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Rui Jin
Laboratory of Remote Sensing and Geospatial Sciences, Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, Gansu, China, 730000

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Journal article
Published: 11 March 2021 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Data fusion can effectively improve the accuracy of remotely sensed (RS) soil moisture (SM) products. Understanding the error structures of RS SM products is beneficial for formulating a data fusion scheme. In this paper, a data fusion scheme is examined on the Tibetan Plateau (TP), and the Soil Moisture Active Passive (SMAP) mission, Soil Moisture and Ocean Salinity (SMOS) mission and Advanced Microwave Scanning Radiometer 2 (AMSR2) products are used as the experimental input datasets. The RS apparent thermal inertia (ATI) is transformed into SM values as the reference data having reliable systemic variability. The ATI-based SM along with three RS SM products are introduced into the triple collocation (TC) method to decompose errors of three RS SM products into the systemic and random errors at each RS pixel. Due to the presence of systemic errors, the temporal mean values and the amplitudes of the three RS SM products were calibrated by those of the ATI-based SM. The rescaled anomalies (including amplitude and random error) were merged according to their random errors estimated by the TC method, and then the merged anomalies were added to the temporal mean values of the ATI-based SM to obtain the final merged results. Compared with the merged ESA CCI passive SM product and input soil moisture datasets, the merged results in this paper exhibit optimal accuracy. The scheme for merging RS SM products shows high data fusion performance and can be further considered a reliable way to obtain a high-quality merged RS SM dataset.

ACS Style

Jian Kang; Rui Jin; Xin Li. An Advanced Framework for Merging Remotely Sensed Soil Moisture Products at the Regional Scale Supported by Error Structure Analysis: A Case Study on the Tibetan Plateau. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 3614 -3624.

AMA Style

Jian Kang, Rui Jin, Xin Li. An Advanced Framework for Merging Remotely Sensed Soil Moisture Products at the Regional Scale Supported by Error Structure Analysis: A Case Study on the Tibetan Plateau. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):3614-3624.

Chicago/Turabian Style

Jian Kang; Rui Jin; Xin Li. 2021. "An Advanced Framework for Merging Remotely Sensed Soil Moisture Products at the Regional Scale Supported by Error Structure Analysis: A Case Study on the Tibetan Plateau." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 3614-3624.

Journal article
Published: 11 January 2021 in Remote Sensing
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In recent decades, microwave remote sensing (RS) has been used to measure soil moisture (SM). Long-term and large-scale RS SM datasets derived from various microwave sensors have been used in environmental fields. Understanding the accuracies of RS SM products is essential for their proper applications. However, due to the mismatched spatial scale between the ground-based and RS observations, the truth at the pixel scale may not be accurately represented by ground-based observations, especially when the spatial density of in situ measurements is low. Because ground-based observations are often sparsely distributed, temporal upscaling was adopted to transform a few in situ measurements into SM values at a pixel scale of 1 km by introducing the temperature vegetation dryness index (TVDI) related to SM. The upscaled SM showed high consistency with in situ SM observations and could accurately capture rainfall events. The upscaled SM was considered as the reference data to evaluate RS SM products at different spatial scales. In regard to the validation results, in addition to the correlation coefficient (R) of the Soil Moisture Active Passive (SMAP) SM being slightly lower than that of the Climate Change Initiative (CCI) SM, SMAP had the best performance in terms of the root-mean-square error (RMSE), unbiased RMSE and bias, followed by the CCI. The Soil Moisture and Ocean Salinity (SMOS) products were in worse agreement with the upscaled SM and were inferior to the R value of the X-band SM of the Advanced Microwave Scanning Radiometer 2 (AMSR2). In conclusion, in the study area, the SMAP and CCI SM are more reliable, although both products were underestimated by 0.060 cm3 cm−3 and 0.077 cm3 cm−3, respectively. If the biases are corrected, then the improved SMAP with an RMSE of 0.043 cm3 cm−3 and the CCI with an RMSE of 0.039 cm3 cm−3 will hopefully reach the application requirement for an accuracy with an RMSE less than 0.040 cm3 cm−3.

ACS Style

Jian Kang; Rui Jin; Xin Li; Yang Zhang. Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products. Remote Sensing 2021, 13, 228 .

AMA Style

Jian Kang, Rui Jin, Xin Li, Yang Zhang. Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products. Remote Sensing. 2021; 13 (2):228.

Chicago/Turabian Style

Jian Kang; Rui Jin; Xin Li; Yang Zhang. 2021. "Mapping High Spatiotemporal-Resolution Soil Moisture by Upscaling Sparse Ground-Based Observations Using a Bayesian Linear Regression Method for Comparison with Microwave Remotely Sensed Soil Moisture Products." Remote Sensing 13, no. 2: 228.

