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Soil moisture downscaling has been extensively investigated in recent years to improve coarse resolution of SM products. However, available methods for downscaling are generally based on pixel-to-pixel strategy, which ignores the information among pixels. Hence, a new downscaling method based on convolutional neural network (CNN) is proposed to solve the problem. Furthermore, weight layer is designed for the input and residual SM is treated as the output of CNN to improve the accuracy. This method is applied to downscale SMAP SM products (i.e., 36-km L3_SM_P and 9-km L3_SM_P_E) from 1st January 2018 to 30th December 2018. Compared with 9-km L3_SM_P_E, the 9-km downscaling result is satisfactory with obtained R, RMSE, and ubRMSE values of 95.81%, 2.77%, and 2.67%, respectively. Moreover, SMAP SM products (36 and 9 km) and downscaling SM (3 and 1 km) are all validated by the in-situ data which are collected by the 109 stations of Oklahoma Mesonet (OKM) SM monitoring network. Mean R, RMSE, and ubRMSE values are 67.92%, 7.94%, and 4.87% for 36-km L3_SM_P; 67.78%, 8.35%, and 4.95% for 9-km L3_SM_P_E; 67.28%, 8.34%, 4.97% for 3-km downscaling SM; 65.90%, 8.40%, 5.18% for 1-km downscaling SM, respectively. The 3-km downscaling SM generated by this method can improve the coarse resolution of 9-km L3_SM_P_E while preserving its accuracy. However, error will remarkable increase in the 1-km downscaling SM. Therefore, the proposed method provides a new strategy for SM downscaling and obtains satisfactory results in practice. Additional studies can be conducted in the future.
Wei Xu; Zhaoxu Zhang; Zehao Long; Qiming Qin. Downscaling SMAP Soil Moisture Products With Convolutional Neural Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 4051 -4062.
AMA StyleWei Xu, Zhaoxu Zhang, Zehao Long, Qiming Qin. Downscaling SMAP Soil Moisture Products With Convolutional Neural Network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):4051-4062.
Chicago/Turabian StyleWei Xu; Zhaoxu Zhang; Zehao Long; Qiming Qin. 2021. "Downscaling SMAP Soil Moisture Products With Convolutional Neural Network." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 4051-4062.
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.
Yuanheng Sun; Qiming Qin; Huazhong Ren; Yao Zhang. Decameter Cropland LAI/FPAR Estimation From Sentinel-2 Imagery Using Google Earth Engine. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -14.
AMA StyleYuanheng Sun, Qiming Qin, Huazhong Ren, Yao Zhang. Decameter Cropland LAI/FPAR Estimation From Sentinel-2 Imagery Using Google Earth Engine. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-14.
Chicago/Turabian StyleYuanheng Sun; Qiming Qin; Huazhong Ren; Yao Zhang. 2021. "Decameter Cropland LAI/FPAR Estimation From Sentinel-2 Imagery Using Google Earth Engine." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.
The occurrence of drought is a complex process and is caused by the interaction of multiple drought-causing factors. The construction of traditional drought models and indexes seldom considers multiple drought-causing factors. This study integrated the precipitation, soil water and heat balance, crop growth during drought. From the beginning of the process of agricultural drought, the atmosphere, soil, and crops that characterize drought are considered, through the principal component analysis method to construct a comprehensive drought monitoring index (CDMI). This index was verified by using the areas covered by drought, areas affected by drought and relative soil moisture. The annual average CDMI had negative correlations with areas covered and affected by drought. The correlation coefficients were -0.5 and -0.7. Moreover, the CDMI value had positive correlations with relative soil moisture. The maximum correlation coefficient was 0.94. Subsequently, the CDMI was applied to long-term drought monitoring in agricultural areas during the summer maize growing season (June to September) in Henan Province. Results showed that the most severe years of agricultural drought in Henan Province were 2004, 2006, 2008, and 2014. The most severe agricultural drought occurred in July and August 2014. Statistics found that Henan Province had high frequencies of severe drought. This study proved that CDMI calculated by multi-source remote sensing is a reliable and effective indicator for monitoring and assessing agricultural drought.
Zhaoxu Zhang; Wei Xu; Zhenwei Shi; Qiming Qin. Establishment of a Comprehensive Drought Monitoring Index Based on Multisource Remote Sensing Data and Agricultural Drought Monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 2113 -2126.
