895). Otherwise, the retrieved AOD results are found to be highly correlated with Aerosol Robotic Network (AERONET) sunphotometer observations (R = 0.931). Compared with the results relying on the MODIS surface reflectance model, preliminary validation is encouraging that the method based on our updated surface reflectance assumptions successfully improved the accuracy, particularly under the clear sky background and over bright surface.

The panchromatic and multispectral (PMS) sensor is a high spatial resolution sensor aboard the GF-1 satellite launched on April 26, 2013. This paper focuses on the cross-calibration of the PMS sensor using Terra/Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat 8/Operational Land Imager (OLI). Two matched-image adjustment factors (MIAFs) are used in the cross-calibration which are the radiance MIAF and reflectance MIAF. Two test sites are chosen as the regions of interest. One is the Dunhuang test site, which has been used for the vicarious calibration of Chinese satellites since later 1990s. The other is the Golmud test site, which is a new site with no ground measured data available. The results show that both the Dunhuang and Golmud test sites can be used for cross-calibration. This paper reveals that the cross-calibration of the PMS sensor using OLI is better than using MODIS, as the calibration coefficient difference between the two test sites with OLI is smaller than that with MODIS. The uncertainty analysis results show that the uncertainty of cross-calibration using OLI is 5%-7% when the ground data are not available.

A novel spectral similarity measure approach, which is named spectral frequency spectrum difference (SFSD), is proposed for hyperspectral image classification based on the frequency spectrum of spectral signature using the Fourier transform. Many important characteristics of spectral signature can be clearly reflected in the frequency spectrum. Therefore, the spectral similarity is defined as the frequency spectrum's difference between the target and reference signatures. The frequency spectrum analysis in this study suggests that the magnitude values of the first few low-frequency components for spectral signature can effectively represent the spectral similarity. To balance the difference between the low- and high-frequency components, the frequency spectrum of the target spectral signature is taken as the normalized factor in the SFSD method. Next, the U.S. Geological Survey spectral data and two hyperspectral remote sensing images were employed as test data in our validation experiments. The new SFSD proposed here was compared with the leading approaches in terms of the spectral discriminability and classification accuracy. Results show that the SFSD exhibits a relatively better performance and has more robust applications for hyperspectral image classification.

This paper describes a methodology that uses Hyperion imagery as reference data to calibrate the Chinese Hyperspectral Imager (HSI) onboard the HJ-1A satellite. Two test sites near Dunhuang in Gansu and in Inner Mongolia were used for the cross-calibration. To account for the uncertainties in the top of atmosphere (TOA) reflectance due to the bidirectional reflectance distribution function (BRDF) and relative spectral response (RSR) differences between the two sensors, a model is adopted to transfer the Hyperion TOA reflectance to the effective HSI TOA reflectance. The influence of BRDF is analyzed, and two BRDF correction approaches are applied to the two test sites, respectively. For the Dunhuang test site, the ground synchronously measured reflectance in different solar zenith angles is used for BRDF correction. For the Inner Mongolia test site, a kernel-driven model is applied. The influence of RSR mismatch is computed using a spectral profile adjustment factor (SPAF), which takes into account the spectral profile of the target and the RSR of each sensor. The SPAF is calculated according to the TOA reflectance simulated using moderate resolution atmospheric transmission. One-point calibration and multipoint calibration coefficients are computed, respectively. Ground reflectance data measured in June 2010 at the Inner Mongolia test site were used to validate the cross-calibration coefficients based on the Hyperion image. The results support the proposal that the cross-calibration method between two hyperspectral sensors is effective, and the use of multipoint calibration coefficient with nonzero offset has great potential for hyperspectral sensor calibration.

This paper analyzes the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) AOD products to study aerosol distribution and regional trends during 2002 to 2010. Firstly, we compared MODIS and MISR AOD with AERONET station AOD. MISR has been found the better correlation (R= 0.93), which dominate MISR perform better than MODIS over eastern China. Our study found that the spatial distribution of aerosol for both sensors is highly associated with human activities. The trend analysis shows that increasing trends are found over study areas and MISR has better applicable over eastern China is evidenced using method of trend evaluation.

One of the significant factors for improving the accuracy of Land Surface Temperature (LST) retrieval is the correct understanding of the directional anisotropy for thermal radiance. In this paper, the multiple scattering effect between heterogeneous non-isothermal surfaces is described rigorously according to the concept of configuration factor, based on which a directional thermal radiance model is built, and the directional radiant character for urban canopy is analyzed. The model is applied to a simple urban canopy with row structure to simulate the change of Directional Brightness Temperature (DBT). The results show that the DBT is aggrandized because of the multiple scattering effects, whereas the change range of DBT is smoothed. The temperature difference, spatial distribution, emissivity of the components can all lead to the change of DBT. The “hot spot” phenomenon occurs when the proportion of high temperature component in the vision field came to a head. On the other hand, the “cool spot” phenomena occur when low temperature proportion came to the head. The “spot” effect disappears only when the proportion of every component keeps invariability. The model built in this paper can be used for the study of directional effect on emissivity, the LST retrieval over urban areas and the adjacency effect of thermal remote sensing pixels.

