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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.

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 μm, 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 emissivity is a key parameter that affects energy exchange and represents the spectral characteristics of land cover. Large-scale mid-infrared (MIR) emissivity can be efficiently obtained using remote sensing technology, but current methods mainly rely on prior knowledge and multi-temporal or multi-angle remote sensing images, and additional errors may be introduced due to the uncertainty of external data such as atmospheric profiles and the inconsistency of multiple source data in spatial resolution, observation time, and other information. In this paper, a new practical method was proposed which can retrieve MIR emissivity with only a single image input by combining the radiance properties of TIR and MIR channels and the spatial information of remote sensing images based on the Sentinel-3 Sea and land surface temperature radiometer (SLSTR) data. Two split-window (SW) algorithms that use TIR channels only and MIR and TIR channels to retrieve land surface temperature (LST) were developed separately, and the initial values of MIR emissivity were obtained from the known LST and TIR emissivity. Under the assumption that the atmospheric conditions in the local area are constant, the radiance transfer equations for adjacent pixels are iterated to optimize the initial values to obtain stable estimation results. The experimental results based on the simulation dataset and real SLSTR images showed that the proposed method can achieve accurate MIR emissivity results. In future work, factors such as angular effects, solar radiance, and the influence of atmospheric water vapor will be further considered to improve performance.

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.

The second-generation Chinese polar-orbit meteorological satellite Fengyun-3D carries the MEdium-Resolution Spectral Imager (MERSI-II). MERSI-II has 19 solar channels and 6 middle and thermal infrared channels and can provide daily global coverage observation at daytime and nighttime. Land surface temperature (LST) products have a wide range of application requirements. Unfortunately, there is no official LST product of MERSI-II yet. This article proposed a temperature-emissivity separation algorithm to estimate LST and emissivity from two mid-infrared (MIR) and two thermal infrared (TIR) nighttime MERSI-II images. The sensitive analysis and ground validation show the following. First, the algorithm can theoretically retrieve LST with an error of less than 0.7 K, emissivity with errors of about 0.025 for MIR channel and 0.01 for TIR channel, respectively. Second, the nighttime LST retrieval error was approximately 2.19 K, and the bias was approximately 0.60 K among 6 SURFRAD sites and 2 PKULSTNet sites. The retrieved emissivity was cross-validated using MODIS emissivity product over a desert area and its difference was obtained as 0.01 and 0.016 for two TIR channels. Finally, the TES algorithm was applied to obtain LST and emissivity images over North China Plain as an example, and the nighttime LST and land surface emissivity (LSE) were obtained well. As a result, this study shows that the proposed TES algorithm provides an effective way to get LST and LSE image from FY-3D/ MERSI-II nighttime observation, which will improve their applications in different fields.

Accurate and timely crop yield estimation is critical for food security and sustainable development. The rapid development of unmanned aerial vehicles (UAVs) offers a new approach to acquire high spatio-temporal resolution imagery of farmland at a low cost. In order to realize the full potential of UAV platform and sensor, machine learning has been introduced to estimate crop yield, but the shortages of field measurements have troubled researchers. In this article, the CW-RF model, a new wheat yield estimation model suitable for the North China plain, was established using random forest, and the crop growth model (the CERES-wheat model) was chosen to simulate abundant training samples for random forest at field plot scale. According to CERES-wheat model simulation, the leaf area index (LAI) and leaf nitrogen content (LNC) at the wheat jointing and heading stages were selected as the most sensitive parameters, and were retrieved from UAV hyperspectral imagery using the directional second derivative and angular insensitivity vegetation index methods, respectively. Then the retrieved LAI and LNC results were input into the CW-RF model to estimate winter wheat yield. The field validation in Luohe, Henan showed that the root-mean-squared error of the retrieved LAI and LNC were 6.27% and 12.17% at jointing stages, 9.21% and 13.64% at heading stages, respectively. The RMSE of estimated yield was 1,008.08 kg/ha, and the mean absolute percent error of estimated yield was 9.36%, demonstrating the available of the CW-RF model in wheat yield estimation at field plot scale. Apart from Luohe, validations in some other fields (e.g., Xiaotangshan, Beijing), prove the wide applicability of the CW-RF model. In addition, the UAV hyperspectral data were found to significantly improve the retrieval accuracy, and further improve CW-RF model estimation accuracy. In conclusion, this article showed that the CERES-Wheat model simulation can be important data source for machine learning-based wheat yield estimation model at field plot scale, and the hyperspectral sensor mounted on a UAV is a feasible remote sensing data acquisition mode for winter wheat growth monitoring and yield estimation.