AMA StyleZhaoxu Zhang, Wei Xu, Zhenwei Shi, Qiming Qin. Establishment of a Comprehensive Drought Monitoring Index Based on Multisource Remote Sensing Data and Agricultural Drought Monitoring. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):2113-2126.
Chicago/Turabian StyleZhaoxu Zhang; Wei Xu; Zhenwei Shi; Qiming Qin. 2021. "Establishment of a Comprehensive Drought Monitoring Index Based on Multisource Remote Sensing Data and Agricultural Drought Monitoring." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 2113-2126.
Drought is a frequent global phenomenon that has the most significant influence on agriculture. Solar-induced chlorophyll fluorescence (SIF) is a by-product of photosynthesis that can be used to monitor vegetation growth and agricultural drought. This study aims to monitor and assess monthly agricultural drought using SIF data with 0.05-degree spatial resolution. The scaled SIF index was calculated during the crop-growing season (March-October, 2000-2017) in agricultural areas of North China Plain (NCP), and the monthly agricultural drought spatial distribution and severity were mapped. Results indicated that NCP experienced mild to severe drought during the study period, the severe drought (proportion more than 50%) affected months including March (2000, 2001, 2003, 2005, 2006, 2010, 2011 and 2012), April (2000, 2001, 2003, 2010 and 2011), May (2000, 2001, 2002 and 2004), June (2000 and 2001), September (2002) and October (2001 and 2002). By statistics, the average drought areas decreased from 2000 to 2017 in NCP. For frequency analysis, the frequencies of mild, moderate and severe droughts were less than 0.4 in most areas of NCP, but severe drought frequency exceeded 0.6 in some areas. The monthly correlation analysis showed that the scaled SIF index had a significant positive correlation with precipitation and crop yield (wheat and corn); the maximum correlation coefficients (R) were 0.53 (September), 0.76 (May) and 0.77 (October). These results indicated that the scaled SIF index is suitable for region agricultural drought monitoring.
Zhaoxu Zhang; Wei Xu; Qiming Qin; Yujia Chen. Monitoring and Assessment of Agricultural Drought Based on Solar-Induced Chlorophyll Fluorescence During Growing Season in North China Plain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 775 -790.
AMA StyleZhaoxu Zhang, Wei Xu, Qiming Qin, Yujia Chen. Monitoring and Assessment of Agricultural Drought Based on Solar-Induced Chlorophyll Fluorescence During Growing Season in North China Plain. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):775-790.
Chicago/Turabian StyleZhaoxu Zhang; Wei Xu; Qiming Qin; Yujia Chen. 2020. "Monitoring and Assessment of Agricultural Drought Based on Solar-Induced Chlorophyll Fluorescence During Growing Season in North China Plain." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 775-790.
This paper proposes a global prototypical network (GPN) to solve the problem of hyperspectral image classification using limited supervised samples (i.e., few-shot problem). In the proposed method, a strategy of global representations learning is adopted to train a network (f) to transfer the samples from the original data space to an embedding-feature space. In the new feature space, a vector called global prototypical representation for each class is learned. In terms of the network (f), we designed an architecture of a deep network consisting of a dense convolutional network and the spectral-spatial attention network (SSAN). For the classification, the similarities between the un-classified samples and the global prototypical representation of each class are evaluated and the classification is finished by Nearest Neighbor (NN) classifier. Several public hyperspectral images were utilized to verify the proposed GPN. The results showed that the proposed GPN obtained the better overall accuracy compared with existing methods. In addition, the time expenditure of the proposed GPN was similar with several existing popular methods. In conclusion, the proposed GPN in this paper is state-of-the-art for solving the problem of hyperspectral image classification using limited supervised samples.
Chengye Zhang; Jun Yue; Qiming Qin. Global Prototypical Network for Few-Shot Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 4748 -4759.
AMA StyleChengye Zhang, Jun Yue, Qiming Qin. Global Prototypical Network for Few-Shot Hyperspectral Image Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):4748-4759.
Chicago/Turabian StyleChengye Zhang; Jun Yue; Qiming Qin. 2020. "Global Prototypical Network for Few-Shot Hyperspectral Image Classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 4748-4759.
