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Prof. Huihui Feng
Central south university

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0 Aerosol
0 Climate Change
0 Satellite
0 environment
0 Land use and cover change

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Satellite
Land use and cover change
Aerosol
Climate Change

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Journal article
Published: 06 July 2021 in Remote Sensing
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Visible and near-infrared (VNIR) spectroscopy technology for soil heavy metal (HM) concentration prediction has been widely studied. However, its spectral response characteristics are still uncertain. In this study, a near standard soil Cd samples (NSSCd) spectra enhanced modeling strategy was developed in order to to reveal the soil cadmium (Cd) spectral response characteristics and predict its concentration. NSSCd were produced by adding the quantitative Cd solution into background soil. Then, prior spectral bands (i.e., the bands with higher variable importance in projection (VIP) score in NSSCd spectra) were used for predicting Cd concentration in soil samples collected from the Hengyang mining area and Baoding agriculture area. The partial least squares (PLS) and competitive adaptive reweighted sampling-partial least squares (CARS-PLS) were used for validation. Compared to using entire VNIR spectral ranges, the new modeling strategy performed very well, with the coefficient of determination (R2) and the ratio of prediction to deviation (RPD) showing an improvement from 0.63 and 1.72 to 0.71 and 1.95 in Hengyang and from 0.54 and 1.57 to 0.76 and 2.19 in Baoding. These results suggest that NSS prior spectral bands are critical for soil HM prediction. Our results represent an exciting finding for the future design of remote sensing sensors for soil HM detection.

ACS Style

Yulong Tu; Bin Zou; Huihui Feng; Mo Zhou; Zhihui Yang; Ying Xiong. A Near Standard Soil Samples Spectra Enhanced Modeling Strategy for Cd Concentration Prediction. Remote Sensing 2021, 13, 2657 .

AMA Style

Yulong Tu, Bin Zou, Huihui Feng, Mo Zhou, Zhihui Yang, Ying Xiong. A Near Standard Soil Samples Spectra Enhanced Modeling Strategy for Cd Concentration Prediction. Remote Sensing. 2021; 13 (14):2657.

Chicago/Turabian Style

Yulong Tu; Bin Zou; Huihui Feng; Mo Zhou; Zhihui Yang; Ying Xiong. 2021. "A Near Standard Soil Samples Spectra Enhanced Modeling Strategy for Cd Concentration Prediction." Remote Sensing 13, no. 14: 2657.

Journal article
Published: 08 April 2021 in Remote Sensing
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The surface shortwave radiation budget (Rsn) is one of the main drivers of Earth’s ecosystems and varies with atmospheric and surface conditions. Land use and cover change (LUCC) alters radiation through biogeophysical effects. However, due to the complex interactions between atmospheric and surface factors, it is very challenging to quantify the sole impacts of LUCC. Based on satellite data from the Global Land Surface Satellite (GLASS) Product and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments, this study introduces an observation-based approach for detecting LUCC influences on the Rsn by examining a humid basin over the Dongting Lake Basin, China from 2001 to 2015. Our results showed that the Rsn of the study area presented a decreasing trend due to the combined effects of LUCC and climate change. Generally, LUCC contributed −0.45 W/m2 to Rsn at the basin scale, which accounted for 2.53% of the total Rsn change. Furthermore, the LUCC contributions reached −0.69 W/m2, 0.21 W/m2, and −0.41 W/m2 in regions with land transitions of forest→grass, grass→forest, and grass→farmland, which accounted for 5.38%, −4.68%, and 2.40% of the total Rsn change, respectively. Physically, LUCC affected surface radiation by altering the surface properties. Specifically, LUCC induced albedo changes of +0.0039 at the basin scale and +0.0061, −0.0020, and +0.0036 in regions with land transitions of forest→grass, grass→forest, and grass→farmland, respectively. Our findings revealed the impact and process of LUCC on the surface radiation budget, which could support the understanding of the physical mechanisms of LUCC’s impact on ecosystems.

ACS Style

Shuchao Ye; Huihui Feng; Bin Zou; Ying Ding; Sijia Zhu; Feng Li; Guotao Dong. Satellite-Based Estimation of the Influence of Land Use and Cover Change on the Surface Shortwave Radiation Budget in a Humid Basin. Remote Sensing 2021, 13, 1447 .