This paper researches on the method how to retrieve the water-leaving reflectance from Chinese HJ1 CCD imagery. The instrument characteristics are first analyzed and then the monochromic atmosphere correction equation is described. Using the radiative transfer model, the correction parameters are worked out by varying different conditions such as sun-observation geometry, aerosol models and so on. The results are stored in look-up table (LUT). Moreover, the MODIS data are used to make certain aerosol load and water vapor volume, which are the input parameters of the pixel-by-pixel procedure. The retrievals of water-leaving reflectance are finally compared to the in Situ measurement and MODIS results. It is found that the correction accuracy is good especially in the blue and green bands. This paper demonstrates that this method is a simple and efficient approach and well worth applying to future conventional production.

In this letter, we generate a temporal/spatial matchup data set between QuikSCAT scatterometer and RADARSAT-1 synthetic aperture radar (SAR) wind products in offshore waters along the U.S. West Coast. Analysis of the resulting three-year database shows that, in general, the wind products from both sensors have characteristics similar to those reported in the literature. Then, we perform an error analysis in the space domain and find that there is significant discrepancy between the two wind products as the matchup points move closer to the coast. The root-mean-square error (rmse) and standard deviation (STD) between the two data sets increases markedly for points matched within about 100 km of the coastline. Beyond 100 km, the rmse, STD, and systematic bias become small and stable. In addition, an empirical relationship between QuikSCAT and SAR winds in coastal region is proposed. Thus, the bias and errors should be taken into account if the standard operational QuikSCAT wind products are used for forcing models in the coastal ocean.

In summer of 2008, an outbreak of Enteromorpha Prolifra (EP), a kind of green algae, occurred in the Yellow Sea of china and posed a serious threat to the 29th Olympic Sailing Games. A color array camera was installed on the aircraft and used to monitoring the spatial distribution of EP. In this paper, we measured the spectral properties of EP and analyzed the R, G, B three band aerial remote sensing images which contains EP, sea water and sunglint. After preprocessing of array images, a decision tree was constituted considering the analysis which can retrieval EP and eliminate the sunglint from images. The retrieval results were validated by field survey.

High altitude photographic work such as Aerial photogrammetry and UAV photography has very high demands on geometric optical parameters of the CCD camera, one millimeter or one pixel error of geometric optical parameters tends to cause a few meters or even dozens of meters error on the ground, so the camera's high-precision geometric optical parameters calibration is a crucial step for data geometry processing after aerial photogrammetry. Four-band CCD camera is a multi-spectral aerial array camera developed by the Institute of Remote Sensing Applications, Chinese Academy of Sciences. In this paper, we introduced the geometric calibration theory and experiment with the Four-band CCD camera. The result of calibration was validated by two methods.

In this paper, a model for light interaction has been developed to compute bidirectional reflectance from realistic 3D canopies approximated by an arbitrary configuration of plants. It can well simulate multi-spectral reflectance of semi-arid nature grassland. There are two important parts of the simulation model. The first part is the generation of the 3D realistic grassland scene. In this model, the Clumped Architecture Model of Plants (CLAMP) is used to. The second part is the determining the visibility and brightness of grass canopy scene using Geometric Optics Model. The simulating model is validated by comparing the simulation result with the HJ satellite data at synchronous time. As a result, it can describe directional reflectance properties of semi-grassland canopies in terms of canopy architecture parameters and optical scattering properties of discrete phytoelements.

In remote chlorophyll-a (Chla) retrieval in Case-II waters, the uncertainties from Chla specific absorption coefficient a*phhas a significant effect on the retrieving accuracy. In this paper, we presented newly improved three-band model and four-band model by a case study in Shitoukoumen Reservoir. The proposed three-band model can be expressed as [Rrs-1(λ1)-Rrs-1(λ2)]×Rrs(λ3)×a*ph-1(λ1), while the improved four-band model is [Rrs-1(λ1)-Rrs-1(λ2)]×[Rrs-1(λ4)×Rrs-1(λ3)]-1×a*ph-1(λ1). Results showed that the latter one was slightly superior to the former one. Comparison with original models without correction of a*ph, the improved models achieved higher precision and stability. The findings underlined the rationale behind the proposed models and demonstrated a potentially use for assessing Chla in Case-II waters.

This paper demonstrates the Ruddick's algorithm to utilize atmospheric correction with the hyper-spectral imagery over Chinese turbid lake water obtained by China first hyperspectral imager (HSI) onboard HJ-1A. The paper studies on the sensor characteristics and analyzes the optical properties of turbid lake water in Taihu Lake. Based on consideration about real circumstances, the paper recalibrates parameterαof value taken as 1.43. Results indicate that the recalibrated parameter could enhance the algorithm performance and improve the accuracy through comparison with the in situ measurements.