This study analyzed changes in nighttime light during the 2019 Spring Festival using Luojia 1-01 nighttime images in six western cities of China (Chengdu, Panzhihua, Kunming, Yuxi, Lhasa, and Jinchang). First, the radiance of the nighttime images was calculated. Second, the light area (LA) and average light intensity (ALI) were estimated for both Spring Festival and non-festival dates. Third, the differences in LA and ALI between the Spring Festival and non-festival were analyzed for all six cities. Migration population data from Baidu Inc. were used to examine the relationship between the changes of nighttime light and the population migration. The results show that, during the non-festival to Spring Festival period, the decrease in LA values coincided with negative net immigration. During the Spring Festival to non-festival period, the LA values increased, which coincided with positive net immigration. The F-test shows that the positive linear relationship between the normalized change in LA and the normalized net immigration is significant at the 0.05 level. This strongly indicates that population migration causes changes in LA. Moreover, while the population is considerably less in these cities during the Spring Festival, the ALI is noticeably higher, which suggests that urban activities are intensified during this period. This study demonstrates the applicability of using Luojia 1-01 nighttime images to detect the nighttime light changes for the Spring Festival in western cities, China, which can then be used to evaluate population migration and urban activities in the Spring Festival. Considering the higher spatial resolution of Luojia 1-01 than NPP (National Polar-orbiting Partnership) / VIIRS (Visible infrared Imaging Radiometer), this study may inspire more applications of Luojia 1-01 to track the activities in a variety of festival-cultures and cities.
Chengye Zhang; Yanqiu Pei; Jun Li; Qiming Qin; Jun Yue. Application of Luojia 1-01 Nighttime Images for Detecting the Light Changes for the 2019 Spring Festival in Western Cities, China. Remote Sensing 2020, 12, 1416 .
AMA StyleChengye Zhang, Yanqiu Pei, Jun Li, Qiming Qin, Jun Yue. Application of Luojia 1-01 Nighttime Images for Detecting the Light Changes for the 2019 Spring Festival in Western Cities, China. Remote Sensing. 2020; 12 (9):1416.
Chicago/Turabian StyleChengye Zhang; Yanqiu Pei; Jun Li; Qiming Qin; Jun Yue. 2020. "Application of Luojia 1-01 Nighttime Images for Detecting the Light Changes for the 2019 Spring Festival in Western Cities, China." Remote Sensing 12, no. 9: 1416.
This study proposes a deep quadruplet network (DQN) for hyperspectral image classification given the limitation of having a small number of samples. A quadruplet network is designed, which makes use of a new quadruplet loss function in order to learn a feature space where the distances between samples from the same class are shortened, while those from a different class are enlarged. A deep 3-D convolutional neural network (CNN) with characteristics of both dense convolution and dilated convolution is then employed and embedded in the quadruplet network to extract spatial-spectral features. Finally, the nearest neighbor (NN) classifier is used to accomplish the classification in the learned feature space. The results show that the proposed network can learn a feature space and is able to undertake hyperspectral image classification using only a limited number of samples. The main highlights of the study include: (1) The proposed approach was found to have high overall accuracy and can be classified as state-of-the-art; (2) Results of the ablation study suggest that all the modules of the proposed approach are effective in improving accuracy and that the proposed quadruplet loss contributes the most; (3) Time-analysis shows the proposed methodology has a similar level of time consumption as compared with existing methods.
Chengye Zhang; Jun Yue; Qiming Qin. Deep Quadruplet Network for Hyperspectral Image Classification with a Small Number of Samples. Remote Sensing 2020, 12, 647 .
AMA StyleChengye Zhang, Jun Yue, Qiming Qin. Deep Quadruplet Network for Hyperspectral Image Classification with a Small Number of Samples. Remote Sensing. 2020; 12 (4):647.
Chicago/Turabian StyleChengye Zhang; Jun Yue; Qiming Qin. 2020. "Deep Quadruplet Network for Hyperspectral Image Classification with a Small Number of Samples." Remote Sensing 12, no. 4: 647.
