Nighttime light (NTL) remote sensing data has been widely used in various fields, such as human activity analysis, urbanization studies, and economic evaluation. However, Earth’s nighttime environment is very complex so that the NTL images are seriously affected by atmospheric effect and moonlight. This complexity primarily stems from the numerous atmospheric scattering and absorption, as well as the incoming moonlight, which can significantly distort and contaminate nighttime light observed by the satellite and consequently reduce the precision and stability of NTL data. In order to improve the quantitatively quality of the NTL data, this paper proposes an innovative atmospheric correction algorithm that leverages the nighttime radiative transfer model (nRTM) considering both atmospheric effect and moonlight effect to get ground radiance of artificial lights from satellite nighttime light images. This model takes into account the complex interactions between light and the atmosphere. By simulating these processes, the algorithm is able to separate the contributions of atmospheric scattering and absorption from the original NTL images. To demonstrate the effectiveness of the proposed algorithm, this paper takes the SDGSAT-1 NTL image of Beijing as a representative case study of atmospheric correction. By comparing the corrected and uncorrected images, it is evident that the atmospheric correction significantly improves the quality of the NTL data and the ground nighttime lighting information becomes clearer and more accurate, effectively removing noise interference and enhancing data reliability. Moreover, it also found that the high-pressure sodium (HPS) lamps and LED lamps in the NTL images presented different radiance values and spectral shapes that can be helpful for classifying different lamps.

The estimation of Sea Surface Temperature (SST) from ocean satellites with large observation angles must account for the angular effects on SST. This study developed an angle-dependent non-linear split-window algorithm (A-NLSW) to retrieve SST from Chinese ocean satellite HY-1D thermal infrared data. The algorithm coefficients were obtained based on the simulated dataset and grouped by initial SSTs, total atmospheric column water vapor content (TCWV), and satellite zenith angle (SZA). The A-NLSW algorithm is validated and re-calibrated using the bulk temperature collected by the iQuam in-situ dataset. After re-calibration, the accuracy of the SST was improved from 1.53 K to 0.87 K for SZA ranging from 0 to 70.5 ° . Nearly 60% of the validation points achieved an accuracy of 0.5 K and over 90% achieved an accuracy of 1.0 K. These findings highlight the robustness of the A-NLSW algorithm in reliably retrieving SST from HY-1D satellite images, even when observations are made at large SZA.

The Diviner sensor provides global lunar surface observation at total nine channels from solar reflective wavelength (0.35-2.8 μm) to thermal infrared wavelength (7.55–400 μm Using the three 8-μm thermal channels (7.55–8.05, 8.10–8.40, and 8.38–8.68 μm), the lunar surface temperature and emissivity can be retrieved using physical temperature-emissivity separation (TES) algorithm and the Empirical regression (ER) algorithm. However, up to now there is no report about the ground validation of the accuracy about the retrieval results. This paper performs the ground validation on the lunar surface emissivity using the samples spectra collected by Apollo missions from 14 to 17, and results showed that the emissivity RMSEs of the three channels of the TES algorithm are respectively 0.025, 0.030 and 0.021 with the total RMSE of 0.026, while those of the ER algorithm are respectively 0.043, 0.038, and 0.030 with the total RMSE of 0.038. The TES algorithm got a better accuracy than the ER algorithm.

Night-time light remote sensing has been widely used in various fields, including human activity analysis, urbanization studies, and economic research. However, Earth’s nighttime environment is very complex so that nighttime light images are seriously affected by atmospheric elements and moonlight. To address this issue, this paper proposed an atmospheric correction algorithm on basis of a newly developed radiative transfer model (RTM) that aims to derive the ground radiance of artificial lights from satellite nighttime light images. The SDGSAT-1 night-time light image of Beijing is used as a case study to demonstrate the effectiveness of the proposed algorithm. Results show that the new algorithm can effectively removes the atmospheric effects and improves the data quality of nighttime light images.

The daytime and nighttime lunar surface temperatures (LSTs) are crucial for investigating lunar surface environment and lunar mineral composition. The Diviner sensor provides global lunar surface observation in seven thermal infrared (TIR) channels from 8 to$400~\mu $m, but the existing LST retrieval methods are more suitable for daytime pixels with high temperature rather than the nighttime or shadowed pixels with low temperature. This letter develops a new method, called as TES-GBR, by combining the conventional temperature-emissivity separation (TES) and gradient boosting regression (GBR) method, to retrieve low LST (e.g., nighttime or shadowed regions) from Diviner’s four longwave infrared channel data. The new method used three emissivity curve shape parameters, maximum-minimum apparent emissivity difference (MMD), maximum-minimum ratio (MMR), and emissivity variance (VAR), to establish their relationship with the minimum emissivity ($\varepsilon _{\mathbf {min}}$). Results indicate that the TES-GBR method can reduce the emissivity error to 0.005 from 0.029 obtained by the conventional TES method and get a general retrieval accuracy of 1.0 K for the low LST. Finally, the TES-GBR method was applied to retrieve the nighttime LST of the year 2015, and it found that there was a period variation in the nighttime temperature.

Urban land surface temperature (ULST) is one of the core parameters in monitoring the urban thermal environment, which has received extensive attention in several study and application areas. Thermal infrared (TIR) remote sensing technology can efficiently observe large-scale land surface thermal radiance information and is a critical approach used to obtain ULST quickly. Traditional LST retrieval algorithms are conducted using the classical radiance transfer equation (RTE) based on the assumption that the land surface is flat, which may be challenging to hold for complex urban landscapes. Moreover, with the improvement of the spatial resolution of remote sensing images, the influence caused by the geometric structure will be more obvious. Various urban thermal radiance transfer models have been proposed and successfully applied to TIR remote sensing images with tens of meters spatial resolutions, such as Landsat, ECOSTRESS, and Gaofen-5. Current airborne TIR sensors can observe remote sensing images with ultra-high spatial resolution (sub-meter). In this paper, using the ensemble learning method based on the ultra-high spatial resolution urban thermal radiance transfer model (UHURT), a new retrieval algorithm is developed to estimate the LST directly from the observed brightness temperature. The proposed new algorithm applies to ultra-high spatial resolution remote sensing images. It has the end-to-end advantage of not relying on atmospheric parameters or land surface emissivity, known as in traditional algorithms, thus avoiding the limitations due to the lack of available input data. Validation results based on the simulation dataset showed that the proposed algorithm has higher theoretical accuracy than the traditional split-window algorithm. As the sky view factor (SVF) decreases, the accuracy advantage becomes more pronounced, growing from 0.149 K (SVF = 1.0) to 1.085 K (SVF = 0.25). The application results in the remote sensing image also indicated that the results of the proposed algorithm (RMSE = 2.093 K) are more accurate than those of the SW algorithm (RMSE = 2.490 K), and the correlation between the resultant error and building density is lower, which can accurately reduce the geometric effect to obtain the ULST better.

The lunar surface Christiansen feature (CF), known as a prominent maximum emissivity centered near$8~\mu \text{m}$, has been widely used to identify silicate mineral types and estimate their compositions. The Diviner sensor provides global lunar surface observation at three 8-$\mu \text{m}$CF channels, and thus, its data have promoted the extraction and mapping of global lunar surface CF. However, the previous studies used the empirical regression (ER) algorithm to extract the CF wavelength (or called CF position) after estimating lunar surface temperature (LST) and emissivity using a three-point parabola approximation, which inevitably leads to uncertainty. The physical temperature–emissivity separation (TES) algorithm was proposed recently by the authors to retrieve LST and emissivity from the Diviner’s three CF channels dataset, on the basis of the surface’s physical radiative transfer equation, and therefore, this algorithm provides a promising way to revise the accuracy in the extraction of CF wavelength. From this point of view, this article first illustrates the difference of LST, emissivity, and CF wavelength between the TES and ER algorithms and finds that the TES algorithm got higher accuracy in extracting CF wavelength. Consequently, the global lunar CF wavelengths are extracted from the Diviner dataset. Compared to the result from the ER algorithm, the CF wavelength from the TES algorithm is found to be revised in a range of −0.051 to$0.372~\mu \text{m}$with a bias of$0.02~\mu \text{m}$, and its value is generally larger than the previous study, indicating that the previous CF wavelength might be underestimated.