FY1C is a polar meteorological satellite of China, which had been worked on orbit about 5 years. In this paper, time series calibration method based on pseudo-invariant target site is applied to monitor the variance of FY1C instrument. Dunhuang test site is chose as the pseudo-invariant site and the FY1C images over this site are processed with some standard. Then the time series calibration result of FY1C seven channels at visible and near-infrared range has been calculated. In order to validate the result, apply the time series calibration coefficients to recalibrate the images of Wuwei test site from 1999 to 2003. The validation result shows that the time series calibration coefficients are efficient and can monitor the radiance status of FY1C instrument.

CBERS02B with three payloads onboard was successfully launched on September 19, 2007 in order to ensure the continuity of CBERS series and CCD is one of three payloads. Calibration of CCD is a precursor for its quantitative application because there isn't onboard calibrator for CCD. A comprehensive vicarious calibration and validation campaign of CCD was performed at Gongger test site on October 12, 2007. The reflectance-based calibration method was used in this campaign with the ground measurements of the surface reflectance and atmospheric characteristics. Then 6S, a radiative transfer code, was used to compute the top-of-atmosphere(TOA) radiance at the sensor. Calibration result was obtained for CCD showing that some change brought to the CCD after launch, especially band 1 and band 2. The in-situ field measurement at the Dunhuang test site was collected validating that the calibration result was good expect for band 4.

Based on the fractal ocean surface model, electromagnetic scattering model under Kirchhoff Approximation and the raw signal simulation procedure of dynamic scene based on time domain, the sea surface of the SAR remote sensing has been simulated. The images of the wave and complex fractal sea surface are in accordance with the hydrodynamic modulation, the tilt modulation and the velocity bunching modulation. The simulation has been developed in the Matlab programming language.

HJ-1A satellite has been lunched in September, 2008. It is calibration and validation that the fundamental of quantitative utilization of HJ-1A IRS imagery. HJ-1A has only one channel in thermal infrared band, compared to MODIS sensor, which has two channels accordingly. The key process of cross-calibration is band match, so this paper uses the MODIS SST product retrieval algorithm as reference for the difference of HJ-1A and MODIS thermal infrared channels characters. TIGR database were used as input parameters into radiative transfer mode Modtran4.0 to obtain band match coefficients by regression analysis. Number 711~1064 datum in TIGR database representing mid-latitude winter were chose according to the selected image's date. Research demonstrates that cross-calibration method is effective to HJ-1A thermal infrared channel 4.

Eco-water is defined as a transformation of precipitation, which is deposited by vegetation layer, humiliated vegetation layer and soil layer. It plays an important role in the water-cycle system. As potential factors on Eco-water and Eco-water layer are different from one season to another, multi-temporal and multi-type remote sensing data, measured spectrum and the routine observation were applied to construct the indices for Eco-water and its inversion model. The four Eco-water indices, including Vegetation Canopy Interception Content, Vegetation Water Content Index, Soil Moisture Index and Eco-water Storage Index, were calculated. The results show that the RS information model can reflect the real soil moisture. The dissertation brings forward the Eco-water Remote Sensing quantitative study based on vegetation layer. The vegetation-based calculation model for Eco-water with quantitative remote rensing technology, which has been identified in the dissertation, possesses significant science affect and practical value; and it can not only advance the methods of Eco-environment study, but also promote the research on water-resources transformation and water-cycle, also enlarge the domains of remote sensing applications.

Cirrus clouds are known to play a key role in the Earth's radiation budget and global climate change, the radiative effects of cirrus clouds depend critically on cloud properties such as optical thickness and particle shape and size. The studies of the optical, microphysical, and physical properties of cirrus have become the popular issue. This paper simulated the bidirectional reflectance distribution function (BRDF) and bidirectional polarization reflectance distribution function (BPDF) at 1380 nm in cirrus cloudy conditions on the basis of an adding-doubling radiative transfer program. Based on the sensitivity of 1380 nm spectral reflectance and polarization reflectance on cirrus optical thickness and aspect ratio, a conceptual approach has been developed to simultaneous retrieve the particle shape and optical thickness of cirrus clouds using the remote sensing data of multi-angular total and polarized at 1380 nm.

Polarization space-borne sensor, just like POLDER (Polarization and Directionality of the Earth's Reflectances), is a new instrument devoted to the global observation of solar radiation reflected by the Earth surface-atmosphere system. It is necessary to acquire polarized information in retrieval of aerosol properties over land surface. Often the aerosol contribution is small compared to the surface, especially by covered vegetation. Atmospheric scattering is much more polarized than the surface reflectance. Using polarized information could solve the inverse problem of separating the surface and atmospheric scattering contributions. This paper presents retrieval of aerosols properties from multi-wavelength polarized measurements. The results suggest that it is feasible and possibility for discriminating the aerosol contribution from the surface in the aerosol retrieval procedure using multidirectional and multiwavelegth polarization measurements.