Autumn phenology is important in determining the growing season length and controlling carbon and energy exchanges in terrestrial ecosystems. However, our knowledge on the interaction processes of vegetation autumn phenology and climate changes remains limited, especially for herbaceous plants. In this study, we comprehensively analyzed the responses of autumn phenology of grassland vegetation to climate changes by using ground-observed brown-down date records of 15 grass species and daily temperature, precipitation, and day length data at six stations. Aside from conducting correlation analysis, we also simulated the brown-down date with a newly developed model by incorporating the effect of drought stress (CDDP) into the traditional chilling-degree-days (CDD) model and compared it with the CDD model. Another revised CDD model included the effect of day length (CDDD) and null model (multiyear average, NM). The statistical results showed a predominant significant negative correlation between the brown-down date and previous temperature/day length in 27.3%/40.9% of site species but a predominant significant positive correlation between the brown-down date and previous precipitation in 54.6% of site species. The opposite effects of previous precipitation and previous temperature/day length on the brown-down date were induced by local thermal–moisture conditions. With regard to the modeling results, the CDDP model was selected as the optimal model for 73% of site species with insufficient water supply in preseason, while the CDD model was selected as the optimal model for 18% of site species with a relatively wet but cold preseason. The CDDD model was selected as the optimal model for only two cases. The average estimation error based on the CDDP model (7.4 days) was lower by 2.0/1.5/1.7 days than that based on the CDD/CDDD/NM model. Overall, this study comprehensively demonstrated the important role of water availability in controlling the autumn phenology process of herbaceous plants.
Shilong Ren; Qiming Qin; Huazhong Ren; Juan Sui; Yao Zhang. New model for simulating autumn phenology of herbaceous plants in the Inner Mongolian Grassland. Agricultural and Forest Meteorology 2019, 275, 136 -145.
AMA StyleShilong Ren, Qiming Qin, Huazhong Ren, Juan Sui, Yao Zhang. New model for simulating autumn phenology of herbaceous plants in the Inner Mongolian Grassland. Agricultural and Forest Meteorology. 2019; 275 ():136-145.
Chicago/Turabian StyleShilong Ren; Qiming Qin; Huazhong Ren; Juan Sui; Yao Zhang. 2019. "New model for simulating autumn phenology of herbaceous plants in the Inner Mongolian Grassland." Agricultural and Forest Meteorology 275, no. : 136-145.
Studying wheat phenology can greatly enhance our understanding of how wheat growth responds to climate change, and guide us to reasonably confront its influence. However, comprehensive global-scale wheat phenology–climate analysis is still lacking. In this study, we extracted the wheat harvest date (WHD) from 1981–2014 from satellite data using threshold-, logistic-, and shape-based methods. Then, we analyzed the effects of heat and drought stress on WHD based on gridded daily temperature and monthly drought data (the Palmer drought severity index (PDSI) and the standardized precipitation evapotranspiration index (SPEI)) over global wheat-growing areas. The results show that WHD was generally delayed from the low to mid latitudes. With respect to variation trends, we detected a significant advancement of WHD in 32.1% of the world’s wheat-growing areas since 1981, with an average changing rate of −0.25 days/yr. A significant negative correlation was identified between WHD and the prior three months’ normal-growing-degree-days across 50.4% of the study region, which implies that greater preseason effective temperature accumulation may cause WHD to occur earlier. Meanwhile, WHD was also found to be significantly and negatively correlated with the prior three months’ extreme-growing-degree-days across only 9.6% of the study region (mainly located in northern South Asia and north Central-West Asia). The effects of extreme heat stress were weaker than those of normal thermal conditions. When extreme drought (measured by PDSI/SPEI) occurred in the current month, in the month prior to WHD, and in the second month prior to WHD, it forced WHD to advance by about 9.0/8.1 days, 13.8/12.2 days, and 10.8/5.3 days compared to normal conditions, respectively. In conclusion, we highlight the effects that heat and drought stress have on advancing wheat harvest timing, which should be a research focus under future climate change.
Shilong Ren; Qiming Qin; Huazhong Ren; Juan Sui; Yao Zhang. Heat and Drought Stress Advanced Global Wheat Harvest Timing from 1981–2014. Remote Sensing 2019, 11, 971 .
AMA StyleShilong Ren, Qiming Qin, Huazhong Ren, Juan Sui, Yao Zhang. Heat and Drought Stress Advanced Global Wheat Harvest Timing from 1981–2014. Remote Sensing. 2019; 11 (8):971.
Chicago/Turabian StyleShilong Ren; Qiming Qin; Huazhong Ren; Juan Sui; Yao Zhang. 2019. "Heat and Drought Stress Advanced Global Wheat Harvest Timing from 1981–2014." Remote Sensing 11, no. 8: 971.