The fraction of absorbed photosynthetically active radiation (FAPAR) is directly linked to the estimation of canopy gross primary production and is a key input parameter in terrestrial ecosystem models. Diffuse FAPAR calculation is necessary for cloudy conditions. Direct and diffuse FAPAR require distinct simulation methods due to different transfer paths, yet most models do not consider diffuse FAPAR independently. In this study, an improved FAPAR-P model （ FAPAR-Pro ） based on the spectral invariant theory, which considers direct and diffuse FAPAR separately, was proposed to simulate all-sky vegetation FAPAR. Based on the proposed FAPAR-Pro model, along with the hourly ratio of diffuse radiance data calculated from Himawari-8 products, the all-sky vegetation FAPAR was calculated. Validation performed with ground measurements showed that the overall RMSE and MAE values are 0.056 and 0.067 for diverse types of vegetation canopies and different diffuse radiation conditions. The results indicate that the model was applicable to diverse types of vegetation canopies and different radiation conditions, and therefore, may provide support for continuous FAPAR time series simulation and various ecological applications in the future.

The Advanced Geostationary Radiation Imager (AGRI) on board of the Chinese geostationary satellite FengYun-4B (FY4B) designs four thermal infrared channels, which has the characteristics of wide observation range, high observation frequency and fixed point observation, with a spatial resolution of 4 km at nadir and a full-disk observation every 15 minutes. Therefore, it can monitor the surface temperature changes on a large time scale, providing important data support for agricultural drought monitoring and climate change. However, there is currently no algorithm for land surface temperature retrieval with this sensor. This paper proposed a three-channel temperature–emissivity separation (TES) algorithm that estimates the LST and emissivity from three thermal-infrared (TIR) images. The analysis shows that the algorithm can theoretically retrieve the LST and emissivity with errors less than 0.8 K and 0.016, respectively.

Leaf chlorophyll content (LCC) is a critical indicator for precision agriculture. Accurately estimating winter wheat LCC based on remote sensing at high spatial resolution is of great significance for agricultural management and decision. In this study, a two-step method was used to retrieve wheat LCC from UAV hyperspectral imagery. The first step is converting canopy reflectance to leaf reflectance using Look-up tables (LUTs) generated from the unified model of bidirectional reflectance distribution function (BRDF). The second step is retrieving wheat LCC from derived leaf reflectance using the PROSPECT-PRO model. Retrieved wheat LAI, leaf reflectance, and LCC were validated against field measurements. The results indicate a good agreement between retrieved and measured LAI with RMSE of 0.092 and R2of 0.605. Leaf reflectance retrieved from UAV canopy reflectance exhibit good consistency with measured leaf reflectance with RMSE of 0.017 and R2of 0.962. Retrieved wheat LCC is of good quality compared to measured LCC, with R2of 0.7675 and RMSE of 4.33 μg/cm2. In conclusion, this study holds potential in estimating wheat LCC from UAV hyperspectral imagery.

The land surface temperature (LST) of urban is a key factor in the field of urban environmental monitoring, and thermal infrared (TIR) remote sensing is an efficient method to obtain it. An important assumption of the traditional thermal radiance transfer model is that the land surface is flat, which has now proven difficult to hold under the urban landscape. Most of the existing urban thermal radiance transfer models have been developed for remote sensing images with a spatial resolution of tens of meters. Currently, airborne TIR sensors have the observation capability to acquire remote sensing images with an ultra-high spatial resolution (1 cm to 1 m), and the model needs to be improved. This paper proposed a new ultra-high spatial resolution urban thermal radiance transfer model (UHURT) after analyzing the transfer processes within the urban canopy at ultra-high spatial resolution. Various radiance components, the emitted radiance, reflected atmospheric downward radiance, and adjacent radiance, were modeled separately. The results of the traditional model and the UHURT model were compared with the results of a ray-tracing computer simulation model, which showed that the new model successfully quantifies the multiple scattering and adjacent effects, and obtained images closer to the computer simulation images. Besides, the LST retrieval of the computer-simulated images was performed using the traditional model and the UHURT model, and the proposed model successfully reduced the errors of the retrieval results and weakened the spatial correlation between the residual distribution and the geometric characteristics of the urban landscape.

The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) onboard the International Space Station (ISS) provides standard land surface temperature (LST) products to meet community concerns on water stress and evapotranspiration. In 2019, an anomaly on the mass storage unit (MSU) changed the data acquisition mode of ECOSTRESS to a direct streaming one, eliminating three bands centered at 1.60, 8.29, and$9.20~\mu \text{m}$. This letter analyzes the feasibility of retrieving LST from ECOSTRESS data using split-window (SW) techniques which need only two thermal infrared (TIR) bands to serve as an alternative to produce LST products. Three different spilt-window algorithms have been developed and analyzed for all possible TIR band combinations using the simulated top-of-atmosphere (TOA) radiance dataset. Besides, the selected SW algorithms were validated using the intercomparison method with ECOSTRESS LST product data and the temperature-based method with surface radiation budget (SURFRAD) ground-based measurements. The result showed that SW algorithms provided higher accuracies than the temperature-emissivity separation (TES) algorithm, with the main advantage that the atmospheric profile data is not demanded prior.

The Urban Canopy Multiple-scattering thermal Radiative Transfer (UCM-RT) model, incorporating the effects of urban geometry and adjacent thermal radiation from neighboring pixels, depicts the process of thermal radiation transfer on the urban surface, and therefore provided new opportunity to develop new retrieval algorithms for urban land surface temperature (ULST). This paper aims at developing an urban split-window (USW) algorithm for deriving ULST from high-spatial-resolution thermal infrared (TIR) data from the Visible and Infrared Multispectral Sensor (VIMS) onboard Chinese GaoFen-5 (GF-5) satellite. The VIMS provides 4-channel TIR image with a spatial resolution of 40 m. The coefficients of the USW algorithm were obtained based on several subranges of atmospheric column water vapors (CWV), emissivity and sky view factors (SVFs) under various land surface conditions, by removing the geometry, adjacent and atmospheric effects. Methods of estimating urban pixel emissivity and CWV in urban areas were also conducted. The sensitive analysis of instrument noise and uncertainty of CWV, pixel emissivity and SVFs demonstrated the reliability of the USW algorithm in ULST retrieval. The accuracy evaluation shows that the root-mean-square errors of the ULST results is less than 0.7 K in theory. Compared with the conventional SW algorithms and publicly released LST products, the USW algorithm obtained better results in estimating ULST, especially in high-density building areas. Finally, the USW algorithm is expected to be beneficial to the application of multiple thermal infrared sensor data, for example, the newly launched GF-5 No.2 satellite images.

Thermal infrared (TIR) remote sensing observation signal is influenced by both atmospheric and land surface conditions that are difficult to separate with conventional multichannel TIR data. Because of the advantage of channel wealth, hyperspectral TIR data can simultaneously estimate the land surface and atmospheric parameters using neural network models or integrating them with physical models. However, the commonly used neural network models do not fully explore the correlation between different channels by treating the input data as discrete features. Thus, this study aims to develop a new deep neural network (DNN) by combining the long short-term memory (LSTM) network and convolutional neural network (CNN) for estimating land surface temperature (LST), emissivity, atmospheric transmittance, upward radiance, and downward radiance more accurately. By applying on the thermal airborne hyperspectral imager (TASI) simulation dataset covering global atmospheric conditions with 32 channels in$8.0- 11.5\,\,\mu \text{m}$, the proposed model achieved results with the LST error of 0.95 K, the emissivity error of less than 0.012 for each channel, and the accuracy of three atmospheric parameters has also been improved compared with the current neural network models. Our model has been applied to a real TASI image, and its validity was further proved by the ground measurement validation data. Therefore, it can provide more reliable initial values for physical optimization models.