Land surface temperature (LST) retrieval from thermal infrared (TIR) remote sensing image requires atmospheric and land surface emissivity (LSE) data that are sometimes unattainable. To overcome th...

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.

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.

One focus of remote-sensing studies is obtaining highly accurate land-cover maps, which is essential for quantifying and monitoring changes in the environment. However, thermal data, which can prov...

The lunar surface temperature (LST) derived from thermal infrared (TIR) measurements can aid in understanding the physical properties of the lunar surface. The Diviner Lunar Radiometer Experiment (herein, Diviner) sensor provides global lunar surface observation in seven TIR channels. However, its retrieval of LST constantly uses a single emissivity value (i.e., 0.95) by ignoring the spatial variation of lunar surface, thereby reducing the accuracy of temperature and day–night temperature difference. To overcome this problem, this study developed a physical method called temperature–emissivity separation (TES) algorithm to retrieve LST and lunar surface emissivity from the daytime observation in three Christiansen Feature (CF) channels (7.55–8.05, 8.10–8.40, and 8.38–8.60 μm) of the Diviner, and then used the emissivity from daytime observation to inverse LST at nighttime observation. Findings showed that the TES algorithm could retrieve LST and emissivity with an error of less than 0.8 K and 0.008, respectively. However, observation noise significantly affected the retrieval accuracy, particularly for the low‐temperature pixels; moreover, high retrieval accuracy requires a surface temperature higher than 240 K. The new algorithm was applied to obtain the daytime and nighttime LST and emissivity from the Diviner images. Results showed that the LST retrieved from the algorithm differed approximately 3.9 K from that calculated from a single emissivity 0.95. Finally, an example of global surface temperature and emissivity were obtained. Consequently, the CF pixels were found to distribute in the latitude range from −60° to 60°; however, they did not have a large distribution in high‐latitude and near‐polar regions.

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.

Landsat 8/thermal infrared sensor (TIRS) is suffering from the problem of stray light that makes an inaccurate radiance for two thermal infrared (TIR) bands and the latest correction was conducted in 2017. This paper focused on evaluation of land surface temperature (LST) retrieval from Landsat 8 before and after the correction using ground-measured LST from five surface radiation budget network (SURFRAD) sites. Results indicated that the correction increased the band radiance at the top of the atmosphere for low temperature but decreased such radiance for high temperature. The root-mean-square error (RMSE) of LST retrieval decreased by 0.27 K for Band 10 and 0.78 K for Band 11 using the single-channel algorithm. For the site with high temperature, the LST retrieval RMSE of the single-channel algorithm for Band 11 even reduced by 1.4 K. However, the accuracy of two of three split-window algorithms adopted in this paper decreased. After correction, the single-channel algorithm for Band 10 and the linear split-window algorithm had the least RMSE (approximately 2.5 K) among five adopted algorithms. Moreover, besides SURFRAD sites, it is necessary to validate using more robust and homogeneous ground-measured datasets to help solely clarify the effect of the correction on LST retrieval.