Journal article
Published: 11 October 2020 in Remote Sensing
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Accurate and continuous monitoring of leaf area index (LAI), a widelyused vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remotesensingderived LAI. This paper evaluates the performance of LAI retrieval from multisource, remotely sensed data through comparisons with continuous field LAI measurements. Firstly, field LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology, over an agricultural region located at the Heihe watershed at northwestern China. Then, cloudfree images from optical satellite sensors, including Landsat 7 the Enhanced Thematic Mapper Plus (ETM+), Landsat 8 the Operational Land Imager (OLI), and Sentinel2A/B Multispectral Instrument (MSI), were collected to derive LAI through inversion of the PROSAIL radiation transfer model using a lookuptable (LUT) approach. Finally, field LAI data were used to validate the multi-temporal LAI retrieved from remotesensing data acquired by different satellite sensors. The results indicate that good accuracy was obtained using different inversion strategies for each sensor, while Green Chlorophyll Index (CIgreen) and a combination of three red-edge bands perform better for Landsat 7/8 and Sentinel2 LAI inversion, respectively. Furthermore, the estimated LAI has good consistency with in situ measurements at vegetative stage (coefficient of determination R2 = 0.74, and root mean square error RMSE = 0.53 m2 m−2). At the reproductive stage, a significant underestimation was found (R2 = 0.41, and 0.89 m2 m−2 in terms of RMSE). This study suggests that timeseries LAI can be retrieved from multisource satellite data through model inversion, and the LAINet instrument could be used as a lowcost tool to provide continuous field LAI measurements to support LAI retrieval.

ACS Style

Lihong Yu; Jiali Shang; Zhiqiang Cheng; Zebin Gao; Zixin Wang; Luo Tian; Dantong Wang; Tao Che; Rui Jin; Jiangui Liu; Taifeng Dong; Yonghua Qu. Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network. Remote Sensing 2020, 12, 3304 .

AMA Style

Lihong Yu, Jiali Shang, Zhiqiang Cheng, Zebin Gao, Zixin Wang, Luo Tian, Dantong Wang, Tao Che, Rui Jin, Jiangui Liu, Taifeng Dong, Yonghua Qu. Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network. Remote Sensing. 2020; 12 (20):3304.

Chicago/Turabian Style

Lihong Yu; Jiali Shang; Zhiqiang Cheng; Zebin Gao; Zixin Wang; Luo Tian; Dantong Wang; Tao Che; Rui Jin; Jiangui Liu; Taifeng Dong; Yonghua Qu. 2020. "Assessment of Cornfield LAI Retrieved from Multi-Source Satellite Data Using Continuous Field LAI Measurements Based on a Wireless Sensor Network." Remote Sensing 12, no. 20: 3304.

Journal article
Published: 02 October 2020 in Sensors
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Recently, wireless sensor networks (WSNs) have been extensively deployed to monitor environments. Sensor nodes are susceptible to fault generation due to hardware and software failures in harsh environments. Anomaly detection for the time-series streaming data of sensor nodes is a challenging but critical fault diagnosis task, particularly in large-scale WSNs. The data-driven approach is becoming essential for the goal of improving the reliability and stability of WSNs. We propose a data-driven anomaly detection approach in this paper, named median filter (MF)-stacked long short-term memory-exponentially weighted moving average (LSTM-EWMA), for time-series status data, including the operating voltage and panel temperature recorded by a sensor node deployed in the field. These status data can be used to diagnose device anomalies. First, a median filter (MF) is introduced as a preprocessor to preprocess obvious anomalies in input data. Then, stacked long short-term memory (LSTM) is employed for prediction. Finally, the exponentially weighted moving average (EWMA) control chart is employed as a detector for recognizing anomalies. We evaluate the proposed approach for the panel temperature and operating voltage of time-series streaming data recorded by wireless node devices deployed in harsh field conditions for environmental monitoring. Extensive experiments were conducted on real time-series status data. The results demonstrate that compared to other approaches, the MF-stacked LSTM-EWMA approach can significantly improve the detection rate (DR) and false rate (FR). The average DR and FR values with the proposed approach are 95.46% and 4.42%, respectively. MF-stacked LSTM-EWMA anomaly detection also achieves a better F2 score than that achieved by other methods. The proposed approach provides valuable insights for anomaly detection in WSNs by detecting anomalies in the time-series status data recorded by wireless sensor nodes.

ACS Style

Minghu Zhang; Jianwen Guo; Xin Li; Rui Jin. Data-Driven Anomaly Detection Approach for Time-Series Streaming Data. Sensors 2020, 20, 5646 .

AMA Style

Minghu Zhang, Jianwen Guo, Xin Li, Rui Jin. Data-Driven Anomaly Detection Approach for Time-Series Streaming Data. Sensors. 2020; 20 (19):5646.

Chicago/Turabian Style

Minghu Zhang; Jianwen Guo; Xin Li; Rui Jin. 2020. "Data-Driven Anomaly Detection Approach for Time-Series Streaming Data." Sensors 20, no. 19: 5646.

Letter
Published: 21 September 2020 in Remote Sensing
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Remote sensing (RS) soil moisture (SM) products have been widely used in various environmental studies. Understanding the error structure of data is necessary to properly apply RS SM products in trend and variation analysis and data fusion. However, a spatially continuous assessment of RS SM datasets is impeded by the limited spatial distribution of ground-based observations. As an alternative, the RS apparent thermal inertia (ATI) data related to the SM are transformed into SM values to expand the validation space. To obtain error components, the ATI-based SM along with the Soil Moisture Active Passive Mission (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) SM are applied with the triple-collocation (TC) method to evaluate the RS SM data regarding random errors and amplitude variances at the regional scale. When the ATI-based SM is regarded as the reference data, the amplitude biases of the other two datasets are determined. The mean bias is also estimated by calculating the mean value difference between the ATI-based and validated RS SM. The results show that the ATI-based SM is a reliable source of reference data that, when combined with the TC method, can correctly estimate the error structure of RS SM datasets in wide space, promoting the reasonable application and calibration of RS SM datasets.

