This page has only limited features, please log in for full access.

Unclaimed
Jing Tian
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 31 March 2020 in Remote Sensing
Reads 0
Downloads 0

Accurate assessment of vegetation dynamics provides important information for ecosystem management. Anthropogenic activities and climate variations are the major factors that primarily influence vegetation ecosystems. This study investigates the spatiotemporal impacts of climate factors and human activities on vegetation productivity changes in China from 1985 to 2015. Actual net primary productivity (ANPP) is used to reflect vegetation dynamics quantitatively. Climate-induced potential net primary productivity (PNPP) is used as an indicator of climate change, whereas the difference between PNPP and ANPP is considered as an indicator of human activities (HNPP). Overall, 91% of the total vegetation cover area shows declining trends for net primary productivity (NPP), while only 9% shows increasing trends before 2000 (base period). However, after 2000 (restoration period), 78.7% of the total vegetation cover area shows increasing trends, whereas 21.3% of the area shows decreasing trends. Moreover, during the base period, the quantitative contribution of climate change to NPP restoration is 0.21 grams carbon per meter square per year (gC m−2 yr−1) and to degradation is 2.41 gC m−2 yr−1, while during the restoration period, climate change contributes 0.56 and 0.29 gC m−2 yr−1 to NPP restoration and degradation, respectively. Human activities contribute 0.36 and 0.72 gC m−2 yr−1 during the base period, and 0.63 and 0.31 gC m−2 yr−1 during the restoration period to NPP restoration and degradation, respectively. The combined effects of climate and human activities restore 0.65 and 1.11 gC m−2 yr−1, and degrade 2.01 and 0.67 gC m−2 yr−1 during the base and restoration periods, respectively. Climate factors affect vegetation cover more than human activities, while precipitation is found to be more sensitive to NPP change than temperature. Unlike the base period, NPP per unit area increases with an increase in the human footprint pressure during the restoration period. Grassland has more variability than other vegetation classes, and the grassland changes are mainly observed in Tibet, Xinjiang, and Inner Mongolia regions. The results may help policy-makers by providing necessary guidelines for the management of forest, grassland, and agricultural activities.

ACS Style

Shahid Naeem; Yongqiang Zhang; Jing Tian; Faisal Mueen Qamer; Aamir Latif; Pranesh Kumar Paul. Quantifying the Impacts of Anthropogenic Activities and Climate Variations on Vegetation Productivity Changes in China from 1985 to 2015. Remote Sensing 2020, 12, 1113 .

AMA Style

Shahid Naeem, Yongqiang Zhang, Jing Tian, Faisal Mueen Qamer, Aamir Latif, Pranesh Kumar Paul. Quantifying the Impacts of Anthropogenic Activities and Climate Variations on Vegetation Productivity Changes in China from 1985 to 2015. Remote Sensing. 2020; 12 (7):1113.

Chicago/Turabian Style

Shahid Naeem; Yongqiang Zhang; Jing Tian; Faisal Mueen Qamer; Aamir Latif; Pranesh Kumar Paul. 2020. "Quantifying the Impacts of Anthropogenic Activities and Climate Variations on Vegetation Productivity Changes in China from 1985 to 2015." Remote Sensing 12, no. 7: 1113.

Journal article
Published: 07 January 2019 in Physics and Chemistry of the Earth, Parts A/B/C
Reads 0
Downloads 0

Everglades National Park (ENP) is a hydro-ecologically enriched wetland with varying salinity contents, which is a concern for terrestrial ecosystem balance and its nearby urban sustainability. In this study, spatio-temporal soil salinity maps are created using remote sensing techniques, coupled with literature review to understand vegetation salt tolerance properties, and field assessments entailing insitu electric conductivity (EC) measurements. The mapping first entailed the execution of a supervised machine learning technique—the maximum likelihood classification algorithm—to delineate seven vegetation-based land cover classes for the area, namely, mangrove forest, mangrove scrub, low-density forest, sawgrass, prairies and marshes, barren lands with woodland hammock and water, for the years 1996, 2000, 2006, 2010 and 2015. The classifications for 1996-2010 yielded accuracies of 82%-94%, and the 2015 classification was supported through ground truthing. Afterwards, EC tolerance thresholds for each vegetation class were established, which yielded soil salinity maps comprising four soil salinity classes—i.e., the non- (EC = 0∼2 dS/m), low- (EC = 2∼4 dS/m) and high-saline (EC ≥4 dS/m) areas. The soil salinity maps visualized the spatial distribution of soil salinity with no significant temporal changes. Furthermore, insitu EC measurements carried out at 23 sampling sites covering all land cover classes mostly validated (91% samples were tested within range) the estimated soil salinity ranges for the latest distribution. The approach of using land cover classes to sense salinity ranges in an urban-proximal ecosystem is pragmatic and application oriented, attributing to novel and useful study upshots considering the diversifying ecological context.

ACS Style

Fahad Khan Khadim; Hongbo Su; Lina Xu; Jing Tian. Soil salinity mapping in Everglades National Park using remote sensing techniques and vegetation salt tolerance. Physics and Chemistry of the Earth, Parts A/B/C 2019, 110, 31 -50.

