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L. Chai
State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, School of Natural Resources, Beijing Normal University, Beijing 100875, China

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Communication
Published: 22 February 2021 in Remote Sensing
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Successfully applied in the carbon research area, sun-induced chlorophyll fluorescence (SIF) has raised the interest of researchers from the water research domain. However, current works focused on the empirical relationship between SIF and plant transpiration (T), while the mechanistic linkage between them has not been fully explored. Two mechanism methods were developed to estimate T via SIF, namely the water-use efficiency (WUE) method and conductance method based on the carbon–water coupling framework. The T estimated by these two methods was compared with T partitioned from eddy covariance instrument measured evapotranspiration at four different sites. Both methods showed good performance at the hourly (R2 = 0.57 for the WUE method and 0.67 for the conductance method) and daily scales (R2 = 0.67 for the WUE method and 0.78 for the conductance method). The developed mechanism methods provide theoretical support and have a great potential basis for deriving ecosystem T by satellite SIF observations.

ACS Style

Huaize Feng; Tongren Xu; Liangyun Liu; Sha Zhou; Jingxue Zhao; Shaomin Liu; Ziwei Xu; Kebiao Mao; Xinlei He; Zhongli Zhu; Linna Chai. Modeling Transpiration with Sun-Induced Chlorophyll Fluorescence Observations via Carbon-Water Coupling Methods. Remote Sensing 2021, 13, 804 .

AMA Style

Huaize Feng, Tongren Xu, Liangyun Liu, Sha Zhou, Jingxue Zhao, Shaomin Liu, Ziwei Xu, Kebiao Mao, Xinlei He, Zhongli Zhu, Linna Chai. Modeling Transpiration with Sun-Induced Chlorophyll Fluorescence Observations via Carbon-Water Coupling Methods. Remote Sensing. 2021; 13 (4):804.

Chicago/Turabian Style

Huaize Feng; Tongren Xu; Liangyun Liu; Sha Zhou; Jingxue Zhao; Shaomin Liu; Ziwei Xu; Kebiao Mao; Xinlei He; Zhongli Zhu; Linna Chai. 2021. "Modeling Transpiration with Sun-Induced Chlorophyll Fluorescence Observations via Carbon-Water Coupling Methods." Remote Sensing 13, no. 4: 804.

Journal article
Published: 23 January 2021 in Remote Sensing of Environment
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Soil moisture (SM) is a fundamental environmental variable for characterizing climate, land surface and atmosphere. In recent years, several SM products have been developed based on remote sensing (RS), land surface model (LSM) or land data assimilation system (LDAS). However, little knowledge is available in understanding spatial patterns of the uncertainty of different SM products and potential regional drivers over the Qinghai-Tibet Plateau (QTP), a complex environment for accurate SM estimation. This paper investigates the sensitivity of the SM uncertainties based on the three-cornered hat (TCH) method and a generalized additive model (GAM) in terms of underlying surface characteristics (sand fraction, soil organic matter, vegetation, land surface temperature, and topography) and near-ground meteorology (precipitation and air temperature) in the third pole environment over the 2015–2018 period. Eleven SM products are involved in this work, including Soil Moisture Active Passive (SMAP), Soil Moisture Ocean Salinity INRA-CESBIO (SMOS-IC), Japan Aerospace Exploration Agency (JAXA), Land Surface Parameter Model (LPRM), Climate Change Initiative - Active/Combined (CCI_A/CCI_C), Global Land Data Assimilation System (GLDAS), European Centre for Medium-Range Weather Forecasts Interim reanalysis (ERA-Interim), Global Land Evaporation Amsterdam Model product a/b (GLEAM_a/GLEAM_b), and Random Forest Soil Moisture (RFSM). Results show that most of the SM products perform well across QTP, while SMOS-IC is strongly affected by radio-frequency interference in this region, JAXA has a relatively higher noise level over QTP, and LPRM has larger relative uncertainties (RUs) in the southeast of QTP. Nonlinear regression analysis demonstrates that variations of RUs in SMOS-IC and JAXA are driven by topography, while LPRM's are mainly controlled by vegetation. In addition, two groups of opposite (positive/negative) effects from topography and vegetation, topography and precipitation, and precipitation and land surface temperature affect the spatial variations of RUs in CCI_A, RFSM, and ERA-Interim, respectively. Meanwhile, more complex relationships are found between multiple surface factors and RUs of different products. In general, the underlying surface factors explain on average 39.41% and 28.34% of the variations in RS and LSM/LDAS SM RUs, respectively. Comparatively, the near-ground meteorology factors have a slightly larger effect on LSM/LDAS products than that on RS products.

ACS Style

Jin Liu; Linna Chai; Jianzhi Dong; Donghai Zheng; J.-P. Wigneron; Shaomin Liu; Ji Zhou; Tongren Xu; Shiqi Yang; Yongze Song; Yuquan Qu; Zheng Lu. Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method. Remote Sensing of Environment 2021, 255, 112225 .

AMA Style

Jin Liu, Linna Chai, Jianzhi Dong, Donghai Zheng, J.-P. Wigneron, Shaomin Liu, Ji Zhou, Tongren Xu, Shiqi Yang, Yongze Song, Yuquan Qu, Zheng Lu. Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method. Remote Sensing of Environment. 2021; 255 ():112225.