ACS Style

Jian Kang; Rui Jin; Xin Li; Yang Zhang. Error Decomposition of Remote Sensing Soil Moisture Products Based on the Triple-Collocation Method Introducing an Unbiased Reference Dataset: A Case Study on the Tibetan Plateau. Remote Sensing 2020, 12, 3087 .

AMA Style

Jian Kang, Rui Jin, Xin Li, Yang Zhang. Error Decomposition of Remote Sensing Soil Moisture Products Based on the Triple-Collocation Method Introducing an Unbiased Reference Dataset: A Case Study on the Tibetan Plateau. Remote Sensing. 2020; 12 (18):3087.

Chicago/Turabian Style

Jian Kang; Rui Jin; Xin Li; Yang Zhang. 2020. "Error Decomposition of Remote Sensing Soil Moisture Products Based on the Triple-Collocation Method Introducing an Unbiased Reference Dataset: A Case Study on the Tibetan Plateau." Remote Sensing 12, no. 18: 3087.

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.

Journal article
Published: 18 March 2019 in Remote Sensing
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Hydro-agricultural applications often require surface soil moisture (SM) information at high spatial resolutions. In this study, daily spatial patterns of SM at a spatial resolution of 1 km over the Babao River Basin in northwestern China were mapped using a Bayesian-based upscaling algorithm, which upscaled point-scale measurements to the grid-scale (1 km) by retrieving SM information using Moderate Resolution Imaging Spectroradiometer (MODIS)-derived land surface temperature (LST) and topography data (including aspect and elevation data) and in situ measurements from a wireless sensor network (WSN). First, the time series of pixel-scale (1 km) representative SM information was retrieved from in situ measurements of SM, topography data, and LST. Second, Bayesian linear regression was used to calibrate the relationship between the representative SM and the WSN measurements. Last, the calibrated relationship was used to upscale a network of in situ measured SM to map spatially continuous SM at a high resolution. The upscaled SM data were evaluated against ground-based SM measurements with satisfactory accuracy—the overall correlation coefficient (r), slope, and unbiased root mean square difference (ubRMSD) values were 0.82, 0.61, and 0.025 m3/m3, respectively. Moreover, when accounting for topography, the proposed upscaling algorithm outperformed the algorithm based only on SM derived from LST (r = 0.80, slope = 0.31, and ubRMSD = 0.033 m3/m3). Notably, the proposed upscaling algorithm was able to capture the dynamics of SM under extreme dry and wet conditions. In conclusion, the proposed upscaled method can provide accurate high-resolution SM estimates for hydro-agricultural applications.

ACS Style

Lei Fan; A. Al-Yaari; Frédéric Frappart; Jennifer J. Swenson; Qing Xiao; Jianguang Wen; Rui Jin; Jian Kang; Xiaojun Li; R. Fernandez-Moran; J.-P. Wigneron. Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures. Remote Sensing 2019, 11, 656 .

AMA Style

Lei Fan, A. Al-Yaari, Frédéric Frappart, Jennifer J. Swenson, Qing Xiao, Jianguang Wen, Rui Jin, Jian Kang, Xiaojun Li, R. Fernandez-Moran, J.-P. Wigneron. Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures. Remote Sensing. 2019; 11 (6):656.

Chicago/Turabian Style

Lei Fan; A. Al-Yaari; Frédéric Frappart; Jennifer J. Swenson; Qing Xiao; Jianguang Wen; Rui Jin; Jian Kang; Xiaojun Li; R. Fernandez-Moran; J.-P. Wigneron. 2019. "Mapping Soil Moisture at a High Resolution over Mountainous Regions by Integrating In Situ Measurements, Topography Data, and MODIS Land Surface Temperatures." Remote Sensing 11, no. 6: 656.

Journal article
Published: 13 December 2018 in Vadose Zone Journal
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Research on land surface processes at the catchment scale has drawn much attention over the past few decades, and a number of watershed observatories have been established worldwide. The Heihe River Basin (HRB), which contains the second largest inland river in China, is an ideal natural field experimental area for investigation of land surface processes involving diverse landscapes and the coexistence of cold and arid regions. The Heihe Integrated Observatory Network was established in 2007. For long-term observations, a hydrometeorological observatory, ecohydrological wireless sensor network, and satellite remote sensing are now in operation. In 2012, a multiscale observation experiment on evapotranspiration over heterogeneous land surfaces was conducted in the midstream region of the HRB, which included a flux observation matrix, wireless sensor network, airborne remote sensing, and synchronized ground measurements. Under an open data policy, the datasets have been publicly released following careful data processing and quality control. The outcomes highlight the integrated research on land surface processes in the HRB and include observed trends, scaling methods, high spatiotemporal resolution remote sensing products, and model–data integration in the HRB, all of which are helpful to other endorheic basins in the “Silk Road Economic Belt.” Henceforth, the goal of the Heihe Integrated Observatory Network is to develop an intelligent monitoring system that incorporates ground-based observatory networks, unmanned aerial vehicles, and multi-source satellites through the Internet of Things technology. Furthermore, biogeochemical processes observation will be improved, and the study of integrating ground observations, remote sensing, and large-scale models will be promoted further. Copyright © 2018. . Copyright © by the Soil Science Society of America, Inc.