Land surface temperature (LST) is one of the key parameters in the process of energy exchange between the land surface and atmosphere, and thermal infrared (TIR) remote sensing is an important approach to efficiently obtain LST over a large area. Algorithms for retrieval of LST from TIR remote sensing data have been studied for decades, and the split-window (SW) algorithm can directly eliminate atmospheric effects by using the brightness temperature at the top of the atmosphere in two adjacent TIR channels and thus is widely applied. Landsat-9, the latest launch in the Landsat series of satellites, provides 2-channel TIR images with the same 100m spatial resolution as Landsat-8, and it is meaningful to develop the SW algorithm for LST retrieval using Landsat-9 data. In this paper, four SW algorithms were developed, and the accuracy and noise sensitivity of the results under different observation conditions were compared based on the simulation dataset to select the algorithm with the best performance. The ground measurement data under different land cover types and the global Landsat-9 LST products, produced by the single-channel algorithm, were selected to verify the accuracy of the proposed algorithm. The results show that the ground validation accuracy is about 1.574 K, better than the Landsat-9 existing LST product. Moreover, the retrieved LST images have similar spatial distribution to the Landsat-9 LST products, with RMSEs from 0.31 K to 2.87 K in various regions.

Thermal infrared remote sensing can acquire large-scale land surface thermal radiance effectively. However, the observed data are affected by surface and atmospheric conditions. Traditional methods require some prior knowledge, such as emissivity in split-window algorithm and atmospheric correction in temperature–emissivity separation algorithm. This information is difficult to obtain directly and accurately. Hyperspectral thermal infrared data provide the possibility for simultaneous retrieval of atmospheric parameters, land surface temperature (LST), and emissivity because of their abundant band information. This study proposed a feature-band linear-format hybrid (FebLihy) algorithm by combining a deep neural network (DNN) model and a physical model with thermal airborne hyperspectral imager (TASI) data. The proposed algorithm was divided into three steps. First, the radiative transfer equation was converted into a linear form, and seven feature bands were chosen to reduce the unknowns. Second, the initial values of atmospheric and land surface parameters were estimated with the DNN model. Finally, least-squares optimization was used in the physical model to retrieve the final results. Results of the simulation data showed that the root-mean-square error (RMSE) of LST was 0.86 K, the RMSE of emissivity was less than 0.015, and the accuracy of atmospheric parameters was improved effectively by the physical model. The FebLihy algorithm was applied in a real TASI image in Fuyun County and verified with CE312 ground measurement data. Accurate results were achieved. The FebLihy algorithm will be optimized in terms of model and data in the future study.

Accurate retrieval of land surface temperature (LST) over urban areas is of great significance for urban thermal environment monitoring. In previous studies, most of the urban LST retrieval methods were developed based on the assumption of a flat surface without considering the influence of urban 3-D geometry structure, which has a significant impact on the retrieval accuracy of LST over urban areas. In this study, a radiative transfer equation (RTE)-based single-channel method was developed to retrieve LST with urban geometry effect correction from the Landsat 8 thermal infrared (TIR) data in band 10. The increase in adjacent radiance from the surrounding pixels and the decrease in atmospheric downwelling radiance caused by urban geometry structure were taken into account in this method. Because it is difficult to directly validate the retrieval accuracy of LST over urban areas usingin situLST measurements, the performance of the RTE-based LST retrieval method was evaluated via comparing brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the discrete anisotropic radiative transfer (DART) model and the urban RTE over three subregions. There is a good agreement between BT at the TOA simulated by the DART model and the urban RTE, with a root-mean-squared error (RMSE) of less than 0.25 K. The variations in LST retrieved with urban geometry effect correction over different local climate zones (LCZs) were analyzed. In general, built-up LCZs have relatively higher LST than land cover LCZs. The differences between LST retrieved without/with urban geometry effect correction over different LCZs are greater than 0.2 K. The largest average LST difference over built-up LCZs is approximately 0.9 K, whereas that over land cover LCZs is approximately 0.65 K. LST retrieved without/with urban geometry effect correction was used to calculate urban heat island intensity (UHII) in terms of the LCZ-based method. The results indicate that UHII calculated from LST with urban geometry effect correction is lower than that calculated from LST without urban geometry effect correction, with an average difference of approximately 0.5 K.

Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) products at regional and global scales have already been extensively and routinely generated from medium-resolution sensors. However, there is a lack of high-resolution LAI/FPAR product, which is especially essential for crop growth and drought monitoring of cropland in patches. This article proposes a processing framework for the derivation of decameter cropland LAI and FPAR in the Northern China plain from Sentinel-2 surface reflectance data with a random forest (RF) algorithm by exploiting the capabilities of the Google Earth Engine (GEE) cloud platform. The training database is generated from the spatially aggregated Sentinel-2 surface reflectance and the corresponding Moderate Resolution Imaging Spectroradiometer (MODIS) LAI/FPAR product over homogeneous cropland, and the training samples are strictly filtered for the best quality. RF is then trained over the processed Sentinel-2 surface reflectance and the filtered MODIS LAI/FPAR under two input groups—one group is for Sentinel-2 spectral bands of 10-m resolution only, and the other group supplements the Sentinel-2 red-edge (RE) and shortwave infrared (SWIR) bands of 20-m resolution. Extensive comparisons and validation are carried out, and they demonstrate that the new method can generate spatial and temporal consistent LAI/FPAR with MODIS at high spatial resolution. The retrieval accuracy is slightly better for 20-m input groups than that for 10-m input groups, confirming the value of RE and/or SWIR in cropland LAI/FPAR estimate. This article also demonstrates that GEE is a suitable high-performance processing tool for high-resolution biophysical variables estimation.

Land surface temperature (LST) is a crucial parameter in the energy and material balance of land surface system. The angle effect of LST makes the accuracy of LST restricted and limits the application of remote sensing LST product. In order to eliminate the influence of viewing angle, this study proposed a novel method to perform angular normalization by constructing a feature space of surface emission radiance and fractional vegetation coverage (Radiance-FVC space). The proposed approach is applied in Hetao Plain as an example. It is found that the Root Mean Square Error (RMSE) can reach 5.1K, and the angular normalization effect is more significant for pixels with larger viewing zenith angle.

The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the two Sentinel-3 satellites provides daily global coverage observation at daytime and nighttime. The split-window (SW) algorithm is currently used to retrieve the land surface temperature (LST) from SLSTR images; however, this algorithm has to utilize visible and near-infrared (VNIR) images and land cover to determine pixel emissivity. For nighttime observation, VNIR cannot be observed, and this limitation complicates the LST retrieval from nighttime images using the SW algorithm. This article proposed a three-channel temperature-emissivity separation (TES) algorithm that estimates the nighttime LST and emissivity from one middle-infrared (MIR) and two thermal-infrared (TIR) nighttime Sentinel-3 SLSTR images. The sensitive analysis showed that the algorithm could theoretically retrieve the LST and emissivity with errors less than 0.8 K and 0.015, respectively. Ground validation showed that the nighttime LST retrieval error was approximately 1.84 K and the bias was approximately -0.33 K. Finally, the TES algorithm was applied to obtain the LST and emissivity images over northern China as an example. The emissivity retrieved from the nighttime observation can be used in the daytime SW algorithm to improve its feasibility in the LST retrieval process.

On the basis of the spectral shape of thermal infrared (TIR) emissivity for sandy land, a remote sensing sand index called the sand differential emissivity index (SDEI) is proposed in this article to simply and conveniently detect sandy land over large areas. The SDEI is evaluated on ground, airborne, and spaceborne thermal emissivity data, and it shows good characterization of sandy land and performs better in sandy land identification than two previous indices. The SDEI was also evaluated in the transition zones of China's four mega-sandy lands and was applied to long-term land surface emissivity to obtain the spatial distribution and variation in China's sandy land from 2000 to 2016. The findings showed that a mean accuracy of 96% and a mean kappa coefficient of 0.83 were obtained in the transition zones, and the sandy land in the transition zone exhibited a decreasing trend over the past 17 years and a significant decline in the Mu Us sandy land. Meanwhile, the sandy land area in China decreased by 3.6×104km2(1.53%) by the end of 2016 compared with that in early 2000.

