This page has only limited features, please log in for full access.
A reliable accuracy is essential for the application of land surface temperature (LST) products. Current satellite retrieved LSTs are mainly validated over a few homogeneous sites. However, most of the existing ground sites are located in inhomogeneous areas: thus, their spatial representativeness on satellite pixel scales is unknown. In this situation, how to evaluate the spatial representativeness of these inhomogeneous sites, quantify the influence introduced by the spatial representativeness and describe the variation of the site's spatial representativeness are critical questions for satellite LST validation. In an attempt to answer those questions, a so-called temporal variation method (TVM) is proposed for evaluating a ground site's spatial representativeness. The method defines a spatial representativeness indicator (SRI), which is the LST difference between a ground radiometer's field-of-view (FOV) and a satellite pixel, and describes a site's spatial representativeness. Based on the temporal variation of LST, the SRI time series consists of three temporal components: ∆ATC, ∆DTCF-P, and ∆USC, which describe the annual, diurnal, and instantaneous variations of SRI, respectively. Associated with the Landsat TM/ETM+ data and weather parameters, the method is implemented and tested at 16 Chinese ground sites for the validation of MODIS and AATSR LST products. Results show that the temporally continuous SRI (SRITPR) shows high correlations with the original SRI (SRIORI). The variation of SRITPR is mainly determined by changes in surface coverage (i.e. NDVI difference on the two scales) and affected by weather conditions (e.g. near-surface air temperature, accumulative downward solar radiation, and wind speed). Since the SRI is defined as the LST difference between the two scales, it can be used as a bridge to convert the in-situ LST to pixel scale to address the spatial scale mismatch in LST validation. With this idea, the in-situ LST at daytime was converted to pixel scale associated with the SRITPR, and the corresponding MODIS and AATSR LST were validated at the 16 ground sites. Results for MODIS and AATSR LST show that the effect of spatial representativeness on the validation results over the sites is large, with mean biases between −1.95 K and 5.60 K and standard deviations between 0.07 K and 3.72 K. Since the TVM method does not rely on a specific satellite or land surface product, it is readily applied to other LST products (e.g. Sentinel-3 SLSTR LST, NOAA VIIRS LST) and surface parameters (e.g. surface longwave radiation).
Jin Ma; Ji Zhou; Shaomin Liu; Frank-Michael Göttsche; Xiaodong Zhang; Shaofei Wang; Mingsong Li. Continuous evaluation of the spatial representativeness of land surface temperature validation sites. Remote Sensing of Environment 2021, 265, 112669 .
AMA StyleJin Ma, Ji Zhou, Shaomin Liu, Frank-Michael Göttsche, Xiaodong Zhang, Shaofei Wang, Mingsong Li. Continuous evaluation of the spatial representativeness of land surface temperature validation sites. Remote Sensing of Environment. 2021; 265 ():112669.
Chicago/Turabian StyleJin Ma; Ji Zhou; Shaomin Liu; Frank-Michael Göttsche; Xiaodong Zhang; Shaofei Wang; Mingsong Li. 2021. "Continuous evaluation of the spatial representativeness of land surface temperature validation sites." Remote Sensing of Environment 265, no. : 112669.
Unmanned aerial vehicle (UAV) thermal infrared (TIR) remote sensing is an important way to obtain land surface temperature (LST) with high spatial and temporal resolutions. Due to wide spectral response function (SRF) ranges of UAV thermal imagers, currently available LST retrieval methods suitable for satellite sensors may induce significant uncertainty when applied to UAV sensors. Despite that some methods have been proposed to retrieve LST from UAV remote sensing, studies considering the adverse effect caused by the SRF ranges are still rare. Here, we present a so-called Temperature Retrieval for UAV Broadband thermal imager data (TRUB) method to retrieve LST from UAV broadband thermal imager data. TRUB's core includes two parts: 1) a simple lookup table (LUT) algorithm for reducing the uncertainty induced by the wide SRF ranges; and 2) models suitable for UAV remote sensing for estimating the atmospheric parameters. Validation from the Heihe River Basin shows that the LST retrieved by TRUB, of which the root mean square error (RMSE) and mean bias error (MBE) is 1.71 and -0.02 K, respectively, is highly consistent with the in situ LST. TRUB is helpful to reduce the uncertainty caused by the wide SRF ranges of UAV thermal imagers and quantify the influence of atmosphere, thus can obtain UAV remote-sensing LST with better accuracy in large-area operating missions.
Ziwei Wang; Ji Zhou; Shaomin Liu; Mingsong Li; Xiaodong Zhang; Zhiming Huang; Weichen Dong; Jin Ma; Lijiao Ai. A Land Surface Temperature Retrieval Method for UAV Broadband Thermal Imager Data. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleZiwei Wang, Ji Zhou, Shaomin Liu, Mingsong Li, Xiaodong Zhang, Zhiming Huang, Weichen Dong, Jin Ma, Lijiao Ai. A Land Surface Temperature Retrieval Method for UAV Broadband Thermal Imager Data. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleZiwei Wang; Ji Zhou; Shaomin Liu; Mingsong Li; Xiaodong Zhang; Zhiming Huang; Weichen Dong; Jin Ma; Lijiao Ai. 2021. "A Land Surface Temperature Retrieval Method for UAV Broadband Thermal Imager Data." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
Automatically registration of unmanned aerial vehicle (UAV) multispectral images is fundamental for subsequent applications. Although many studies exist for visible camera images and satellite multispectral image registration, studies for UAV are still rare. Under this context, this study firstly evaluates the performance of several widely used traditional methods (i.e., SIFT, SURF, ORB, and CFOG) for visible camera and satellite images in UAV multispectral image registration. This study further proposes an unsupervised and end-to-end deep learning network (i.e., DSIM) for multispectral image registration. An evident feature of DSIM is to regress the homography parameters with convolutional neural networks and to use the pyramid structural similarity loss to optimize the network. 1200 groups of UAV multispectral images acquired over three different sites in four months are used to comprehensively test the aforementioned five methods. Results show that CFOG achieves the highest correct matching rate in the test set, followed by DSIM and SIFT. Nevertheless, DSIM is more robust in images with weak or repeated texture than CFOG and SIFT. In addition, performance of CFOG and SIFT is closely related to the number of the found matching points. Based on the findings, we propose a multi-method ensemble strategy to combine CFOG, DSIM, and SIFT according to the number of the found matching points. This strategy outperforms the individual methods with a correct matching rate of 96.2%. Lower correct matching rate of CFOG + SIFT confirms that DSIM and traditional methods are very complementary in UAV multispectral image registrations.