Snow cover is an essential climate variable of the Global Climate Observing System. Gaofen-4 (GF-4) is the first Chinese geostationary satellite to obtain optical imagery with high spatial and temporal resolution, which presents unique advantages in snow cover monitoring. However, the panchromatic and multispectral sensor (PMS) onboard GF-4 lacks the shortwave infrared (SWIR) band, which is crucial for snow cover detection. To reach the potential of GF-4 PMS in snow cover monitoring, this study developed a novel method termed the restored snow index (RSI). The SWIR reflectance of snow cover is restored firstly, and then the RSI is calculated with the restored reflectance. The distribution of snow cover can be mapped with a threshold, which should be adjusted according to actual situations. The RSI was validated using two pairs of GF-4 PMS and Landsat-8 Operational Land Imager images. The validation results show that the RSI can effectively map the distribution of snow cover in these cases, and all of the classification accuracies are above 95%. Signal saturation slightly affects PMS images, but cloud contamination is an important limiting factor. Therefore, we propose that the RSI is an efficient method for monitoring snow cover from GF-4 PMS imagery without requiring the SWIR reflectance.
Tianyuan Zhang; Huazhong Ren; Qiming Qin; Yuanheng Sun. Snow Cover Monitoring with Chinese Gaofen-4 PMS Imagery and the Restored Snow Index (RSI) Method: Case Studies. Remote Sensing 2018, 10, 1871 .
AMA StyleTianyuan Zhang, Huazhong Ren, Qiming Qin, Yuanheng Sun. Snow Cover Monitoring with Chinese Gaofen-4 PMS Imagery and the Restored Snow Index (RSI) Method: Case Studies. Remote Sensing. 2018; 10 (12):1871.
Chicago/Turabian StyleTianyuan Zhang; Huazhong Ren; Qiming Qin; Yuanheng Sun. 2018. "Snow Cover Monitoring with Chinese Gaofen-4 PMS Imagery and the Restored Snow Index (RSI) Method: Case Studies." Remote Sensing 10, no. 12: 1871.
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.
Yuanheng Sun; Huazhong Ren; Tianyuan Zhang; Chengye Zhang; Qiming Qin. Crop Leaf Area Index Retrieval Based on Inverted Difference Vegetation Index and NDVI. IEEE Geoscience and Remote Sensing Letters 2018, 15, 1662 -1666.
AMA StyleYuanheng Sun, Huazhong Ren, Tianyuan Zhang, Chengye Zhang, Qiming Qin. Crop Leaf Area Index Retrieval Based on Inverted Difference Vegetation Index and NDVI. IEEE Geoscience and Remote Sensing Letters. 2018; 15 (11):1662-1666.
Chicago/Turabian StyleYuanheng Sun; Huazhong Ren; Tianyuan Zhang; Chengye Zhang; Qiming Qin. 2018. "Crop Leaf Area Index Retrieval Based on Inverted Difference Vegetation Index and NDVI." IEEE Geoscience and Remote Sensing Letters 15, no. 11: 1662-1666.
Road information as a type of basic geographic information is very important for services such as city planning and traffic navigation, as such there is an urgent need for updating road information in a timely manner. Scholars have proposed various methods of extracting roads from remote sensing images, but most of them are not applicable to rural roads with diverse materials, large curvature changes, and a severe shelter problem. In view of these problems, we propose a road extraction method based on geometric feature inference. In this method, we make full use of the linear characteristics of roads, and construct a geometric knowledge base of rural roads using information on selected sample road segments. Based on the knowledge base, we identify the parallel line pairs in images, and further conduct grouping and connection instructed by knowledge reasoning, and finally obtain complete rural roads. The case study in Xiangtan City of China’s Hunan Province validates the performance of the proposed method.
Jian Liu; Qiming Qin; Jun Li; Yunpeng Li. Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference. ISPRS International Journal of Geo-Information 2017, 6, 314 .
AMA StyleJian Liu, Qiming Qin, Jun Li, Yunpeng Li. Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference. ISPRS International Journal of Geo-Information. 2017; 6 (10):314.
Chicago/Turabian StyleJian Liu; Qiming Qin; Jun Li; Yunpeng Li. 2017. "Rural Road Extraction from High-Resolution Remote Sensing Images Based on Geometric Feature Inference." ISPRS International Journal of Geo-Information 6, no. 10: 314.
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 and Landsat images. Summer-average emissivity products in four narrowbands (Landsat 5/Thematic Mapper Band 6, Landsat 7/Enhanced Thematic Mapper Plus Band 6, and 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. Intercomparisons in different places showed that the new emissivity was close to Advanced Spaceborne Thermal Emission and Reflection Radiometer 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.