Land surface component temperatures (LSCTs), i.e., the temperatures of soil and vegetation, are important parameters in many applications, such as estimating evapotranspiration and monitoring droughts. However, the multiangle algorithm is affected due to different spatial resolution between nadir and oblique views. Therefore, we propose a combined retrieval algorithm that uses dual-angle and multipixel observations together. The sea and land surface temperature radiometer onboard ESA's Sentinel-3 satellite allows for quasi-synchronous dual-angle observations, from which LSCTs can be retrieved using dual-angle and multipixel algorithms. The better performance of the combined algorithm is demonstrated using a sensitivity analysis based on a synthetic dataset. The spatial errors in the oblique view due to different spatial resolution can reach 4.5 K and have a large effect on the multiangle algorithm. The introduction of multipixel information in a window can reduce the effect of such spatial errors, and the retrieval results of LSCTs can be further improved by using multiangle information for a pixel. In the validation, the proposed combined algorithm performed better, with LSCT root mean squared errors of 3.09 K and 1.91 K for soil and vegetation at a grass site, respectively, and corresponding values of 3.71 K and 3.42 K at a sparse forest site, respectively. Considering that the temperature differences between components can reach 20 K, the results confirm that, in addition to a pixel-average LST, the combined retrieval algorithm can provide information on LSCTs. This article demonstrates the potential of utilizing additional information sources for better LSCT results, which makes the presented combined strategy a promising option for deriving large-scale LSCT products.

It is important to understand temporal and spatial variations in the structure and photosynthetic capacity of tropical rainforests in a world of changing climate, increased disturbances and human appropriation. The equatorial rainforests of Central Africa are the second largest and least disturbed of the biodiversly-rich and highly productive rainforests on Earth. Currently, there is a dearth of knowledge about the phenological behavior and long-term changes that these forests are experiencing. Thus, this study reports on leaf area seasonality and its time trend over the past two decades as assessed from multiple remotely sensed datasets. Seasonal variations of leaf area in Congolese forests derived from MODIS data co-vary with the bimodal precipitation pattern in this region, with higher values during the wet season. Independent observational evidence derived from MISR and EPIC sensors in the form of angular reflectance signatures further corroborate this seasonal behavior of leaf area. The bimodal patterns vary latitudinally within this large region. Two sub-seasonal cycles, each consisting of a dry and wet season, could be discerned clearly. These exhibit different sensitivities to changes in precipitation. Contrary to a previous published report, no widespread decline in leaf area was detected across the entire extent of the Congolese rainforests over the past two decades with the latest MODIS Collection 6 dataset. Long-term precipitation decline did occur in some localized areas, but these had minimal impacts on leaf area, as inferred from MODIS and MISR multi-angle observations.

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.

Fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter in ecosystem productivity and carbon balance estimation. FPAR can be estimated from various satellite images but its product might have significant differences due to the usage of various algorithms. This work proposes a generalized FPAR retrieval method for Landsat 5/ Thematic Mapper (TM), Landsat 7/Enhanced Thematic Mapper Plus (ETM+), Landsat 8/ Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer(MODIS), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), SPOT/VEGETATION, and HJ-1/CCD in the form of two linear models, namely, the BOA (bottom of atmosphere) model and the TOA (top of atmosphere) model, to reduce FPAR discrepancy among sensors. The BOA model estimates canopy FPAR from land surface multiband reflectance after atmospheric correction, whereas the TOA model estimates FPAR from apparent multiband reflectance at the TOA. Analysis results found that the FPAR errors from the BOA and TOA models were approximately 0.03 and 0.06, respectively, and the difference among FPAR estimated from different sensors was turned out to be less than 0.015 in theory. In addition, the FPAR difference between the two models was generally small, especially under low aerosol optical depth (AOD) and densely vegetated conditions. Ground validation using the datasets from the HiWATER and Validation of Land European Remote Sensing Instruments (VALERI) programs showed that the FPAR errors were 0.16 and 0.18 for the BOA and TOA models, respectively, which might be affected by the time interval of ground and satellite observation, spatial scale effect, and atmospheric correction errors. Moreover, this paper applied the new methods to estimate FPAR in different dates at the Heihe River basin and conduct a cross-comparison of FPAR from various sensors, and consequently obtained acceptable results.

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.