Lingxuan Meng; Ji Zhou; Shaomin Liu; Lirong Ding; Jirong Zhang; Shaofei Wang; Tianjie Lei. Investigation and evaluation of algorithms for unmanned aerial vehicle multispectral image registration. International Journal of Applied Earth Observation and Geoinformation 2021, 102, 102403 .
AMA StyleLingxuan Meng, Ji Zhou, Shaomin Liu, Lirong Ding, Jirong Zhang, Shaofei Wang, Tianjie Lei. Investigation and evaluation of algorithms for unmanned aerial vehicle multispectral image registration. International Journal of Applied Earth Observation and Geoinformation. 2021; 102 ():102403.
Chicago/Turabian StyleLingxuan Meng; Ji Zhou; Shaomin Liu; Lirong Ding; Jirong Zhang; Shaofei Wang; Tianjie Lei. 2021. "Investigation and evaluation of algorithms for unmanned aerial vehicle multispectral image registration." International Journal of Applied Earth Observation and Geoinformation 102, no. : 102403.
Estimating future temporal patterns of Surface Urban Heat Islands (SUHIs) on multiple time scales is an ongoing research endeavor. Among these time scales, estimation of next-day SUHIs is of special significance to urban residents, yet we currently lack a simple but efficient approach for making such estimations. In the present study, we propose a statistical strategy for estimating next-day nighttime SUHIs, based on incorporating various SUHI controls into a support vector machine regression (SVR) model. The majority of both the surface controls (including factors related to land cover and solar radiation) and meteorological controls (including temperature fluctuations, relative humidity, accumulated precipitation, wind speed, aerosol optical depth, and soil moisture) that have previously been found to account for daily SUHI variations were used as estimators, and we provide estimations for both the overall SUHI intensity (SUHII) and pixel-by-pixel Gaussian-based LSTs over 59 Chinese megacities. For the overall SUHII, the mean absolute error (MAE) is 0.67 K on average, and the mean absolute percentage error (MAPE) is no more than 25% for more than 90% of the cities. For the pixel-by-pixel LSTs, the associated MAE is less than 2.0 K in most scenarios. In addition, the contribution from each selected estimator to SUHII estimation is assessed comprehensively. Among all the estimators, the contribution from relative humidity is the greatest, followed by rural surface temperature and surface air temperature. Moreover, for nearly 78% of the cities, the estimators related to day-to-day SUHI variations make a larger contribution than those related to intra-annual SUHI variations. We conclude that our simple yet effective statistical approach for estimating next-day SUHIs can potentially help urban residents to better adapt to urban heat stress.
Jiameng Lai; Wenfeng Zhan; Jinling Quan; Benjamin Bechtel; Kaicun Wang; Ji Zhou; Fan Huang; Tirthankar Chakraborty; Zihan Liu; Xuhui Lee. Statistical estimation of next-day nighttime surface urban heat islands. ISPRS Journal of Photogrammetry and Remote Sensing 2021, 176, 182 -195.
AMA StyleJiameng Lai, Wenfeng Zhan, Jinling Quan, Benjamin Bechtel, Kaicun Wang, Ji Zhou, Fan Huang, Tirthankar Chakraborty, Zihan Liu, Xuhui Lee. Statistical estimation of next-day nighttime surface urban heat islands. ISPRS Journal of Photogrammetry and Remote Sensing. 2021; 176 ():182-195.
Chicago/Turabian StyleJiameng Lai; Wenfeng Zhan; Jinling Quan; Benjamin Bechtel; Kaicun Wang; Ji Zhou; Fan Huang; Tirthankar Chakraborty; Zihan Liu; Xuhui Lee. 2021. "Statistical estimation of next-day nighttime surface urban heat islands." ISPRS Journal of Photogrammetry and Remote Sensing 176, no. : 182-195.
An all-weather land surface temperature (LST) dataset at moderate to high spatial resolutions (e.g. 1 km) has been in urgent need, especially in areas frequently covered in clouds (i.e. the Tibetan Plateau). Merging satellite thermal infrared (TIR) and passive microwave (PMW) observations is a widely-adopted approach to derive such LST datasets, whereas the swath gap of the PMW data leads to considerable data deficiency or low reliability in the merged LST, especially at the low-mid latitudes. Fortunately, reanalyzed data provides the spatiotemporally continuous LST and thus, is promising to be merged with TIR data for reconstructing the all-weather LST without this issue. However, few studies along this direction have been reported. In this context, based on the decomposition model of LST time series, this study proposes a novel reanalysis and thermal infrared remote sensing data merging (RTM) method to reconstruct the 1-km all-weather LST. The method was applied to merge Aqua/Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and Global/China Land Data Assimilation System (GLDAS/CLDAS) data over the Tibetan Plateau and the surrounding area. Results show that the RTM LST has RMSEs of 2.03–3.98 K and coefficients of determination of 0.82–0.93 under all-weather conditions when validated against the ground measured LST. Besides, from comparison between RTM LST and current existing PMW-TIR merged LST, it is found the former LST efficiently outperforms the latter one in terms of accuracy and image quality, especially over the MW swath gap-covered area. In addition, compared to the MODIS-CLDAS merged all-weather LST based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the two LSTs have comparable accuracy while the RTM LST has higher spatial completeness. The method is promising for generating a long-term all-weather LST record at moderate to high spatiotemporal resolutions at large scales, which would be beneficial to associated studies and applications.