Chicago/Turabian Style

Jin Liu; Linna Chai; Jianzhi Dong; Donghai Zheng; J.-P. Wigneron; Shaomin Liu; Ji Zhou; Tongren Xu; Shiqi Yang; Yongze Song; Yuquan Qu; Zheng Lu. 2021. "Uncertainty analysis of eleven multisource soil moisture products in the third pole environment based on the three-corned hat method." Remote Sensing of Environment 255, no. : 112225.

Journal article
Published: 11 December 2020 in IEEE Transactions on Geoscience and Remote Sensing
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Here, four normalized difference water index (NDWI) variants, i.e., NDWI(860,970), NDWI(860,1240), NDWI(860,1640), and NDWI(1240,1640) are generated from the corn-oriented PROSAIL radiative transfer model. It is found that, instead of the linear relationship derived in previous studies, corn canopy water content (CWC) is best approximated as an exponential function of NDWI. Following the analysis of the PROSAIL-generated results, a newly optimized NDWI-based scheme is proposed for estimating corn CWC according to variations in the performance of the four NDWI variants under different CWC conditions. Validation results based on independent field data from the SMEX02, HiWATER2012, and Baoding2018 field experiments verify that this optimized NDWI-based corn CWC estimating scheme has a higher accuracy (R = 0.87 ± 0.03, RMSE = 0.2068 ± 0.0145 kg/m²) than existing NDWI-based strategies for corn CWC retrieval. The feasibility of retrieving corn vegetation water content (VWC) based on the optimized NDWI-based scheme is also investigated, and the superiority of the optimized NDWI-based scheme for retrieving corn VWC is assessed. By comparing with four other NDWI-based corn VWC estimating methods, as well as the corn VWC parameterization scheme applied in the SMAP soil moisture algorithm, it is shown that our optimized NDWI-based scheme has the best VWC estimation accuracy, with the highest R of 0.89 ± 0.02 and the lowest RMSE of 0.7179 ± 0.0555 kg/m².

ACS Style

Linna Chai; Haiying Jiang; Wade T. Crow; Shaomin Liu; Shaojie Zhao; Jin Liu; Shiqi Yang. Estimating Corn Canopy Water Content From Normalized Difference Water Index (NDWI): An Optimized NDWI-Based Scheme and Its Feasibility for Retrieving Corn VWC. IEEE Transactions on Geoscience and Remote Sensing 2020, PP, 1 -14.

AMA Style

Linna Chai, Haiying Jiang, Wade T. Crow, Shaomin Liu, Shaojie Zhao, Jin Liu, Shiqi Yang. Estimating Corn Canopy Water Content From Normalized Difference Water Index (NDWI): An Optimized NDWI-Based Scheme and Its Feasibility for Retrieving Corn VWC. IEEE Transactions on Geoscience and Remote Sensing. 2020; PP (99):1-14.

Chicago/Turabian Style

Linna Chai; Haiying Jiang; Wade T. Crow; Shaomin Liu; Shaojie Zhao; Jin Liu; Shiqi Yang. 2020. "Estimating Corn Canopy Water Content From Normalized Difference Water Index (NDWI): An Optimized NDWI-Based Scheme and Its Feasibility for Retrieving Corn VWC." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-14.

Journal article
Published: 11 October 2020 in Journal of Hydrology
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Microwave remote sensing is able to retrieve soil moisture (SM) at an adequate level of accuracy. However, these microwave remotely sensed SM products usually have a spatial resolution of tens of kilometers which cannot satisfy the requirements of fine to medium scale applications such as agricultural irrigation and local water resource management. Several SM downscaling methods have been proposed to solve this mismatch by downscaling the coarse-scale SM to fine-scale (several kilometers or hundreds of meters). Although studies have been conducted over different climatic zones and from different data sets with good results, there is still a lack of a comprehensive comparison and evaluation between them to guide the production of high-resolution and high-accuracy SM data. Therefore, in this study we compared several SM downscaling methods (from 0.25° to 0.01°) based on polynormal fitting, physical model, machine learning and geostatistics over the Qinghai-Tibet plateau where there is a wide range of climate conditions from four aspects, that is, comparison with the original microwave product, comparison with in situ measurements, inter-comparison based on three-cornered hat (TCH) method, and a spatial feasibility analysis. The comparison results show that the method based on a physical model, in this case the Disaggregation based on Physical And Theoretical scale Change (DisPATCh) method, has the highest ability on preserving the coarse-scale feature of original microwave SM product, while to some extent, this ability could be a disadvantage for improving the accuracy of the downscaling results. In addition, soil evaporation efficiency (SEE) alone is not sufficient to represent SM spatial patterns over complex land surface. Geostatistics based area-to-area regression Kriging (ATARK) introduces the highest uncertainty caused by the overcorrection during the residual interpolation process while this process can also improve correlation (R) and correct the bias as well as provide more feasible spatial patterns and details. Two machine learning methods, the random forest (RF) and Gaussian process regression (GPR) show high stability on all comparison results but provide smoother spatial patterns. The multivariate statistical regression (MSR) method performs worst due to the fact that its simple linear regression model could not meet the requirement of SM fitting on complicated land surface. Moreover, all five downscaling methods show a declining accuracy after downscaling. This phenomenon may be caused by the spatial mismatch on fine-scale. In addition, this could also be caused by the tendency that downscaled results will usually provide more spatial details from downscaling predictors, while they cannot capture the temporal changes of the microwave SM product well. In general, this phenomenon tends to be more significant over heterogeneous land surface. All in all, five widely used soil moisture downscaling methods were compared based on a comprehensive comparison scheme to add to the body of knowledge in applicability of downcaling methods under different weather conditions.