The high resolution (HR) camera, designed to have the capability of providing 2.36-meter panchromatic images, is one of three sensors onboard the CBERS-02B satellite. Because the orbit ephemeris and camera parameters needed for rigorous geometric correction are not included in the "leader files" of the HR images, approximate geometric correction algorithms have to be testified to establish the suitable geometric correction methods. In this paper, the emphasis is put on the experimental analysis and precision comparison of different kinds of approximate correction algorithms using the real CBERS-02B HR image from the viewpoints of GCP number, precision, the algorithm complexity and the applicability. Experimental results show that if the balance among precision, complexity, requirements for known data should be considered when choosing methods for the CBERS-02B HR images geometric correction, the improved polynomial method is an ideal choice. After precisely geometric correction, the HR images can be used in the surveying and mapping, land management, town planning, etc.

It is difficult to determinate the aerosol model when retrieving the aerosol over land surfaces. In this paper, from the products of AErosol RObotic NETwork (AERONET), the aerosol model in city area of Beijing was analyzed. Then, the aerosol properties was retrieved from polarized data of the Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a LIDAR (PARASOL), and validated by the AOD products of AERONET. The results show: (1) the aerosol model based on ground based observations in Beijing is different from standard aerosol model of POLDER; (2) the particular aerosol model over Beijing can improve the retrieval accuracy over Beijing.

Because of a series of complex environmental and instrumental effects, there are strip-pattern noises in images of many bands of MODIS, especially in that of Band 5. Several destriping algorithms have been used to remove the strips in MODIS images, but since they could not properly take account of physical or spectral relevance maintained in the data, the destriping results are hardly ideal. A new algorithm is proposed for the removal of strips in Band 5 images. It can locate the strips accurately so as to retain as much original information of the image as possible. Additionally, the new algorithm makes full use of the spectral correlation of multi-band remote sensed data, and can reconstruct the Band 5 value of strip-pixels based on appropriate interpolation. The validation and comparison with other algorithms indicate that, this new algorithm could remove strips perfectly and the reconstructed values have a small deviation of about 3% from the "true values".

Tropospheric aerosols play an important role in the Earth radiation budget directly through scattering and absorption of solar and infrared radiation, and indirectly by modifying cloud microphysical and radiative properties. Satellite remote sensing provides a means to derive aerosol distribution at global scales. The common operational algorithm to retrieve aerosol optical depth (AOD) is dark pixels method based on the atmospheric effect on the path radiance. However, when the land surface has a high reflectance, this method always becomes invalid. Another way was contrast reduction method (or structure function method) using the change in contrast for several scenes to determine the optical thickness between the scenes. China Brazil Earth Resources Satellite (CBERS) series carry high resolution CCD camera with a small scan degree, which is fit to retrieve AOD using contrast reduction method. CBERS02B was launched on 19thSeptember, 2007, and the CCD camera has been calibrated on board. This paper retrieves AOD from CBERS02B using contrast reduction method in Beijing urban area. The experiment result overestimates the AOD by about 0.2 while has the right tend.

This paper, we detect clouds in China regions from combination of POLarization and Directionality of the Earth's Reflectances(POLDER) data and Moderate Resolution Imaging Spectroradiometer(MODIS) data, based on the spectral, multi- angular, and polarized characteristics of cloud. Four tests are applied to the measurements. The first one is blue channel reflectance test. The second one is the test on polarization at 865 nm. The third one is the test on reflectance at 1380 nm. The fourth one is the test on reflectance at 645 nm and 1640 nm. At last, the performance of method is evaluated using a large dataset of surface face observations of cloud cover. The result demonstrate the methods of cloud detection are feasible and believable..

A good calibration of satellite is necessary to derive reliable quantitative measurements of the surface parameters or to compare data obtain from different sensors. DCSRS (Demonstration Center for Spaceborne Remote Sensing of China National Space Administration) went to inner-Mongolia Plateau to seek fairly uniform reflectance sites as a part of Beijing multi functional test site network in May and October 2006, and four quite flat and homogenous sites were selected as potential test sites. These four sites have many good calibration site characteristics: they are large and flat; the rain is little and the evaporation is much larger than precipitation, so the water vapor content is little in atmosphere; the elevation is about 1100m and the weather is sunny in most time. In this study, more than 200 MODIS level 1 images of these four sites were obtained and the average reflectance and relative mean squared deviation of each image were calculated. In the end, the variation of reflectance with solar zenith, month and season were analyzed, and the result was consistent with in-situ investigation.

