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.

Carbon dioxide (CO2) is the most important atmospheric contributor for global warming. Satellite remote sensing is a commonly used method for high-precision CO2 detection, but it often suffers from striping issues, which hinders its ability to achieve full coverage. Therefore, the limited availability of data poses challenges for global carbon accounting. In this article, we generate a global seamless and high-resolution dataset of column-averaged dry air CO2 mole fractions (XCO2) from 2017 to 2020 by integrating multiple data sources using deep learning techniques feedforward neural network (FNN). The data sources primarily include satellite, ground-based, and reanalysis XCO2 products, satellite vegetation index data, and meteorological data. The spatial resolution of the dataset is 0.1°, and the temporal resolution is one day. Moreover, this article also investigated the importance of features in deep learning models and examined the spatiotemporal variations of global XCO2. The results demonstrate that the FNN approach with time series features yields an improved dataset (${R} =0.98$and RMSE =0.82 ppm) compared to the other methods. In an FNN, the known model data [Carbon Tracker (CT) XCO2] are considered as the most important features. We find that the global XCO2 has increased by approximately 7.5 ppm from 2017 to 2020. This seamless and fine-scale dataset provides valuable support for understanding global carbon cycling and formulating carbon emission reduction policies.

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.

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.

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.

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.

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.

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.

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.

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.

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".

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.

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.

The ear is the top layer of mature wheat and has very different geometric and thermal characteristics from that of leaves. Compared to the directional brightness temperature (DBT) of wheat canopy without ears, the DBT at the ear stage has specific features, and the ear effects could not be explained by previous models. This paper proposes a hybrid geometric optical and radiative transfer model to reveal the combined influences of the geometric structure of ears and leaf; the temperature distribution of ear, leaf, and soil; and the Sun-target-sensor geometry on the canopy DBT. The soil, leaf, and ear layers are taken into account in the model so it is named as the Soil Leaf Ear Combined (SLEC) DBT model. We compare the model prediction with the field measurement data. The results show that the new SLEC DBT model can simulate the DBT of wheat at the ear stage with an accuracy of 0.78 K.

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.

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.