ACS Style

Yuquan Qu; Zhongli Zhu; Carsten Montzka; Linna Chai; Shaomin Liu; Yong Ge; Jin Liu; Zheng Lu; Xinlei He; Jie Zheng; Tian Han. Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China. Journal of Hydrology 2020, 592, 125616 .

AMA Style

Yuquan Qu, Zhongli Zhu, Carsten Montzka, Linna Chai, Shaomin Liu, Yong Ge, Jin Liu, Zheng Lu, Xinlei He, Jie Zheng, Tian Han. Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China. Journal of Hydrology. 2020; 592 ():125616.

Chicago/Turabian Style

Yuquan Qu; Zhongli Zhu; Carsten Montzka; Linna Chai; Shaomin Liu; Yong Ge; Jin Liu; Zheng Lu; Xinlei He; Jie Zheng; Tian Han. 2020. "Inter-comparison of several soil moisture downscaling methods over the Qinghai-Tibet Plateau, China." Journal of Hydrology 592, no. : 125616.

Journal article
Published: 01 August 2020 in ISPRS Journal of Photogrammetry and Remote Sensing
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Xiaodong 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.

Journal article
Published: 28 August 2019 in Remote Sensing
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The sensing depth of passive microwave remote sensing is a significant factor in quantitative frozen soil studies. In this paper, a microwave radiation response depth (MRRD) was proposed to describe the source of the main signals of passive microwave remote sensing. The main goal of this research was to develop a simple and accurate parameterized model for estimating the MRRD of frozen soil. A theoretical model was introduced first to describe the emission characteristics of a three-layer case, which incorporates multiple reflections at the two boundaries. Based on radiative transfer theory, the total emission of the three layers was calculated. A sensitivity analysis was then performed to demonstrate the effects of soil properties and frequency on the MRRD based on a simulation database comprising a wide range of soil characteristics and frequencies. Sensitivity analysis indicated that soil temperature, soil texture, and frequencies are three of the primary variables affecting MRRD, and a definite empirical relationship existed between the three parameters and the MRRD. Thus, a parameterized model for estimating MRRD was developed based on the sensitivity analysis results. A controlled field experiment using a truck-mounted multi-frequency microwave radiometer (TMMR) was designed and performed to validate the emission model of the soil freeze–thaw cycle and the parameterized model of MRRD developed in this work. The results indicated that the developed parameterized model offers a relatively accurate and simple way of estimating the MRRD. The total root mean square error (RMSE) between the calculated and measured MRRD of frozen loam soil was approximately 0.5 cm for the TMMR’s four frequencies.

ACS Style

Tao Zhang; Lingmei Jiang; Shaojie Zhao; Linna Chai; Yunqing Li; Yuhao Pan. Development of a Parameterized Model to Estimate Microwave Radiation Response Depth of Frozen Soil. Remote Sensing 2019, 11, 2028 .

AMA Style

Tao Zhang, Lingmei Jiang, Shaojie Zhao, Linna Chai, Yunqing Li, Yuhao Pan. Development of a Parameterized Model to Estimate Microwave Radiation Response Depth of Frozen Soil. Remote Sensing. 2019; 11 (17):2028.

Chicago/Turabian Style

Tao Zhang; Lingmei Jiang; Shaojie Zhao; Linna Chai; Yunqing Li; Yuhao Pan. 2019. "Development of a Parameterized Model to Estimate Microwave Radiation Response Depth of Frozen Soil." Remote Sensing 11, no. 17: 2028.

Journal article
Published: 02 April 2019 in Remote Sensing
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High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms.

ACS Style

Jin Liu; Linna Chai; Zheng Lu; Shaomin Liu; Yuquan Qu; Deyuan Geng; Yongze Song; Yabing Guan; Zhixia Guo; Jian Wang; Zhongli Zhu. Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas. Remote Sensing 2019, 11, 792 .

AMA Style

Jin Liu, Linna Chai, Zheng Lu, Shaomin Liu, Yuquan Qu, Deyuan Geng, Yongze Song, Yabing Guan, Zhixia Guo, Jian Wang, Zhongli Zhu. Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas. Remote Sensing. 2019; 11 (7):792.

Chicago/Turabian Style

Jin Liu; Linna Chai; Zheng Lu; Shaomin Liu; Yuquan Qu; Deyuan Geng; Yongze Song; Yabing Guan; Zhixia Guo; Jian Wang; Zhongli Zhu. 2019. "Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas." Remote Sensing 11, no. 7: 792.