Xiaodong Zhang; Ji Zhou; Shunlin Liang; Dongdong Wang. A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature. Remote Sensing of Environment 2021, 260, 112437 .
AMA StyleXiaodong Zhang, Ji Zhou, Shunlin Liang, Dongdong Wang. A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature. Remote Sensing of Environment. 2021; 260 ():112437.
Chicago/Turabian StyleXiaodong Zhang; Ji Zhou; Shunlin Liang; Dongdong Wang. 2021. "A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature." Remote Sensing of Environment 260, no. : 112437.
A widely used approach for all-weather land surface temperature (LST) estimation is the integration of satellite passive microwave (MW) and thermal infrared (TIR) remote sensing observations. However, there are still few methods for estimating near-real time (NRT) all-weather (AW) (NRT-AW) LST. Besides, estimation of the LST within the swath gap of the satellite MW images is still greatly limited. This letter proposes a so-called NRT-AW method for the estimation of NRT-AW LST. NRT-AW firstly fills up the brightness temperatures (BT) inside the AMSR2 swath gap. Then, the NRT AW LST is estimated by learning the mapping between the time series of AMSR2 BT and MODIS LST on the annual scales. The results of the application of NRT-AW in the Heihe River Basin (HRB) show that the NRT-AW LST is spatially continuous and highly consistent with the original MODIS LST with a standard deviation (STD) of 1.27-1.77 K. Validation based on in situ LST indicates that the NRT-AW LST estimate has a root mean square error (RMSE) of 2.46-4.62 K. This method is beneficial for rapid mapping of all-weather LST over large areas and, thus, can satisfy associated applications.
Wenbin Tang; Dongjian Xue; Zhiyong Long; Xiaodong Zhang; Ji Zhou. Near-Real-Time Estimation of 1-km All-Weather Land Surface Temperature by Integrating Satellite Passive Microwave and Thermal Infrared Observations. IEEE Geoscience and Remote Sensing Letters 2021, PP, 1 -5.
AMA StyleWenbin Tang, Dongjian Xue, Zhiyong Long, Xiaodong Zhang, Ji Zhou. Near-Real-Time Estimation of 1-km All-Weather Land Surface Temperature by Integrating Satellite Passive Microwave and Thermal Infrared Observations. IEEE Geoscience and Remote Sensing Letters. 2021; PP (99):1-5.
Chicago/Turabian StyleWenbin Tang; Dongjian Xue; Zhiyong Long; Xiaodong Zhang; Ji Zhou. 2021. "Near-Real-Time Estimation of 1-km All-Weather Land Surface Temperature by Integrating Satellite Passive Microwave and Thermal Infrared Observations." IEEE Geoscience and Remote Sensing Letters PP, no. 99: 1-5.
Cross-view image matching has attracted extensive attention due to its huge potential applications, such as localization and navigation. Unmanned aerial vehicle (UAV) technology has been developed rapidly in recent years, and people have more opportunities to obtain and use UAV-view images than ever before. However, the algorithms of cross-view image matching between the UAV view (oblique view) and the satellite view (vertical view) are still in their beginning stage, and the matching accuracy is expected to be further improved when applied in real situations. Within this context, in this study, we proposed a cross-view matching method based on location classification (hereinafter referred to LCM), in which the similarity between UAV and satellite views is considered, and we implemented the method with the newest UAV-based geo-localization dataset (University-1652). LCM is able to solve the imbalance of the input sample number between the satellite images and the UAV images. In the training stage, LCM can simplify the retrieval problem into a classification problem and consider the influence of the feature vector size on the matching accuracy. Compared with one study, LCM shows higher accuracies, and [email protected] (K{1, 5, 10}) and the average precision (AP) were improved by 5–10%. The expansion of satellite-view images and multiple queries proposed by the LCM are capable of improving the matching accuracy during the experiment. In addition, the influences of different feature sizes on the LCM’s accuracy are determined, and we found that 512 is the optimal feature size. Finally, the LCM model trained based on synthetic UAV-view images was evaluated in real-world situations, and the evaluation result shows that it still has satisfactory matching accuracy. The LCM can realize the bidirectional matching between the UAV-view image and the satellite-view image and can contribute to two applications: (i) UAV-view image localization (i.e., predicting the geographic location of UAV-view images based on satellite-view images with geo-tags) and (ii) UAV navigation (i.e., driving the UAV to the region of interest in the satellite-view image based on the flight record).
Lirong Ding; Ji Zhou; Lingxuan Meng; Zhiyong Long. A Practical Cross-View Image Matching Method between UAV and Satellite for UAV-Based Geo-Localization. Remote Sensing 2020, 13, 47 .
AMA StyleLirong Ding, Ji Zhou, Lingxuan Meng, Zhiyong Long. A Practical Cross-View Image Matching Method between UAV and Satellite for UAV-Based Geo-Localization. Remote Sensing. 2020; 13 (1):47.
Chicago/Turabian StyleLirong Ding; Ji Zhou; Lingxuan Meng; Zhiyong Long. 2020. "A Practical Cross-View Image Matching Method between UAV and Satellite for UAV-Based Geo-Localization." Remote Sensing 13, no. 1: 47.