On 29 and 31 May 2006, a comprehensive vicarious calibration experiment for the Moderate Resolute Imaging Spectroradiometer(MODIS) visible and near-infrared bands was performed at Gongger test site located in Inner Mongolia, which is a flat and uniform area. The reflectance-based method was used for calibration of MODIS visible and near-infrared bands. In situ measurements of surface and atmospheric conditions were carried out. By computing the surface reflectance of the site, it was concluded that the site was appropriate for calibration because of its stable and uniform characteristics. These data were then inputted to a radiative transfer code, 6S, to compute top-of- atmosphere (TOA) radiances and TOA reflectances, which were compared with the MODIS on-board calibration results. The in situ estimated results were in good agreement with the MODIS on-board calibration results on May 31 with the variations about 2%, while vicarious calibration results on May 29 were slightly inconsistent with those of on-board calibration, whose differences were about 7%.

The feasibility for collecting the coordinates of GCPs (Ground Control Points) makes Real-Time Kinematic GPS (GPS-RTK) the best optimal choice in the photogrammetric field work. In this paper a new digital photogrammetric camera system is introduced, firstly. And then how the GPS-RTK technique is used in the new camera system is illustrated. Some preliminary results are made following the data processing and precision analysis in the end.

In this paper, based on the normalized polarized radiances at top of atmosphere (TOA) have been simulated with different viewing geometry (along-track viewing angle and viewing angle number), the aerosol optical depths (AOD) have been retrieved. The retrieved AODs have been used to analyze the best viewing geometry for the new multi-angle polarized camera design. The results show that the accuracy of AOD is better as the viewing angle number is getting more, but for alongtrack viewing angle, the accuracy of AOD does not increase while the along-track viewing angle range changes from ±40° to ±60°.

An atmospheric correction algorithm for ocean color data with multiple viewing and polarization information is proposed. The correction is based on using the directional and polarized data for 865 nm and 665 nm to estimate the properties of aerosols over the ocean. The aerosol models used in atmospheric correction consist of bimodal size distributions. And, a best-fit optical thickness is grossly determined using the generated look up table of the upwelling radiation. Moreover, Validation of the improved algorithm with the standard POLDER atmospheric correction algorithm is given.

The consistent end-to-end simulation of them is an important task, sometimes the only way for the adaptation and optimisation of a sensor and its observation conditions, the choice and test of algorithms for data processing, error estimation and the evaluation of the capabilities of the whole sensor system. It is essential to accomplish simulation of atmospheric radiative transfer, if a complete imaging simulating system is to be expected. Based on given resolution and directional capabilities of the instrument, and combination with land surface temperature and emissivity data obtained from airborne imagery, TOA (top of atmosphere) radiance images have been simulated pixel by pixel coupling the atmospheric radiative transfer analytic model extended from MODTRAN4 and the atmospheric adjacency effect model derived from point spread function (for atmospheric directional and adjacency effect). In this way, all major scattering and emission contribution of atmosphere were considered. Through analysing results, it indicates that analytic model and adjacency effect model is more adequate for thermal infrared imaging simulation than others existing models.

This Paper described that in-flight radiometric calibration for thermal channels of TERRA-MODIS sensors using a new calibration test site-Dali-lake, China. The radiance of water surface was measured by CE312, and the spectral transmittance and upward radiance of the atmosphere was calculated using radiance transfer model MODTRAN4. At the same time the spectral response of Satellite sensor and that of ground-based sensor are coupled. At last the apparent radiance of sensor spectral channels is compared to the digital count of satellite's output to give the calibration coefficient. The calibration result in May 31 showed the difference between inflight and on-board calibration was equivalent to a brightness temperature of 1.44 k for TERRA-MODIS channel 31 and 0.35 K for channel 32 respectively.

Satellite observations use very narrow field of view (FOV) (less than 0.01deg) but the field measurements use generally very wide FOV (often between 10deg and 40deg) in order to obtain representative sampling size. This difference may introduce some large errors. The objective of this paper is to propose a practical modeling method and evaluate field of view effect on the field reflectance measurements for row crops. The model considers a row crop as a repetition of rectangular walls and then translates to a grid image with sufficient spatial resolution. The wide FOV reflectance is determined by averaging the reflectance of the elements. This model has been used for studying a typical row structure crop (maize canopy) for different observation heights (from 1 m to 5m), different observation angles (from -60deg to + 60deg with step of 5deg) and for three FOV (10deg,25deg,45deg). The results show that wide FOV measurements generally underestimate the reflectance in the red domain (up to -25% of relative reflectance when a 25deg of FOV) and have overestimation in the near infrared domain (up to 10% of relative reflectance when a 25deg of FOV). For different viewing angles, the vegetation contribution is overestimated in the illumination direction and underestimated in the opposite direction; This study can be used not only to analyze that FOV effects, but also to make optimal design of observation geometry (FOV, measurement height, measurement spatial size etc.) for minimizing the measurement errors, and/or to introduce some corrections to reduce the FOV effects.