Journal article
Published: 21 March 2019 in Remote Sensing
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Time series of soil moisture (SM) data in the Qinghai–Tibet plateau (QTP) covering a period longer than one decade are important for understanding the dynamics of land surface–atmosphere feedbacks in the global climate system. However, most existing SM products have a relatively short time series or show low performance over the challenging terrain of the QTP. In order to improve the spaceborne monitoring in this area, this study presents a random forest (RF) method to rebuild a high-accuracy SM product over the QTP from 19 June 2002 to 31 March 2015 by adopting the advanced microwave scanning radiometer for earth observing system (AMSR-E), and the advanced microwave scanning radiometer 2 (AMSR2), and tracking brightness temperatures with latitude and longitude using the International Geosphere–Biospheres Programme (IGBP) classification data, the digital elevation model (DEM) and the day of the year (DOY) as spatial predictors. Brightness temperature products (from frequencies 10.7 GHz, 18.7 GHz and 36.5 GHz) of AMSR2 were used to train the random forest model on two years of Soil Moisture Active Passive (SMAP) SM data. The simulated SM values were compared with third year SMAP data and in situ stations. The results show that the RF model has high reliability as compared to SMAP, with a high correlation (R = 0.95) and low values of root mean square error (RMSE = 0.03 m3/m3) and mean absolute percent error (MAPE = 19%). Moreover, the random forest soil moisture (RFSM) results agree well with the data from five in situ networks, with mean values of R = 0.75, RMSE = 0.06 m3/m3, and bias = −0.03 m3/m3 over the whole year and R = 0.70, RMSE = 0.07 m3/m3, and bias = −0.05 m3/m3 during the unfrozen seasons. In order to test its performance throughout the whole region of QTP, the three-cornered hat (TCH) method based on removing common signals from observations and then calculating the uncertainties is applied. The results indicate that RFSM has the smallest relative error in 56% of the region, and it performs best relative to the Japan Aerospace Exploration Agency (JAXA), Global Land Data Assimilation System (GLDAS), and European Space Agency’s Climate Change Initiative (ESA CCI) project. The spatial distribution shows that RFSM has a similar spatial trend as GLDAS and ESA CCI, but RFSM exhibits a more distinct spatial distribution and responds to precipitation more effectively than GLDAS and ESA CCI. Moreover, a trend analysis shows that the temporal variation of RFSM agrees well with precipitation and LST (land surface temperature), with a dry trend in most regions of QTP and a wet trend in few north, southeast and southwest regions of QTP. In conclusion, a spatiotemporally continuous SM product with a high accuracy over the QTP was obtained.

ACS Style

Yuquan Qu; Zhongli Zhu; Linna Chai; Shaomin Liu; Carsten Montzka; Jin Liu; Xiaofan Yang; Zheng Lu; Rui Jin; Xiang Li; Zhixia Guo; Jie Zheng. Rebuilding a Microwave Soil Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness Temperature and SMAP over the Qinghai–Tibet Plateau, China. Remote Sensing 2019, 11, 683 .

AMA Style

Yuquan Qu, Zhongli Zhu, Linna Chai, Shaomin Liu, Carsten Montzka, Jin Liu, Xiaofan Yang, Zheng Lu, Rui Jin, Xiang Li, Zhixia Guo, Jie Zheng. Rebuilding a Microwave Soil Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness Temperature and SMAP over the Qinghai–Tibet Plateau, China. Remote Sensing. 2019; 11 (6):683.

Chicago/Turabian Style

Yuquan Qu; Zhongli Zhu; Linna Chai; Shaomin Liu; Carsten Montzka; Jin Liu; Xiaofan Yang; Zheng Lu; Rui Jin; Xiang Li; Zhixia Guo; Jie Zheng. 2019. "Rebuilding a Microwave Soil Moisture Product Using Random Forest Adopting AMSR-E/AMSR2 Brightness Temperature and SMAP over the Qinghai–Tibet Plateau, China." Remote Sensing 11, no. 6: 683.

Conference paper
Published: 01 July 2018 in IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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Vegetation water content (VWC) is a key variable in land-atmosphere interactions and plays an important role in agriculture, climate and hydrology. Based on the first-order scattering model, simulation database of corn backscattering coefficients at L-band was established. The simulations were used to train an artificial neural network (ANN) to establish an inverse model for corn VWC estimation during corn growth periods. The inverse accuracy of the trained ANN was evaluated using ground corn samplings and radar data acquired by the Passive and Active L- and S-band (PALS) airborne microwave sensor during the Soil Moisture Experiments in 2002 (SMEX02). Moreover, the corn VWC inversion results were compared to those obtained from an empirical method using the radar vegetation index (RVI). Result showed that the ANN method is superior to the RVI method and capable of estimating corn VWC with a correlation coefficient (R) of 0.7987, a root mean square error (RMSE) of 0.6033 kg/m 2 and a mean absolute relative error (MARE) of 12.00%.

ACS Style

Wenxing Hu; Linna Chai; Shaojie Zhao. Vegetation Water Content Estimation for Corn by Means of Inverse Modeling from Simulations of the First-Order Scattering Model. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium 2018, 7597 -7600.

AMA Style

Wenxing Hu, Linna Chai, Shaojie Zhao. Vegetation Water Content Estimation for Corn by Means of Inverse Modeling from Simulations of the First-Order Scattering Model. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 2018; ():7597-7600.