Urban land surface temperature (ULST) is a key surface feature parameter in urban heat island studies. However, the geometry and adjacency effects are usually neglected in conventional land surface temperature (LST) retrieval methods. In this study, considering the geometry and adjacency effects, a new urban canopy multiple-scattering thermal radiative transfer (UCM-RT) model and an urban temperature-emissivity separation (UTES) algorithm were developed for ULST retrieval from Chinese GaoFen-5 (GF-5) satellite thermal infrared (TIR) images. The UCM-RT model and the UTES algorithm incorporate multiple scattering within the urban building canopy and consider the thermal radiance contribution from adjacent pixels and the atmosphere. Two schemes were designed to evaluate the geometry and adjacency effects quantitatively in this study: (1) evaluating their impact on ground temperature and top of atmosphere (TOA) brightness temperature for GF-5 four TIR bands from the simulation dataset, and (2) comparing the retrieval temperature difference between the UTES algorithm and conventional temperature-emissivity separation (TES) algorithm (without considering geometry and adjacency effects). For a specific simulation situation, the magnitude of the geometric effects on urban ground temperatures were found to be approximately 3.2 K, 4.2 K, 4.0 K, and 3.9 K for the four GF-5 TIR bands, while the effects on the TOA brightness temperature reached 2.4 K, 3.3 K, 3.3 K, and 3.0 K for those bands, respectively. The results show that the geometry effects have a significant influence on the thermal radiative transfer process. The temperature error of the UTES algorithm was generally up to 0.8 K, much lower than that of the conventional TES algorithm (~2.2 K) in urban surface. The results indicate that the UTES algorithm is suitable for ULST retrieval, and the retrieval temperature difference between the UTES algorithm and conventional TES algorithm ranges from 0.1 K to 2.1 K, especially for dense building pixels. Additionally, the thermal radiation from adjacent pixels in high-spatial-resolution TIR images has significant influence on ULST retrieval. The comparison between the ULST algorithm for urban land surface and the conventional TES algorithm for the flat surface indicated that the three-dimensional structure inside the pixel has a significant influence on the ULST retrieval. The findings of this study indicate that the geometry and adjacency effects must be considered in ULST retrieval for highly accurate results.

Thermal infrared (TIR) remote sensing technology is capable of acquiring large-scale land surface temperature (LST), which is a key factor in the energy exchange between land surface and atmosphere. The visible and infrared multispectral sensor (VIMS) equipped in the Chinese Gaofen-5 (GF-5) satellite can obtain four channels of TIR images with a 40 m spatial resolution. However, due to the change of working environment, the TIR sensor suffers a low-accurate radiometric calibration that needs improvement. This paper puts forward a new cross-calibration for GF-5/VIMS TIR images by linking the top of the atmosphere (TOA) radiance of the vertical angle MODIS observation with the GF-5/VIMS image to estimate the radiometric calibration coefficients, Gain and Offset. To verify the recalibration performance, a new nonlinear two-channel split-window (SW) algorithm and a light gradient boosting machine (LightGBM) method which is used to refine LST from the SW algorithm by minimizing the residuals, were developed for the recalibrated GF-5/VIMS TIR 3 and TIR 4 channels. The radiometric cross-calibration algorithm and the optimized SW algorithm were applied to real GF-5/VIMS TIR images. The validation results showed that the brightness temperature and LST were improved significantly, and the LST retrieval error was reduced greatly to 1.79 K after recalibration, indicating a large improvement of the LST retrieval for GF-5/VIMS TIR image using the proposed cross-calibration and SW algorithms.

Accurate and efficient in situ measurement methods of leaf area index (LAI) and leaf angle distribution (LAD) are needed to estimate the fluxes of water and energy in agricultural settings. However, available methods: to estimate these two parameters, especially LAD, are limited. In this study, we propose a field measurement method using multi-angular digital images to estimate LAI and LAD simultaneously from the area proportions of: (i) sunlit soil; (ii) sunlit leaves; (iii) shaded soil; and (iv) shaded leaves. A new expression of the fraction of sunlit leaves is developed based on the radiative transfer theory. Coupling the measured and modeled fractions with an optimization scheme, LAI and the LAD parameters are derived from inverting a fractional model of sunlit and shaded leaves and soil. Through four tests using simulated scenes and in situ measurements for row crops, it is determined that our method performs well. The absolute error of LAI estimation is less than 0.1 when LAI is low (i.e., 3), so little background soil is seen. The sensitivity and uncertainty analysis is consistent with the estimation errors. Theoretically, the application of this method is not limited to row crops or to field measurement, as the derived formulae of sunlit and shaded components can be used for other types of vegetation by introducing the clumping index and can be used in the modeling of canopy vegetation parameters (e.g., canopy reflectance).