Aerosol Optical Depth (AOD) is an important physical quantity of atmospheric turbidity and a key factor for the atmospheric correction of optical remote sensing image. Several approaches have been developed to retrieve AOD from remote sensing data, and at ground level the sunphotometer is usually used to measure AOD. However, the sunphotometer is expensive and not portable for field campaign, which make it difficult to synchronously obtain AOD data along with other relevant measurement. In order to overcome this problem, this study proposed a new way to estimate AOD using Ground-measured Spectral Skylight Ratio method (GSSR) on the basis that skylight ratio is highly determined by such parameter. In the GSSR method, a look-up table (LUT) containing spectral skylight ratio in 400-1000nm was first established from the MODTRAN code under various AOD levels, solar zenith angles (SZA), and atmospheric types. Based on the LUT and actual skylight ratio measured from a ground spectrometer, the AOD was then determined using a shortest distance between the measurement and the spectral values in the LUT. Finally, the AOD derived from the GSSR method was validated using the AOD data from the CE318 sunphotometer data, and it was found that AOD error was about 0.0185, which demonstrated that the GSSR method can be used to estimate AOD accurately, and can be regarded as an alternative method to estimate AOD in the field work if no sunphotometer is available.

Earthquake is a very serious geological disaster. At present, the pre-earthquake prediction is not very accurate and effective in the world, but the quick, accurate and comprehensive understanding of the earthquake situation after the earthquake, and the arrangement of the implementation of rescue work, as far as possible to reduce the loss of recovery, has become the focus of the work of earthquake prevention and mitigation. The application of remote sensing technology in earthquake disaster monitoring and emergency rescue can greatly avoid the time-consuming and laborious shortcomings of the traditional ground survey method. Optical remote sensing has always been the mainstream means in the field of remote sensing. Collapsed buildings pose a direct threat to human life, so it is an urgent task to quickly extract the collapse of buildings in the earthquake. The object-oriented detection method of building damage change is to classify the pre-earthquake and post-earthquake images by object-oriented method, and then compare and analyze them on the basis of object to determine the change area. It can extract high-resolution buildings effectively and accurately. In the post-earthquake emergency rescue, the extraction of building damage information can quickly understand the traffic situation of each disaster area, and more effectively carry out targeted rescue operations.

The estimation of leaf area index (LAI) from optical remotely sensed data based on vegetation indices (VIs) is a quick and practical approach to acquire LAI over vast areas. Reflectance in the red-edge bands is sensitive to vegetation status, and its information is thought to be useful in agricultural applications. Based on three red-edge band observations (represented as RE1, RE2, and RE3 for bands 5-7) from the Multispectral Instrument (MSI) onboard the Sentinel-2 satellite, this article aims to investigate the feasibility and performance of using red-edge bands for LAI estimates with the VI method and ground-measured LAI data sets. Sensitivity analysis from PROSAIL simulations revealed that RE1 is mainly affected by the influence of the leaf chlorophyll content, and this uncertainty should not be ignored during LAI estimation. For the normalized difference vegetation index (NDVI), modified simple ratio (MSR), chlorophyll index (CI), and wide dynamic range vegetation index (WDRVI), the optimal combination of Sentinel-2 bands for LAI estimation was RE2 and RE3, with a minimum root-mean-square error (RMSE) of 0.75. Four 3-band red-edge VIs were proposed to exploit the full content of the red-edge bands of Sentinel-2, and their performance in LAI estimation improved slightly. However, both 2-band red-edge VIs and 3-band red-edge VIs remained slightly saturated at high LAI levels; therefore, a segmental estimation with a threshold was suggested for large LAIs. The results indicate that the optimal 2-band red-edge VIs and proposed 3-band red-edge VIs are effective tools for crop LAI estimation in multiple-growth stages with Sentinel-2 MSI images.

Spatial distribution and disappearance of sandy land is important for ecosystem management of desert regions and provides highly valuable information on desertification and climate change studies in arid environments. Based on the field measurement in the Gurbantonggut Desert, Xinjiang, China and the analysis of the spectral features of sandy land, a new sand differential emissivity index (SDEI) was proposed first for sandy land detection. Compared with the previous vegetation index, which can only distinguish green plants from bare land, SDEI can make a distinction well between sandy land and dry vegetation. For large regional mapping of sandy land, SDEI was applied on the ASTER Global Emissivity Dataset based on the Google Earth Engine platform. And then, four emissivity simulation schemes of different mixed pixels were conducted to determine the best threshold of sandy land mapping. The results show that when the threshold value is larger than 0.041, the sand distribution can be well extracted. Finally, the sandy land area of China extracted by SDEI is 160.67×104km2for year 2008, which is close to the data released by the China’s State Forestry Administration. These experimental results indicated that SDEI is applicable to identification of sandy land, and therefore satellite remotely-sensed thermal infrared observations have good potential in sandy land detection.

Urban area is the main and important space of human activities with a large number of population. Compared with rural and other natural areas, the dense buildings and high-intensity land use are the most different features of urban areas. Therefore, the urban area has obvious texture in remote sensing images. Effective and accurate identification of urban area can play an important role in urban study, urban planning and other urban-related fields. In this paper, a new method based on urban and non-urban scene classification using Convolutional Neural Network (CNN) technique is developed to identify the boundary of urban areas and is applied in Beijing as an example. An acceptable result of the urban area identification was obtained, indicating a great potential of deep learning method in urban related studies.

In this study, we used HyMap-C airborne hyperspectral imagery and ground samples collected synchronously to explore the estimation of soil heavy metal concentration. Preprocessing methods such as first-order derivative were used to enhance the weak spectral information related heavy metals. The multivariate stepwise regression method was used to select the spectral characteristics and establish the inversion model. The samples were divided into 3 parts, model set, validation set and test set. For the arsenic (As) the errors of the samples sets were 0.55, 0.75, 0.44, and the root-mean-square error were 51.20, 30.12, 32.78 mg/kg respectively. The results show that this method can predict the heavy metals arsenic in the study area.

Thermal infrared (TIR) remote sensing technology is an effective way to obtain a large-scale land surface temperature. The GaoFen-5 (GF-5) satellite, the fifth satellite in the national high-resolution Earth observation project of China, was launched May 9, 2018. The Visible and Infrared Multiple-channel Sensor (VIMS) onboard GF-5 can observe land surface thermal emission in four TIR channels within 8.0-12.0 μm with a spatial resolution of 40 m, much finer than that of ASTER (90 m), Landsat 8 (100 m), and MODIS (1 km). Under the support of national high-resolution earth observation project of China, several algorithms have been developed to retrieve LST and emissivity from VIMS image and generate products. This paper will present an overview the GF-5 LST retrieval algorithms.

With the deepening of urbanization and industrialization in China, large amount immigration flocked into the metropolis. As the capital of China, Beijing has become a huge immigration community. Most of the immigrants live in temporary buildings in urban-rural fringe areas because of high rents. It is difficult to supervise the quantitative changes of immigrants living in urban-rural fringe areas using traditional method. In order to study the variation of immigration communities, we try to detect and analyze the changes in temporary buildings using remote sensing data, in Daxing district, Beijing. Firstly, the TBI (Temporary Building Index) extraction method is used to extract Temporary buildings from 5 periods Sentinel-2A remote sensing images of Daxing District from 2016 to 2018. According to the extraction results, we found the immigration community is changing rapidly and intensely in three years. Affected by policy, the immigrants gathered close to the urban area, is rapidly moving away and the number of immigrants lived in suburban is increasing significantly.

Winter wheat is one of the main food crops in China, accurately forecasting the yield of winter wheat is of great significance for agricultural management and decision. UAV remote sensing has the advantages of high spatial-time resolution, low cost, flexibility and repeatability. In this paper the growth condition remote sensing and yield estimation of winter wheat were carried out using UAV hyperspectral sensor in Xiaotangshan Town, Changping District, Beijing. Based on the DSD (directional second differential) method and AIVI (Angular Insensitivity Vegetation Index), LAI (leaf area index) and LNC (leaf nitrogen content) of winter wheat at heading and filling periods were retrieved, and according to the result of DSSAT simulation, the forecasting model between LAI, LNC at heading and filling periods and yield of winter wheat was established by random forest algorithm. The R2of yield estimation model is 0.787 and RMSE is 727.87 kg/ha, which shows the yield estimation model can accurately and effectively estimate winter wheat yield.