ACS Style

Shaomin Liu; Xin Li; Ziwei Xu; Tao Che; Qing Xiao; Mingguo Ma; Qinhuo Liu; Rui Jin; Jianwen Guo; Liangxu Wang; Weizhen Wang; Yuan Qi; Hongyi Li; Tongren Xu; Youhua Ran; Xiaoli Hu; ShengJin Shi; Zhongli Zhu; Junlei Tan; Yang Zhang; Zhiguo Ren. The Heihe Integrated Observatory Network: A Basin-Scale Land Surface Processes Observatory in China. Vadose Zone Journal 2018, 17, 180072 .

AMA Style

Shaomin Liu, Xin Li, Ziwei Xu, Tao Che, Qing Xiao, Mingguo Ma, Qinhuo Liu, Rui Jin, Jianwen Guo, Liangxu Wang, Weizhen Wang, Yuan Qi, Hongyi Li, Tongren Xu, Youhua Ran, Xiaoli Hu, ShengJin Shi, Zhongli Zhu, Junlei Tan, Yang Zhang, Zhiguo Ren. The Heihe Integrated Observatory Network: A Basin-Scale Land Surface Processes Observatory in China. Vadose Zone Journal. 2018; 17 (1):180072.

Chicago/Turabian Style

Shaomin Liu; Xin Li; Ziwei Xu; Tao Che; Qing Xiao; Mingguo Ma; Qinhuo Liu; Rui Jin; Jianwen Guo; Liangxu Wang; Weizhen Wang; Yuan Qi; Hongyi Li; Tongren Xu; Youhua Ran; Xiaoli Hu; ShengJin Shi; Zhongli Zhu; Junlei Tan; Yang Zhang; Zhiguo Ren. 2018. "The Heihe Integrated Observatory Network: A Basin-Scale Land Surface Processes Observatory in China." Vadose Zone Journal 17, no. 1: 180072.

Journal article
Published: 12 July 2018 in Remote Sensing
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Land surface temperature (LST) products derived from the moderate resolution imaging spectroradiometer (MODIS) sensor are one of the most important data sources used to research land surface energy and water balance at regional and global scales. However, MODIS data are severely contaminated by cloud cover, which limits the applications of LST products. In this paper, based on the spatio-temporal autocorrelation of land surface variables, a reconstruction algorithm depending on the correlations between spatial pixels in multiple time phases from available MODIS LST data is developed to reconstruct clear-sky LST values for missing pixels. Considering the impacts of correlation and bias between predictors and reconstructed data on the modeling error, the known data in the reconstructed time phase are combined with the data temporally nearest to them as predictor variables to establish their temporal relationships with the reconstructed data. The reconstructed results are validated by a series of evaluation indices. The average correlation coefficient between the reconstructed results and ground-based observations is 0.87, showing high temporal change accuracy. The difference in Moran’s I, representing spatial structure characteristics between the known and reconstructed data, is 0.03 on average, indicating a slight loss of spatial accuracy. The average reconstruction rate is approximately 87.0%. The modeling error, as part of the reconstruction error, is only 1.40 K on average and accounts for 5.0% of the total error. If the product and modeling errors are removed, the residual error represents approximately 3.5 K and 5.6 K of the annual mean difference between the cloudy and cloudless LST at night and during the day, respectively. In addition, different reconstruction cases are demonstrated using various predictor data, including many combinations of multi-temporal MODIS LST data, the microwave brightness temperature, and the combination of the normalized difference vegetation index and terrain data. Comparisons among cases show that the known MODIS LST data are more reliable as predictor variables and that the data combination advocated in this paper is optimal.

ACS Style

Jian Kang; Junlei Tan; Rui Jin; Xin Li; Yang Zhang. Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information. Remote Sensing 2018, 10, 1112 .

AMA Style

Jian Kang, Junlei Tan, Rui Jin, Xin Li, Yang Zhang. Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information. Remote Sensing. 2018; 10 (7):1112.

Chicago/Turabian Style

Jian Kang; Junlei Tan; Rui Jin; Xin Li; Yang Zhang. 2018. "Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information." Remote Sensing 10, no. 7: 1112.

Letter
Published: 28 January 2018 in Remote Sensing
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Available ground-based observation networks for the validation of soil moisture remote sensing products are commonly sparse; thus, ground truth determinations are difficult at the validated remote sensing pixel scale. Based on the consistency of temporal trends between ground truth and in situ measurements, it is feasible to estimate ground truth by building a linear relationship between temporal sparse ground observations and truth samples. Herein, auxiliary remote sensing data with a moderate spatial resolution can be transformed into truth samples depending on the stronger representation of remote sensing data to spatial heterogeneity in the validated pixel relative to limited sites. When solving weighting coefficients for the relationship model, the underlying correlations among the in situ measurements cause the multicollinearity problem, leading to failed predictions. An upscaling algorithm called ridge regression (RR) addresses this by introducing a regularization parameter. With sparse sites, the RR method is tested in two cases employing six and nine sites, and compared with the ordinary least squares and the arithmetic mean. The upscaling results of the RR method show higher prediction accuracies compared to the other two methods. When the RR method is used, the six-site case has the same estimation accuracy as the nine-site case due to maintaining the diversity of in situ measurements through the analysis of the ridge trace and variance inflation factor (VIF). Thus, the ridge trace and VIF analysis is considered as the optimal selection method for the existing observation networks if the RR method will be used in future validation work. With a different number of sites, the RR method always displays the best estimation accuracy and is not sensitive to the number of sites, which indicates that the RR method can potentially upscale sparse sites. However, if the sites are too few, e.g., one to four, it is difficult to perform the upscaling method.