Land surface temperature (LST) plays an important role in the research of climate change and various land surface processes. Before 2000, global LST products with relatively high temporal and spatial resolutions are scarce, despite a variety of operational satellite LST products. In this study, a global 0.05∘×0.05∘ historical LST product is generated from NOAA advanced very-high-resolution radiometer (AVHRR) data (1981–2000), which includes three data layers: (1) instantaneous LST, a product generated by integrating several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST; and (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapor content of 0.04 and 1.0 g cm−2, respectively, evaluated against the simulation dataset, the RF-SWA method has a mean bias error (MBE) of less than 0.10 K and a standard deviation (SD) of 1.10 K. To compensate for the influence of orbital drift on LST, the retrieved RF-SWA LST was normalized with an improved ODC method. The RF-SWA LST were validated with in situ LST from Surface Radiation Budget (SURFRAD) sites and water temperatures obtained from the National Data Buoy Center (NDBC). Against the in situ LST, the RF-SWA LST has a MBE of 0.03 K with a range of −1.59–2.71 K, and SD is 1.18 K with a range of 0.84–2.76 K. Since water temperature only changes slowly, the validation of ODC LST was limited to SURFRAD sites, for which the MBE is 0.54 K with a range of −1.05 to 3.01 K and SD is 3.57 K with a range of 2.34 to 3.69 K, indicating good product accuracy. As global historical datasets, the new AVHRR LST products are useful for filling the gaps in long-term LST data. Furthermore, the new LST products can be used as input to related land surface models and environmental applications. Furthermore, in support of the scientific research community, the datasets are freely available at https://doi.org/10.5281/zenodo.3934354 for RF-SWA LST (Ma et al., 2020a), https://doi.org/10.5281/zenodo.3936627 for ODC LST (Ma et al., 2020c), and https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b).
Jin Ma; Ji Zhou; Frank-Michael Göttsche; Shunlin Liang; Shaofei Wang; Mingsong Li. A global long-term (1981–2000) land surface temperature product for NOAA AVHRR. Earth System Science Data 2020, 12, 3247 -3268.
AMA StyleJin Ma, Ji Zhou, Frank-Michael Göttsche, Shunlin Liang, Shaofei Wang, Mingsong Li. A global long-term (1981–2000) land surface temperature product for NOAA AVHRR. Earth System Science Data. 2020; 12 (4):3247-3268.
Chicago/Turabian StyleJin Ma; Ji Zhou; Frank-Michael Göttsche; Shunlin Liang; Shaofei Wang; Mingsong Li. 2020. "A global long-term (1981–2000) land surface temperature product for NOAA AVHRR." Earth System Science Data 12, no. 4: 3247-3268.
Most previous studies of surface urban heat islands (SUHIs) have focused solely on their controlling factors on a seasonal/annual timescale, while the controls on daily variations are largely unknown. By extracting the daily variations of nighttime SUHI features using the Gaussian model and investigating their correlations with various explanatory factors, we have attempted to determine the controls on SUHIs on a daily-basis over Chinese cities. Specific controls of weather conditions on the intensity, extent, shape, and centroid of the SUHIs were identified. Our results show that: (1) SUHI intensity (SUHII) was considerably more sensitive to weather conditions than the SUHI footprint (i.e., extent, shape, and centroid). (2) Meteorological variables including relative humidity, accumulated precipitation, and aerosol optical depth, had the greatest impact on SUHI intensity; whereas factors related to temperature fluctuations (day-to-day fluctuations of surface and air temperature) were the main factors influencing SUHI extent, shape, and the direction in which SUHI centroid varies. (3) Antecedent precipitation substantially impacted the subsequent SUHIs under clear-skies, changing both the SUHI itself and its sensitivity to other factors. Typically, the clear-sky SUHIs directly following rainfall showed a higher dependence on the relative humidity, soil moisture and aerosol, but were less affected by wind. (4) The meteorological contributions to the daily nighttime SUHIIs varied among Chinese cities with different bioclimatic conditions. In general, they were stronger in temperate zones than in subtropical zones. Our results provide an improved understanding of the controls on SUHIs on a daily timescale, as well as a foundation for predicting daily SUHIs based on the influencing meteorological variables.
Jiameng Lai; Wenfeng Zhan; James Voogt; Jinling Quan; Fan Huang; Ji Zhou; Benjamin Bechtel; Leiqiu Hu; Kaicun Wang; Chang Cao; Xuhui Lee. Meteorological controls on daily variations of nighttime surface urban heat islands. Remote Sensing of Environment 2020, 253, 112198 .
AMA StyleJiameng Lai, Wenfeng Zhan, James Voogt, Jinling Quan, Fan Huang, Ji Zhou, Benjamin Bechtel, Leiqiu Hu, Kaicun Wang, Chang Cao, Xuhui Lee. Meteorological controls on daily variations of nighttime surface urban heat islands. Remote Sensing of Environment. 2020; 253 ():112198.
Chicago/Turabian StyleJiameng Lai; Wenfeng Zhan; James Voogt; Jinling Quan; Fan Huang; Ji Zhou; Benjamin Bechtel; Leiqiu Hu; Kaicun Wang; Chang Cao; Xuhui Lee. 2020. "Meteorological controls on daily variations of nighttime surface urban heat islands." Remote Sensing of Environment 253, no. : 112198.