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.

Surface water extraction from remote sensing images is crucial for monitoring water resources and studying global environmental change. This study introduced a method for surface water extraction based on the LBV transformation (WE-LBV) from Landsat 8 Operational Land Imager (OLI) imagery. To avoid inadequate or redundant utilization of remote sensing data, input band combinations were selected using the band index method, and the LBV transformation equations for OLI images were subsequently derived. Characteristics of water and nonwater pixels were investigated after the LBV transformation. The established extraction rule stated that pixels should be classified as water if the B value is larger than the V value. For validation, the WE-LBV was used to extract water pixels from OLI images covering different water types and outside conditions and compared with other water indices on the global scale. Results showed that the producer accuracy (95.75%) and user accuracy (99.15%) of the WE-LBV were more stable and its overall accuracy (98.02%) and Kappa coefficient (0.9582) were higher than those of other methods. The WE-LBV classified shallow water and shadow pixels accurately, whereas other methods had problems. Water pixels with different colors and mixed pixels with water percentage higher than 80% could be extracted by the WE-LBV effectively. The WE-LBV did not require manually selected threshold, thus avoiding the effect of subjective human factors. Therefore, the WE-LBV is proposed as an accurate, simple, and robust method for surface water extraction, especially in situations when surface water needs to be identified in bulk automatically and accurately.

Land surface emissivity is a crucial parameter for obtaining the land surface temperature and estimating the land surface energy budget from remote sensing data. The current emissivity products always have a coarser spatial resolution than the products from the visible and near-infrared data. This study focused on the generation of an emissivity product at a spatial resolution of 30 m using a new global land cover product called Finer Resolution Observation and Monitoring of Global Land Cover (FROM-GLC) and Landsat images. Summer-average emissivity products in four narrowbands (Landsat 5/ Thematic Mapper (TM) Band 6, Landsat 7/ Enhanced Thematic Mapper Plus(ETM+) Band 6, Landsat 8 Thermal Infrared Sensor bands 1 and 2) and two broadbands (3–14 μm and 8–13.5 μm) were produced in China. Results illustrated that the narrowband emissivities ranged from 0.95 to 0.99, whereas the broadband emissivities ranged from 0.93 to 0.99 in the study area. Inter-comparisons in different places showed that the new emissivity was close to Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) emissivity with a difference of about 0.015 for narrowband emissivity and about 0.02 for broadband emissivity on a regional scale. For application purposes, the emissivities were released in the Worldwide Reference System 2 and geographic coordinate systems with several spatial resolutions resampled from its original scale of 30 m.

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.

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.

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 R2 of 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.

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.

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.

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.

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.

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.

The Fraction of Absorbed Photosynthetically Active Radiation (FPAR) has been identified as one of the key parameters in calculating ecosystem productivity. The objective of this paper is to model the vertical profile of FPAR in the canopy using a radiative transfer model, the Modified Simultaneous Heat and Water (MSHAW) radiation model. Model analysis indicated that the vertical distribution of the canopy FPAR was dependent on the leaf area index (LAI), average leaf orientation angle (ALA), solar position, and sky conditions. In the validation of the MSHAW model with three varieties of wheat leaf profile at different growth stages, two parabolic functions were developed to approximately reconstruct the shape of the wheat leaf for the first time and, consequently, the vertical profiles of LAI and ALA used to drive the MSHAW model were estimated. The validation results indicated that the estimated FPAR was close to the measurements made with the SunScan canopy analysis system with an RMSE of approximately 0.15 for the continuous canopy. Finally, this paper also discusses a promising method to perform time normalisation on canopy FPAR data using multiple temporal remotely sensed data observations and to retrieve FPAR from remotely sensed data based on the analysis of the MSHAW model.

