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Cracking on the surface of soda saline-alkali soil is very common. In most previous studies, spectral prediction models of soil salinity were less accurate since spectral measurements were usually performed on 2 mm soil samples which cannot represent true soil surface condition very well. The objective of our research is to provide a procedure to improve soil property estimation of soda saline-alkali soil based on spectral measurement considering the texture feature of the soil surface with cracks. To achieve this objective, a cracking test was performed with 57 soil samples from Songnen Plain of China, the contrast (CON) texture feature of crack images of soil samples was then extracted from grey level co-occurrence matrix (GLCM). The original reflectance was then measured and the mixed reflectance considering the CON texture feature was also calculated from both the block soil samples (soil blocks separated by crack regions) and the comparison soil samples (soil powders with 2 mm particle size). The results of analysis between spectra and the main soil properties indicate that surface cracks can reduce the overall reflectivity of the soda saline-alkali soil and thus increasing the spectral difference among the block soil samples with different salinity levels. The results also show that both univariate and multivariate linear regression models considering the CON texture feature can greatly improve the prediction accuracy of main soil properties of soda saline-alkali soils, such as Na+, EC and salinity, which also can reduce the intensity of field spectral measurements under natural condition.
Jianhua Ren; Xiaojie Li; Sijia Li; Honglei Zhu; Kai Zhao. Quantitative Analysis of Spectral Response to Soda Saline-AlkaliSoil after Cracking Process: A Laboratory Procedure to Improve Soil Property Estimation. Remote Sensing 2019, 11, 1406 .
AMA StyleJianhua Ren, Xiaojie Li, Sijia Li, Honglei Zhu, Kai Zhao. Quantitative Analysis of Spectral Response to Soda Saline-AlkaliSoil after Cracking Process: A Laboratory Procedure to Improve Soil Property Estimation. Remote Sensing. 2019; 11 (12):1406.
Chicago/Turabian StyleJianhua Ren; Xiaojie Li; Sijia Li; Honglei Zhu; Kai Zhao. 2019. "Quantitative Analysis of Spectral Response to Soda Saline-AlkaliSoil after Cracking Process: A Laboratory Procedure to Improve Soil Property Estimation." Remote Sensing 11, no. 12: 1406.
The parameter bp in the tuo-omega (τ–ω) model is important for retrieving soil moisture data from passive microwave brightness temperatures. Theoretically, bp depends on the observation mode (polarization, frequency, and incidence angle) and vegetation properties and varies with vegetation growth. For simplicity, previous studies have taken bp to be a constant. However, to reduce the uncertainty of soil moisture retrieval further, the present study is of the dynamics of bp based on the SMAPVEX12 experimental dataset by combining a genetic algorithm and the L-MEB microwave radiative transfer model of vegetated soil. The results show the following. First, bp decreases nonlinearly with vegetation water content (VWC), decreasing critically when VWC becomes less than 2 kg/m2. Second, there is a power law between bp and VWC for both horizontal and vertical polarizations (R2 = 0.919 and 0.872, respectively). Third, the effectiveness of this relationship is verified by comparing its soil-moisture inversion accuracy with the previous constant-bp method based on the HiWATER dataset. Doing so reveals that the dynamic bp method reduces the root-mean-square error of the retrieved soil moisture by approximately 0.06 cm3/cm3, and similar improvement is obtained for the calibrated SMAPVEX12 dataset. Our results indicate that the dynamic bp method is reasonable for different vegetation growth stages and could improve the accuracy of soil moisture retrieval.
Tao Jiang; Kai Zhao; Xingming Zheng; Si Chen; Xiangkun Wan. Dynamic bp in the L Band and Its Role in Improving the Accuracy of Soil Moisture Retrieval. Chinese Geographical Science 2019, 29, 283 -292.
AMA StyleTao Jiang, Kai Zhao, Xingming Zheng, Si Chen, Xiangkun Wan. Dynamic bp in the L Band and Its Role in Improving the Accuracy of Soil Moisture Retrieval. Chinese Geographical Science. 2019; 29 (2):283-292.