AMA Style

Shuchao Ye, Huihui Feng, Bin Zou, Ying Ding, Sijia Zhu, Feng Li, Guotao Dong. Satellite-Based Estimation of the Influence of Land Use and Cover Change on the Surface Shortwave Radiation Budget in a Humid Basin. Remote Sensing. 2021; 13 (8):1447.

Chicago/Turabian Style

Shuchao Ye; Huihui Feng; Bin Zou; Ying Ding; Sijia Zhu; Feng Li; Guotao Dong. 2021. "Satellite-Based Estimation of the Influence of Land Use and Cover Change on the Surface Shortwave Radiation Budget in a Humid Basin." Remote Sensing 13, no. 8: 1447.

Journal article
Published: 30 March 2020 in Remote Sensing
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Current reported spatiotemporal solutions for fusing multisensor aerosol optical depth (AOD) products used to recover gaps either suffer from unacceptable accuracy levels, i.e., fixed rank smooth (FRS), or high time costs, i.e., Bayesian maximum entropy (BME). This problem is generally more serious when dealing with multiple AOD products in a long time series or over large geographic areas. This study proposes a new, effective, and efficient enhanced FRS method (FRS-EE) to fuse satellite AOD products with uncertainty constraints. AOD products used in the fusion experiment include Moderate Resolution Imaging SpectroRadiometer (MODIS) DB/DT_DB_Combined AOD and Multiangle Imaging SpectroRadiometer (MISR) AOD across mainland China from 2016 to 2017. Results show that the average completeness of original, initial FRS fused, and FRS-EE fused AODs with uncertainty constraints are 22.80%, 95.18%, and 65.84%, respectively. Although the correlation coefficient (R = 0.77), root mean square error (RMSE = 0.30), and mean bias (Bias = 0.023) of the initial FRS fused AODs are relatively lower than those of original AODs compared to Aerosol Robotic Network (AERONET) AOD records, the accuracy of FRS-EE fused AODs, which are R = 0.88, RMSE = 0.20, and Bias = 0.022, is obviously improved. More importantly, in regions with fully missing original AODs, the accuracy of FRS-EE fused AODs is close to that of original AODs in regions with valid retrievals. Meanwhile, the time cost of FRS-EE for AOD fusion was only 2.91 h; obviously lower than the 30.46 months taken for BME.

ACS Style

Bin Zou; Ning Liu; Wei Wang; Huihui Feng; Xiangping Liu; Yan Lin. An Effective and Efficient Enhanced Fixed Rank Smoothing Method for the Spatiotemporal Fusion of Multiple-Satellite Aerosol Optical Depth Products. Remote Sensing 2020, 12, 1102 .

AMA Style

Bin Zou, Ning Liu, Wei Wang, Huihui Feng, Xiangping Liu, Yan Lin. An Effective and Efficient Enhanced Fixed Rank Smoothing Method for the Spatiotemporal Fusion of Multiple-Satellite Aerosol Optical Depth Products. Remote Sensing. 2020; 12 (7):1102.

Chicago/Turabian Style

Bin Zou; Ning Liu; Wei Wang; Huihui Feng; Xiangping Liu; Yan Lin. 2020. "An Effective and Efficient Enhanced Fixed Rank Smoothing Method for the Spatiotemporal Fusion of Multiple-Satellite Aerosol Optical Depth Products." Remote Sensing 12, no. 7: 1102.