Atmospheric water vapor (wv) is required for the accurate retrieval of the land surface temperature from remote sensing data and other applications. This work aims to estimate wv from Landsat 8 Thermal InfraRed Sensor (TIRS) images using a new modified split-window covariance-variance ratio (MSWCVR) method on the basis of the brightness temperatures of two thermal infrared bands. Results show that the MSWCVR method can theoretically retrieve wv with an accuracy better than 0.3 g/cm2 for dry atmosphere (wv

Fractional vegetation cover (FVC) is one of the most important criteria for surface vegetation status. This criterion corresponds to the complement of gap fraction unity at the nadir direction and accounts for the amount of horizontal vegetation distribution. This study aims to directly validate the accuracy of FVC products over crops at coarse resolutions (1 km) by employing field measurements and high-resolution data. The study area was within an oasis in the Heihe Basin, Northwest China, where the Heihe Watershed Allied Telemetry Experimental Research was conducted. Reference FVC was generated through upscaling, which fitted field-measured data with spaceborne and airborne data to retrieve high-resolution FVC, and then high-resolution FVC was aggregated with a coarse scale. The fraction of green vegetation cover product (i.e., GEOV1 FVC) of SPOT/VEGETATION data taken during the GEOLAND2 project was compared with reference data. GEOV1 FVC was generally overestimated for crops in the study area compared with our estimates. Reference FVC exhibits a systematic uncertainty, and GEOV1 can overestimate FVC by up to 0.20. This finding indicates the necessity of reanalyzing and improving GEOV1 FVC over croplands.

This paper developed a practical split-window (SW) algorithm to estimate land surface temperature (LST) from Thermal Infrared Sensor (TIRS) aboard Landsat 8. The coefficients of the SW algorithm were determined based on atmospheric water vapor sub-ranges, which were obtained through a modified split-window covariance–variance ratio method. The channel emissivities were acquired from newly released global land cover products at 30 m and from a fraction of the vegetation cover calculated from visible and near-infrared images aboard Landsat 8. Simulation results showed that the new algorithm can obtain LST with an accuracy of better than 1.0 K. The model consistency to the noise of the brightness temperature, emissivity and water vapor was conducted, which indicated the robustness of the new algorithm in LST retrieval. Furthermore, based on comparisons, the new algorithm performed better than the existing algorithms in retrieving LST from TIRS data. Finally, the SW algorithm was proven to be reliable through application in different regions. To further confirm the credibility of the SW algorithm, the LST will be validated in the future.

This paper proposes a modified model based on the PROSPECT-5 model to simulate the spectral reflectance of copper-stressed leaves. Compared with PROSPECT-5, the modified model adds the copper content of leaves as one of input variables, and the specific absorption coefficient related to copper (Kcu) was estimated and fixed in the modified model. The specific absorption coefficients of other biochemical components (chlorophyll, carotenoid, water, dry matter) were the same as those in PROSPECT-5. Firstly, based on PROSPECT-5, we estimated the leaf structure parameters (N), using biochemical contents (chlorophyll, carotenoid, water, and dry matter) and the spectra of all the copper-stressed leaves (samples). Secondly, the specific absorption coefficient related to copper (Kcu) was estimated by fitting the simulated spectra to the measured spectra using 22 samples. Thirdly, other samples were used to verify the effectiveness of the modified model. The spectra with the new model are closer to the measured spectra when compared to that with PROSPECT-5. Moreover, for all the datasets used for validation and calibration, the root mean square errors (RMSEs) from the new model are less than that from PROSPECT-5. The differences between simulated reflectance and measured reflectance at key wavelengths with the new model are nearer to zero than those with the PROSPECT-5 model. This study demonstrated that the modified model could get more accurate spectral reflectance from copper-stressed leaves when compared with PROSPECT-5, and would provide theoretical support for monitoring the vegetation stressed by copper using remote sensing.

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.

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.

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.

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.

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.

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.