Huazhong Ren; Rongyuan Liu; Qiming Qin; Wenjie Fan; Le Yu; Chen Du. Mapping finer-resolution land surface emissivity using Landsat images in China. Journal of Geophysical Research: Atmospheres 2017, 122, 6764 -6781.
AMA StyleHuazhong Ren, Rongyuan Liu, Qiming Qin, Wenjie Fan, Le Yu, Chen Du. Mapping finer-resolution land surface emissivity using Landsat images in China. Journal of Geophysical Research: Atmospheres. 2017; 122 (13):6764-6781.
Chicago/Turabian StyleHuazhong Ren; Rongyuan Liu; Qiming Qin; Wenjie Fan; Le Yu; Chen Du. 2017. "Mapping finer-resolution land surface emissivity using Landsat images in China." Journal of Geophysical Research: Atmospheres 122, no. 13: 6764-6781.
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.
Xin Ye; Huazhong Ren; Rongyuan Liu; Qiming Qin; Yao Liu; Jijia Dong. Land Surface Temperature Estimate From Chinese Gaofen-5 Satellite Data Using Split-Window Algorithm. IEEE Transactions on Geoscience and Remote Sensing 2017, 55, 5877 -5888.
AMA StyleXin Ye, Huazhong Ren, Rongyuan Liu, Qiming Qin, Yao Liu, Jijia Dong. Land Surface Temperature Estimate From Chinese Gaofen-5 Satellite Data Using Split-Window Algorithm. IEEE Transactions on Geoscience and Remote Sensing. 2017; 55 (10):5877-5888.
Chicago/Turabian StyleXin Ye; Huazhong Ren; Rongyuan Liu; Qiming Qin; Yao Liu; Jijia Dong. 2017. "Land Surface Temperature Estimate From Chinese Gaofen-5 Satellite Data Using Split-Window Algorithm." IEEE Transactions on Geoscience and Remote Sensing 55, no. 10: 5877-5888.
Exploring how human activity impacts land use/cover change (LUCC) is a hot research topic in the field of geography and sustainability management. Researchers have primarily used socioeconomic variables to measure human activity. However, the human activity indexes mainly based on socioeconomic variables have a spatial resolution that is coarser than traditional LUCC datasets, which hinders a deep and comprehensive analysis. In view of these problems, we selected China’s Lijiang River Basin as our study area and proposed the use of GPS trajectory data for analyzing the impact of human activity on LUCC from two perspectives: (1) Type of population: we used the kernel density estimation method to extract the spatial distribution of activity intensity of local residents and tourists, investigated their correlation with the LUCC result, and found these two populations have different impacts on each land cover; (2) Flow of population: we used the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm and a network analysis method to build a flow network of population from raw trajectories, conducted regression analysis with LUCC, and found that the flow of population is an important factor driving LUCC and is sometimes a more important factor than the static distribution of the population. Experimental results validated that the proposed method can be used to uncover the impact mechanism of human activity on LUCC at fine-grained scales and provide more accurate planning and instructions for sustainability management.
Jun Li; Yuan Zhang; Qiming Qin; Yueguan Yan. Investigating the Impact of Human Activity on Land Use/Cover Change in China’s Lijiang River Basin from the Perspective of Flow and Type of Population. Sustainability 2017, 9, 383 .
AMA StyleJun Li, Yuan Zhang, Qiming Qin, Yueguan Yan. Investigating the Impact of Human Activity on Land Use/Cover Change in China’s Lijiang River Basin from the Perspective of Flow and Type of Population. Sustainability. 2017; 9 (3):383.
Chicago/Turabian StyleJun Li; Yuan Zhang; Qiming Qin; Yueguan Yan. 2017. "Investigating the Impact of Human Activity on Land Use/Cover Change in China’s Lijiang River Basin from the Perspective of Flow and Type of Population." Sustainability 9, no. 3: 383.
This study proposed a novel method to extract endmembers from hyperspectral image based on discrete firefly algorithm (EE-DFA). Endmembers are the input of many spectral unmixing algorithms. Hence, in this paper, endmember extraction from hyperspectral image is regarded as a combinational optimization problem to get best spectral unmixing results, which can be solved by the discrete firefly algorithm. Two series of experiments were conducted on the synthetic hyperspectral datasets with different SNR and the AVIRIS Cuprite dataset, respectively. The experimental results were compared with the endmembers extracted by four popular methods: the sequential maximum angle convex cone (SMACC), N-FINDR, Vertex Component Analysis (VCA), and Minimum Volume Constrained Nonnegative Matrix Factorization (MVC-NMF). What’s more, the effect of the parameters in the proposed method was tested on both synthetic hyperspectral datasets and AVIRIS Cuprite dataset, and the recommended parameters setting was proposed. The results in this study demonstrated that the proposed EE-DFA method showed better performance than the existing popular methods. Moreover, EE-DFA is robust under different SNR conditions.