Neural networks, especially the latest deep learning, have exhibited good ability in estimating surface parameters from satellite remote sensing. However, thorough examinations of neural networks in the estimation of land surface temperature (LST) from satellite passive microwave (MW) observations are still lacking. Here, we examined the performances of the traditional neural network (NN), deep belief network (DBN), and convolutional neural network (CNN) in estimating LST from the AMSR-E and AMSR2 data over the Chinese landmass. The examinations were based on the same training set, validation set, and test set extracted from 2003, 2004, and 2009, respectively, for AMSR-E with a spatial resolution of 0.25°. For AMSR2, the three sets were extracted from 2013, 2014, and 2016 with a spatial resolution of 0.1°, respectively. MODIS LST played the role of “ground truth” in the training, validation, and testing. The examination results show that CNN is better than NN and DBN by 0.1–0.4 K. Different combinations of input parameters were examined to get the best combinations for the daytime and nighttime conditions. The best combinations are the brightness temperatures (BTs), NDVI, air temperature, and day of the year (DOY) for the daytime and BTs and air temperature for the nighttime. By adding three and one easily obtained parameters on the basis of BTs, the accuracies of LST estimates can be improved by 0.8 K and 0.3 K for the daytime and nighttime conditions, respectively. Compared with the MODIS LST, the CNN LST estimates yielded root-mean-square differences (RMSDs) of 2.19–3.58 K for the daytime and 1.43–2.14 K for the nighttime for diverse land cover types for AMSR-E. Validation against the in-situ LSTs showed that the CNN LSTs yielded root-mean-square errors of 2.10–4.72 K for forest and cropland sites. Further intercomparison indicated that ~50% of the CNN LSTs were closer to the MODIS LSTs than ESA’s GlobTemperature AMSR-E LSTs, and the average RMSDs of the CNN LSTs were less than 3 K over dense vegetation compared to NASA’s global land parameter data record air temperatures. This study helps better the understanding of the use of neural networks for estimating LST from satellite MW observations.
Shaofei Wang; Ji Zhou; Tianjie Lei; Hua Wu; Xiaodong Zhang; Jin Ma; Hailing Zhong. Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network. Remote Sensing 2020, 12, 2691 .
AMA StyleShaofei Wang, Ji Zhou, Tianjie Lei, Hua Wu, Xiaodong Zhang, Jin Ma, Hailing Zhong. Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network. Remote Sensing. 2020; 12 (17):2691.
Chicago/Turabian StyleShaofei Wang; Ji Zhou; Tianjie Lei; Hua Wu; Xiaodong Zhang; Jin Ma; Hailing Zhong. 2020. "Estimating Land Surface Temperature from Satellite Passive Microwave Observations with the Traditional Neural Network, Deep Belief Network, and Convolutional Neural Network." Remote Sensing 12, no. 17: 2691.
All-weather remotely sensed land surface temperature (LST) with a 1-km resolution from combined satellite passive microwave (MW) and thermal infrared (TIR) remote sensing data has been urgently needed during the past decades. However, due to considerable temporal gap between AMSR-E and AMSR2 observation from November 2011 to May 2012, current MODIS-AMSR-E/2 integrated LST is not really all-weather available. Therefore, an AMSR-E/2-like brightness temperature (BT) without the temporal gap for 2011–2012 is highly desirable. Despite the Chinese Fengyun-3B MWRI BT is qualified to reconstruct such a BT, the swath gap in its BT has to be effectively filled as the gap not only greatly decreases the spatiotemporal coverage of the TIR-MW integrated LST but also limits the application of satellite MW BT data. However, the gap issue has not been effectively addressed by previous research. In this context, this study proposes an novel method to (i) reconstruct a spatial-seamless (i.e. without the two gaps) AMSR-E/2-like MW BT based on MWRI data for 2011–2012 over the Tibetan Plateau and (ii) estimate a realistic 1-km all-weather LST by integrating reconstructed MW BT with Aqua-MODIS data. Results show that the reconstructed MW BT is spatiotemporally continuous and has a high accuracy with a root-mean-square error (RMSE) of 0.89–2.61 K compared to original AMSR-E/2 BT. This exhibits the method’s potential to greatly extend the spatiotemporal coverage of currently available MW BT-based remote sensing data. In addition, the estimated LST has an RMSE of 1.45–3.36 K when validated against the ground measurements, which outperforms current TIR-MW integrated LST products. Therefore, this study would be valuable for facilitating satellite MW data and generating a realistic and reliable 1-km all-weather remotely sensed LST at large scales.
Xiaodong Zhang; Ji Zhou; Shunlin Liang; Linna Chai; Dongdong Wang; Jin Liu. Estimation of 1-km all-weather remotely sensed land surface temperature based on reconstructed spatial-seamless satellite passive microwave brightness temperature and thermal infrared data. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 167, 321 -344.
AMA StyleXiaodong Zhang, Ji Zhou, Shunlin Liang, Linna Chai, Dongdong Wang, Jin Liu. Estimation of 1-km all-weather remotely sensed land surface temperature based on reconstructed spatial-seamless satellite passive microwave brightness temperature and thermal infrared data. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 167 ():321-344.
Chicago/Turabian StyleXiaodong Zhang; Ji Zhou; Shunlin Liang; Linna Chai; Dongdong Wang; Jin Liu. 2020. "Estimation of 1-km all-weather remotely sensed land surface temperature based on reconstructed spatial-seamless satellite passive microwave brightness temperature and thermal infrared data." ISPRS Journal of Photogrammetry and Remote Sensing 167, no. : 321-344.
Land surface temperature (LST) is an important indicator of global ecological environment and climate change. The Sea and Land Surface Temperature Radiometer (SLSTR) onboard the recently launched Sentinel-3 satellites provides high-quality observations for estimating global LST. The algorithm of the official SLSTR LST product is a split-window algorithm (SWA) that implicitly assumes and utilizes knowledge of land surface emissivity (LSE). The main objective of this study is to investigate alternative SLSTR LST retrieval algorithms with an explicit use of LSE. Seventeen widely accepted SWAs, which explicitly utilize LSE, were selected as candidate algorithms. First, the SWAs were trained using a comprehensive global simulation dataset. Then, using simulation data as well as in-situ LST, the SWAs were evaluated according to their sensitivity and accuracy: eleven algorithms showed good training accuracy and nine of them exhibited low sensitivity to uncertainties in LSE and column water vapor content. Evaluation based on two global simulation datasets and a regional simulation dataset showed that these nine SWAs had similar accuracy with negligible systematic errors and RMSEs lower than 1.0 K. Validation based on in-situ LST obtained for six sites further confirmed the similar accuracies of the SWAs, with the lowest RMSE ranges of 1.57–1.62 K and 0.49−0.61 K for Gobabeb and Lake Constance, respectively. While the best two SWAs usually yielded good accuracy, the official SLSTR LST generally had lower accuracy. The SWAs identified and described in this study may serve as alternative algorithms for retrieving LST products from SLSTR data.