Chicago/Turabian StyleTao Jiang; Kai Zhao; Xingming Zheng; Si Chen; Xiangkun Wan. 2019. "Dynamic bp in the L Band and Its Role in Improving the Accuracy of Soil Moisture Retrieval." Chinese Geographical Science 29, no. 2: 283-292.
The spectral features of soils are a comprehensive representation of their physicochemical parameters, surface states, and internal structures. To date, spectral measurements have been mostly performed for powdered soils and smooth aggregate soils, but rarely for cracked soils; a common state of soda-saline soils. In this study, we measured the spectral features of 57 saline soil samples in powdered, aggregate, and cracked states for comparison. We then explored in depth the factors governing soil spectral features to build up simple and multiple linear regression models between the spectral features and physicochemical parameters (salt content, Na+, pH, and electronic conductivity (EC)) of saline soils in different states. We randomly selected 40 samples to construct the models, and used the remaining 17 samples for validation. Our results indicated that the regression models worked more effectively in predicting physicochemical parameters for cracked soils than for other soils. Subsequently, the crack ratio (CR) was introduced into the regression models to modify the spectra of soils in powdered and aggregate states. The accuracy of prediction was improved, evidenced by a 2–11% decrease in the parameters mean absolute error (MAE).
Xiaojie Li; Jianhua Ren; Kai Zhao; Zhengwei Liang. Correlation between Spectral Characteristics and Physicochemical Parameters of Soda-Saline Soils in Different States. Remote Sensing 2019, 11, 388 .
AMA StyleXiaojie Li, Jianhua Ren, Kai Zhao, Zhengwei Liang. Correlation between Spectral Characteristics and Physicochemical Parameters of Soda-Saline Soils in Different States. Remote Sensing. 2019; 11 (4):388.
Chicago/Turabian StyleXiaojie Li; Jianhua Ren; Kai Zhao; Zhengwei Liang. 2019. "Correlation between Spectral Characteristics and Physicochemical Parameters of Soda-Saline Soils in Different States." Remote Sensing 11, no. 4: 388.
The Songnen Plain in China has a typical type of soda saline-alkali soil that frequently shrinks and cracks under natural conditions, especially during water evaporation. This study aims to study the effects of salt contents on the cracking process of soda saline-alkali soils and to make a...
Jianhua Ren; Xiaojie Li; Kai Zhao; Xingming Zheng; Tao Jiang. Quantitative Research on the Relationship between Salinity and Crack Length of Soda Saline-Alkali Soil. Polish Journal of Environmental Studies 2018, 28, 823 -832.
AMA StyleJianhua Ren, Xiaojie Li, Kai Zhao, Xingming Zheng, Tao Jiang. Quantitative Research on the Relationship between Salinity and Crack Length of Soda Saline-Alkali Soil. Polish Journal of Environmental Studies. 2018; 28 (2):823-832.
Chicago/Turabian StyleJianhua Ren; Xiaojie Li; Kai Zhao; Xingming Zheng; Tao Jiang. 2018. "Quantitative Research on the Relationship between Salinity and Crack Length of Soda Saline-Alkali Soil." Polish Journal of Environmental Studies 28, no. 2: 823-832.
Passive microwave data of a snow-covered land surface received by a passive microwave radiometer is composed of microwave radiation from the snow cover and the underlying surface. The western Jilin Province of China is an important area with seasonal snow. Here, the land cover types mainly include characteristically saline-alkaline farmland and grassland. The main aim of this paper is to obtain more accurate snow depth (SD) data in the western Jilin Province of China. First, a multifrequency passive microwave unmixing method is proposed for the study area. Second, based on Fengyun-3B microwave radiation imagery (MWRI) data and advanced microwave scanning radiometer 2 (AMSR2) data, the SD retrieval results are evaluated using four methods: using MWRI data with Fosters algorithm and with the standard MWRI algorithm, and using the AMSR2 data with Fosters algorithm and with the standard AMSR2 algorithm. The experimental results demonstrate that using MWRI data with the standard MWRI algorithm yields the highest accuracy for SD retrieval, with average bias and root-mean-square-error (RMSE) values of approximately 6.9 and 7.5 cm, respectively. Finally, we combined the unmixing method with the optimal SD retrieval method, and obtained more accurate SD data for the study area. Using MWRI data with the standard MWRI algorithm, the average bias and RMSE improved by approximately 9.7% and 7% when using the unmixed pixels compared with using the original mixed pixels.