Chicago/Turabian Style

Wenxing Hu; Linna Chai; Shaojie Zhao. 2018. "Vegetation Water Content Estimation for Corn by Means of Inverse Modeling from Simulations of the First-Order Scattering Model." IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium , no. : 7597-7600.

Journal article
Published: 16 May 2018 in IEEE Geoscience and Remote Sensing Letters
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The matrix doubling (MD) model is a numerical solution to the radiative transfer equation. It can achieve better accuracy in simulating microwave signals from vegetated terrain by considering multiple-scattering effects. However, it is difficult to apply the MD model to retrieving work due to its high complexity. This letter presents a case study performed on corn to demonstrate a multiangular (5°-65°), multiband (1.4/6.925/10.65 GHz) microwave emission model considering multiple-scattering effects by parameterizing the MD model. The simulated emissivity differences between the theoretical model and parameterized model are small. The mean absolute percent errors are all less than 1%, and the root mean square errors (RMSEs) are all within the range of 10⁻³. Validations using airborne polarimetric L-band microwave radiometer data and ground-based trunk-mounted multifrequency microwave radiometer data indicate that the parameterized model achieves good accuracy with overall RMSEs within 8K at all three bands.

ACS Style

Linna Chai; Qian Zhang; Jiancheng Shi; Shaomin Liu; Shaojie Zhao; Haiying Jiang. A Parameterized Multiangular Microwave Emission Model of L-, C-, and X-Bands for Corn Considering Multiple-Scattering Effects. IEEE Geoscience and Remote Sensing Letters 2018, 15, 1249 -1253.

AMA Style

Linna Chai, Qian Zhang, Jiancheng Shi, Shaomin Liu, Shaojie Zhao, Haiying Jiang. A Parameterized Multiangular Microwave Emission Model of L-, C-, and X-Bands for Corn Considering Multiple-Scattering Effects. IEEE Geoscience and Remote Sensing Letters. 2018; 15 (8):1249-1253.

Chicago/Turabian Style

Linna Chai; Qian Zhang; Jiancheng Shi; Shaomin Liu; Shaojie Zhao; Haiying Jiang. 2018. "A Parameterized Multiangular Microwave Emission Model of L-, C-, and X-Bands for Corn Considering Multiple-Scattering Effects." IEEE Geoscience and Remote Sensing Letters 15, no. 8: 1249-1253.

Journal article
Published: 27 September 2017 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Microwave portion of the electromagnetic spectrum are effective for observations of vegetation not only to leaf but also woody parts of vegetation. Specifically, microwave emissivity varies strongly with surface roughness, polarization, look–angle and water content. Microwave Vegetation Index (MVI) is one of the microwave indexes that are based on zero-order model. The zero order models applicable when the scattering contribution with in the vegetation is negligible. In this paper MVI in different frequencies (Base on WCOM project) and different angles (Base on SMOS data) are calculated by Matrix Doubling model to take in to account multi-scattering effects within the vegetation. Then because MVI is depends on vegetation information we tried to analysis its behaviour in different densities of corn canopy by comparing to vegetation optical depth. The result shows linear relationship with height correlation between MVI and effective optical depth. So it can be a useful index in vegetation study for future satellite mission as WCOM.

ACS Style

S. Talebi; J. Shi; T. Zhao; Y. Li; X. Chuan; L. Chai. EVALUATION OF MICROWAVE VEGETTION INDEX (MVI) IN MULTI-FREQUENCY AND MULTI-ANGLE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2017, XLII-4/W4, 259 -263.

AMA Style

S. Talebi, J. Shi, T. Zhao, Y. Li, X. Chuan, L. Chai. EVALUATION OF MICROWAVE VEGETTION INDEX (MVI) IN MULTI-FREQUENCY AND MULTI-ANGLE. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2017; XLII-4/W4 ():259-263.

Chicago/Turabian Style

S. Talebi; J. Shi; T. Zhao; Y. Li; X. Chuan; L. Chai. 2017. "EVALUATION OF MICROWAVE VEGETTION INDEX (MVI) IN MULTI-FREQUENCY AND MULTI-ANGLE." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W4, no. : 259-263.

Proceedings article
Published: 01 July 2017 in 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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According to the microwave radiation characteristics, this paper introduced a new frozen soil dielectric model to calculate the dielectric constant of frozen and thawed soil based on the Helsinki University of Technology (HUT) microwave snow emission model. The Advanced Integrated Emission Model (AIEM) was used to calculate surface emissivity. The multi-frequency microwave radiation model and soil freeze/thaw discriminant algorithm were improved. The classification accuracies of original and improved soil freeze/thaw discriminant algorithms were validated using AMSR2 Level 3 daily gridded 0.25° brightness temperature products and the measured values obtained by ground-based microwave radiometer. The results showed that compared to the original discriminant algorithm, the frozen soil classification accuracy of the improved discriminant algorithm was effectively improved and the overall classification accuracy reached 82%. It turned out to be a comparatively reliable mode of discrimination.

ACS Style

Wenxing Hu; Linna Chai; Shaojie Zhao; Tianjie Zhao; Zheng Lu. Improvement on soil freeze/thaw discriminant algorithm under complex surface conditions in cold regions. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) 2017, 1599 -1602.