Gap frequency is the probability that a light penetrates through the canopy unintercepted and reaches the surface under the vegetation. Previous study revealed that 22h local time, the brightness components of maize field were stable, so the they were easy to be distinguished in TIR images. To investigate the directional variation of gap fraction over a medium dense maize canopy (LAI=3.64) by night TIR data, an experiment was carried out on August 23th 2005 in Huailai county of Hebei Province. The acquisition of TIR images was conducted by a system consisting of a broadband TIR camera, and a truck platform whose height is about 2.5 m. The gap frequency can be calculated by threshold value method. The result showed the main characteristics of row crop that very little azimuth variation appears for gap frequency except for the observations along row direction, in the row direction the gap frequency is 0.35 and declines slowly with the zenith angles and converges to a unchanged value 0.14 along row direction; gap frequency declines from the nadir slowly; in other azimuth directions, gap frequency declines sharply. Put the measured parameters into a GORT model, the directional gap frequency can be simulated. Compared the measured gap frequency with the simulated one, the two had good agreement. The differences between them were due to reasons, such as the disagreement of the objects, the angle variation was not controlled accurately in experiment, the selection of projection function and clumping parameter et al.

In this paper, a synthetic strategy has been employed to model 3D canopy's radiation transfer in the whole optical spectral domains. 3D plant architecture model (the Clumped Architecture Model of Plants: CLAMP) (1) is used to generate the realistic vegetation scene. In the visible and NIR region, the canopy BRDF was decomposed into three parts: single scattering contribution from leaves, single scattering contribution from the soil, and multiple scattering part of the canopy. The single scattering contributions come from illuminated leaves and soil components which are computed by the reverse ray-tracing procedure (2) with their corresponding reflectance. The multiple scattering contribution is approximated by the four-stream theory. As a result, the modeling of VNIR region is more efficient and fairly accurately describes the anisotropically scattering features of vegetation. In the TIR region, the directional brightness temperature of canopy is calculated as the linear combination of four component's (illuminated leaves, illuminated ground, shadowed leaves, and shadowed ground) brightness temperature multiplied by its fractional cover computed by the reverse ray-tracing procedure. Initial modeling results show typical features of vegetation's anisotropic scattering and directional temperature distributions, for example, hot spot, bowl shape and reach a good agreement with theoretical results in those three domains. This strategy shows potential of exploring the impact of canopy structure on the radiometric response measured by remote sensors.

The symmetric kernel-driven operator equations play an important role in mathematical physics, engineering, atmospheric image processing and remote sensing sciences. Such problems are usually ill-posed in the sense that even if a unique solution exists, the solution need not depend continuously on the input data. One common technique to overcome the difficulty is applying the Tikhonov regularization to the symmetric kernel operator equations, which is more generally called the Lavrentiev regularization. It has been shown that the iterative implementation of the Tikhonov regularization can improve the rate of convergence. Therefore in this paper, we study the iterative Lavrentiev regularization method in a similar way when applying it to symmetric kernel problems which appears frequently in applications, say digital image restoration problems. We first prove the convergence property, and then under the widely used Morozov discrepancy principle(MDP), we prove the regularity of the method. Numerical performance for digital image restoration is included to confirm the theory. It seems that the iterated Lavrentiev regularization with the MDP strategy is appropriate for solving symmetric kernel problems.

Numerous researchers have observed that the distribution of brightness temperature field over an agricultural canopy strongly depended on canopy biophysical and phenological features coupling with environment conditions. Recently, an in situ experiment was conducted over a maize canopy in Avignon of France to investigate the temporal variation of brightness temperature distributions as a function of canopy geometrical structure. The experiment lasted 3 months throughout maize growth cycle. The results revealed that component brightness temperature (CBT) values are not independent with each other. The correlation of sunlit soil temperature minus vegetation temperature (Tsb-Tv) and shaded soil temperature minus vegetation temperature (Tso-Tv) is 0.85 with a RMSE of 1.1degC. In the analysis of maize geometrical structure when it was within its first two of three growth stages, good relations have been found between geometrical parameters with leaf area index LAI. To analyze the relation between CBT and LAI, the differences of CBTs were compared with LAI as a function of date of year, where we found that Tsb-Tsohas the best relation with LAI. An initial interpretation was given to this result, which is still under further analysis based on more experiment observations. The tie between CBT and LAI is expected to be utilized in the researches on canopy energy balance estimation directly

A field experiment has been performed from May to August in 1999 over a maize canopy at INRA Avignon branch in south France. Directional brightness temperature (DBT) features were extracted from thermal infrared (TIR) images captured by a TIR camera mounted upon a crane platform. Different from the method with a goniometer, another approach based on the different principles has been developed that the camera can view different part of the field to collect DBT of different angles by means of rotating to different directions instead of moving the camera to make it always focus on a defined sample area. After the description of the measurement method, the evaluation of data acquisition is presented, where the quality and limits of this measurement method are discussed. In this study, a compromise is made in sampling design based on the analysis of canopy heterogeneity and FOV effect. An example of acquiring hemispherical DBT under natural conditions was given at last. In the analysis of the measurement at 13:10 of local time on June 24, 1999, the results showed the spatial heterogeneity of field thermal radiance played as the most important role. Using the new sampling method, the total error for acquiring hemispherical DBT was about 1.4degC in maximum with 95% confidence, when the amplitude of directional variation was larger than 7degC. The errors changed with measurement conditions, which made it different for each measurement