AMA Style

Fahad Khan Khadim, Hongbo Su, Lina Xu, Jing Tian. Soil salinity mapping in Everglades National Park using remote sensing techniques and vegetation salt tolerance. Physics and Chemistry of the Earth, Parts A/B/C. 2019; 110 ():31-50.

Chicago/Turabian Style

Fahad Khan Khadim; Hongbo Su; Lina Xu; Jing Tian. 2019. "Soil salinity mapping in Everglades National Park using remote sensing techniques and vegetation salt tolerance." Physics and Chemistry of the Earth, Parts A/B/C 110, no. : 31-50.

Journal article
Published: 22 November 2018 in Physics and Chemistry of the Earth, Parts A/B/C
Reads 0
Downloads 0

Significant changes of surface emissivity have taken place in cities with the rapid urbanization. Surface emissivity influences the radiation budget and the surface energy balance, thereby influences city climate. It is an important parameter in retrieving surface temperature and has close relationship with city heat island effect. This paper attempts to explore the possibility of using soil moisture index (SMI) to estimate soil emissivity (SE). The advantage of using SMI over soil moisture (SM) is that SMI is easily obtained with remotely sensed data. Its advantage over soil reflectance (SR) is that SMI can reflect soil water condition better. On the basis of the careful measurements of the variations of SE and SR with SM for four soil samples, the following results were obtained: (1) Logarithmic function is better than linear function in describing the relationship between SE and SM/SMI; (2) Similar correlation coefficient (R2) and the Root Mean Square Error (RMSE) between SMI and SE were obtained in comparison with that between SM and SE. Therefore, SMI can replace SM to estimate SE. (3) The index of Water Index SOIL (WISOIL) is the best in the estimation of SE. (4) SE in MODIS 29 waveband is the most sensitive to the change of SM in comparison with SE in the other MODIS thermal wavebands regardless of soil texture.

ACS Style

Jing Tian; Shangkun Song; Honglin He. The relationship between soil emissivity and soil reflectance under the effects of soil water content. Physics and Chemistry of the Earth, Parts A/B/C 2018, 110, 133 -137.

AMA Style

Jing Tian, Shangkun Song, Honglin He. The relationship between soil emissivity and soil reflectance under the effects of soil water content. Physics and Chemistry of the Earth, Parts A/B/C. 2018; 110 ():133-137.

Chicago/Turabian Style

Jing Tian; Shangkun Song; Honglin He. 2018. "The relationship between soil emissivity and soil reflectance under the effects of soil water content." Physics and Chemistry of the Earth, Parts A/B/C 110, no. : 133-137.

Original articles
Published: 15 March 2018 in International Journal of Digital Earth
Reads 0
Downloads 0

This paper compared two soil moisture downscaling methods using three scaling factors. Level 3 soil moisture product of advanced microwave scanning radiometer for EOS (AMSR-E) is downscaled from 25 to 1 km. The downscaled results are compared with the soil moisture observations from polarimetric scanning radiometer (PSR) microwave radiometer and field sampling. The results show that (1) the scaling factor of normalized soil thermal inertia (NSTIs) and vegetation temperature condition index (VTCI) are better than soil evaporative efficiency in reflecting soil moisture; (2) for method 1, NSTIS is the best in the downscaling of soil moisture. For method 2, VTCI is the best; (3) no significant differences of the correlation coefficients (R2) and the biases were found between the two methods for the same scaling factors. However, method 2 shows a better potential than method 1 in the time-series applications of the downscaling of soil moisture; (4) compared with the relationship between the area-averaged soil moisture of AMSR-E and that of PSR, R2 of the 6 sets of the downscaled soil moisture almost do not decrease, which suggests the validity of the downscaling of soil moisture with the two downscaling methods using the three scaling factors.

ACS Style

Jing Tian; Xiangzheng Deng; Hongbo Su. Intercomparison of two trapezoid-based soil moisture downscaling methods using three scaling factors. International Journal of Digital Earth 2018, 12, 485 -499.

AMA Style

Jing Tian, Xiangzheng Deng, Hongbo Su. Intercomparison of two trapezoid-based soil moisture downscaling methods using three scaling factors. International Journal of Digital Earth. 2018; 12 (4):485-499.

Chicago/Turabian Style

Jing Tian; Xiangzheng Deng; Hongbo Su. 2018. "Intercomparison of two trapezoid-based soil moisture downscaling methods using three scaling factors." International Journal of Digital Earth 12, no. 4: 485-499.

Articles
Published: 08 February 2018 in International Journal of Remote Sensing
Reads 0
Downloads 0