AMA Style

Wenxing Hu, Linna Chai, Shaojie Zhao, Tianjie Zhao, Zheng Lu. Improvement on soil freeze/thaw discriminant algorithm under complex surface conditions in cold regions. 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2017; ():1599-1602.

Chicago/Turabian Style

Wenxing Hu; Linna Chai; Shaojie Zhao; Tianjie Zhao; Zheng Lu. 2017. "Improvement on soil freeze/thaw discriminant algorithm under complex surface conditions in cold regions." 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) , no. : 1599-1602.

Journal article
Published: 08 June 2017 in Remote Sensing
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A method using a nonlinear auto-regressive neural network with exogenous input (NARXnn) to retrieve time series soil moisture (SM) that is spatially and temporally continuous and high quality over the Heihe River Basin (HRB) in China was investigated in this study. The input training data consisted of the X-band dual polarization brightness temperature (TB) and the Ka-band V polarization TB from the Advanced Microwave Scanning Radiometer II (AMSR2), Global Land Satellite product (GLASS) Leaf Area Index (LAI), precipitation from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), and a global 30 arc-second elevation (GTOPO-30). The output training data were generated from fused SM products of the Japan Aerospace Exploration Agency (JAXA) and the Land Surface Parameter Model (LPRM). The reprocessed fused SM from two years (2013 and 2014) was inputted into the NARXnn for training; subsequently, SM during a third year (2015) was estimated. Direct and indirect validations were then performed during the period 2015 by comparing with in situ measurements, SM from JAXA, LPRM and the Global Land Data Assimilation System (GLDAS), as well as precipitation data from TRMM and GPM. The results showed that the SM predictions from NARXnn performed best, as indicated by their higher correlation coefficients (R ≥ 0.85 for the whole year of 2015), lower Bias values (absolute value of Bias ≤ 0.02) and root mean square error values (RMSE ≤ 0.06), and their improved response to precipitation. This method is being used to produce the NARXnn SM product over the HRB in China.

ACS Style

Zheng Lu; Linna Chai; Shaomin Liu; Huizhen Cui; Yanghua Zhang; Lingmei Jiang; Rui Jin; Ziwei Xu. Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China. Remote Sensing 2017, 9, 574 .

AMA Style

Zheng Lu, Linna Chai, Shaomin Liu, Huizhen Cui, Yanghua Zhang, Lingmei Jiang, Rui Jin, Ziwei Xu. Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China. Remote Sensing. 2017; 9 (6):574.

Chicago/Turabian Style

Zheng Lu; Linna Chai; Shaomin Liu; Huizhen Cui; Yanghua Zhang; Lingmei Jiang; Rui Jin; Ziwei Xu. 2017. "Estimating Time Series Soil Moisture by Applying Recurrent Nonlinear Autoregressive Neural Networks to Passive Microwave Data over the Heihe River Basin, China." Remote Sensing 9, no. 6: 574.

Journal article
Published: 29 January 2016 in Remote Sensing
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Land surface temperature (LST) plays a major role in the study of surface energy balances. Remote sensing techniques provide ways to monitor LST at large scales. However, due to atmospheric influences, significant missing data exist in LST products retrieved from satellite thermal infrared (TIR) remotely sensed data. Although passive microwaves (PMWs) are able to overcome these atmospheric influences while estimating LST, the data are constrained by low spatial resolution. In this study, to obtain complete and high-quality LST data, the Bayesian Maximum Entropy (BME) method was introduced to merge 0.01° and 0.25° LSTs inversed from MODIS and AMSR-E data, respectively. The result showed that the missing LSTs in cloudy pixels were filled completely, and the availability of merged LSTs reaches 100%. Because the depths of LST and soil temperature measurements are different, before validating the merged LST, the station measurements were calibrated with an empirical equation between MODIS LST and 0~5 cm soil temperatures. The results showed that the accuracy of merged LSTs increased with the increasing quantity of utilized data, and as the availability of utilized data increased from 25.2% to 91.4%, the RMSEs of the merged data decreased from 4.53 °C to 2.31 °C. In addition, compared with the filling gap method in which MODIS LST gaps were filled with AMSR-E LST directly, the merged LSTs from the BME method showed better spatial continuity. The different penetration depths of TIR and PMWs may influence fusion performance and still require further studies.

ACS Style

Xiaokang Kou; Lingmei Jiang; Yanchen Bo; Shuang Yan; Linna Chai. Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method. Remote Sensing 2016, 8, 105 .

AMA Style

Xiaokang Kou, Lingmei Jiang, Yanchen Bo, Shuang Yan, Linna Chai. Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method. Remote Sensing. 2016; 8 (2):105.

Chicago/Turabian Style

Xiaokang Kou; Lingmei Jiang; Yanchen Bo; Shuang Yan; Linna Chai. 2016. "Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method." Remote Sensing 8, no. 2: 105.