ACS Style

Jian Kang; Rui Jin; Xin Li; Yang Zhang; Zhongli Zhu. Spatial Upscaling of Sparse Soil Moisture Observations Based on Ridge Regression. Remote Sensing 2018, 10, 192 .

AMA Style

Jian Kang, Rui Jin, Xin Li, Yang Zhang, Zhongli Zhu. Spatial Upscaling of Sparse Soil Moisture Observations Based on Ridge Regression. Remote Sensing. 2018; 10 (2):192.

Chicago/Turabian Style

Jian Kang; Rui Jin; Xin Li; Yang Zhang; Zhongli Zhu. 2018. "Spatial Upscaling of Sparse Soil Moisture Observations Based on Ridge Regression." Remote Sensing 10, no. 2: 192.

Journal article
Published: 03 October 2017 in IEEE Geoscience and Remote Sensing Letters
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This letter summarizes a special stream of the IEEE Geoscience and Remote Sensing Letters devoted to understanding the heterogeneity in soil moisture, evapotranspiration, and other related ecohydrological variables based on multiscale observations from satellite-based and airborne remote sensors, a flux observation matrix, and an ecohydrological wireless sensor network in the Heihe Watershed Allied Telemetry Experimental Research project. Scaling and uncertainty are the key issues in the remote-sensing research community, especially regarding the heterogeneous land surface. However, a lack of understanding and an inadequate theoretical basis impede the development and innovation of forward radiative transfer models, as well as the quantitative retrieval and validation of remote-sensing products. We summarize the prior considerations regarding surface heterogeneity research and report the main outcomes and contributions of this special stream. The highlights of this stream are related to spatial sampling, upscaling, uncertainty analysis, the validation of remote-sensing products, and accounting for heterogeneity in remote-sensing models.

ACS Style

R. Jin; X. Li; S. M. Liu. Understanding the Heterogeneity of Soil Moisture and Evapotranspiration Using Multiscale Observations From Satellites, Airborne Sensors, and a Ground-Based Observation Matrix. IEEE Geoscience and Remote Sensing Letters 2017, 14, 2132 -2136.

AMA Style

R. Jin, X. Li, S. M. Liu. Understanding the Heterogeneity of Soil Moisture and Evapotranspiration Using Multiscale Observations From Satellites, Airborne Sensors, and a Ground-Based Observation Matrix. IEEE Geoscience and Remote Sensing Letters. 2017; 14 (11):2132-2136.

Chicago/Turabian Style

R. Jin; X. Li; S. M. Liu. 2017. "Understanding the Heterogeneity of Soil Moisture and Evapotranspiration Using Multiscale Observations From Satellites, Airborne Sensors, and a Ground-Based Observation Matrix." IEEE Geoscience and Remote Sensing Letters 14, no. 11: 2132-2136.

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: 06 December 2016 in IEEE Geoscience and Remote Sensing Letters
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Block kriging (BK) is a common method of predicting the true value at the pixel scale when validating remote sensing retrieval products. However, measurement errors (MEs) increase the prediction uncertainty. In this letter, an extended interpolation technique - BK with MEs (BKMEs) - is developed. The properties of BKME are proven through derivation and demonstrated in a case study of soil moisture (SM) upscaling. Three prediction scenarios - one without MEs (BK), BK with homogeneous MEs (BKHOME), and BK with heterogeneous MEs (BKHEME) - are considered for the upscaling of SM data observed by a distributed wireless sensor network, and the results are compared. Both BK and BKHOME yield the same upscaling results, which differ from those of BKHEME, and the prediction results of BKHEME show less bias than those of the other scenarios. Because both BKHOME and BKHEME consider MEs, their prediction results show smaller kriging variances than do the BK results. Three primary conclusions are drawn. The first is that the optimal kriging coefficients assigned to the observations are affected not only by spatial distance but also by the MEs when the MEs of the samples are unequal. The second is that when the MEs are equal, it may not be necessary to consider the MEs to predict the value for an unobserved location. The third is that although the prediction uncertainty can be reduced by considering MEs, it is more meaningful to consider unequal MEs than equal MEs in the prediction process. BKME is an advanced upscaling method that achieves improved prediction accuracy by considering MEs.

ACS Style

Jian Kang; Rui Jin; Xin Li; Yang Zhang. Block Kriging With Measurement Errors: A Case Study of the Spatial Prediction of Soil Moisture in the Middle Reaches of Heihe River Basin. IEEE Geoscience and Remote Sensing Letters 2016, 14, 87 -91.

AMA Style

Jian Kang, Rui Jin, Xin Li, Yang Zhang. Block Kriging With Measurement Errors: A Case Study of the Spatial Prediction of Soil Moisture in the Middle Reaches of Heihe River Basin. IEEE Geoscience and Remote Sensing Letters. 2016; 14 (1):87-91.