Jiajia Yang; Ji Zhou; Frank-Michael Göttsche; Zhiyong Long; Jin Ma; Ren Luo. Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data. International Journal of Applied Earth Observation and Geoinformation 2020, 91, 102136 .
AMA StyleJiajia Yang, Ji Zhou, Frank-Michael Göttsche, Zhiyong Long, Jin Ma, Ren Luo. Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data. International Journal of Applied Earth Observation and Geoinformation. 2020; 91 ():102136.
Chicago/Turabian StyleJiajia Yang; Ji Zhou; Frank-Michael Göttsche; Zhiyong Long; Jin Ma; Ren Luo. 2020. "Investigation and validation of algorithms for estimating land surface temperature from Sentinel-3 SLSTR data." International Journal of Applied Earth Observation and Geoinformation 91, no. : 102136.
The Heihe River Basin (HRB), with unique landscapes and coexisting cold and arid regions, is the second largest endorheic basin in northwestern China. The Heihe integrated observatory network was established in 2007, which included multi-element, multiscale, distributed, and incorporating satellite-airborne-ground based observations via Internet of Things technology; these observations span the main land cover of the basin. This observatory provides a great opportunity to analyze the spatiotemporal variation in evapotranspiration (ET) in the HRB, and ET characteristics were investigated on three scales (typical ecosystems, oasis-desert systems, watershed) taking the HRB as a research object. The average annual ET in typical ecosystems is approximately 380–530 mm upstream (alpine meadow, Qinghai spruce, shrub), 640–1000 mm midstream (maize, wetland), 610–680 mm downstream (riparian forest), and 190 mm and 50 mm in midstream and downstream desert surfaces, respectively. The ET from plant surfaces is strongly controlled by available energy in upstream and midstream regions, while it is controlled by vapor pressure deficit (VPD) and surface conductance downstream. The ET in oasis and desert systems is characterized by three gradients: plant, residential area/barren land, and desert, with a maximum difference of annual ET more than 500 mm. This difference is primarily caused by variations of soil moisture among different underlying surfaces. For watershed ET, higher ET was observed upstream, and it decreased from midstream to downstream, with the highest values in the oasis. The annual ET in the main plant surfaces was approximately 500–700 mm, 600–800 mm, and 600–700 mm in the up-, mid-, and downstream regions, respectively, while the ET was approximately 100–250 mm and 50–200 mm in desert and barren or sparsely vegetation surfaces in the mid- and downstream regions, respectively. The spatiotemporal variations of ET were primarily influenced by land cover, soil moisture, vegetation condition and available energy. The results improve our understanding of the spatiotemporal variations in ET in the HRB and apply to comparable endorheic basins with similar climatic and landscape conditions.
Ziwei Xu; Shaomin Liu; Zhongli Zhu; Ji Zhou; Wenjiao Shi; Tongren Xu; Xiaofan Yang; Yuan Zhang; Xinlei He. Exploring evapotranspiration changes in a typical endorheic basin through the integrated observatory network. Agricultural and Forest Meteorology 2020, 290, 108010 .
AMA StyleZiwei Xu, Shaomin Liu, Zhongli Zhu, Ji Zhou, Wenjiao Shi, Tongren Xu, Xiaofan Yang, Yuan Zhang, Xinlei He. Exploring evapotranspiration changes in a typical endorheic basin through the integrated observatory network. Agricultural and Forest Meteorology. 2020; 290 ():108010.
Chicago/Turabian StyleZiwei Xu; Shaomin Liu; Zhongli Zhu; Ji Zhou; Wenjiao Shi; Tongren Xu; Xiaofan Yang; Yuan Zhang; Xinlei He. 2020. "Exploring evapotranspiration changes in a typical endorheic basin through the integrated observatory network." Agricultural and Forest Meteorology 290, no. : 108010.
Accurate estimation of surface evapotranspiration (ET) with high quality and fine spatiotemporal resolution is one of the biggest obstacles for routine applications of remote sensing in eco-hydrological studies and water resource management at basin scale. Integrating multi-source remote sensing data is one of the main ideas for many scholars to obtain synthesized frequent high spatial resolution surface ET. This study was based on the theoretically robust surface energy balance system (SEBS) model, which the model mechanism needs further investigation, including the applicability and the influencing factors, such as local environment, heterogeneity of the landscape, and optimized parametric scheme, for improving estimation accuracy. In addition, due to technical and budget limitations, so far, no single sensor provides both high spatial resolution and high temporal resolution. Optical remote sensing data is missing due to frequent cloud contamination and other poor atmospheric conditions. The passive microwave (PW) remote sensing has a better ability in overcoming the influences of clouds and rainy. The accurate "all-weather" ET estimation method had been proposed through blending multi-source remote sensing data acquired by optical, thermal infrared (TIR) and PW remote sensors on board polar satellite platforms. The estimation had been carried out for daily ET of the River Source Region in Southwest China, and then the "All-weather" remotely sensed ET results showed that the daily ET estimates had a mean absolute percent error (MAPE) of 36% and a root mean square error (RMSE) of 0.88 mm/day relative to ground measurements from 12 eddy covariance (EC) sites in the study area. The validation results indicated good accuracy using multi-source remote sensing data in cloudy and mountainous regions.
Yanfei Ma; Ji Zhou; Shaomin Liu. Monitoring of "All-weather" Evapotranspiration Using Multi-source Remote Sensing Imagery in Cloudy and Mountainous Regions in Southwest China. 2020, 1 .