Lingjia Gu; Ruizhi Ren; Xiaofeng Li; Kai Zhao. Snow Depth Retrieval Based on a Multifrequency Passive Microwave Unmixing Method for Saline-Alkaline Land in the Western Jilin Province of China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2018, 11, 2210 -2222.
AMA StyleLingjia Gu, Ruizhi Ren, Xiaofeng Li, Kai Zhao. Snow Depth Retrieval Based on a Multifrequency Passive Microwave Unmixing Method for Saline-Alkaline Land in the Western Jilin Province of China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2018; 11 (7):2210-2222.
Chicago/Turabian StyleLingjia Gu; Ruizhi Ren; Xiaofeng Li; Kai Zhao. 2018. "Snow Depth Retrieval Based on a Multifrequency Passive Microwave Unmixing Method for Saline-Alkaline Land in the Western Jilin Province of China." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 11, no. 7: 2210-2222.
Soil moisture (SM) retrieval from SMOS (the Soil Moisture and Ocean Salinity mission) and SMAP (the Soil Moisture Active/Passive mission) passive microwave data over forested areas with required accuracy is of great significance and poses some challenges. In this paper, we used Ground Wireless Sensor Network (GWSN) SM measurements from 9 September to 5 November 2015 to validate SMOS and SMAP Level 3 (L3) SM products over forested areas in northeastern China. Our results found that neither SMOS nor SMAP L3 SM products were ideal, with respective RMSE (root mean square error) values of 0.31 cm3/cm3 and 0.17 cm3/cm3. Nevertheless, some improvements in SM retrieval might be achievable through refinements of the soil dielectric model with respect to high percentage of soil organic matter (SOM) in the forested area. To that end, the potential of the semi-empirical soil dielectric model proposed by Jun Liu (Liu’s model) in improving SM retrieval results over forested areas was investigated. Introducing Liu’s model into the retrieval algorithms of both SMOS and SMAP missions produced promising results. For SMAP, the RMSE of L3 SM products improved from 0.16 cm3/cm3 to 0.07 cm3/cm3 for AM (local solar time around 06:00 am) data, and from 0.17 cm3/cm3 to 0.05 cm3/cm3 for PM (local solar time around 06:00 pm) data. For SMOS ascending orbit products, the accuracy was improved by 56%, while descending orbit products improved by 45%.
Mengjie Jin; Xingming Zheng; Tao Jiang; Xiaofeng Li; Xiao-Jie Li; Kai Zhao. Evaluation and Improvement of SMOS and SMAP Soil Moisture Products for Soils with High Organic Matter over a Forested Area in Northeast China. Remote Sensing 2017, 9, 387 .
AMA StyleMengjie Jin, Xingming Zheng, Tao Jiang, Xiaofeng Li, Xiao-Jie Li, Kai Zhao. Evaluation and Improvement of SMOS and SMAP Soil Moisture Products for Soils with High Organic Matter over a Forested Area in Northeast China. Remote Sensing. 2017; 9 (4):387.
Chicago/Turabian StyleMengjie Jin; Xingming Zheng; Tao Jiang; Xiaofeng Li; Xiao-Jie Li; Kai Zhao. 2017. "Evaluation and Improvement of SMOS and SMAP Soil Moisture Products for Soils with High Organic Matter over a Forested Area in Northeast China." Remote Sensing 9, no. 4: 387.