Chicago/Turabian Style

Jian Kang; Rui Jin; Xin Li; Yang Zhang. 2016. "Block Kriging With Measurement Errors: A Case Study of the Spatial Prediction of Soil Moisture in the Middle Reaches of Heihe River Basin." IEEE Geoscience and Remote Sensing Letters 14, no. 1: 87-91.

Journal article
Published: 26 November 2016 in Remote Sensing
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Validation is mandatory to quantify the reliability of remote sensing products (RSPs). However, this process is not straightforward and usually presents formidable challenges in terms of both theory and real-world operations. In this context, a dedicated validation initiative was launched in China, and we identified a validation strategy (VS). This overall VS focuses on validating regional-scale RSPs with a systematic site-to-network concept, consisting of four main components: (1) general guidelines and technical specifications to guide users in validating various land RSPs, particularly aiming to further develop in situ sampling schemes and scaling approaches to acquire ground truth at the pixel scale over heterogeneous surfaces; (2) sound site-based validation activities, conducted through multi-scale, multi-platform, and multi-source observations to experimentally examine and improve the first component; (3) a national validation network to allow for comprehensive assessment of RSPs from site or regional scales to the national scale across various zones; and (4) an operational RSP evaluation system to implement operational validation applications. Research progress on the development of these four components is described in this paper. Some representative research results, with respect to the development of sampling methods and site-based validation activities, are also highlighted. The development of this VS improves our understanding of validation issues, especially to facilitate validating RSPs over heterogeneous land surfaces both at the pixel scale level and the product level.

ACS Style

Shuguo Wang; Xin Li; Yong Ge; Rui Jin; Mingguo Ma; Qinhuo Liu; Jianguang Wen; Shaomin Liu. Validation of Regional-Scale Remote Sensing Products in China: From Site to Network. Remote Sensing 2016, 8, 980 .

AMA Style

Shuguo Wang, Xin Li, Yong Ge, Rui Jin, Mingguo Ma, Qinhuo Liu, Jianguang Wen, Shaomin Liu. Validation of Regional-Scale Remote Sensing Products in China: From Site to Network. Remote Sensing. 2016; 8 (12):980.

Chicago/Turabian Style

Shuguo Wang; Xin Li; Yong Ge; Rui Jin; Mingguo Ma; Qinhuo Liu; Jianguang Wen; Shaomin Liu. 2016. "Validation of Regional-Scale Remote Sensing Products in China: From Site to Network." Remote Sensing 8, no. 12: 980.

Journal article
Published: 01 December 2015 in Agricultural and Forest Meteorology
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Irrigated oases are the main water consumers in arid and semi-arid regions. As plant evapotranspiration (ET) in these regions mainly depends on irrigated water, accurate quantification of evapotranspiration (ET) on the irrigated oases is crucial for allocation and management of irrigation water resources. In this study, we integrated the soil moisture retrieved from Polarimetric L-band Multibeam Radiometer (PLMR) into the Surface Energy Balance System (SEBS) model for improving ET estimates under water stress conditions. The study area is the irrigated oasis in the middle reaches of the Heihe River where airborne and satellite-borne remotely sensed data as well as in situ observations are available through the Heihe Watershed Allied Telemetry Experimental Research (HiWATER). The main goal of this experiment is to monitor the energy-water exchange between near-surface atmosphere and land surface, and to assess its influencing factors within the oasis–desert ecosystem. The soil moisture data were retrieved using the L-band Microwave Emission of the Biosphere (L-MEB) model fed with the airborne dual-polarized and multi-angular viewing of PLMR. The comparison of soil moisture retrieval from PLMR data with the soil moisture measured by a wireless sensor network (WSN) showed good consistency, with an absolute mean error (ME) <0.004 cm3 cm−3 and a root mean square error (RMSE) value <0.05 cm3 cm−3. Further, the actual daily evapotranspiration was estimated using the soil moisture integrated (SM-integrated) SEBS algorithm fed with the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) images and soil moisture data retrieved from PLMR data. The sensible heat fluxes and daily evapotranspiration (ETdaily) obtained by the SM-integrated SEBS and the original SEBS were compared with the eddy correlation (EC) measurements collected from HiWATER experiment. The results indicate an obvious improvement when soil moisture information is integrated into the SEBS. This method overcomes the weakness of remote sensing based (RS-based) surface energy balance models of overestimating evapotranspiration particularly in semi-arid and arid regions. It shows a prospect that the combination of optical and microwave remote sensing can further improve the RS-based ET estimation.

ACS Style

Yan Li; Jian Zhou; Haijing Wang; Dazhi Li; Rui Jin; Yanzhao Zhou; Qingguo Zhou. Integrating soil moisture retrieved from L-band microwave radiation into an energy balance model to improve evapotranspiration estimation on the irrigated oases of arid regions in northwest China. Agricultural and Forest Meteorology 2015, 214-215, 306 -318.

AMA Style

Yan Li, Jian Zhou, Haijing Wang, Dazhi Li, Rui Jin, Yanzhao Zhou, Qingguo Zhou. Integrating soil moisture retrieved from L-band microwave radiation into an energy balance model to improve evapotranspiration estimation on the irrigated oases of arid regions in northwest China. Agricultural and Forest Meteorology. 2015; 214-215 ():306-318.