As one of the ultimate approaches to clearly define and understand directional features of canopy thermal radiation, ground level experimental studies of directional brightness temperature (DBT) have been concerned for many years. In this study, a recent crane based thermal camera system developed by INRA-Avignon of France is presented, and then four measuring methods for hemispherical DBT observations are introduced. These methods are based on different measurement principles: (1) view the same surface from different angle by moving the crane and platform to assure the sensor always toward the target; (2) view different part of the field to collect DBT of different angles by means of rotating the camera to different directions instead of moving the camera; (3) view different part of the field by moving the camera along the row direction; and (4) scan the field with a high imaging frequency while the camera moves along the crane bar vertical to the row direction. During the comparison of these methods, special emphasis is on the analysis of temporal and spatial variations of maize canopy brightness temperature distribution, from which the main errors of these methods are analyzed. The study also presents common shortcomings and limitation of the field observations with this system, such as the less capacity of hot spot observation and the sensitivities to field environmental influences. Finally, recommendations for optimal field measurement of hemispherical DBT with the system are given from an application-oriented viewpoint.

A modelling study on the hemispherical variations of directional brightness temperature (DBT) for row-structured building arrays was carried out based on the ground observations over a residential in urban areas. The model assumes that the DBT is a function of component brightness temperatures and their directional fractions. Their fractions in the scene depend on sun-view geometry and the geometry of buildings. The methodology for brightness temperature component classification and temporal variations of component values were analyzed based on the experiment dedicated to urban brightness temperature distribution. Results reveal that the number of typical objects of an urban area and their brightness temperature values vary with time of day due to the complex of urban thermal feature and geometrical structures. Generally, the trend of brightness temperature variation is similar for asphalt road and concrete place, grass and trees, walls of the building toward different directions, and the windows of the walls. In the simulation of urban areas hemispherical DBT, the results reveal an evident row-direction-oriented stripe in DBT polar map, where no hot spot appears. As an initial attempt, the research only focus on the simplified conditions, more complicated structure may be considered in the further researches.

A study on modeling the variations of directional brightness temperature (DBT) for row-structure crops was carried out with the images captured by a large-aperture thermal infrared camera over a maize canopy. The model assumes that the DBT is a function of target component brightness temperatures and their directional fractions. The canopy has three brightness temperature components: the sunlit soil, the shaded soil, and the vegetation. Their fractions in the scene depend on the sun-view geometry and the distributions of gaps within and between plant rows. To describe canopy geometrical features, a series of porous hedgerows with a rectangular cross section is used. The directional variations of gap fractions are described by the Kuusk function. The model demonstrated how the features of DBT depend on the sun-view geometry, canopy geometrical structure, and component brightness temperatures. In the simulation of DBT over a middle-density canopy near the local noontime, the results revealed an evident row-direction-oriented hot stripe in DBT polar maps, where the hot spot appeared along the sun direction. The sensitivities of the model to the input parameters were tested. Further validation demonstrated a close correlation between predicted DBT and field observations.

In order to modeling directional thermal radiation and energy balance for a partially covered canopy, surface brightness temperature is usually classified into several components. This paper researches the methodology for brightness temperature component classification and temporal variations of component number and values by an in situ experiment, dedicated to analyze maize canopy brightness temperature distribution. The measurement was carried out by using a TIR camera and a visible camera mounted on an industrial crane, the experiment lasted 3 months throughout a maize growth cycle. In the analysis of the brightness temperature, a Gaussian distribution has been assumed. Results show the number of components and their brightness temperature values vary with time of day and biomass density. Three brightness temperature components of vegetation, sunlit and shaded soil could be identified at midday during the measurement period. In the daytime, temperature variability of sunlit soil is much larger than the other two components when the canopy's density is not high. When the canopy is fully covered, vegetation brightness temperature has a wider range.

A modeling study on the variations of directional brightness temperature (DBT) for row-structure crops was carried out with the help of the images captured by a large aperture thermal infrared camera over a maize canopy. The model assumes that the DBT is a function of component brightness temperatures and their directional fractions. The canopy has three brightness temperature components: sunlit soil, shaded soil and vegetation, each component has a unique temperature value. Component fractions in the scene of view depend on sun-view geometry and the distributions of gaps within and between plant rows. To describe canopy geometrical features, a system of porous hedgerows with rectangle cross-section has been used; the directional variations of gap fraction are described by Nilson function. The model demonstrates directional variations of DBT as a function of sun-viewing geometry and canopy geometrical structure as well as component brightness temperatures. In the simulation of DBT over a middle dense canopy near the noontime, the results reveal an evident row-direction-oriented hot stripe in the DBT polar map, where appeared the hot spot along the sun direction. The sensitivities of the model to the input parameters have been tested. Further validation analysis has also been conducted which demonstrates modeled DBT agreeing closely with field observations.