A high temporal frequency of high-resolution thermal data is required in regional evapotranspiration (ET) studies. In this article, a spatial-temporal thermal remote-sensing sharpening scheme, which can be used to perform temporally stable land surface temperature (LST) mapping with high spatial resolution and further facilitate the estimation of ET, is discussed in the context of the Soil Moisture Experiment of 2002. To demonstrate this scheme, relationships between LST and three remote-sensing parameters (normalized difference vegetation index (NDVI), fractional vegetation cover (FVC), and Bowen ratio) were first used in a thermal disaggregation procedure for retrieving LSTs at a 250-m scale. Then, the spatial and temporal adaptive reflectance fusion (STARFM) model was applied to the 250-m LSTs, producing LST data at a fine resolution of 60 m and a fine temporal resolution of 1 day. Two remote-sensing-based energy balance models were then used to retrieve the ET at the Moderate Resolution Imaging Spectroradiometer overpass time respectively using 250- and 60-m LSTs. The results showed that the Bowen ratio-based LSTs were matched field observations better than did the LSTs obtained with the other two approaches (NDVI- and FVC-based) at the 250-m scale, and consequently produced 250-m ET mapping that better matched the observed tower-based values. When combined with the STARFM fusion model, the 250-m Bowen ratio-based LSTs produced more accurate time-series LSTs and ET at the 60-m scale. The Bowen ratio, which is more related to surface energy principles and the soil moisture variation, was effective in disaggregating LSTs and promoting the estimation of ET. Overall, sharpened LSTs using the combination of thermal disaggregation procedure and the STARFM fusion model could substantially improve remote-sensing-based ET estimates. Moreover, the STARFM model that can fuse LST from 250 to ~100 m should be given more attention as long as the thermal disaggregation procedure that can disaggregate LST from 1000 to 250 m, provided that it contributed approximately 10.1% to further improving ET retrieval performance.

ACS Style

Kai Liu; Hongbo Su; Jing Tian; Xueke Li; Weimin Wang; Lijun Yang; Hong Liang. Assessing a scheme of spatial-temporal thermal remote-sensing sharpening for estimating regional evapotranspiration. International Journal of Remote Sensing 2018, 39, 3111 -3137.

AMA Style

Kai Liu, Hongbo Su, Jing Tian, Xueke Li, Weimin Wang, Lijun Yang, Hong Liang. Assessing a scheme of spatial-temporal thermal remote-sensing sharpening for estimating regional evapotranspiration. International Journal of Remote Sensing. 2018; 39 (10):3111-3137.

Chicago/Turabian Style

Kai Liu; Hongbo Su; Jing Tian; Xueke Li; Weimin Wang; Lijun Yang; Hong Liang. 2018. "Assessing a scheme of spatial-temporal thermal remote-sensing sharpening for estimating regional evapotranspiration." International Journal of Remote Sensing 39, no. 10: 3111-3137.

Original paper
Published: 28 April 2017 in Theoretical and Applied Climatology
Reads 0
Downloads 0

Surface air temperature is an essential variable for monitoring the atmosphere, and it is generally acquired at meteorological stations that can provide information about only a small area within an r m radius (r-neighborhood) of the station, which is called the representable radius. In studies on a local scale, ground-based observations of surface air temperatures obtained from scattered stations are usually interpolated using a variety of methods without ascertaining their effectiveness. Thus, it is necessary to evaluate the spatial representativeness of ground-based observations of surface air temperature before conducting studies on a local scale. The present study used remote sensing data to estimate the spatial distribution of surface air temperature using the advection-energy balance for air temperature (ADEBAT) model. Two target stations in the study area were selected to conduct an analysis of spatial representativeness. The results showed that one station (AWS 7) had a representable radius of about 400 m with a possible error of less than 1 K, while the other station (AWS 16) had the radius of about 250 m. The representable radius was large when the heterogeneity of land cover around the station was small.

ACS Style

Suhua Liu; Hongbo Su; Jing Tian; Weizhen Wang. An analysis of spatial representativeness of air temperature monitoring stations. Theoretical and Applied Climatology 2017, 132, 857 -865.

AMA Style

Suhua Liu, Hongbo Su, Jing Tian, Weizhen Wang. An analysis of spatial representativeness of air temperature monitoring stations. Theoretical and Applied Climatology. 2017; 132 (3-4):857-865.

Chicago/Turabian Style

Suhua Liu; Hongbo Su; Jing Tian; Weizhen Wang. 2017. "An analysis of spatial representativeness of air temperature monitoring stations." Theoretical and Applied Climatology 132, no. 3-4: 857-865.

Journal article
Published: 05 August 2016 in Remote Sensing
Reads 0
Downloads 0

Evapotranspiration (ET) is an essential part of the hydrological cycle and accurately estimating it plays a crucial role in water resource management. Surface energy balance (SEB) models are widely used to estimate regional ET with remote sensing. The presence of horizontal advection, however, perturbs the surface energy balance system and contributes to the uncertainty of energy influxes. Thus, it is vital to consider horizontal advection when applying SEB models to estimate ET. This study proposes an innovative and simplified approach, the surface energy balance-advection (SEB-A) method, which is based on the energy balance theory and also takes into account the horizontal advection to determine ET by remote sensing. The SEB-A method considers that the actual ET consists of two parts: the local ET that is regulated by the energy balance system and the exotic ET that arises from horizontal advection. To evaluate the SEB-A method, it was applied to the middle region of the Heihe River in China. Instantaneous ET for three days were acquired and assessed with ET measurements from eddy covariance (EC) systems. The results demonstrated that the ET estimates had a high accuracy, with a correlation coefficient (R2) of 0.713, a mean average error (MAE) of 39.3 W/m2 and a root mean square error (RMSE) of 54.6 W/m2 between the estimates and corresponding measurements. Percent error was calculated to more rigorously assess the accuracy of these estimates, and it ranged from 0% to 35%, with over 80% of the locations within a 20% error. To better understand the SEB-A method, the relationship between the ET estimates and land use types was analyzed, and the results indicated that the ET estimates had spatial distributions that correlated with vegetation patterns and could well demonstrate the ET differences caused by different land use types. The sensitivity analysis suggested that the SEB-A method requested accurate estimation of the available energy, Rn−G, but was less constrained with the difference between ground and air temperature, T0−Ta–loc.