Journal article
Published: 24 August 2015 in Remote Sensing
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The Soil Moisture and Ocean Salinity (SMOS) mission was initiated in 2009 with the goal of acquiring global soil moisture data over land using multi-angular L-band radiometric measurements. Specifically, surface soil moisture was estimated using the L-band Microwave Emission of the Biosphere (L-MEB) radiative transfer model. This study evaluated the applicability of this model to the Heihe River Basin in Northern China for specific underlying surfaces by simulating brightness temperature (BT) with the L-MEB model. To analyze the influence of a ground sampling strategy on the simulations, two resampling methods based on ground observations were compared. In the first method, the simulated BT of each point observation was initially acquired. The simulations were then resampled at a 1 km resolution. The other method was based on gridded data with a resolution of 1 km averaged from point observations, such as soil moisture, soil temperature, and soil texture. The simulated BTs at a 1 km resolution were then obtained using the L-MEB model. Because of the large variability in soil moisture, the resampling method based on gridded data was used in the simulation. The simulated BTs based on the calibrated parameters were validated using airborne L-band data from the Polarimetric L-band Multibeam Radiometer (PLMR) acquired during the HiWATER project. The root mean square errors (RMSEs) between the simulated results and the PLMR data were 6 to 7 K for V-polarization and 3 to 5 K for H-polarization at different angles. These results demonstrate that the model effectively represents agricultural land surfaces, and this study will serve as a reference for applying the L-MEB model in arid regions and for selecting a ground sampling strategy.

ACS Style

Shuang Yan; Lingmei Jiang; Linna Chai; Juntao Yang; Xiaokang Kou. Calibration of the L-MEB Model for Croplands in HiWATER Using PLMR Observation. Remote Sensing 2015, 7, 10878 -10897.

AMA Style

Shuang Yan, Lingmei Jiang, Linna Chai, Juntao Yang, Xiaokang Kou. Calibration of the L-MEB Model for Croplands in HiWATER Using PLMR Observation. Remote Sensing. 2015; 7 (8):10878-10897.

Chicago/Turabian Style

Shuang Yan; Lingmei Jiang; Linna Chai; Juntao Yang; Xiaokang Kou. 2015. "Calibration of the L-MEB Model for Croplands in HiWATER Using PLMR Observation." Remote Sensing 7, no. 8: 10878-10897.

Journal article
Published: 17 August 2015 in Remote Sensing
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In this study, an algorithm to retrieve the gravimetric vegetation water content (GVWC, %) of corn was developed. First, the method for obtaining the optical depth from L-band (1.4 GHz) bi-angular, dual-polarized brightness temperatures (TB) for short vegetation was investigated. Then, the quantitative relationship between the corn optical depth, corn GVWC and corn leaf area index (LAI) was constructed. Finally, using the Polarimetric L-band Microwave Radiometer (PLMR) airborne data in the 2012 Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project, the Global Land Surface Satellite (GLASS) LAI product, the height and areal density of the corn stalks, the corn GVWC was estimated (corn GLASS-GVWC). Both the in situ measured corn GVWC and the corn GVWC retrieved based on the in situ measured corn LAI (corn LAINET-GVWC) were used to validate the accuracy of the corn GLASS-GVWC. The results show that the GVWC retrieval method proposed in this study is feasible for monitoring the corn GVWC. However, the accuracy of the retrieval results is highly sensitive to the accuracy of the LAI input parameters.

ACS Style

Qi Wang; Linna Chai; Shaojie Zhao; Zhongjun Zhang. Gravimetric Vegetation Water Content Estimation for Corn Using L-Band Bi-Angular, Dual-Polarized Brightness Temperatures and Leaf Area Index. Remote Sensing 2015, 7, 10543 -10561.

AMA Style

Qi Wang, Linna Chai, Shaojie Zhao, Zhongjun Zhang. Gravimetric Vegetation Water Content Estimation for Corn Using L-Band Bi-Angular, Dual-Polarized Brightness Temperatures and Leaf Area Index. Remote Sensing. 2015; 7 (8):10543-10561.

Chicago/Turabian Style

Qi Wang; Linna Chai; Shaojie Zhao; Zhongjun Zhang. 2015. "Gravimetric Vegetation Water Content Estimation for Corn Using L-Band Bi-Angular, Dual-Polarized Brightness Temperatures and Leaf Area Index." Remote Sensing 7, no. 8: 10543-10561.

Journal article
Published: 17 November 2014 in International Journal of Remote Sensing
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ACS Style

Linna Chai; Lixin Zhang; Yuanyuan Zhang; Zhenguo Hao; Lingmei Jiang; Shaojie Zhao. Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery. International Journal of Remote Sensing 2014, 35, 7631 -7649.

AMA Style

Linna Chai, Lixin Zhang, Yuanyuan Zhang, Zhenguo Hao, Lingmei Jiang, Shaojie Zhao. Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery. International Journal of Remote Sensing. 2014; 35 (22):7631-7649.

Chicago/Turabian Style

Linna Chai; Lixin Zhang; Yuanyuan Zhang; Zhenguo Hao; Lingmei Jiang; Shaojie Zhao. 2014. "Comparison of the classification accuracy of three soil freeze–thaw discrimination algorithms in China using SSMIS and AMSR-E passive microwave imagery." International Journal of Remote Sensing 35, no. 22: 7631-7649.