AMA StyleYanfei Ma, Ji Zhou, Shaomin Liu. Monitoring of "All-weather" Evapotranspiration Using Multi-source Remote Sensing Imagery in Cloudy and Mountainous Regions in Southwest China. . 2020; ():1.
Chicago/Turabian StyleYanfei Ma; Ji Zhou; Shaomin Liu. 2020. "Monitoring of "All-weather" Evapotranspiration Using Multi-source Remote Sensing Imagery in Cloudy and Mountainous Regions in Southwest China." , no. : 1.
Spatiotemporal data fusion is a methodology to generate images with both high spatial and temporal resolution. Most spatiotemporal data fusion methods generate the fused image at a prediction date based on pairs of input images from other dates. The performance of spatiotemporal data fusion is greatly affected by the selection of the input image pair. There are two criteria for selecting the input image pair: the ``similarity'' criterion, in which the image at the base date should be as similar as possible to that at the prediction date, and the ``consistency'' criterion, in which the coarse and fine images at the base date should be consistent in terms of their radiometric characteristics and imaging geometry. Unfortunately, the ``consistency'' criterion has not been quantitatively considered by previous selection strategies. We thus develop a novel method (called ``cross-fusion'') to address the issue of the determination of the base image pair. The new method first chooses several candidate input image pairs according to the ``similarity'' criterion and then takes the ``consistency'' criterion into account by employing all of the candidate input image pairs to implement spatiotemporal data fusion between them. We applied the new method to MODIS-Landsat Normalized Difference Vegetation Index (NDVI) data fusion. The results show that the cross-fusion method performs better than four other selection strategies, with lower average absolute difference (AAD) values and higher correlation coefficients in various vegetated regions including a deciduous forest in Northeast China, an evergreen forest in South China, cropland in North China Plain, and grassland in the Tibetan Plateau. We simulated scenarios for the inconsistency between MODIS and Landsat data and found that the simulated inconsistency is successfully quantified by the new method. In addition, the cross-fusion method is less affected by cloud omission errors. The fused NDVI time-series data generated by the new method tracked various vegetation growth trajectories better than previous selection strategies. We expect that the cross-fusion method can advance practical applications of spatiotemporal data fusion technology.
Yang Chen; Ruyin Cao; Jin Chen; Xiaolin Zhu; Ji Zhou; Guangpeng Wang; Miaogen Shen; Xuehong Chen; Wei Yang. A New Cross-Fusion Method to Automatically Determine the Optimal Input Image Pairs for NDVI Spatiotemporal Data Fusion. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 5179 -5194.
AMA StyleYang Chen, Ruyin Cao, Jin Chen, Xiaolin Zhu, Ji Zhou, Guangpeng Wang, Miaogen Shen, Xuehong Chen, Wei Yang. A New Cross-Fusion Method to Automatically Determine the Optimal Input Image Pairs for NDVI Spatiotemporal Data Fusion. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (7):5179-5194.
Chicago/Turabian StyleYang Chen; Ruyin Cao; Jin Chen; Xiaolin Zhu; Ji Zhou; Guangpeng Wang; Miaogen Shen; Xuehong Chen; Wei Yang. 2020. "A New Cross-Fusion Method to Automatically Determine the Optimal Input Image Pairs for NDVI Spatiotemporal Data Fusion." IEEE Transactions on Geoscience and Remote Sensing 58, no. 7: 5179-5194.
Vegetation green-up date (GUD), an important phenological characteristic, is usually estimated from time-series of satellite-based normalized difference vegetation index (NDVI) data at regional and global scales. However, GUD estimates in seasonally snow-covered areas suffer from the effect of spring snowmelt on the NDVI signal, hampering our realistic understanding of phenological responses to climate change. Recently, two snow-free vegetation indices were developed for GUD detection: the normalized difference phenology index (NDPI) and normalized difference greenness index (NDGI). Both were found to improve GUD detection in the presence of spring snowmelt. However, these indices were tested at several field phenological camera sites and carbon flux sites, and a detailed evaluation on their performances at the large spatial scale is still lacking, which limits their applications globally. In this study, we employed NDVI, NDPI, and NDGI to estimate GUD at northern middle and high latitudes (north of 40° N) and quantified the snowmelt-induced uncertainty of GUD estimations from the three vegetation indices (VIs) by considering the changes in VI values caused by snowmelt. Results showed that compared with NDVI, both NDPI and NDGI improve the accuracy of GUD estimation with smaller GUD uncertainty in the areas below 55° N, but at higher latitudes (55°N-70° N), all three indices exhibit substantially larger GUD uncertainty. Furthermore, selecting which vegetation index to use for GUD estimation depends on vegetation types. All three indices performed much better for deciduous forests, and NDPI performed especially well (5.1 days for GUD uncertainty). In the arid and semi-arid grasslands, GUD estimations from NDGI are more reliable (i.e., smaller uncertainty) than NDP-based GUD (e.g., GUD uncertainty values for NDGI vs. NDPI are 4.3 d vs. 7.2 d in Mongolia grassland and 6.7 d vs. 9.8 d in Central Asia grassland), whereas in American prairie, NDPI performs slightly better than NDGI (GUD uncertainty for NDPI vs. NDGI is 3.8 d vs. 4.7 d). In central and western Europe, reliable GUD estimations from NDPI and NDGI were acquired only in those years without snowfall before green-up. This study provides important insights into the application of, and uncertainty in, snow-free vegetation indices for GUD estimation at large spatial scales, particularly in areas with seasonal snow cover.
Ruyin Cao; Yan Feng; Xilong Liu; Miaogen Shen; Ji Zhou. Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes. Remote Sensing 2020, 12, 190 .
AMA StyleRuyin Cao, Yan Feng, Xilong Liu, Miaogen Shen, Ji Zhou. Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes. Remote Sensing. 2020; 12 (1):190.