Snow depth parameter inversion in the farmland using passive microwave remote sensing is of great significance to the agricultural production in Northeast China. Firstly, the Helsinki University of Technology (HUT) snow emission model was validated in the farmland based on microwave radiation imager (MWRI) onboard FengYun-3B satellite (FY-3B). The results showed that there was a big difference between the brightness temperature of HUT model simulation and MWRI for 18.7 GHz horizontal polarization (18.7 H) and 36.5 GHz horizontal polarization (36.5 H). To improve HUT model, the empirical parameter in the model was localized. Then the localized HUT (LHUT) model was built, where the extinction coefficient was calculated by the new extinction coefficient formula. Next, LHUT model was validated based on MWRI data and compared with HUT model. The results showed that LHUT underestimates slightly the brightness temperature with 0.91 and 4.19 K for 18.7 and 36.5 H respectively, and LHUT is superior to HUT model. Finally, the genetic algorithm (GA) was used to invert snow depth based on LHUT. The results showed that snow depth was underestimated with 6.79 cm based on LHUT. The inverted snow depth based on LHUT model is in better agreement with the measured snow depth.
Lili Wu; Xiaofeng Li; Kai Zhao; Xingming Zheng. Snow Depth Inversion Using the Localized HUT Model Based on FY-3B MWRI Data in the Farmland of Heilongjiang Province, China. Journal of the Indian Society of Remote Sensing 2016, 45, 89 -100.
AMA StyleLili Wu, Xiaofeng Li, Kai Zhao, Xingming Zheng. Snow Depth Inversion Using the Localized HUT Model Based on FY-3B MWRI Data in the Farmland of Heilongjiang Province, China. Journal of the Indian Society of Remote Sensing. 2016; 45 (1):89-100.
Chicago/Turabian StyleLili Wu; Xiaofeng Li; Kai Zhao; Xingming Zheng. 2016. "Snow Depth Inversion Using the Localized HUT Model Based on FY-3B MWRI Data in the Farmland of Heilongjiang Province, China." Journal of the Indian Society of Remote Sensing 45, no. 1: 89-100.
Fractional vegetation cover (FVC) is one of the most critical parameters in monitoring vegetation status. Accurate estimates of FVC are crucial to the use in land surface models. The dimidiate pixel model is the most widely used method for retrieval of FVC. The normalized difference vegetation index (NDVI) of bare soil endmember (NDVIsoil) is usually assumed to be invariant without taking into account the spatial variability of soil backgrounds. Two NDVIsoil determining methods were compared for estimating FVC. The first method used an invariant NDVIsoil for the Northeast China. The second method used the historical minimum NDVI along with information on soil types to estimate NDVIsoil for each soil type. We quantified the influence of variations of NDVIsoil derived from the second method on FVC estimation for each soil type and compared the differences in FVC estimated by these two methods. Analysis shows that the uncertainty in FVC estimation introduced by NDVIsoil variability can exceed 0.1 (root mean square error—RMSE), with the largest errors occurring in vegetation types with low NDVI. NDVIsoil with higher variation causes greater uncertainty on FVC. The difference between the two versions of FVC in Northeast China, is about 0.07 with an RMSE of 0.07. Validation using fine-resolution FVC reference maps shows that the second approach yields better estimates of FVC than using an invariant NDVIsoil value. The accuracy of FVC estimates is improved from 0.1 to 0.07 (RMSE), on average, in the croplands and from 0.04 to 0.03 in the grasslands. Soil backgrounds have impacts not only on NDVIsoil but also on other VIsoil. Further focus will be the selection of optimal vegetation indices and the modeling of the relationships between VIsoil and soil properties for predicting VIsoil.
Yanling Ding; Xingming Zheng; Kai Zhao; Xiaoping Xin; Huanjun Liu. Quantifying the Impact of NDVIsoil Determination Methods and NDVIsoil Variability on the Estimation of Fractional Vegetation Cover in Northeast China. Remote Sensing 2016, 8, 29 .
AMA StyleYanling Ding, Xingming Zheng, Kai Zhao, Xiaoping Xin, Huanjun Liu. Quantifying the Impact of NDVIsoil Determination Methods and NDVIsoil Variability on the Estimation of Fractional Vegetation Cover in Northeast China. Remote Sensing. 2016; 8 (1):29.
Chicago/Turabian StyleYanling Ding; Xingming Zheng; Kai Zhao; Xiaoping Xin; Huanjun Liu. 2016. "Quantifying the Impact of NDVIsoil Determination Methods and NDVIsoil Variability on the Estimation of Fractional Vegetation Cover in Northeast China." Remote Sensing 8, no. 1: 29.