ACS Style

Suhua Liu; Hongbo Su; Renhua Zhang; Jing Tian; Shaohui Chen; Weizhen Wang. Regional Estimation of Remotely Sensed Evapotranspiration Using the Surface Energy Balance-Advection (SEB-A) Method. Remote Sensing 2016, 8, 644 .

AMA Style

Suhua Liu, Hongbo Su, Renhua Zhang, Jing Tian, Shaohui Chen, Weizhen Wang. Regional Estimation of Remotely Sensed Evapotranspiration Using the Surface Energy Balance-Advection (SEB-A) Method. Remote Sensing. 2016; 8 (8):644.

Chicago/Turabian Style

Suhua Liu; Hongbo Su; Renhua Zhang; Jing Tian; Shaohui Chen; Weizhen Wang. 2016. "Regional Estimation of Remotely Sensed Evapotranspiration Using the Surface Energy Balance-Advection (SEB-A) Method." Remote Sensing 8, no. 8: 644.

Journal article
Published: 24 June 2016 in Sensors
Reads 0
Downloads 0

In the inversion of land surface temperature (LST) from satellite data, obtaining the information on land surface emissivity is most challenging. How to solve both the emissivity and the LST from the underdetermined equations for thermal infrared radiation is a hot research topic related to quantitative thermal infrared remote sensing. The academic research and practical applications based on the temperature-emissivity retrieval algorithms show that directly measuring the emissivity of objects at a fixed thermal infrared waveband is an important way to close the underdetermined equations for thermal infrared radiation. Based on the prior research results of both the authors and others, this paper proposes a new approach of obtaining the spectral emissivity of the object at 8–14 µm with a single-band CO2 laser at 10.6 µm and a 102F FTIR spectrometer. Through experiments, the spectral emissivity of several key samples, including aluminum plate, iron plate, copper plate, marble plate, rubber sheet, and paper board, at 8–14 µm is obtained, and the measured data are basically consistent with the hemispherical emissivity measurement by a Nicolet iS10 FTIR spectrometer for the same objects. For the rough surface of materials, such as marble and rusty iron, the RMSE of emissivity is below 0.05. The differences in the field of view angle and in the measuring direction between the Nicolet FTIR method and the method proposed in the paper, and the heterogeneity in the degree of oxidation, polishing and composition of the samples, are the main reasons for the differences of the emissivities between the two methods.

ACS Style

Ren-Hua Zhang; Hong-Bo Su; Jing Tian; Su-Juan Mi; Zhao-Liang Li. Non-Contact Measurement of the Spectral Emissivity through Active/Passive Synergy of CO2 Laser at 10.6 µm and 102F FTIR (Fourier Transform Infrared) Spectrometer. Sensors 2016, 16, 970 .

AMA Style

Ren-Hua Zhang, Hong-Bo Su, Jing Tian, Su-Juan Mi, Zhao-Liang Li. Non-Contact Measurement of the Spectral Emissivity through Active/Passive Synergy of CO2 Laser at 10.6 µm and 102F FTIR (Fourier Transform Infrared) Spectrometer. Sensors. 2016; 16 (7):970.

Chicago/Turabian Style

Ren-Hua Zhang; Hong-Bo Su; Jing Tian; Su-Juan Mi; Zhao-Liang Li. 2016. "Non-Contact Measurement of the Spectral Emissivity through Active/Passive Synergy of CO2 Laser at 10.6 µm and 102F FTIR (Fourier Transform Infrared) Spectrometer." Sensors 16, no. 7: 970.

Journal article
Published: 31 August 2015 in Remote Sensing
Reads 0
Downloads 0

Water use efficiency (WUE) is a useful indicator to illustrate the interaction of carbon and water cycles in terrestrial ecosystems. MODIS gross primary production (GPP) and evapotranspiration (ET) products have been used to analyze the spatial and temporal patterns of WUE and their relationships with environmental factors at regional and global scales. Although MODIS GPP and ET products have been evaluated using eddy covariance flux measurements, the accuracy of WUE estimated from MODIS products has not been well quantified. In this paper, we evaluated WUE estimated from MODIS GPP and ET products against eddy covariance measurements of GPP and ET during 2003–2008 at eight sites of the Chinese flux observation and research network (ChinaFLUX) and conducted sensitivity analysis to investigate the possible key contributors to the bias of MODIS products. Results show that MODIS products underestimate eight-day water use efficiency in four forest ecosystems and one cropland ecosystem with the bias from −0.36–−2.28 g·C·kg−1 H2O, while overestimating it in three grassland ecosystems with the bias from 0.26–1.11 g·C·kg−1 H2O. Mean annual WUE was underestimated by 14%–54% at four forest sites, 45% at one cropland site and 7% at an alpine grassland site, but overestimated by 66% and 9% at a temperate grassland site and an alpine meadow site, respectively. The underestimation of WUE by MODIS data results from underestimated GPP and overestimated ET at four forest sites, while MODIS WUE values are significantly overvalued mainly due to underestimated ET in the three grassland ecosystems. The maximum light use efficiency and fraction of photosynthetically-active radiation (FPAR) were the two most sensitive factors to the estimation of WUE derived from the MODIS GPP and ET algorithms. The error in meteorological data partly caused the overestimation of ET and accordingly underestimation in WUE in subtropical and tropical forests. The bias of MODIS-produced WUE was also derived from the uncertainties in eddy flux data due to gap-filling processes and unbalanced surface energy issue. Their contributions to the uncertainty in estimated WUE at both eight-day and annual scales still need to be further quantified.