Chicago/Turabian StyleRuyin Cao; Yan Feng; Xilong Liu; Miaogen Shen; Ji Zhou. 2020. "Uncertainty of Vegetation Green-Up Date Estimated from Vegetation Indices Due to Snowmelt at Northern Middle and High Latitudes." Remote Sensing 12, no. 1: 190.
Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety.
Lingxuan Meng; Zhixing Peng; Ji Zhou; Jirong Zhang; Zhenyu Lu; Andreas Baumann; Yan Du. Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety. Remote Sensing 2020, 12, 182 .
AMA StyleLingxuan Meng, Zhixing Peng, Ji Zhou, Jirong Zhang, Zhenyu Lu, Andreas Baumann, Yan Du. Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety. Remote Sensing. 2020; 12 (1):182.
Chicago/Turabian StyleLingxuan Meng; Zhixing Peng; Ji Zhou; Jirong Zhang; Zhenyu Lu; Andreas Baumann; Yan Du. 2020. "Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety." Remote Sensing 12, no. 1: 182.
Ground measured component radiative temperatures are basic inputs for modelling energy and hydrological processes and for simulating land surface temperature (LST) as “viewed” by remote sensors. However, knowledge of factors affecting the component temperatures and about their potential for upscaling LST over sparsely vegetated surfaces with high heterogeneity is still lacking. Here, a MUlti-Scale Observation Experiment on land Surface temperature (MUSOES) was performed under HiWATER over an arid sparsely vegetated surface. Component temperatures were obtained with different instruments on multiple spatial scales; for LST upscaling, a three-dimensional scene model was employed for two forest stations (MFS and PFS) and a two-dimensional model for a shrub station (SUP). Results show that intrinsic characteristics contribute to the temperature variability between different components and even within a single component. Using a thermal infrared (TIR) imager at MFS, average temperature difference of 24.9 K between sunlit bare soil and tree canopy was found; different components exhibit different internal temperature differences at direction-level and pixel-level. Furthermore, illumination conditions, viewing directions, and instrument types significantly affected the measured component temperatures. The measurements of the TIR radiometer and the imager can deviate considerably (e.g. 14.9 K for sunlit bare soil at MFS). When the longwave radiometers were selected as target sensors, the component temperatures measured by the imager exhibit good potential for LST upscaling: the upscaled LST has MBD/RMSD values of 2.0 K/2.3 K at MFS and 2.0 K/2.5 K at PFS. The TIR radiometer’s measurements introduce large uncertainties into LST upscaling at MFS and PFS, but result in good accuracy at SUP, mainly due to its simpler land cover and surface structure. Findings from this study can benefit our understanding of factors affecting observations of component temperatures and the LST upscaling process and are, therefore, relevant for further studying the evaluation of satellite LST products.
Mingsong Li; Ji Zhou; Zhixing Peng; Shaomin Liu; Frank-Michael Göttsche; Xiaodong Zhang; Lisheng Song. Component radiative temperatures over sparsely vegetated surfaces and their potential for upscaling land surface temperature. Agricultural and Forest Meteorology 2019, 276-277, 107600 .
AMA StyleMingsong Li, Ji Zhou, Zhixing Peng, Shaomin Liu, Frank-Michael Göttsche, Xiaodong Zhang, Lisheng Song. Component radiative temperatures over sparsely vegetated surfaces and their potential for upscaling land surface temperature. Agricultural and Forest Meteorology. 2019; 276-277 ():107600.
Chicago/Turabian StyleMingsong Li; Ji Zhou; Zhixing Peng; Shaomin Liu; Frank-Michael Göttsche; Xiaodong Zhang; Lisheng Song. 2019. "Component radiative temperatures over sparsely vegetated surfaces and their potential for upscaling land surface temperature." Agricultural and Forest Meteorology 276-277, no. : 107600.
Xiaodong Zhang; Ji Zhou; Frank-Michael Gottsche; Wenfeng Zhan; Shaomin Liu; Ruyin Cao. Correction to “A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations” [Feb 19 4670-4691]. IEEE Transactions on Geoscience and Remote Sensing 2019, 57, 6254 -6254.
AMA StyleXiaodong Zhang, Ji Zhou, Frank-Michael Gottsche, Wenfeng Zhan, Shaomin Liu, Ruyin Cao. Correction to “A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations” [Feb 19 4670-4691]. IEEE Transactions on Geoscience and Remote Sensing. 2019; 57 (8):6254-6254.
Chicago/Turabian StyleXiaodong Zhang; Ji Zhou; Frank-Michael Gottsche; Wenfeng Zhan; Shaomin Liu; Ruyin Cao. 2019. "Correction to “A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations” [Feb 19 4670-4691]." IEEE Transactions on Geoscience and Remote Sensing 57, no. 8: 6254-6254.
Xiaodong Zhang; Ji Zhou; Frank-Michael Gottsche; Wenfeng Zhan; Shaomin Liu; Ruyin Cao. A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations. IEEE Transactions on Geoscience and Remote Sensing 2019, 57, 4670 -4691.
AMA StyleXiaodong Zhang, Ji Zhou, Frank-Michael Gottsche, Wenfeng Zhan, Shaomin Liu, Ruyin Cao. A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations. IEEE Transactions on Geoscience and Remote Sensing. 2019; 57 (7):4670-4691.
Chicago/Turabian StyleXiaodong Zhang; Ji Zhou; Frank-Michael Gottsche; Wenfeng Zhan; Shaomin Liu; Ruyin Cao. 2019. "A Method Based on Temporal Component Decomposition for Estimating 1-km All-Weather Land Surface Temperature by Merging Satellite Thermal Infrared and Passive Microwave Observations." IEEE Transactions on Geoscience and Remote Sensing 57, no. 7: 4670-4691.