Fractional vegetation cover (FVC) is one of the most critical parameters in monitoring vegetation status. Comprehensive assessment of the FVC products is critical for their improvement and use in land surface models. This study investigates the performances of two major long time serial FVC products: GEOV1 and Australian MODIS. The spatial and temporal consistencies of these products were compared during the 2000–2012 period over the main biome types across the Australian continent. Their accuracies were validated by 443 FVC in-situ measurements during the 2011–2012 period. Our results show that there are strong correlations between the GEOV1 and Australian MODIS FVC products over the main Australian continent while they exhibit large differences and uncertainties in the coastal regions covered by dense forests. GEOV1 and Australian MODIS describe similar seasonal variations over the main biome types with differences in magnitude, while Australian MODIS exhibit unstable temporal variations over grasslands and shifted seasonal variations over evergreen broadleaf forests. The GEOV1 and Australian MODIS products overestimate FVC values over the biome types with high vegetation density and underestimate FVC in sparsely vegetated areas and grasslands. Overall, the GEOV1 and Australian MODIS FVC products agree with in-situ FVC values with a RMSE around 0.10 over the Australian continent.
Yanling Ding; Xingming Zheng; Tao Jiang; Kai Zhao. Comparison and Validation of Long Time Serial Global GEOV1 and Regional Australian MODIS Fractional Vegetation Cover Products Over the Australian Continent. Remote Sensing 2015, 7, 5718 -5733.
AMA StyleYanling Ding, Xingming Zheng, Tao Jiang, Kai Zhao. Comparison and Validation of Long Time Serial Global GEOV1 and Regional Australian MODIS Fractional Vegetation Cover Products Over the Australian Continent. Remote Sensing. 2015; 7 (5):5718-5733.
Chicago/Turabian StyleYanling Ding; Xingming Zheng; Tao Jiang; Kai Zhao. 2015. "Comparison and Validation of Long Time Serial Global GEOV1 and Regional Australian MODIS Fractional Vegetation Cover Products Over the Australian Continent." Remote Sensing 7, no. 5: 5718-5733.
Validation is an essential and important step before the application of remote sensing products. This paper introduces a prototype of the validation network for remote sensing products in China (VRPC). The VRPC aims to improve remote sensing products at a regional scale in China. These improvements will enhance the applicability of the key remote sensing products in understanding and interpretation of typical land surface processes in China. The framework of the VRPC is introduced first, including its four basic components. Then, the basic selection principles of the observation sites are described, and the principles for the validation of the remote sensing products are established. The VRPC will be realized in stages. In the first stage, four stations that have improved remote sensing observation facilities have been incorporated according to the selection principles. Certain core observation sites have been constructed at these stations. Next the Heihe Station is introduced in detail as an example. The three levels of observation (the research base, pixel-scale validation sites, and regional coverage) adopted by the Heihe Station are carefully explained. The pixel-scale validation sites with nested multi-scale observation systems in this station are the most unique feature, and these sites aim to solve some key scientific problems associated with remote sensing product validation (e.g., the scale effect and scale transformation). Multi-year of in situ measurements will ensure the high accuracy and inter-annual validity of the land products, which will provide dynamic regional monitoring and simulation capabilities in China. The observation sites of the VRPC are open, with the goal of increasing cooperation and exchange with global programs.
Mingguo Ma; Tao Che; Xin Li; Qing Xiao; Kai Zhao; Xiaoping Xin. A Prototype Network for Remote Sensing Validation in China. Remote Sensing 2015, 7, 5187 -5202.
AMA StyleMingguo Ma, Tao Che, Xin Li, Qing Xiao, Kai Zhao, Xiaoping Xin. A Prototype Network for Remote Sensing Validation in China. Remote Sensing. 2015; 7 (5):5187-5202.
Chicago/Turabian StyleMingguo Ma; Tao Che; Xin Li; Qing Xiao; Kai Zhao; Xiaoping Xin. 2015. "A Prototype Network for Remote Sensing Validation in China." Remote Sensing 7, no. 5: 5187-5202.