Journal article
Published: 01 November 2019 in Science of The Total Environment
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Benefiting from the advantages of a wide spatial sampling range and strong continuity, hyperspectral analysis provides a potential way to detect heavy metals in soil. However, it is still a great challenge to identify the spectral response characteristics of heavy metals from naturally polluted soil samples. This paper innovatively produces near standard soil samples for exploring the exact spectral response of cadmium (Cd) in soil and presents a novel method by combining the direct standardization (DS) and Spiking algorithms for integrating multisource spectra to improve the accuracy of Cd concentration estimation. A total of 46 naturally polluted soil samples were collected from a known Cd-contaminated mining area in Xiangjiang River Basin, China. The soil spectra of the naturally polluted soil samples were synchronously measured in the field. Moreover, clean soils with low heavy metal contaminants were collected to produce 65 near standard soil samples with known Cd levels. Then, the spectra and Cd concentrations of all 111 soil samples were measured under laboratory conditions. The principle component stepwise regression (PCSR) analysis results illustrated that the reflectance at all the wavelengths (380–2460 nm) is indicative of the differences in the soil Cd concentrations. Among these, the sensitivity of the spectral reflectance is the strongest at approximately 400 nm, 1000 nm and above 2300 nm. Additionally, the integrated multisource spectra significantly improved the accuracy of soil Cd concentration estimation (coefficient of determination, R2 = 0.96; root mean square error, RMSE = 0.29; ratio of prediction to deviation, RPD = 1.21) when 30 transfer samples and 15 training samples were simultaneously implemented in the combined DS and Spiking algorithm. This will provide a feasible scheme for exploration of spectral response characteristics of multiple soil heavy metals, and highlight the potential of developing low-level and satellite remote sensing on a large scale.

ACS Style

Bin Zou; Xiaolu Jiang; Huihui Feng; Yulong Tu; Chao Tao. Multisource spectral-integrated estimation of cadmium concentrations in soil using a direct standardization and Spiking algorithm. Science of The Total Environment 2019, 701, 134890 .

AMA Style

Bin Zou, Xiaolu Jiang, Huihui Feng, Yulong Tu, Chao Tao. Multisource spectral-integrated estimation of cadmium concentrations in soil using a direct standardization and Spiking algorithm. Science of The Total Environment. 2019; 701 ():134890.

Chicago/Turabian Style

Bin Zou; Xiaolu Jiang; Huihui Feng; Yulong Tu; Chao Tao. 2019. "Multisource spectral-integrated estimation of cadmium concentrations in soil using a direct standardization and Spiking algorithm." Science of The Total Environment 701, no. : 134890.

Journal article
Published: 06 July 2019 in Remote Sensing of Environment
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The aerosol forcing is an essential factor of global climate change, which can be estimated by various models. However, the model results ranging from −2.8 to 2.2 K remain controversial because of unavoidable uncertainty, leaving a great gap for global change prediction. This study aims to evaluate the forcing on the land surface temperature (Ts) using satellite-based observations. Based on the Blackbody radiation and surface radiation budget, first, a semi-physical framework is developed to estimate the Ts. Subsequently, the aerosol forcing is calculated by measuring the Ts difference between the changing aerosol scenario and baseline scenario with a fixed aerosol amount. Results show that the framework simulates Ts with acceptable accuracy (R = 0.62 and RMSE = 1.48 K), which supports the estimation of aerosol forcing. Generally, the change in the aerosol contributes 0.005 ± 0.237 K to the global Ts, which presents significant temporal and spatial variabilities. Temporally, the forcing shows a decreasing trend of −0.0006 K/year (R2 = 0.29, p = 0.031). Spatially, the forcing tends to warm the surface in regions with arid climate, low-cloud fraction, and moderate vapor or in sparsely vegetated and cool regions because of the potential interactions with climatic and environmental factors. The result of this study helps to reduce the uncertainty and validate the model results, which further supports the research on global climatic and environmental change.

ACS Style

Huihui Feng; Bin Zou. Satellite-based estimation of the aerosol forcing contribution to the global land surface temperature in the recent decade. Remote Sensing of Environment 2019, 232, 111299 .

AMA Style

Huihui Feng, Bin Zou. Satellite-based estimation of the aerosol forcing contribution to the global land surface temperature in the recent decade. Remote Sensing of Environment. 2019; 232 ():111299.

Chicago/Turabian Style

Huihui Feng; Bin Zou. 2019. "Satellite-based estimation of the aerosol forcing contribution to the global land surface temperature in the recent decade." Remote Sensing of Environment 232, no. : 111299.