ACS Style

Li Zhang; Jing Tian; Honglin He; Xiaoli Ren; Xiaomin Sun; Guirui Yu; Qianqian Lu; Linyu Lv. Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China. Remote Sensing 2015, 7, 11183 -11201.

AMA Style

Li Zhang, Jing Tian, Honglin He, Xiaoli Ren, Xiaomin Sun, Guirui Yu, Qianqian Lu, Linyu Lv. Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China. Remote Sensing. 2015; 7 (9):11183-11201.

Chicago/Turabian Style

Li Zhang; Jing Tian; Honglin He; Xiaoli Ren; Xiaomin Sun; Guirui Yu; Qianqian Lu; Linyu Lv. 2015. "Evaluation of Water Use Efficiency Derived from MODIS Products against Eddy Variance Measurements in China." Remote Sensing 7, no. 9: 11183-11201.

Journal article
Published: 24 August 2015 in Remote Sensing
Reads 0
Downloads 0

With the development of quantitative remote sensing, regional evapotranspiration (ET) modeling based on the feature space has made substantial progress. Among those feature space based evapotranspiration models, accurate determination of the dry/wet lines remains a challenging task. This paper reports the development of a new model, named DDTI (Determination of Dry line by Thermal Inertia), which determines the theoretical dry line based on the relationship between the thermal inertia and the soil moisture. The Simplified Thermal Inertia value estimated in the North China Plain is consistent with the value measured in the laboratory. Three evaluation methods, which are based on the comparison of the locations of the theoretical dry line determined by two models (DDTI model and the heat energy balance model), the comparison of ET results, and the comparison of the evaporative fraction between the estimates from the two models and the in situ measurements, were used to assess the performance of the new model DDTI. The location of the theoretical dry line determined by DDTI is more reasonable than that determined by the heat energy balance model. ET estimated from DDTI has an RMSE (Root Mean Square Error) of 56.77 W/m2 and a bias of 27.17 W/m2; while the heat energy balance model estimated ET with an RMSE of 83.36 W/m2 and a bias of −38.42 W/m2. When comparing the coeffcient of determination for the two models with the observations from Yucheng, DDTI demonstrated ET with an R2 of 0.9065; while the heat energy balance model has an R2 of 0.7729. When compared with the in situ measurements of evaporative fraction (EF) at Yucheng Experimental Station, the ET model based on DDTI reproduces the pixel scale EF with an RMSE of 0.149, much lower than that based on the heat energy balance model which has an RMSE of 0.220. Also, the EF bias between the DDTI model and the in situ measurements is 0.064, lower than the EF bias of the heat energy balance model, which is 0.084.

ACS Style

Sujuan Mi; Hongbo Su; Renhua Zhang; Jing Tian. Using Simplified Thermal Inertia to Determine the Theoretical Dry Line in Feature Space for Evapotranspiration Retrieval. Remote Sensing 2015, 7, 10856 -10877.

AMA Style

Sujuan Mi, Hongbo Su, Renhua Zhang, Jing Tian. Using Simplified Thermal Inertia to Determine the Theoretical Dry Line in Feature Space for Evapotranspiration Retrieval. Remote Sensing. 2015; 7 (8):10856-10877.

Chicago/Turabian Style

Sujuan Mi; Hongbo Su; Renhua Zhang; Jing Tian. 2015. "Using Simplified Thermal Inertia to Determine the Theoretical Dry Line in Feature Space for Evapotranspiration Retrieval." Remote Sensing 7, no. 8: 10856-10877.

Journal article
Published: 13 May 2015 in Remote Sensing
Reads 0
Downloads 0

This paper presents a method of estimating regional distributions of surface air temperature (Ta) and surface vapor pressure (ea), which uses remotely-sensed data and meteorological data as its inputs. The method takes into account the effects of both local driving force and horizontal advection on Ta and ea. Good correlation coefficients (R2) and root mean square error (RMSE) between the measurements of Ta/ea at weather stations and Ta/ea estimates were obtained; with R2 of 0.77, 0.82 and 0.80 and RMSE of 0.42K, 0.35K and 0.20K for Ta and with R2 of 0.85, 0.88, 0.88 and RMSE of 0.24hpa, 0.35hpa and 0.16hpa for ea, respectively, for the three-day results. This result is much better than that estimated from the inverse distance weighted method (IDW). The performance of Ta/ea estimates at Dongping Lake illustrated that the method proposed in the paper also has good accuracy for a heterogeneous surface. The absolute biases of Ta and ea estimates at Dongping Lake from the proposed method are less than 0.5Kand 0.7hpa, respectively, while the absolute biases of them from the IDW method are more than 2K and 3hpa, respectively. Sensitivity analysis suggests that the Ta estimation method presented in the paper is most sensitive to surface temperature and that the ea estimation method is most sensitive to available energy.