Chicago/Turabian Style

Yan Li; Jian Zhou; Haijing Wang; Dazhi Li; Rui Jin; Yanzhao Zhou; Qingguo Zhou. 2015. "Integrating soil moisture retrieved from L-band microwave radiation into an energy balance model to improve evapotranspiration estimation on the irrigated oases of arid regions in northwest China." Agricultural and Forest Meteorology 214-215, no. : 306-318.

Journal article
Published: 09 October 2015 in Remote Sensing
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High spatial resolution soil moisture (SM) data are crucial in agricultural applications, river-basin management, and understanding hydrological processes. Merging multi-resource observations is one of the ways to improve the accuracy of high spatial resolution SM data in the heterogeneous cropland. In this paper, the Bayesian Maximum Entropy (BME) methodology is implemented to merge the following four types of observed data to obtain the spatial distribution of SM at 100 m scale: soil moisture observed by wireless sensor network (WSN), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)-derived soil evaporative efficiency (SEE), irrigation statistics, and Polarimetric L-band Multi-beam Radiometer (PLMR)-derived SM products (~700 m). From the poor BME predictions obtained by merging only WSN and SEE data, we observed that the SM heterogeneity caused by irrigation and the attenuating sensitivity of the SEE data to SM caused by the canopies result in BME prediction errors. By adding irrigation statistics to the merged datasets, the overall RMSD of the BME predictions during the low-vegetated periods can be successively reduced from 0.052 m3·m−3to 0.033 m3·m−3. The coefficient of determination (R2) and slope between the predicted and in situ measured SM data increased from 0.32 to 0.64 and from 0.38 to 0.82, respectively, but large estimation errors occurred during the moderately vegetated periods (RMSD = 0.041 m3·m−3, R = 0.43 and the slope = 0.41). Further adding the downscaled SM information from PLMR SM products to the merged datasets, the predictions were satisfactorily accurate with an RMSD of 0.034 m3·m−3, R2 of 0.4 and a slope of 0.69 during moderately vegetated periods. Overall, the results demonstrated that merging multi-resource observations into SM estimations can yield improved accuracy in heterogeneous cropland.

ACS Style

Lei Fan; Qing Xiao; Jianguang Wen; Qiang Liu; Rui Jin; Dongqing You; Xiaowen Li. Mapping High-Resolution Soil Moisture over Heterogeneous Cropland Using Multi-Resource Remote Sensing and Ground Observations. Remote Sensing 2015, 7, 13273 -13297.

AMA Style

Lei Fan, Qing Xiao, Jianguang Wen, Qiang Liu, Rui Jin, Dongqing You, Xiaowen Li. Mapping High-Resolution Soil Moisture over Heterogeneous Cropland Using Multi-Resource Remote Sensing and Ground Observations. Remote Sensing. 2015; 7 (10):13273-13297.

Chicago/Turabian Style

Lei Fan; Qing Xiao; Jianguang Wen; Qiang Liu; Rui Jin; Dongqing You; Xiaowen Li. 2015. "Mapping High-Resolution Soil Moisture over Heterogeneous Cropland Using Multi-Resource Remote Sensing and Ground Observations." Remote Sensing 7, no. 10: 13273-13297.

Journal article
Published: 01 May 2015 in Arctic, Antarctic, and Alpine Research
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Rui Jin; Tingjun Zhang; Xin Li; Xingguo Yang; Youhua Ran. Mapping Surface Soil Freeze-Thaw Cycles in China Based on SMMR and SSM/I Brightness Temperatures from 1978 to 2008. Arctic, Antarctic, and Alpine Research 2015, 47, 213 -229.

AMA Style

Rui Jin, Tingjun Zhang, Xin Li, Xingguo Yang, Youhua Ran. Mapping Surface Soil Freeze-Thaw Cycles in China Based on SMMR and SSM/I Brightness Temperatures from 1978 to 2008. Arctic, Antarctic, and Alpine Research. 2015; 47 (2):213-229.

Chicago/Turabian Style

Rui Jin; Tingjun Zhang; Xin Li; Xingguo Yang; Youhua Ran. 2015. "Mapping Surface Soil Freeze-Thaw Cycles in China Based on SMMR and SSM/I Brightness Temperatures from 1978 to 2008." Arctic, Antarctic, and Alpine Research 47, no. 2: 213-229.

Journal article
Published: 20 April 2015 in ELECTROPHORESIS
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Insertion/deletion polymorphisms have become a research hot spot in forensic science due to their tremendous potential in recent years. In the present study, we investigated 30 indel loci in a Chinese Yi ethnic group. The allele frequencies of the short allele of the 30 indel loci were in the range of 0.1025 to 0.9221. The power of discrimination values were observed ranging from 0.6607 (HLD70 locus) to 0.2630 (HLD111 locus) and probability of exclusion values ranged from 0.0189 (HLD111 locus) to 0.2343 (HLD56 locus). The combined power of discrimination and power of exclusion for 30 loci in the studied Yi group were 0.99999999995713 and 0.97746, respectively, which showed tremendous potential for forensic personal identification in the Yi group. Moreover, the DA distances, phylogenetic tree, PCA and cluster analysis showed the Yi group had close genetic relationships with the Tibetan, South Korean, Chinese Han and She groups. This article is protected by copyright. All rights reserved

ACS Style

Yu-Dang Zhang; Chun-Mei Shen; Rui Jin; Ya-Ni Li; Bo Wang; Li-Xia Ma; Hao-Tian Meng; Jiang-Wei Yan; Hong- Dan Wang; Ze-Long Yang; Bo-Feng Zhu. Forensic evaluation and population genetic study of 30 insertion/deletion polymorphisms in a Chinese Yi group. ELECTROPHORESIS 2015, 36, 1196 -1201.