Review articles
Published: 26 June 2019 in Journal of Spatial Science
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This study compares land use regression (LUR) and geographically weighted regression (GWR) models in PM2.5 concentration mapping over California (USA). Results show that R2 values of LUR model are 0.78, 0.74 and 0.74 times lower than those of GWR model at annual, seasonal and monthly scales. Relative errors are 2.18, 1.79 and 1.60 times higher, and root mean square error (RMSE) are 1.48, 1.32 and 1.28 higher. Furthermore, performance difference is significant under polluted conditions, but is minor under clean conditions. It demonstrates that LUR model is effective under low concentration at short time scales.

ACS Style

Bin Zou; Xin Fang; Huihui Feng; Xiang Zhou. Simplicity versus accuracy for estimation of the PM2.5 concentration: a comparison between LUR and GWR methods across time scales. Journal of Spatial Science 2019, 66, 279 -297.

AMA Style

Bin Zou, Xin Fang, Huihui Feng, Xiang Zhou. Simplicity versus accuracy for estimation of the PM2.5 concentration: a comparison between LUR and GWR methods across time scales. Journal of Spatial Science. 2019; 66 (2):279-297.

Chicago/Turabian Style

Bin Zou; Xin Fang; Huihui Feng; Xiang Zhou. 2019. "Simplicity versus accuracy for estimation of the PM2.5 concentration: a comparison between LUR and GWR methods across time scales." Journal of Spatial Science 66, no. 2: 279-297.

Journal article
Published: 09 January 2019 in Ecological Indicators
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There is a consensus that climate plays a crucial role in the formation and evolution of air pollution. However, air pollution also affects climate by radiation absorption and scattering. Whether air pollution or climate dominates their mutual interaction remains unclear, thus leaving much uncertainty in efforts to clarify a causal relationship. This study aimed to identify the dominant variable of the air pollution-climate interaction using global datasets of fine particulate matter (PM2.5) concentrations, precipitation and air temperature. The results show that the global evolution of air pollution from 1998 to 2015 was characterized by the “Polluting in Polluted and Cleaning in Clean” (PIPCIC) paradigm. Air pollution is mainly negatively and positively correlated to precipitation and temperature in tropical, arid and temperate regions. However, the conditions are much more complex in cold regions. In the air pollution-precipitation interaction, the threshold values of PM2.5 concentration and precipitation are 21.07 μg/m3 and 846.10 mm for negative and positive correlations in cold regions, respectively. Precipitation dominates the interaction in wet and clean conditions, resulting in a negative correlation. In contrast, air pollution dominates in dry and polluted conditions, leading to the enhancement of precipitation and a positive correlation. In the air pollution-temperature interaction, air pollution acts as the dominant variable, whereas temperature exhibits a limited influence. The threshold value of PM2.5 concentration is 20.47 μg/m3 for negative and positive correlations in cold regions. Specifically, radiative scattering dominates interactions in seriously polluted atmospheres, tending to cool the atmosphere and leading to a negative correlation. However, absorption is the dominant variable in clear skies; absorption warms the air and causes a positive correlation. The results of this study will help to clarify the causal relationship and support pollution control under the changing global climate.

ACS Style

Huihui Feng; Bin Zou; Jinyan Wang; Xiaodong Gu. Dominant variables of global air pollution-climate interaction: Geographic insight. Ecological Indicators 2019, 99, 251 -260.

AMA Style

Huihui Feng, Bin Zou, Jinyan Wang, Xiaodong Gu. Dominant variables of global air pollution-climate interaction: Geographic insight. Ecological Indicators. 2019; 99 ():251-260.

Chicago/Turabian Style

Huihui Feng; Bin Zou; Jinyan Wang; Xiaodong Gu. 2019. "Dominant variables of global air pollution-climate interaction: Geographic insight." Ecological Indicators 99, no. : 251-260.