The directional anisotropy of canopy emissivity and brightness temperature in the thermal infrared band has widely been studied. However, the contribution of different scattering orders has been an open scientific question for many years. The recently proposed CE-P model enables us to analytically evaluate the different scattering orders. Herein, we derive expressions for the first double collisions (DCE12) and first triple collisions (DCE123). Our result shows that DCE123can simulate the observed emissivity with an error less than 0.001 and that DCE12is reasonably accurate when leaf emissivity is greater than 0.96. Numerical analysis shows that the contribution of quadruple or greater collisions can be ignored when the leaf (soil) emissivity is no less than 0.90. Furthermore, we develop the CBT-P model to simulate the directional brightness temperature (DBT) based on the new optimized CE-P model (DCE123) and validate it by 4SAIL (4-Stream Radiative Transfer Theory of Scattering by Arbitrary Inclined Leaves) and DART (Discrete Anisotropic Radiative Transfer) models. Both of isothermal (soil temperature is equal to leaf temperature) and nonisothermal (soil temperature is higher than leaf temperature) cases are considered. The maximum differences between the CBT-P model and 4SAIL (DART) are less than 0.35 K (0.42 K), the average differences between CBT-P and 4SAIL (DART) are less than 0.10 K (0.13 K), and the R2is over 0.99 (0.95) with component emissivities larger than 0.90 and the difference between soil and leaf temperatures less than 20 K. The directional anisotropy of DBT is dominated by the zero-scattering and the single scattering terms according to the new developed CBT-P model.

A new directional canopy emissivity model (CE-P) based on spectral invariants can separate the multiple scattering effect and single scattering in vegetation canopy. So we can further evaluate the contribution of scattering effect to the canopy emissivity and brightness temperature based on CE-P model. Numerical analysis shows that the contribution of more than three times scattering can be ignored when the leaf (soil) emissivity is no less than 0.90. Then, we optimize CE-P model and obtain the expressions containing the first twice collisions (ε2) and first three times collisions (ε3). The result shows that ε3 can simulate the emissivity in any case with an error less than 0.001. Furthermore, we simulate the brightness temperature distribution using the optimized model and compare it with DART model. The difference between them is less than 0.3K and the R2of them is over 0.96 in all of the selected samples.

While change of lakes in the Tibetan Plateau (TP) is generally attributed to change of natural conditions, it is also important to evaluate the influence of human activities on lakes with the rapid increase of population in the plateau. Using a total of 786 clear-sky images of Landsat, we try to study the trend of lakes' area and shape in the TP and investigate the role human activities play. During the past 25 years, the area of lakes in the TP has experienced a rapid increase, and reservoirs and salt lakes also appeared to expand, but the shape and area of the lakes close to cities and roads have little change. In general, total 43 lakes affected by human activities increased 841.3 km2in the surface area, and all of them located in the northeast or southwest of the Tibetan Plateau.

Leaf chlorophyll content was a mediate indicator for monitoring winter wheat growing status. Based on 8-day composited time series dataset of MODIS surface reflectance with 1 km spatial resolution, a new Modified Ratio Vegetation Index (MRVI) was developed for monitoring leaf chlorophyll content of winter wheat in Hengshui City, Hebei Province of China. Comparing with the existing vegetation indexes, MRVI showed the better linear correlation with leaf chlorophyll content, with R2of 0.62 and RMSE of 8.34. The MRVI also revealed spatial patterns of leaf nitrogen status of winter wheat all over the Hengshui City in 2017, with R2of 0.73 in pure pixels and R2of 0.59 in all pixels. Our results demonstrated that MRVI would be a timely, economically and promising way for monitoring winter wheat nutritional condition from MODIS data in the future.

Land Surface Albedo is an important surface parameter and is widely applied to the surface energy balance, mid-term and long-term weather forecast and atmospheric general circulation model. GF-4 satellite is the first geostationary orbit satellite which combines high spatial resolution and high temporal resolution in China. In order to explore the feasibility of semi-empirical kernel-driven BRDF model applied on GF-4 satellite data, the earth's surface classification is joined to give kernel factors initial value, and Powell iteration algorithm is used to optimize the result of model. Then the land surface narrow band albedos of each band could be gained through angle integration on BRDF model. On this basis, combining spectral library with spectral response function of GF-4 satellite, the conversions of narrow to broadband albedo for GF-4 satellite data is built for the first time. And albedo inversion in short wave band (0.3-3μm) are acquired. Finally, cross validation used by MODIS albedo product indicates that an accurate land surface albedo could be acquired by this method.

Due to limitation of observation angles and resolution of satellite sensor, the resolution of surface albedo products retrieved based on angle information is usually coarse, such as MODIS albedo products and GLASS albedo products. Downscaling method of stratified linear regression proposed in this study solved this problem. First, under the assumption of non-anisotropy surface, Landsat8 primary albedo is obtained by converting narrow albedo to broadband albedo. Under the resolution of 500m, the correlation degree of Landsat8 primary shortwave albedo and MCD43A3 shortwave albedo shows higher after classification. Therefore, a linear regression model for each land cover is established. By fusing Landsat8 data with MCD43A3 albedo, downscaled shortwave albedo with high-resolution is obtained. Finally, it is validated with data of four observation sites in the United States. The results show that downscaled albedo has high precision (bias is 0.01 and sd is 0.012) and rich details, and the algorithm is reliable for different land cover, indicating its potential to become an operational algorithm for high-resolution albedo product.

Spectral vegetation indices are powerful tools in statistically estimating leaf area index (LAI) with remotely sensed imagery. However, the band selection in some generic vegetation indices influenced their performance to a great extent due to the rapid development of new sensors. As the latest launched satellite carried with multispectral sensors, Sentinel-2 provides 3 extra red-edge bands and 1 extra SWIR band. For the purpose of statistical LAI retrieval based on Sentinel-2 data, the optimal bands combination of the DVI, SR and NDVI-formed spectral indices were selected on the basis of correlation analysis. The experiment results demonstrated that band 7 (red-edge) and 8 (near-infrared) of Sentinel-2 MSI data were the optimal bands combination of LAI retrieval. And finally the optimal spectral index were validate with the in-situ LAI observations, which performed satisfied estimated accuracy.

An accurate and quick detection and seismic classification of the building earthquake damage is significant for disaster emergency and rescue. This paper aimed at quickly, precisely and efficiently detecting building damage information. According to the analysis of the problems existing in present research, a technical process for the VHR remote sensing image of the building earthquake damage information object-oriented change detection was proposed for its extraction through different degrees of improvement and innovation for the key technologies. The final building earthquake damage information result was output by accuracy evaluation through using classification evaluation criteria and other indicators. The change detection accuracy of the Yushu small area is 88.45% with the Kappa coefficient was 0.8411. It was proved and verified that the proposed method can make up for the classification deficiency based on the single data source, and realized the complementary advantages among the classifiers, which can improve the classification accuracy.

Urban thermal environment plays a crucial part in urban disaster prevention, urban planning, and environmental protection. Urban shadow distributions and land surface components are considered as the most influential factors in the thermal radiation of urban environment. This paper proposes a new method to determine the two factors from high-spatial-resolution digital surface models (DSM) and remote sensing multispectral images respectively, and then urban thermal radiation can be determined by combining temperature measurement on the ground level. Finally, The proposed method is applied to simulate the urban thermal radiation in Beijing as an example, using a three-meter DSM and Landsat 8 images.

Leaf area index (LAI), an important parameter describing a crop canopy structure and its growth status, can be estimated from remote sensing data by statistical methods involving vegetation indices (VIs). This letter reports the development of a new VI, the inverted difference vegetation index (IDVI), for crop LAI retrieval. The IDVI can overcome the saturation issue of the normalized difference vegetation index (NDVI) at high LAI values and exhibits robust insensitivity to crop leaf water and chlorophyll content. By combining the IDVI and NDVI with a scaling factor, we constructed a novel statistical regression model with parameters that can be calibrated to a specific region to estimate the LAI. Validations on simulated data and in situ observations show that the proposed retrieval method with the IDVI is stable for low and high LAIs and obtains better results than the empirical method involving the NDVI at the regional scale. Findings in this letter will benefit future agricultural applications.