The development of an efficient ground sampling strategy is critical to assess uncertainties associated with moderate- or coarse-resolution remote-sensing products. This work presents a comparison of estimating spatial means from fine spatial resolution images using spatial random sampling (SRS), Block Kriging (BK), and Means of Surface with Nonhomogeneity (MSN) at 1 km2 spatial scale. Towards this goal, we focus on the sampling strategies for ground data measurements and provide an assessment of the MODIS LAI product validated by the spatial means estimated by the above-mentioned three methods. The results of this study indicate that: (1) for its effective stratification strategies and its criteria of minimum mean square estimation error, MSN demonstrates the lowest mean squared estimation error for estimating the means of stratified nonhomogeneous surface; (2) BK is efficient in estimating the means of homogeneous surfaces without bias and with minimum mean squared estimation errors. The MODIS LAI product is assessed using the means estimated by SRS, BK, and MSN based on Landsat 8 OLI and SPOT HRV fine-resolution LAI maps. For heterogeneous surfaces, MSN results in low RMSE and high accuracy of MODIS LAI product compared with BK and SRS, whereas for homogeneous surfaces, the statistical parameters outputted by these three methods are similar. These results reveal that MSN is an effective method for estimating the spatial means for heterogeneous surfaces. There are differences in the accuracies of MODIS LAI product assessed by these three methods.
Yanling Ding; Yong Ge; Maogui Hu; Jinfeng Wang; Jianghao Wang; Xingming Zheng; Kai Zhao. Comparison of spatial sampling strategies for ground sampling and validation of MODIS LAI products. International Journal of Remote Sensing 2014, 35, 7230 -7244.
AMA StyleYanling Ding, Yong Ge, Maogui Hu, Jinfeng Wang, Jianghao Wang, Xingming Zheng, Kai Zhao. Comparison of spatial sampling strategies for ground sampling and validation of MODIS LAI products. International Journal of Remote Sensing. 2014; 35 (20):7230-7244.
Chicago/Turabian StyleYanling Ding; Yong Ge; Maogui Hu; Jinfeng Wang; Jianghao Wang; Xingming Zheng; Kai Zhao. 2014. "Comparison of spatial sampling strategies for ground sampling and validation of MODIS LAI products." International Journal of Remote Sensing 35, no. 20: 7230-7244.
Row structure causes the anisotropy of microwave brightness temperature (TB) of soil surface, and it also can affect soil moisture retrieval accuracy when its influence is ignored in the inversion model. To study the effect of typical row structure on the retrieved soil moisture and evaluate if there is a need to introduce this effect into the inversion model, two ground-based experiments were carried out in 2011. Based on the observed C-band TB, field soil and vegetation parameters, row structure rough surface assumption (Q p model and discrete model), including the effect of row structure, and flat rough surface assumption (Q p model), ignoring the effect of row structure, are used to model microwave TB of soil surface. Then, soil moisture can be retrieved, respectively, by minimizing the difference of the measured and modeled TB. The results show that soil moisture retrieval accuracy based on the row structure rough surface assumption is approximately 0.02 cm3/cm3 better than the flat rough surface assumption for vegetated soil, as well as 0.015 cm3/cm3 better for bare and wet soil. This result indicates that the effect of row structure cannot be ignored for accurately retrieving soil moisture of farmland surface when C-band is used.
Zheng Xingming; Zhao Kai; Li Yangyang; Ren Jianhua; Ding Yanling. The Effect of Row Structure on Soil Moisture Retrieval Accuracy from Passive Microwave Data. The Scientific World Journal 2014, 2014, 1 -7.
AMA StyleZheng Xingming, Zhao Kai, Li Yangyang, Ren Jianhua, Ding Yanling. The Effect of Row Structure on Soil Moisture Retrieval Accuracy from Passive Microwave Data. The Scientific World Journal. 2014; 2014 ():1-7.
Chicago/Turabian StyleZheng Xingming; Zhao Kai; Li Yangyang; Ren Jianhua; Ding Yanling. 2014. "The Effect of Row Structure on Soil Moisture Retrieval Accuracy from Passive Microwave Data." The Scientific World Journal 2014, no. : 1-7.