Journal article
Published: 15 December 2018 in Science of The Total Environment
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The surface-air temperature difference (Ts-Ta) is a critical variable for tracking climatic and environmental change. Vegetation has unavoidably affected the temperature by altering surface properties, while the magnitude of this effect has remained unknown. This study aimed to investigate the patterns of global Ts-Ta and quantify the contribution of vegetation change. Trend analysis, correlation analysis and a trajectory-based method were adopted for the investigation. The results demonstrated that the global Ts-Ta decreased by −0.140 K from 2001 to 2016. The greening trend covered 24.46% of the land and played a profound role in changing Ts-Ta. In particular, vegetation changes resulted in −0.0022 K, −0.0092 K and − 0.0043 K of the Ts-Ta decreases at the global, greening and browning levels, respectively accounting for 11.58%, 35.38% and 20.38% of the total decrease. Physically, vegetation influenced Ts-Ta mainly by altering atmospheric properties, rather than surface properties. Specifically, the greening of the surface reduced the albedo at a rate of −0.0003/year over 20% of the global land and enhanced atmospheric water vapor by 3 × 10−5 g/m3 over approximately 40% of the land. Meanwhile, the effect of vegetation change varied with coverage. A reduction in albedo caused by vegetation change occurred equally over different vegetated conditions, while the enhancement of atmospheric water vapor occurred mainly in sparsely (0.10 < NDVI < 0.30) and densely (0.55 < NDVI < 0.70) vegetated regions. Under these conditions, the vegetation change mainly affected Ts-Ta in sparsely vegetated regions (NDVI < 0.4). The results of this study are helpful for understanding the physical mechanism behind changes in global Ts-Ta and support climatic adaptation and environmental management.

ACS Style

Huihui Feng; Bin Zou. A greening world enhances the surface-air temperature difference. Science of The Total Environment 2018, 658, 385 -394.

AMA Style

Huihui Feng, Bin Zou. A greening world enhances the surface-air temperature difference. Science of The Total Environment. 2018; 658 ():385-394.

Chicago/Turabian Style

Huihui Feng; Bin Zou. 2018. "A greening world enhances the surface-air temperature difference." Science of The Total Environment 658, no. : 385-394.

Article
Published: 02 September 2017 in Remote Sensing
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Under rapid urbanization, many cities in China suffer from serious fine particulate matter (PM2.5) pollution. As the emission sources or adsorption sinks, land use and the corresponding landscape pattern unavoidably affect the concentration. However, the correlation varies with different regions and scales, leaving a significant gap for urban planning. This study clarifies the correlation with the aid of in situ and satellite-based spatial datasets over six urban agglomerations in China. Two coverage and four landscape indices are adopted to represent land use and landscape pattern. Specifically, the coverage indices include the area ratios of forest (F_PLAND) and built-up areas (C_PLAND). The landscape indices refer to the perimeter-area fractal dimension index (PAFRAC), interspersion and juxtaposition index (IJI), aggregation index (AI), Shannon’s diversity index (SHDI). Then, the correlation between PM2.5 concentration with the selected indices are evaluated from supporting the potential urban planning. Results show that the correlations are weak with the in situ PM2.5 concentration, which are significant with the regional value. It means that land use coverage and landscape pattern affect PM2.5 at a relatively large scale. Furthermore, regional PM2.5 concentration negatively correlate to F_PLAND and positively to C_PLAND (significance at p < 0.05), indicating that forest helps to improve air quality, while built-up areas worsen the pollution. Finally, the heterogeneous landscape presents positive correlation to the regional PM2.5 concentration in most regions, except for the urban agglomeration with highly-developed urban (i.e., the Jing-Jin-Ji and Chengdu-Chongqing urban agglomerations). It suggests that centralized urbanization would be helpful for PM2.5 pollution controlling by reducing the emission sources in most regions. Based on the results, the potential urban planning is proposed for controlling PM2.5 pollution for each urban agglomeration.

ACS Style

Huihui Feng; Bin Zou; Yumeng Tang. Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning. Remote Sensing 2017, 9, 918 .

AMA Style

Huihui Feng, Bin Zou, Yumeng Tang. Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning. Remote Sensing. 2017; 9 (9):918.

Chicago/Turabian Style

Huihui Feng; Bin Zou; Yumeng Tang. 2017. "Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning." Remote Sensing 9, no. 9: 918.