AMA Style

Yu-Dang Zhang, Chun-Mei Shen, Rui Jin, Ya-Ni Li, Bo Wang, Li-Xia Ma, Hao-Tian Meng, Jiang-Wei Yan, Hong- Dan Wang, Ze-Long Yang, Bo-Feng Zhu. Forensic evaluation and population genetic study of 30 insertion/deletion polymorphisms in a Chinese Yi group. ELECTROPHORESIS. 2015; 36 (9-10):1196-1201.

Chicago/Turabian Style

Yu-Dang Zhang; Chun-Mei Shen; Rui Jin; Ya-Ni Li; Bo Wang; Li-Xia Ma; Hao-Tian Meng; Jiang-Wei Yan; Hong- Dan Wang; Ze-Long Yang; Bo-Feng Zhu. 2015. "Forensic evaluation and population genetic study of 30 insertion/deletion polymorphisms in a Chinese Yi group." ELECTROPHORESIS 36, no. 9-10: 1196-1201.

Journal article
Published: 14 October 2014 in Sensors
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The eco-hydrological wireless sensor network (EHWSN) in the middle reaches of the Heihe River Basin in China is designed to capture the spatial and temporal variability and to estimate the ground truth for validating the remote sensing productions. However, there is no available prior information about a target variable. To meet both requirements, a hybrid model-based sampling method without any spatial autocorrelation assumptions is developed to optimize the distribution of EHWSN nodes based on geostatistics. This hybrid model incorporates two sub-criteria: one for the variogram modeling to represent the variability, another for improving the spatial prediction to evaluate remote sensing productions. The reasonability of the optimized EHWSN is validated from representativeness, the variogram modeling and the spatial accuracy through using 15 types of simulation fields generated with the unconditional geostatistical stochastic simulation. The sampling design shows good representativeness; variograms estimated by samples have less than 3% mean error relative to true variograms. Then, fields at multiple scales are predicted. As the scale increases, estimated fields have higher similarities to simulation fields at block sizes exceeding 240 m. The validations prove that this hybrid sampling method is effective for both objectives when we do not know the characteristics of an optimized variables.

ACS Style

Jian Kang; Xin Li; Rui Jin; Yong Ge; Jinfeng Wang; Jianghao Wang. Hybrid Optimal Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of the Heihe River Basin, China. Sensors 2014, 14, 19095 -19114.

AMA Style

Jian Kang, Xin Li, Rui Jin, Yong Ge, Jinfeng Wang, Jianghao Wang. Hybrid Optimal Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of the Heihe River Basin, China. Sensors. 2014; 14 (10):19095-19114.

Chicago/Turabian Style

Jian Kang; Xin Li; Rui Jin; Yong Ge; Jinfeng Wang; Jianghao Wang. 2014. "Hybrid Optimal Design of the Eco-Hydrological Wireless Sensor Network in the Middle Reach of the Heihe River Basin, China." Sensors 14, no. 10: 19095-19114.

Journal article
Published: 13 June 2014 in IEEE Geoscience and Remote Sensing Letters
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The ground truth estimated by in situ measurements is important for accurately evaluating retrieved remote sensing products, particularly over heterogeneous land surfaces. This letter analyzes the role of multisource remote sensing observations on the upscaling of soil moisture observed by a wireless sensor network at the pixel scale via the regression kriging (RK) method. Three types of auxiliary remote sensing information are employed, including Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Temperature Vegetation Dryness Index (TVDI; 90 m), Polarimetric L-band Multiband Radiometer brightness temperature (700 m), and Moderate Resolution Image Spectroradiometer TVDI (1000 m). Moreover, a comparison with the ordinary kriging method is analyzed. The spatial inferences show that the RK method is more accurate and that its spatial pattern is more consistent with the auxiliary data when the trend is successfully removed, particularly when spatial continuity is destroyed by irrigation. The ASTER TVDI has a higher resolution and stronger correlation with soil moisture and yields more accurate interpolation results than the other types of remote sensing information. Although medium-resolution data do not substantially contribute to capture the spatial patterns of soil moisture, such data may still improve the prediction accuracy.

ACS Style

Jian Kang; Rui Jin; Xin Li. Regression Kriging-Based Upscaling of Soil Moisture Measurements From a Wireless Sensor Network and Multiresource Remote Sensing Information Over Heterogeneous Cropland. IEEE Geoscience and Remote Sensing Letters 2014, 12, 92 -96.

AMA Style

Jian Kang, Rui Jin, Xin Li. Regression Kriging-Based Upscaling of Soil Moisture Measurements From a Wireless Sensor Network and Multiresource Remote Sensing Information Over Heterogeneous Cropland. IEEE Geoscience and Remote Sensing Letters. 2014; 12 (1):92-96.

Chicago/Turabian Style

Jian Kang; Rui Jin; Xin Li. 2014. "Regression Kriging-Based Upscaling of Soil Moisture Measurements From a Wireless Sensor Network and Multiresource Remote Sensing Information Over Heterogeneous Cropland." IEEE Geoscience and Remote Sensing Letters 12, no. 1: 92-96.