ACS Style

Renhua Zhang; Yuan Rong; Jing Tian; Hongbo Su; Zhao-Liang Li; Suhua Liu. A Remote Sensing Method for Estimating Surface Air Temperature and Surface Vapor Pressure on a Regional Scale. Remote Sensing 2015, 7, 6005 -6025.

AMA Style

Renhua Zhang, Yuan Rong, Jing Tian, Hongbo Su, Zhao-Liang Li, Suhua Liu. A Remote Sensing Method for Estimating Surface Air Temperature and Surface Vapor Pressure on a Regional Scale. Remote Sensing. 2015; 7 (5):6005-6025.

Chicago/Turabian Style

Renhua Zhang; Yuan Rong; Jing Tian; Hongbo Su; Zhao-Liang Li; Suhua Liu. 2015. "A Remote Sensing Method for Estimating Surface Air Temperature and Surface Vapor Pressure on a Regional Scale." Remote Sensing 7, no. 5: 6005-6025.

Journal article
Published: 22 April 2013 in Remote Sensing
Reads 0
Downloads 0

This study aims to investigate the impact of the spatial size of the study domain on the performance of the triangle method using progressively smaller domains and Moderate Resolution Imaging Spectroradiometer (MODIS) observations in the Heihe River basin located in the arid region of northwestern China. Data from 10 clear-sky days during the growing season from April to September 2009 were used. Results show that different dry/wet edges in the surface temperature-vegetation index space directly led to the deviation of evapotranspiration (ET) estimates due to the variation of the spatial domain size. The slope and the intercept of the limiting edges are dependent on the range and the maximum of surface temperature over the spatial domain. The difference of the limiting edges between different domain sizes has little impact on the spatial pattern of ET estimates, with the Pearson correlation coefficient ranging from 0.94 to 1.0 for the 10 pairs of ET estimates at different domain scales. However, it has a larger impact on the degree of discrepancies in ET estimates between different domain sizes, with the maximum of 66 W∙m−2. The largest deviation of ET estimates between different domain sizes was found at the beginning of the growing season.

ACS Style

Jing Tian; Hongbo Su; Xiaomin Sun; Shaohui Chen; Honglin He; Linjun Zhao. Impact of the Spatial Domain Size on the Performance of the Ts-VI Triangle Method in Terrestrial Evapotranspiration Estimation. Remote Sensing 2013, 5, 1998 -2013.

AMA Style

Jing Tian, Hongbo Su, Xiaomin Sun, Shaohui Chen, Honglin He, Linjun Zhao. Impact of the Spatial Domain Size on the Performance of the Ts-VI Triangle Method in Terrestrial Evapotranspiration Estimation. Remote Sensing. 2013; 5 (4):1998-2013.

Chicago/Turabian Style

Jing Tian; Hongbo Su; Xiaomin Sun; Shaohui Chen; Honglin He; Linjun Zhao. 2013. "Impact of the Spatial Domain Size on the Performance of the Ts-VI Triangle Method in Terrestrial Evapotranspiration Estimation." Remote Sensing 5, no. 4: 1998-2013.

Correction
Published: 11 September 2012 in Sensors
Reads 0
Downloads 0

Note: In lieu of an abstract, this is an excerpt from the first page. There are two mistakes in this article [1]. On page 523, lines 10–11, the sentence “the value of the SAM is 1, but the value of the ESAM is less than 1” should be “the value of the SAM is 0, but the value of the ESAM is great than 0”. Line 12, “but the value of the ESAM is less than 1” should be “but the value of the ESAM is great than 0 for even n”.

ACS Style

Shaohui Chen; Hongbo Su; Renhua Zhang; Jing Tian; Lihu Yang. Correction: Chen, S. et al. The Tradeoff Analysis for Remote Sensing Image Fusion Using Expanded Spectral Angle Mapper. Sensors 2008, 8, 520-528. Sensors 2012, 12, 12374 -12374.

AMA Style

Shaohui Chen, Hongbo Su, Renhua Zhang, Jing Tian, Lihu Yang. Correction: Chen, S. et al. The Tradeoff Analysis for Remote Sensing Image Fusion Using Expanded Spectral Angle Mapper. Sensors 2008, 8, 520-528. Sensors. 2012; 12 (9):12374-12374.

Chicago/Turabian Style

Shaohui Chen; Hongbo Su; Renhua Zhang; Jing Tian; Lihu Yang. 2012. "Correction: Chen, S. et al. The Tradeoff Analysis for Remote Sensing Image Fusion Using Expanded Spectral Angle Mapper. Sensors 2008, 8, 520-528." Sensors 12, no. 9: 12374-12374.