International audience; Land surface temperature (LST) is a key variable in the study of the energy exchange between the land surface and the atmosphere. Among the different methods proposed to estimate LST, the quadratic split-window (SW) method has achieved considerable popularity. This method works well when the emissivities are high in both channels. Unfortunately, it performs poorly for low land surface emissivities (LSEs). To solve this problem, assuming that the LSE is known, the constant in the quadratic SW method was calculated by maintaining the other coefficients the same as those obtained for the black body condition. This procedure permits transfer of the emissivity effect to the constant. The result demonstrated that the constant was influenced by both atmospheric water vapour content (W) and atmospheric temperature (T 0) in the bottom layer. To parameterize the constant, an exponential approximation between W and T 0 was used. A LST retrieval algorithm was proposed. The error for the proposed algorithm was RMSE = 0.70 K. Sensitivity analysis results showed that under the consideration of NE∆T = 0.2 K, 20% uncertainty in W and 1% uncertainties in the channel mean emissivity and the channel emissivity difference, the RMSE was 1.29 K. Compared with AST 08 product, the proposed algorithm underestimated LST by about 0.8 K for both study areas when ASTER L1B data was used as a proxy of Gaofen-5 (GF-5) satellite data. The GF-5 satellite is scheduled to be launched in 2017.

The radiometric performance of remotely-sensed images is important for the applications of such data in monitoring land surface, ocean and atmospheric status. One requirement placed on the Thermal Infrared Sensor (TIRS) onboard Landsat 8 was that the noise-equivalent change in temperature (NEΔT) should be ≤0.4 K at 300 K for its two thermal infrared bands. In order to optimize the use of TIRS data, this study investigated the on-orbit NEΔT of the TIRS two bands from a scene-based method using clear-sky images over uniform ground surfaces, including lake, deep ocean, snow, desert and Gobi, as well as dense vegetation. Results showed that the NEΔTs of the two bands were 0.051 and 0.06 K at 300 K, which exceeded the design specification by an order of magnitude. The effect of NEΔT on the land surface temperature (LST) retrieval using a split window algorithm was discussed, and the estimated NEΔT could contribute only 3.5% to the final LST error in theory, whereas the required NEΔT could contribute up to 26.4%. Low NEΔT could improve the application of TIRS images. However, efforts are needed in the future to remove the effects of unwanted stray light that appears in the current TIRS images.

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.

Since the launch of landsat8, high-quality surface observation data have been acquired. But the corresponding sea surface temperature (SST) products have not been seen. Moreover, the existing SST inversion algorithm do not carefully consider the effect of water vapor on the accuracy. In this study, a new method is proposed for the retrieve of SST from Landsat 8 Thermal Infrared Remote Sensing (TIRS) data based on the variation of atmospheric water vapor content. The steps are briefly described as follows: 1) constructing the SST retrieval model of thermal infrared remote sensing by using the radiation transfer equation with atmospheric profile data (air temperature and pressure); 2) retrieving SST of Zhoushan sea area, China from Landsat 8 TIRS Data; 3) evaluating the accuracy of proposed model by simulation data and AVHRR SST product. The bias and RMSE based on the simulation data are within 0.4 K. The bias and RMSE based on AVHRR SST product are 1.6063 K and 1.8507 K, respectively. The results show that the accurate SST with high spatial resolution could be obtained by using the method.

We are the first to apply the copper stress vegetation index (CSVI) on a remotely sensed image and the purpose is to verify the effectiveness of CSVI at the satellite-image scale. The study area was located at the Dexing Copper Mine, Jiangxi Province, China. First, the data preprocessing for the Hyperion image was conducted, including bands removal, radiometric calibration, and atmospheric correction. Second, the regions with high vegetation cover were extracted based on endmembers extraction and spectral unmixing. The CSVI was calculated on the high-vegetation-cover regions. Third, the samples of soil and leaves were collected from the study area and the copper contents in the samples were measured for the assessment. The results showed that the high values of the CSVI were near the functional regions for copper mining and the polluted rivers. What is more, there was a significant positive correlation between the CSVI calculated from the Hyperion image and the copper content in soil/leaves. We demonstrate that CSVI is applicable for monitoring the copper stress on vegetation using satellite hyperspectral images. In addition, we provide a complete example for the application of CSVI at the satellite-image scale for the first time, which is helpful for the community in remote sensing of copper-stressed vegetation.