A new directional canopy emissivity model (CE-P) based on spectral invariants is proposed in this paper. First, we prove the existence of the spectral invariant properties in the thermal infrared (TIR) band using a Monte Carlo model. Based on it, the equation of the new model is derived from the perspective of absorption. In this expression, single-scattering and multiscattering effects are separated analytically in the TIR band. We find that the overall contribution of multiple scatterings is less than 0.005 when the component emissivities are over 0.90, and the overall contribution decreases with increasing leaf or soil emissivity. Furthermore, the new model can avoid the logical difficulty encountered when using the traditional cavity effect factor to simulate the emissivity of a sparse vegetation canopy. The results of 4SAIL and Discrete Anisotropic Radiative Transfer (DART) are selected to do cross validation. The CE-P can achieve a high accuracy compared with 4SAIL and DART, with an absolute bias less than 0.002 when the leaf (soil) emissivity is equal to 0.98 (0.94). Four widely used analytical models are selected for comparison. The resulting accuracies of these models are ordered from CE-P to REN15, FR97, FR02, and VALOR96 with the most serious error up to 0.002, 0.002, 0.007, 0.013, and 0.014, respectively. Three main conclusions are obtained through the sensitivity analysis: the multiscattering between vegetation and the background can be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), the second and higher order scattering within the vegetation can also be ignored when the leaf (soil) emissivity is no less than 0.94 (0.90), and the single-scattering effect within the canopy should be considered which can be calculated using three view factors.

Land surface temperature (LST) is a key surface feature parameter. Temperature and emissivity separation (TES) and split-window (SW) algorithms are two typical LST estimation algorithms that have been applied to a variety of sensors to generate LST products. The TES algorithm can synchronously obtain LST and emissivity, but it requires high accuracy for atmospheric correction of the thermal infrared (TIR) data and does not perform well for surfaces with low spectral emissivity contrast. On the contrary, the SW algorithm can retrieve LST without detailed atmospheric data because the linear or nonlinear combination of brightness temperatures in the two adjacent TIR channels can reduce the atmospheric effect; however, this algorithm requires prior accurate pixel emissivity. Combining the two algorithms can improve the accuracy of LST estimation because the emissivity calculated from the TES algorithm can be used in the SW algorithm, and the LST from the SW algorithm can then be applied to the TES algorithm as an initial value to refine emissivity and LST. This paper investigates the aforementioned hybrid algorithm using Chinese Gaofen-5 satellite data, which will provide four-channel data for TIR at 40 m for synchronously retrieving LST and emissivity. The results showed that the hybrid algorithm was less sensitive to instrument noise and atmospheric data error, and can obtain LST and emissivity with an error less than 1 K and 0.015, respectively, which is better than those obtained with the single TES or SW algorithm. Finally, the hybrid algorithm was tested in simulated image and ground-measured data, and obtained accurate results.

GPP (Gross Primary Productivity) is an important index to reflect the productivity of vegetation. The MODIS 8-day GPP time-series products in the study area were rebuilt using Savitzky-Golay filter from 2001 to 2012. After phenological parameters' variation was analyzed based on dynamic threshold method, the time series of GPP were analyzed in this paper. Correlation analysis between annual accumulated GPP and influencing factors showed that annual accumulated temperature rather than precipitation has a remarkable positive correlation on GPP. The growing season length of study area was prolonged obviously over the dozen years but no obvious correlation was found between growing season length and annual accumulated GPP.

Gaofen-5 (GF-5) satellite is designed to provide high resolution imagery for land observation and disaster monitoring and will be launched in 2017. As true GF-5's data is unavailable, image simulation can be a useful tool to provide test images for research on relevant key techniques. In this paper, an emissivity image simulation method is proposed for 4 thermal infrared (TIR) bands on one of GF-5's instruments. In this method, thermal infrared images of airborne hyperspectral sensors are chosen as data source. For bands TIR1-3, their emissivity images are simulated through spectral integration based on hyperspectral emissivity images. For band TIR4, abundance inversion is conducted on emissivity images of the data source with surface endmembers, and emissivity images of band TIR4 are finally generated through spectral mixing. As the data source has a higher spatial and spectral resolution than the sensor on GF-5, this method can reproduce emissivity images with good accuracy.

According to the problems (e.g. the strong dependence on satellite sensors and atmospheric correction) in the current Fraction of absorbed Photosynthetically Active Radiation (FPAR) retrieval from remote sensing data, this study developed a generalized FPAR retrieval methods that can be applied to Landsat 5/TM, Landsat7/ETM+, Landsat 8/OLI, MODIS, ASTER, SPOT/VEGETATION and HJ/CCD based on a new radiative transfer model, and validated the result using VALERI data.

Leaf Area Index (LAI) is an important parameter in describing leaf density and canopy structure of plants, which could be estimated by remote sensing data conveniently by empirical methods with vegetation indexes. Due to the saturation of Normalized Difference Vegetation Index (NDVI) in high LAI value, Inverted Difference Vegetation Index (IDVI), which possessed a robust insensitivity on leaf water content and chlorophyll content, was proposed in this paper. Then we combined the statistical regression model base on NDVI and IDVI with a dynamic scale factor to estimate LAI. Our result demonstrated that this new retrieval method was quite stable in both low and high level of LAI, which indicated that it would be a promising way to retrieval LAI from remote sensing data in the future.

Fractional Vegetation Cover (FVC) describes the distribution and growing condition of vegetation on land surface ecosystem, and it could be estimated in regional and global scale with remote sensing techniques conveniently. The multispectral sensor equipped on China's GaoFen-4 (GF-4) geostationary satellite which was launched in December 2015 has a high spatial resolution of 50 m, thus makes it a powerful data source for vegetation monitoring and FVC estimation in large spatial scale timely. In this paper, we conducted a FVC estimation experiment in Northeast Inner Mongolian, China with GF-4 data acquired in August 2016 based on 2 most widely used method, and the estimation results were validated by a simultaneous Unmanned Aerial Vehicle (UAV) measurement afterwards. Our results showed that the vegetation index method with pure endmember pixel of soil and vegetation NDVI (Normalized Difference Vegetation Index) value extracted in a 2-dimensional spectral space was most accurate with the near ground UAV measurement results, and it would be a promising method in GF-4 FVC estimation in northern temperate semi-arid area of China in the future.

In order to improve the low spatial resolution of remote sensed LST, two methods based on multiple scale factors are proposed. Considering the optimal scale factor is usually not unique under environment of different land cover types, a stratified linear regression model is built, which shows a higher accuracy than global linear regression with one scale factor. In view of the relationship between scale factor and LST showing fluctuant under different conditions, such as season, size of scale and land cover type, where the errors of linear regression model often come from, a method of downscaling based on BP neural network is proposed. Multiple scale factors as input, this method shows as almost precise as the first method globally, especially for mountainous areas and mixed areas, which shows a stronger robustness.

In hyperspectral thermal infrared remote sensing, iterative spectrally smooth temperature / emissivity separation (ISSTES) is currently the most popular method to retrieve land surface temperature (LST) and emissivities (LSEs) at the same time. However, a serious problem may occur when noise reaches certain intensities, which causes ISSTES to fall into a false minimum, and thus the errors of LST and LSEs are far beyond tolerance. In this paper, both simulated and measured data were used to show how the problem would occur, and the ISSTES-Extreme (ISSTES-E) method was proposed to fix the problem. The results reveal that the new method is able to prevent the false minimum when the original method fails to come to a valid answer.

Leaf area index inversion using remote sensing is crucial for obtain vegetation information and monitoring global climate change. For the invalid uniform continuous canopy hypothesis of SAIL model, fractional canopy cover (FCC) is introduced and a simultaneous retrieval method of LAI and FCC is developed. SAIL model, PSO algorithm, and linear spectral mixture theory are combined in the novel method. Different fitness functions are designed and tested with field measurement data and Landsat-8 OLI data. Results show that the underestimation of LAI of canopy caused by the invalid hypothesis of SAIL model is well restrained when appropriate fitness function is adopted. The RMSE of the new method is only 0.489, which indicates the satisfactory retrieval performance. Therefore, the novel method is suggested as an effective LAI inversion technique.