Journal article
Published: 26 February 2009 in Sensors
Reads 0
Downloads 0

For scaling up low resolution multispectral images (LRMIs) with high resolution panchromatic image (HRPI), intensity-hue-saturation (IHS) can produce satisfactory spatial enhancement but usually introduces spectral distortion in the fused high resolution multispectral images (HRMIs). In this paper, to minimize this problem, we present a generalized intensity modulation (GIM) by extending the IHS transform to an arbitrary number of LRMIs, which uses the information of the spectral response functions (SRFs) of the multispectral and panchromatic sensors. Before modulation, the generalized intensity is enhanced by injecting details extracted from the HRPI by means of empirical mode decomposition. After the enhanced generalized intensity is substituted for the old one, the HRMIs are obtained through the GIM. Quickbird images are used to illustrate the superiority of this proposed method. Extensive comparison results based on visual analysis and Wald’s protocol demonstrate that the proposed method is more encouraging for scaling up the LRMIs with the HRPI spectrally and spatially than the tested fusion methods.

ACS Style

Shaohui Chen; Renhua Zhang; Hongbo Su; Jing Tian; Jun Xia. Scaling-up Transformation of Multisensor Images with Multiple Resolutions. Sensors 2009, 9, 1370 -1381.

AMA Style

Shaohui Chen, Renhua Zhang, Hongbo Su, Jing Tian, Jun Xia. Scaling-up Transformation of Multisensor Images with Multiple Resolutions. Sensors. 2009; 9 (3):1370-1381.

Chicago/Turabian Style

Shaohui Chen; Renhua Zhang; Hongbo Su; Jing Tian; Jun Xia. 2009. "Scaling-up Transformation of Multisensor Images with Multiple Resolutions." Sensors 9, no. 3: 1370-1381.

Journal article
Published: 01 October 2008 in Sensors
Reads 0
Downloads 0

In order to make the prediction of land surface heat fluxes more robust, two improvements were made to an operational two-layer model proposed previously by Zhang. These improvements are: 1) a surface energy balance method is used to determine the theoretical boundary lines (namely ‘true wet/cool edge’ and ‘true dry/warm edge’ in the trapezoid) in the scatter plot for the surface temperature versus the fractional vegetation cover in mixed pixels; 2) a new assumption that the slope of the Tm – f curves is mainly controlled by soil water content is introduced. The variables required by the improved method include near surface vapor pressure, air temperature, surface resistance, aerodynamic resistance, fractional vegetation cover, surface temperature and net radiation. The model predictions from the improved model were assessed in this study by in situ measurements, which show that the total latent heat flux from the soil and vegetation are in close agreement with the in situ measurement with an RMSE (Root Mean Square Error) ranging from 30 w/m2~50 w/m2,which is consistent with the site scale measurement of latent heat flux. Because soil evaporation and vegetation transpiration are not measured separately from the field site, in situ measured CO2 flux is used to examine the modeled λEveg. Similar trends of seasonal variations of vegetation were found for the canopy transpiration retrievals and in situ CO2 flux measurements. The above differences are mainly caused by 1) the scale disparity between the field measurement and the MODIS observation; 2) the non-closure problem of the surface energy balance from the surface fluxes observations themselves. The improved method was successfully used to predict the component surface heat fluxes from the soil and vegetation and it provides a promising approach to study the canopy transpiration and the soil evaporation quantitatively during the rapid growing season of winter wheat in northern China.

ACS Style

Renhua Zhang; Jing Tian; Hongbo Su; Xiaomin Sun; Shaohui Chen; Jun Xia. Two Improvements of an Operational Two-Layer Model for Terrestrial Surface Heat Flux Retrieval. Sensors 2008, 8, 6165 -6187.

AMA Style

Renhua Zhang, Jing Tian, Hongbo Su, Xiaomin Sun, Shaohui Chen, Jun Xia. Two Improvements of an Operational Two-Layer Model for Terrestrial Surface Heat Flux Retrieval. Sensors. 2008; 8 (10):6165-6187.

Chicago/Turabian Style

Renhua Zhang; Jing Tian; Hongbo Su; Xiaomin Sun; Shaohui Chen; Jun Xia. 2008. "Two Improvements of an Operational Two-Layer Model for Terrestrial Surface Heat Flux Retrieval." Sensors 8, no. 10: 6165-6187.

Original articles
Published: 01 September 2008 in International Journal of Remote Sensing
Reads 0
Downloads 0

In this paper, a general formula has been modified, proving that the scaling difference of a surface parameter depends not only on the variance of the surface parameter itself but also on the function structure of the surface parameter. Through quantitatively describing the relationship between scaling differences and measuring scale, in terms of the concept of information fractal dimension and topological dimension, a definition of information fractal dimension used in remote sensing was proposed. By computing the information fractal dimension of Leaf Area Index and surface temperature, we found that the method describes not only the information on spatial texture and spatial structure of remotely sensed data as the traditional methods did, but also illustrates the connection between the scaling difference and measuring scale. Where the information fractal dimension of a surface parameter in some areas is known, the scaling difference can be obtained according to the measuring scale, then it can be eliminated and more accurate results could be achieved after scaling transform. At last, the problems about the relativity of true values of surface parameters were discussed.

ACS Style

Renhua Zhang; Jing Tian; Zhaoliang Li; Xiaomin Sun; Xiaoguang Jiang. Spatial scaling and information fractal dimension of surface parameters used in quantitative remote sensing. International Journal of Remote Sensing 2008, 29, 5145 -5159.