This study aims at developing an autonomous atmospheric correction method, that is exploiting the information content in the image of the satellite considered. The signal recorded by the sensor contains information relative both to the atmosphere and the surface. Aerosol characteristics are the most difficult to evaluate because they vary rapidly with time and space. The spectral variation of the radiance signal, when enough sampled by the sensor, generally allows decoupling aerosol effects from that of the surface. However, on non vegetated areas or region with low vegetation amount, the decoupling is more difficult. For this reason, we propose to use the spatial variation of the signal to better constrain the decoupling process, assuming that the aerosol vary over typically scales of few tenth of kilometers, while the surface varies at shorter distances. A data base was created using Radiative Transfer Model simulations. It contains the satellite top of atmosphere (TOA) reflectances along with the corresponding aerosol optical thickness (AOT). The method is applied to the MERIS sensor, a neural network is then trained at relating AOT to reflectance TOA. Conclusions are drawn on its advantages and limits and possible application to other sensors.

Composite scene of row crops induced an unavoidable error in ground measurements of directional brightness temperature (DBT) due to the use of wide field of view (FOV). The measurement results vary with sample size and position, detector height and view direction, and bias due to project principle. This is called FOV effect. The study focuses on the estimation of FOV effect on the measurements of maize canopy using a computational geometric 2D model. The model was developed to simulate the fractional variations of canopy brightness temperature components. The simulation results revealed that the errors caused by FOV effect have a complex feature. Generally, vegetation fraction is always over counted in the nadir view, errors increase dramatically with the decrease of detector height as well as the enlargement of sample size, the deviation of the error corresponding to detect position is small; in oblique view, the errors are limited to a low level due to an effect called compensation effect. However, the deviation of the error keeps large when the sample size is small. Nevertheless, the best approach to reduce FOV effect in ground observation is levering the detector to a higher altitude as the model suggested.

In order to estimate directional variation of gap fraction over a high dense maize canopy, a geometric optical and radiative transfer (GORT) model was improved to simulate the hemispherical gap fraction, the model was validated by a crane borne experiment using a narrow FOV thermal infrared camera conducted at night. The research revealed that the path length is a function of canopy geometrical structure, view direction and position, which leads to the row effect on hemispherical gap fraction: azimuthal variation of gap fraction is insignificant except for the observations parallel to the rows. For dense canopies, the value of gap fraction declined quickly in small view zenith rather than in large view zenith range, which leads the curves to show a concave shape. The experiment was conducted at 22 h local time on July 26 in 1999 (LAI=5) for the validation. At the time, brightness temperatures of leaves and soil had Gaussian distribution, their mean values presented a significant difference (24.3/spl deg/C and 26.5/spl deg/C) comparing to their small standard deviations (0.52 degC and 0.44 degC), gap fraction could be discriminated from canopy background. Observations showed that most gaps appeared between the adjacent rows, which lead the high dense canopy still to keep row feature in thermal infrared images. As conclusion of the comparison, the model could capture the main features of the measured gap fraction. With a proper adjustment of input leaf optical parameters, the simulated gap fraction showed a fairly good agreement with observed gap fraction.

A field experiment has been conducted from May to August in 1999 over a maize canopy at INRA Avignon branch in France. The experiment covered the whole growth period of maize plants in order to observe directional brightness temperature (TBD) variations of row structure canopy as a function of measuring time and date. The TBD was extracted from thermal infrared (TIR) images captured by a TIR camera mounted upon a crane. The results show that TBD were highly date and time specific. Comparing the variations of DBT near the noontime with different biomass, the measurements over a middle dense canopy revealed an evident row-direction-oriented hot stripe in the DBT polar map, where a hot spot appeared along the solar direction. Similarly, for a low cover condition, a wider hot stripe appeared in the polar map, the hottest area in the band is around the solar position. However, for a high biomass condition when the field was nearly wholly covered by the leaves, the lowest temperature appeared along the row direction, which formed a cool strip in the polar map. Comparing the variations of DBT on the same day, for a measurement over middle dense canopy, in the morning and afternoon, with a large solar zenith, the range of DBT variation decreased, hot strip features weakened and turned wider, solar position was out of the hot strip. As a conclusion of the field experiment, the maize TBD feature is dominated by sun-sensor geometry, complex effects of canopy structures coupled with spatial distributions of canopy brightness temperatures. A row structure effect appeared clearly in the TBD polar map throughout the whole measurement. Quantitative explanations are expected in further research on the physical models.

Local statistic-based fusion algorithms are discussed, which can be applied to fuse high-resolution VNIR images and a single low-resolution TIR image. These algorithms are based the experiments that structural information of observed objects In visible and near-infared spectral range (VNIR) is essentially correlated with the environmental information (especially moisture information) in thermal infrared spectral range (TIR). The local is performed at three scales. This algorithm can be useful method to merge the TM image and VNIR images.