Damaged building detection from high spatial resolution remote sensing image helps to rapid disaster losses assessment. However, the majority of traditional methods relies on only a single category feature of the damaged building. This letter presents a new strategy for detecting damaged buildings from postquake remote sensing image by multiple-feature analysis, in which the integrity of the building edge and the interior roof was both considered. The intactness of the building edge was assessed by proposing a new feature parameter, edge significance (ES), ES using significance test to quantify the difference between the gradient values on the edge and in the edge buffer. In addition, the gradient orientation inside the building was analyzed and local gradient orientation entropy (LOE) parameter was adopted to determine whether the interior roof was damaged. In general, damaged buildings have lower ES values because of broken edges and higher LOE values owing to debris, final decision was made on the basis of both feature parameters. A Quickbird image of Yushu, China, was used in the experiment and, among a total of 327 buildings, 266 were detected correctly. The overall accuracy was 84.10%, which is better than traditional methods.

The Gaofen-5 (GF-5) satellite, the only satellite that provides the thermal infrared (TIR) sensor in the national high-resolution earth observation project of China, will observe earth surface at a spatial resolution of 40 m in four TIR channels. This paper aims at developing a new nonlinear, four-channel split-window (SW) algorithm to retrieve land surface temperature (LST) from GF-5 image. In the SW algorithm, its coefficients were obtained based on several subranges of atmospheric column water vapors (CWV) under various land surface conditions, in order to remove the atmospheric effect and improve the retrieval accuracy. Results showed that the new algorithm can obtain LST with root-mean-square errors of less than 1 K. Compared with previous two- and three-channel SW algorithms, the four-channel SW algorithm obtained better results in estimating LST, especially under moist atmospheres. Methods of estimating CWV and pixel emissivity were also conducted. The sensitive analysis of LST retrieval to instrument noise and uncertainty of pixel emissivity and water vapor demonstrated the good performance of the proposed algorithm. At last, the new SW algorithm was validated using ground-measured data at six sites, and some simulated images from airborne hyperspectral TIR data.

Based on our previous studies, a simple fusion algorithm is proposed to estimate surface shortwave fluxes with polar-orbiting and geostationary satellite data. A shortwave flux component of one geostationary moment can be retrieved by only five inputs which include the known flux of one polar-orbiting moment, solar zenith angles and cloud fractions of the two moments. The preliminary validations are performed in terms of both the simulated and realistic datasets. The R2for each component is higher than 0.90 in the simulated case. The accuracy is relatively lower for the more complicated realistic situations. All of the validation results show that the simple and practical fusion algorithm has the potential to estimate surface shortwave fluxes with acceptable accuracy. With the combination of polar-orbiting (MODIS) and geostationary (Fengyun-2C) satellite data, surface shortwave fluxes with the temporal resolution of one hour were retrieved over the Tibetan Plateau.

Scale effect, which is caused by a combination of model nonlinearity and surface heterogeneity, has been of interest to the remote sensing community for decades. However, there is no current analysis of scale effect in the ground-based indirect measurement of leaf area index (LAI), where model nonlinearity and surface heterogeneity also exist. This paper examines the scale effect on the indirect measurement of LAI. We built multiscale data sets based on realistic scenes and field measurements. We then implemented five representative methods of indirect LAI measurement at scales (segment lengths) that range from meters to hundreds of meters. The results show varying degrees of deviation and fluctuation that exist in all five methods when the segment length is shorter than 20 m. The retrieved LAI from either Beer's law or the gap-size distribution method shows a decreasing trend with increasing segment lengths. The length at which the LAI values begin to stabilize is about a full period of row in row crops and 100 m in broadleaf or coniferous forests. The impacts of segment length on the finite-length averaging method, the combination of gap-size distribution and finite-length methods, and the path-length distribution method are relatively small. These three methods stabilize at the segment scale longer than 20 m in all scenes. We also find that computing the average LAI of all of the short segment lengths, which is commonly done, is not as good as merging these short segments into a longer one and computing the LAI value of the merged one.

The urbanization process changed the urban ecological land and consequently affected the quality of urban residents' environment, and it was very important to obtain urban ecological land cover information. In this paper, an object-oriented method was proposed to extract urban ecological land cover from the multiple-channel images acquired by Chinese Gaofen-1 (GF-1) satellite. Taking Beijing City as an example, five ecological land covers, including water, vegetation, road, building land and bare land, were classified using new classification rules based on the spectral, geometry and texture information in the GF-1 image. The result showed that the urban land covers were accurately identified and its validation accuracy was up to 90%.

Canopy water content (CWC) is one of the most important biochemical properties of plants, which can be estimated from remote sensing data conveniently by using vegetation water indices. This paper started from the analysis of some existing indices and then proposed two novel indices to estimate CWC. First, the area under part of near infrared and shortwave infrared reflectance curve were calculated. Then two indices, Area-based Normalized Index (ABNI) and Area-Based Ratio Index (ABRI) were developed by using ratio method and normalization method, respectively. From the validation results, the new indices were found to exponentially correlate with CWC more significantly than some classical indices, and the determination coefficient (R2) and root mean square error (RMSE) of the new method were 0.89 and 0.04, which indicated that the novel indices provided a promising way to monitor CWC.

On the basis of the thermal infrared radiative transfer theory, this paper addressed the retrieval of Land Surface Temperature (LST) from Landsat 8-the latest satellite in the Landsat Data Continuity Mission (LDCM) project in two thermal infrared channels, using the Generalized Split-Window (GSW) algorithm. Meanwhile, a linear bidirectional reflectance distribution function (BRDF) models were used to estimate the emissivity according to different surface classification. A series of ranging of typical surface emissivity and the atmospheric water vapor content (WV) were used into an accurate atmospheric radiative transfer model MODTRAN 4.3 to derive the coefficients in the algorithm. The simulation result showed the LST estimated by the algorithm with the Root Mean Square Error (RMSE) is 1.26K for the all ranges of the atmospheric WV and the results could be better in lower atmospheric WV condition.

Fraction of Absorbed Photosynthetically Active Radiation (FPAR), determined from remote sensing data, can vary with the spatial resolution, the different retrieval algorithms and viewing angels of the used data. This paper aimed at evaluating MODIS, POLDER and CYCLOPE global FPAR products, and found that the MODIS FPAR was larger than CYCLOPES, and their difference ranged within 0.1~0.2, especially at the forest area where MODIS product always presented seasonal variation in this area while CYCLOPES products kept relatively stable. For other vegetation covers, their difference was less than 0.1. Furthermore, the comparison of MODIS and POLDER FPAR products shown that the MODIS FPAR was also larger than the POLDER and their difference was up to 0.1 to 0.2.

Fraction of abstracted Photosynthetically Active Radiation (FPAR) is a fundamental terrestrial state variable in most ecosystem productivity models and is also one of the key terrestrial products. This paper proposed a new Direct-Algorithm to retrieve FPAR from apparent reflectance of MODIS's seven bands in the visible, near-infrared and short-wave wavelengths. The Direct-Algorithm developed from the dataset simulated by radiative transfer models of canopy and atmosphere with different canopy structures and atmosphere conditions, estimated direct FPAR (FPARdir), and scattering FPAR (FPARsct), and total FPAR of the canopy (FPARtot) by using linear equations of TOA reflectance. Result showed that the estimated FPAR product were close to that of MODIS products except the forest, perhaps because the homogenous canopy of the SAIL model is not suitable for the forest canopy.

This objective of this paper is to estimate atmospheric water vapor (wv) from the latest Landsat 8 Thermal InfRared Sensor (TIRS) image by using a new modified split-window covariance-variance ratio (MSWCVR) method. Model analysis showed that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.45 g/cm2for most atmospheric moisture conditions. The MSWCVR was evaluated by using AERONET ground-measured data and cross-compared with MODIS products in 2013 at forty two ground sites, and results presented that the retrieved wv from TIRS data was highly correlated with but generally larger (about 1.0 g/cm2) than two others. The reasons for this uncertainty were mainly ascribed to data systematic noise and radiative calibration error. Future work must pay more attention to the data quality and radiative calibration of Landsat 8 TIRS data.