AMA Style

Renhua Zhang, Jing Tian, Zhaoliang Li, Xiaomin Sun, Xiaoguang Jiang. Spatial scaling and information fractal dimension of surface parameters used in quantitative remote sensing. International Journal of Remote Sensing. 2008; 29 (17-18):5145-5159.

Chicago/Turabian Style

Renhua Zhang; Jing Tian; Zhaoliang Li; Xiaomin Sun; Xiaoguang Jiang. 2008. "Spatial scaling and information fractal dimension of surface parameters used in quantitative remote sensing." International Journal of Remote Sensing 29, no. 17-18: 5145-5159.

Journal article
Published: 08 April 2008 in Sensors
Reads 0
Downloads 0

Empirical mode decomposition (EMD) is good at analyzing nonstationary and nonlinear signals while support vector machines (SVMs) are widely used for classification. In this paper, a combination of EMD and SVM is proposed as an improved method for fusing multifocus images. Experimental results show that the proposed method is superior to the fusion methods based on à-trous wavelet transform (AWT) and EMD in terms of quantitative analyses by Root Mean Squared Error (RMSE) and Mutual Information (MI).

ACS Style

Shaohui Chen; Hongbo Su; Renhua Zhang; Jing Tian; Lihu Yang. Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion. Sensors 2008, 8, 2500 -2508.

AMA Style

Shaohui Chen, Hongbo Su, Renhua Zhang, Jing Tian, Lihu Yang. Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion. Sensors. 2008; 8 (4):2500-2508.

Chicago/Turabian Style

Shaohui Chen; Hongbo Su; Renhua Zhang; Jing Tian; Lihu Yang. 2008. "Improving Empirical Mode Decomposition Using Support Vector Machines for Multifocus Image Fusion." Sensors 8, no. 4: 2500-2508.

Journal article
Published: 24 January 2008 in Sensors
Reads 0
Downloads 0

Image fusion is a useful tool in integrating a high-resolution panchromatic image (HRPI) with a low-resolution multispectral image (LRMI) to produce a high-resolution multispectral image (HRMI). To date, many image fusion techniques have been developed to try to improve the spatial resolution of the LRMI to that of the HRPI with its spectral property reliably preserved. However, many studies have indicated that there exists a trade- off between the spatial resolution improvement and the spectral property preservation of the LRMI, and it is difficult for the existing methods to do the best in both aspects. Based on one minimization problem, this paper mathematically analyzes the tradeoff in fusing remote sensing images. In experiment, four fusion methods are evaluated through expanded spectral angle mapper (ESAM). Results clearly prove that all the tested methods have this property.

ACS Style

Shaohui Chen; Hongbo Su; Renhua Zhang; Jing Tian; Lihu Yang. The Tradeoff Analysis for Remote Sensing Image Fusion Using Expanded Spectral Angle Mapper. Sensors 2008, 8, 520 -528.

AMA Style

Shaohui Chen, Hongbo Su, Renhua Zhang, Jing Tian, Lihu Yang. The Tradeoff Analysis for Remote Sensing Image Fusion Using Expanded Spectral Angle Mapper. Sensors. 2008; 8 (1):520-528.

Chicago/Turabian Style

Shaohui Chen; Hongbo Su; Renhua Zhang; Jing Tian; Lihu Yang. 2008. "The Tradeoff Analysis for Remote Sensing Image Fusion Using Expanded Spectral Angle Mapper." Sensors 8, no. 1: 520-528.

Journal article
Published: 08 January 2008 in Sensors
Reads 0
Downloads 0

In this paper, the design of an automatic instrument for measuring the spatial distribution of land surface emissivity is presented, which makes the direct in situ measurement of the spatial distribution of emissivity possible. The significance of this new instrument lies in two aspects. One is that it helps to investigate the spatial scaling behavior of emissivity and temperature; the other is that, the design of the instrument provides theoretical and practical foundations for the implement of measuring distribution of surface emissivity on airborne or spaceborne. To improve the accuracy of the measurements, the emissivity measurement and its uncertainty are examined in a series of carefully designed experiments. The impact of the variation of target temperature and the environmental irradiance on the measurement of emissivity is analyzed as well. In addition, the ideal temperature difference between hot environment and cool environment is obtained based on numerical simulations. Finally, the scaling behavior of surface emissivity caused by the heterogeneity of target is discussed.

ACS Style

Jing Tian; Renhua Zhang; Hongbo Su; Xiaomin Sun; Shaohui Chen; Jun Xia. An Automatic Instrument to Study the Spatial Scaling Behavior of Emissivity. Sensors 2008, 8, 800 -816.

AMA Style

Jing Tian, Renhua Zhang, Hongbo Su, Xiaomin Sun, Shaohui Chen, Jun Xia. An Automatic Instrument to Study the Spatial Scaling Behavior of Emissivity. Sensors. 2008; 8 (2):800-816.

Chicago/Turabian Style

Jing Tian; Renhua Zhang; Hongbo Su; Xiaomin Sun; Shaohui Chen; Jun Xia. 2008. "An Automatic Instrument to Study the Spatial Scaling Behavior of Emissivity." Sensors 8, no. 2: 800-816.