Chengye Zhang; Qiming Qin; Tianyuan Zhang; Yuanheng Sun; Chao Chen. Endmember extraction from hyperspectral image based on discrete firefly algorithm (EE-DFA). ISPRS Journal of Photogrammetry and Remote Sensing 2017, 126, 108 -119.
AMA StyleChengye Zhang, Qiming Qin, Tianyuan Zhang, Yuanheng Sun, Chao Chen. Endmember extraction from hyperspectral image based on discrete firefly algorithm (EE-DFA). ISPRS Journal of Photogrammetry and Remote Sensing. 2017; 126 ():108-119.
Chicago/Turabian StyleChengye Zhang; Qiming Qin; Tianyuan Zhang; Yuanheng Sun; Chao Chen. 2017. "Endmember extraction from hyperspectral image based on discrete firefly algorithm (EE-DFA)." ISPRS Journal of Photogrammetry and Remote Sensing 126, no. : 108-119.
Damaged building detection from high spatial resolution remote sensing image helps to rapid disaster losses assessment. However, the majority of traditional methods relies on only a single category feature of the damaged building. This letter presents a new strategy for detecting damaged buildings from postquake remote sensing image by multiple-feature analysis, in which the integrity of the building edge and the interior roof was both considered. The intactness of the building edge was assessed by proposing a new feature parameter, edge significance (ES), ES using significance test to quantify the difference between the gradient values on the edge and in the edge buffer. In addition, the gradient orientation inside the building was analyzed and local gradient orientation entropy (LOE) parameter was adopted to determine whether the interior roof was damaged. In general, damaged buildings have lower ES values because of broken edges and higher LOE values owing to debris, final decision was made on the basis of both feature parameters. A Quickbird image of Yushu, China, was used in the experiment and, among a total of 327 buildings, 266 were detected correctly. The overall accuracy was 84.10%, which is better than traditional methods.
Xin Ye; Mingchao Liu; Jun Wang; Qiming Qin; Huazhong Ren; Jianhua Wang; Jian Hui. Building-Based Damage Detection From Postquake Image Using Multiple-Feature Analysis. IEEE Geoscience and Remote Sensing Letters 2017, 14, 1 -5.
AMA StyleXin Ye, Mingchao Liu, Jun Wang, Qiming Qin, Huazhong Ren, Jianhua Wang, Jian Hui. Building-Based Damage Detection From Postquake Image Using Multiple-Feature Analysis. IEEE Geoscience and Remote Sensing Letters. 2017; 14 (4):1-5.
Chicago/Turabian StyleXin Ye; Mingchao Liu; Jun Wang; Qiming Qin; Huazhong Ren; Jianhua Wang; Jian Hui. 2017. "Building-Based Damage Detection From Postquake Image Using Multiple-Feature Analysis." IEEE Geoscience and Remote Sensing Letters 14, no. 4: 1-5.
Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity between roads and impervious structures, the current methods solely using spectral characteristics are often ineffective. By contrast, the detailed information discernible from the high-resolution aerial images enables road extraction with spatial texture features. In this study, a knowledge-based method is established and proposed; this method incorporates the spatial texture feature into urban road extraction. The spatial texture feature is initially extracted by the local Moran’s I, and the derived texture is added to the spectral bands of image for image segmentation. Subsequently, features like brightness, standard deviation, rectangularity, aspect ratio, and area are selected to form the hypothesis and verification model based on road knowledge. Finally, roads are extracted by applying the hypothesis and verification model and are post-processed based on the mathematical morphology. The newly proposed method is evaluated by conducting two experiments. Results show that the completeness, correctness, and quality of the results could reach approximately 94%, 90% and 86% respectively, indicating that the proposed method is effective for urban road extraction.
Jianhua Wang; Qiming Qin; Zhongling Gao; Jianghua Zhao; Xin Ye. A New Approach to Urban Road Extraction Using High-Resolution Aerial Image. ISPRS International Journal of Geo-Information 2016, 5, 114 .