Coal-bed methane (CBM), as an increasingly promising resource for the energy supply, deserves further exploration and accurate reservoir evaluation. It is also required to dynamically monitor the reservoirs (>200 m). Remote sensing methods in regular wavebands may fail in the depth sounding, with only imaging geo-objects shallower than 100 m. In contrast, the Super-Low Frequency (SLF) remote sensing technique has outstanding traits over others, including lower attenuation, all-weather and deeper penetration. In this paper, we have developed a non-imaging remote sensor to acquire electromagnetic signals in the Super-Low Frequency bands (i.e. SLF signals), which also enables us to fast and efficiently pre-process signals in a real-time display. In order to accurately identify producing CBM reservoirs, we mainly extract electromagnetic radiation (EMR) anomalies from processed SLF signals, and then dynamic analysis can be achieved. This technique has been validated by field experiments in Qin shui Basin, China.

Shortwave (SW) radiative flux (usually within 0.3∼3μm) is the dominant energy source of our planet, which drives the climate as well as the matter and energy cycle of the Earth system. It is an indispensable component of surface total energy balance. Considering the importance of SW radiation, during the past decades, more and more studies have conducted for estimating surface SW radiation using satellite-based data, such as MODIS, CERES, GOES etc. Although great effort has been made, most researches neglect the topographic effect and mainly focus on the retrieval of SW radiation over ideal horizontal surfaces for both instantaneous and time-integrated radiation. For this point, we propose a topographic SW radiation model based on the existing studies. Based on this, the SW radiative flux components are derived from MODIS data by fully accounting for the surface topographic effect. The results show that the errors induced in the retrieved daily SW radiation can reach up to 400W/m2at 1km scale. For instantaneous radiation, the uncertainties of derived SW radiation can reach up to 300W/m2even at 5km scale due to topographic effect. The findings of this paper prove the importance of topographic modeling of surface radiation over rugged terrain.

This paper aimed at the case of nonisothermal pixels and proposed a daytime temperature-independent spectral indices (TISI) method to retrieve directional emissivity and effective temperature from daytime multiangular observed images in both middle and thermal infrared (MIR and TIR) channels by combining the kernel-driven bidirectional reflectance distribution function (BRDF) model and the TISI method. Four groups of angular observations and two groups of MIR and TIR channels with narrow and broad bandwidths were used to investigate the influence of angular observations and bandwidth on the retrieval accuracy. Model sensitivity analysis indicated that the new method can generally obtain directional emissivity and temperature with an error less than 0.015 and 1.5 K if the noise included in the measured directional brightness temperature (DBT) and atmospheric data was no more than 1.0 K and 10%, respectively. The analysis also indicated that 1) large-angle intervals among the angular observations and a larger viewing zenith angle, with respect to nadir direction, can improve the retrieval accuracy because those angle conditions can result in significant difference for components' fractions and DBT under different viewing directions; 2) narrow channels can produce better results than broad channels. The new method was finally applied to a multiangular MIR and TIR data set acquired by an airborne system, and a modified kernel-driven BRDF model was used for angular normalization to the surface temperature for the first time. The difference of the retrieved emissivity and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity was found to be approximately 0.012 in the study area.

The ground-measured emissivity is always affected by many kinds of noises, which lead the retrieval accuracy to be out of expectation. This paper investigates the influence of three major noises (formula simplification, surface temperature measurement, and temperature emissivity separation algorithm) on the spectral emissivity by using simulation data based on radiative transfer model and field measured data from portable 102F infrared spectrometer. The findings of this paper can provide some suggestions for the further emissivity measurement.

This paper describes two new methods that were used to generate 26 years (1985–2010) of broadband emissivity (BBE) products with spatiotemporal continuity at the global scale from satellite data recorded by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Very High Resolution Radiometer (AVHRR). On the basis of emissivity libraries, the study began with establishing relationships for converting channel emissivities of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and MODIS to BBEs for the 8–13.5-$\mu\hbox{m}$spectral window and then developed two new algorithms from simultaneous ASTER emissivity products to estimate BBEs over vegetated surfaces using the MODIS and AVHRR data. The MODIS-data-based algorithm (MDBA) uses linear equations with MODIS normalized difference vegetation index (NDVI) and seven channels' albedo; the AVHRR-data-based algorithm uses nonlinear equations with AVHRR red and near-infrared reflectances. The proposed algorithms were first validated with ASTER emissivity products. Results indicated that the root-mean-square errors of both the proposed algorithms were less than 0.015 and their biases were less than 0.003. Comparison with MODIS emissivity products from the day/night algorithm showed that the estimated BBEs using the MDBA were generally smaller than the MODIS products. Cross-comparisons were also made between the proposed algorithms and the NDVI threshold method. Finally, strategies for mapping global BBE products from the MODIS and AVHRR data are presented, and some examples are discussed. The global BBE products are planned to be released throughout the network in the near future.

Accurate spectral calibration of the in-flight sensors is crucial for processing and exploration of remotely sensed data. This paper developed a strategy to make spectral recalibration (i.e., spectral response function, central wavelength, and bandwidth) for in-flight broadband sensor using a device-responsivity-decomposition model with a priori knowledge and an optimization algorithm. Sensitivity analysis indicates that an accurate result requires the targets to be observed under a dry and clear atmospheric condition (column water vapor2and visibility > 23 km) and no more than 5% error is included in the measured data. The new strategy was used to retrieve the spectral parameters along with radiometric calibration coefficients for a multichannel camera onboard an unmanned aerial vehicle from simultaneously remotely sensed and ground measured data sets over 19 (15 color-scaled and four gray-scaled) man-made surface targets, and the retrieved results were validated with a similar data set over another four man-made targets. It demonstrated that the camera's spectral parameters were accurately retrieved and an error less than 3.5 W/m2/μm/sr was brought to the channel radiance.

This paper presents a portable Multi-Angle Observation System (MAOS) to quickly collect bi-directional reflectance factor (BRF) and directional thermal radiance of land surface along with the spectroradiometer and thermal radiometer. The new system is able to make more than 13 zenith measurements in six minutes at an arbitrary azimuth direction, with the angle-controlling accuracy better than 2°. More observations are sampled in the hot-spot direction. All operations of the MAOS and data-processing are automatically controlled by the computer. Field campaign of winter wheat canopy shows that the MAOS had captured the angular variations of the BRF.

Based on the theory of radiation transfer model, this paper modified the Simultaneous Heat and Water model to calculate FPAR vertical distribution in maize canopy and analyzed the relationships between FPAR and some parameters like maize canopy structure, solar zenith, soil reflectance, etc. The validation results using field measurements prove the model to be accurate.

Land surface temperature (LST) at both high spatial and high temporal resolution is required for routine monitoring of surface energy fluxes. Disaggregating LST to the NDVI-pixel resolution is possible because of significant inverse relationship between LST and vegetation indices. A modified algorithm (SWISF) has been proposed for thermal imagery sharpening, in which multiple least-squares regression relationships between LST and vegetation indices were acquired for bins of pixels with different soil wetness index values. Applying both SWISF and Distrad which is originally proposed by Kustas et al. to simulated thermal maps at 360 m resolution and sharpening down to 90 m shows that the new algorithm slightly outperform the old one. Moreover, DisTrad does not have the ability to consider the fact that two pairs of pixels with the same NDVI difference may have distinct LST difference under different soil moisture conditions, while SWISF algorithm could consider it to some extent.

Accurate radiometric and spectral calibrations of hyperspectral remote sensing instruments are essential for optimum data processing and exploitation. Two improved methods for the refinement of the spectral calibration of air- and spaceborne imaging spectrometers are presented in this paper. Both spectral channel position and width can be retrieved by modeling the atmospheric absorption features around 760, 940, 1140, and 2060 nm without making use of external atmospheric or surface parameters. A sensitivity analysis based on synthetic data demonstrated that, for each of the two methods, the root-mean-square errors to be expected were less than 0.18 nm for the retrieval of channel wavelength center and less than 0.8 nm for channel full-width at half-maximum. The application of the proposed methods to a real Hyperion data set showed quite-similar cross-track variations in the spectral calibration for the two methods, although relatively large differences in magnitude were found near the 940- and 1140-nm H2O absorption features. The significant improvement of the reflectance spectra derived after the refinement of the instrument spectral calibration confirms the good performance of the proposed methods.