Journal article
Published: 05 September 2014 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Freeze/thaw erosion is the third largest soil erosion type, after water erosion and wind erosion and is a serious threat to agricultural land and various structures, especially those used in water projects. In this paper, an investigation into the feasibility of using passive microwave remote sensing to monitor freeze/thaw erosion area and intensity in China is presented. At the core of the method are two important indices: average annual cumulative frozen days and average daily normalized phase change water. The relationships between the first index and erosion area and between the second index and erosion intensity were explored. The results show that the index of average annual cumulative frozen days can distinguish freeze/thaw erosion areas from nonfreeze/thaw erosion areas if an appropriate threshold is selected and that the index of average daily normalized phase change water is correlated with the freeze/thaw erosion intensity, which can be described using a logarithmic function. Finally, the freeze/thaw erosion area and intensity obtained from passive microwave remote sensing were compared with a grading thematic map of freeze/thaw erosion obtained in the First National Census for Water sponsored by the Ministry of Water Resources of China. The results agree with the grading thematic map of the freeze/thaw erosion intensity in China. The results also demonstrate that passive microwave remote sensing has the potential to monitor freeze/thaw erosion, and it is feasible to use the two indices to determine freeze/thaw erosion area and intensity.

ACS Style

Linna Chai; Lixin Zhang; Xiaoran Lv; Zhenguo Hao; Shuzhen Liu. An Investigation Into the Feasibility of Using Passive Microwave Remote Sensing to Monitor Freeze/Thaw Erosion in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2014, 8, 4460 -4469.

AMA Style

Linna Chai, Lixin Zhang, Xiaoran Lv, Zhenguo Hao, Shuzhen Liu. An Investigation Into the Feasibility of Using Passive Microwave Remote Sensing to Monitor Freeze/Thaw Erosion in China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2014; 8 (9):4460-4469.

Chicago/Turabian Style

Linna Chai; Lixin Zhang; Xiaoran Lv; Zhenguo Hao; Shuzhen Liu. 2014. "An Investigation Into the Feasibility of Using Passive Microwave Remote Sensing to Monitor Freeze/Thaw Erosion in China." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, no. 9: 4460-4469.

Proceedings article
Published: 01 July 2014 in 2014 IEEE Geoscience and Remote Sensing Symposium
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Soil moisture is an important parameter in many fields. Since the dielectric constant of soil is directly related with its moisture content, many soil dielectric constant models have been established and used in the application of soil moisture inversion. As an effective composition of soil, organic matter could increase the adsorption of soil particles and affect the dielectric constant. However, due to its little content, it was seldom considered in soil moisture inversion and brightness simulation. In this study, a semi-empirical organic dielectric model was used in the forward simulation of brightness temperature in Genhe River basin. The results show that it has a higher accuracy about 1.6k~2.4k than using TMD model at C-band and X-band.

ACS Style

Xiaokang Kou; Lingmei Jiang; Shaojie Zhao; Shuang Yan; Linna Chai. Evaluation of organic matter effect on brightness temperature simulated over Genhe region, China. 2014 IEEE Geoscience and Remote Sensing Symposium 2014, 3307 -3310.

AMA Style

Xiaokang Kou, Lingmei Jiang, Shaojie Zhao, Shuang Yan, Linna Chai. Evaluation of organic matter effect on brightness temperature simulated over Genhe region, China. 2014 IEEE Geoscience and Remote Sensing Symposium. 2014; ():3307-3310.

Chicago/Turabian Style

Xiaokang Kou; Lingmei Jiang; Shaojie Zhao; Shuang Yan; Linna Chai. 2014. "Evaluation of organic matter effect on brightness temperature simulated over Genhe region, China." 2014 IEEE Geoscience and Remote Sensing Symposium , no. : 3307-3310.

Conference paper
Published: 01 July 2014 in 2014 IEEE Geoscience and Remote Sensing Symposium
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A useful indicator to evaluate the soil freeze-thaw intensity is the amount of phase transition water content (PTWC) in soil pores. In this research, a power function relation between soil phase transition water content (PTWC) and the variation rate of land surface temperature (VTS) was found by analyzing of ground measured soil moisture and temperature data obtained in the Tibet plateau during the winter of 2012. Then a downscaling approach combining MODIS VTs and AMSR2 products was employed to retrieve high resolution PTWC. The downscaled result was tested using in situ observations from the CTP-SMTMN network and found that it quite followed the trend of ground data with a RMSE of 0.0034 (m3/m3) and MAE of 0.0025 (m3/m3). The comparisons indicate that PTWC-VTS model has combined the advantage of microwave remote sensing and optical remote sensing; it has a high precision and can generate PTWC in small scale.

ACS Style

Qinyu Ye; Linna Chai; Lingmei Jiang; Shaojie Zhao. A downscaling approach of phase transition water content using AMSR2 and MODIS products. 2014 IEEE Geoscience and Remote Sensing Symposium 2014, 3323 -3326.

AMA Style

Qinyu Ye, Linna Chai, Lingmei Jiang, Shaojie Zhao. A downscaling approach of phase transition water content using AMSR2 and MODIS products. 2014 IEEE Geoscience and Remote Sensing Symposium. 2014; ():3323-3326.

Chicago/Turabian Style

Qinyu Ye; Linna Chai; Lingmei Jiang; Shaojie Zhao. 2014. "A downscaling approach of phase transition water content using AMSR2 and MODIS products." 2014 IEEE Geoscience and Remote Sensing Symposium , no. : 3323-3326.