AMA StyleJianhua Wang, Qiming Qin, Zhongling Gao, Jianghua Zhao, Xin Ye. A New Approach to Urban Road Extraction Using High-Resolution Aerial Image. ISPRS International Journal of Geo-Information. 2016; 5 (7):114.
Chicago/Turabian StyleJianhua Wang; Qiming Qin; Zhongling Gao; Jianghua Zhao; Xin Ye. 2016. "A New Approach to Urban Road Extraction Using High-Resolution Aerial Image." ISPRS International Journal of Geo-Information 5, no. 7: 114.
The interaction between human activity and landscape pattern has been a hot research topic during the last few decades. However, scholars used to measure human activity by social, economic and humanistic indexes. These indexes cannot directly reflect human activity and are not suitable for fine-grained analysis due to the coarse spatial resolution. In view of the above problems, this paper proposes a method that obtains the intensity of human activity from GPS trajectory data, collects landscape information from remote sensing images and further analyzes the interaction between human activity and landscape pattern at a fine-grained scale. The Lijiang River Basin is selected as the study area. Experimental results show that human activity and landscape pattern interact synergistically in this area. Built-up land and water boost human activity, while woodland restrains human activity. The effect of human activity on landscape pattern differs by the land cover category. Overall, human activities make natural land, such as woodland and water, scattered and fragmented, but cause man-built land, such as built-up land and farmland, clustered and regular. Nevertheless, human activities inside and outside urban areas are the opposite. The research findings in this paper are helpful for designing and implementing sustainable management plans.
Jun Li; Yuan Zhang; Xiang Wang; Qiming Qin; Zhuangzhuang Wei; Jingze Li. Application of GPS Trajectory Data for Investigating the Interaction between Human Activity and Landscape Pattern: A Case Study of the Lijiang River Basin, China. ISPRS International Journal of Geo-Information 2016, 5, 104 .
AMA StyleJun Li, Yuan Zhang, Xiang Wang, Qiming Qin, Zhuangzhuang Wei, Jingze Li. Application of GPS Trajectory Data for Investigating the Interaction between Human Activity and Landscape Pattern: A Case Study of the Lijiang River Basin, China. ISPRS International Journal of Geo-Information. 2016; 5 (7):104.
Chicago/Turabian StyleJun Li; Yuan Zhang; Xiang Wang; Qiming Qin; Zhuangzhuang Wei; Jingze Li. 2016. "Application of GPS Trajectory Data for Investigating the Interaction between Human Activity and Landscape Pattern: A Case Study of the Lijiang River Basin, China." ISPRS International Journal of Geo-Information 5, no. 7: 104.
The prevalence of moving object data (MOD) brings new opportunities for behavior related research. Periodic behavior is one of the most important behaviors of moving objects. However, the existing methods of detecting periodicities assume a moving object either does not have any periodic behavior at all or just has a single periodic behavior in one place. Thus they are incapable of dealing with many real world situations whereby a moving object may have multiple periodic behaviors mixed together. Aiming at addressing this problem, this paper proposes a probabilistic periodicity detection method called MPDA. MPDA first identifies high dense regions by the kernel density method, then generates revisit time sequences based on the dense regions, and at last adopts a filter-refine paradigm to detect mixed periodicities. At the filter stage, candidate periods are identified by comparing the observed and reference distribution of revisit time intervals using the chi-square test, and at the refine stage, a periodic degree measure is defined to examine the significance of candidate periods to identify accurate periods existing in MOD. Synthetic datasets with various characteristics and two real world tracking datasets validate the effectiveness of MPDA under various scenarios. MPDA has the potential to play an important role in analyzing complicated behaviors of moving objects.
Jun Li; Jingjing Wang; Junfei Zhang; Qiming Qin; Tanvi Jindal; Jiawei Han. A probabilistic approach to detect mixed periodic patterns from moving object data. GeoInformatica 2016, 20, 715 -739.
AMA StyleJun Li, Jingjing Wang, Junfei Zhang, Qiming Qin, Tanvi Jindal, Jiawei Han. A probabilistic approach to detect mixed periodic patterns from moving object data. GeoInformatica. 2016; 20 (4):715-739.
Chicago/Turabian StyleJun Li; Jingjing Wang; Junfei Zhang; Qiming Qin; Tanvi Jindal; Jiawei Han. 2016. "A probabilistic approach to detect mixed periodic patterns from moving object data." GeoInformatica 20, no. 4: 715-739.