Journal article
Published: 23 September 2016 in Remote Sensing
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The total nitrogen concentration (NC, g/100 g) of wetland plants is an important parameter to estimate the wetland health status and to calculate the nitrogen storage of wetland plants. Remote sensing has been widely used to estimate biophysical, physiological, and biochemical parameters of plants. However, current studies place little emphasis on NC estimations by only taking nitrogen’s vertical distribution into consideration, resulting in limited accuracy and decreased practical value of the results. The main goal of this study is to develop a model, considering a non-uniform vertical nitrogen distribution to estimate the total NC of the reed canopy, which is one of the wetland’s dominant species, using hyperspectral data. Sixty quadrats were selected and measured based on an experimental design that considered vertical layer divisions within the reed canopy. Using the measured NCs of different leaf layers and corresponding spectra from the quadrats, the results indicated that the vertical distribution law of the NC was distinct, presenting an initial increase and subsequent decrease from the top layer to the bottom layer. The spectral indices MCARI/MTVI2, TCARI/OSAVI, MMTCI, DCNI, and PPR/NDVI had high R2 values when related to NC (R2 > 0.5) and low R2 when related to LAI (R2 < 0.2) and could minimize the influence of LAI and increase the sensitivity to changes in NC of the reed canopy. The relative variation rates (Rv, %) of these spectral indices, calculated from each quadrat, also indicated that the top three layers of the reed canopy were an effective depth to estimate NCs using hyperspectral data. A model was developed to estimate the total NC of the whole reed canopy based on PPR/DNVI with R2 = 0.88 and RMSE = 0.37%. The model, which considered the vertical distribution patterns of the NC and the effective canopy layers, has demonstrated great potential to estimate the total NC of the whole reed canopy.

ACS Style

Juhua Luo; Ronghua Ma; Huihui Feng; Xinchuan Li. Estimating the Total Nitrogen Concentration of Reed Canopy with Hyperspectral Measurements Considering a Non-Uniform Vertical Nitrogen Distribution. Remote Sensing 2016, 8, 789 .

AMA Style

Juhua Luo, Ronghua Ma, Huihui Feng, Xinchuan Li. Estimating the Total Nitrogen Concentration of Reed Canopy with Hyperspectral Measurements Considering a Non-Uniform Vertical Nitrogen Distribution. Remote Sensing. 2016; 8 (10):789.

Chicago/Turabian Style

Juhua Luo; Ronghua Ma; Huihui Feng; Xinchuan Li. 2016. "Estimating the Total Nitrogen Concentration of Reed Canopy with Hyperspectral Measurements Considering a Non-Uniform Vertical Nitrogen Distribution." Remote Sensing 8, no. 10: 789.

Technical note
Published: 29 April 2015 in Remote Sensing
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In-situ soil moisture was widely used to validate and calibrate the satellite-retrieved data of different footprints. However, it contained unavoidable uncertainty when used as spatial representative. This paper examined the uncertainty in pixel-wise soil moisture designed for satellite validation in the HiWATER project. Two in-situ data sets were used for the examination, which were carefully designed to capture the spatial heterogeneity of soil moisture at different scales. Our results indicated that the pixel-wise uncertainty increased with increasing extent. At a small area, the uncertainty referred to the natural spatial variability of in-situ soil moisture. With respect to a large area, sampling error of spatial soil moisture played an important role, particularly of dry condition. Temporally, the uncertainty was higher during rainfall than that after then. It suggested that in-situ soil moisture could be more spatially representative at a small area after rainfall, valuable for satellite validation. Uncertainty was correlated to soil moisture. It was strongly correlated to spatial mean at a small scale and was to the spatial pattern at a large scale. Results of this study offered some clues to examine the uncertainty of in-situ soil moisture for satellite validation.

ACS Style

Huihui Feng; Yuanbo Liu; Guiping Wu. Temporal Variability of Uncertainty in Pixel-Wise Soil Moisture: Implications for Satellite Validation. Remote Sensing 2015, 7, 5398 -5415.

AMA Style

Huihui Feng, Yuanbo Liu, Guiping Wu. Temporal Variability of Uncertainty in Pixel-Wise Soil Moisture: Implications for Satellite Validation. Remote Sensing. 2015; 7 (5):5398-5415.

Chicago/Turabian Style

Huihui Feng; Yuanbo Liu; Guiping Wu. 2015. "Temporal Variability of Uncertainty in Pixel-Wise Soil Moisture: Implications for Satellite Validation." Remote Sensing 7, no. 5: 5398-5415.