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P-band PolInSAR technique has been validated to be a useful tool for forest height inversion. Aiming at the residual ground scattering contribution and temporal decorrelation in repeat-pass interferometry that affect the inversion accuracy, we proposed a dual-baseline method based on the RVoG + VTD model. This method is firstly applied to E-SAR P-band data acquired over the Krycklan Catchment, Sweden with mixed forests, and the influence of ground scattering contribution and temporal decorrelation related to vertical wavenumber kz and height hv are theoretically and experimentally explored by comparing the traditional three-stage inversion method, the fixed extinction method and the proposed method. The experimental results show that the traditional three-stage inversion method has a overall serious overestimation for the whole study area, resulting in a R2 of ca. 0.4 and RMSE of ca. 7 m. Compared with the three-stage inversion method, the fixed extinction method improves the overestimation in the near range but has little effect in the far range. After introducing the RVoG + VTD model, both the overestimation in the near and far range are improved. In general, the study area is affected by both the influence of ground scattering contribution and temporal decorrelation. The overestimation in the near range (with kz larger than ca. 0.1 rad/m) is mainly caused by ground scattering contribution while for the far range (with kz smaller than ca. 0.06 rad/m) temporal decorrelation is the dominated reason. The simulation experiment further reveals the relationship between the influence of ground scattering contribution, temporal decorrelation and kz. Simulative results also show that ground scattering contribution has a greater influence for a large kz while temporal decorrelation generally brings overestimation, and has a greater impact for a small kz inversely. The proposed method simultaneously reduces the effects of ground scattering contribution and temporal decorrelation, and produces the highest accuracy with R2 reaching to ca. 0.6 and RMSE reducing to ca. 3.48 m, further demonstrating its validity for forest height inversion.
Yue Shi; Binbin He; Zhanmang Liao. An improved dual-baseline PolInSAR method for forest height inversion. International Journal of Applied Earth Observation and Geoinformation 2021, 103, 102483 .
AMA StyleYue Shi, Binbin He, Zhanmang Liao. An improved dual-baseline PolInSAR method for forest height inversion. International Journal of Applied Earth Observation and Geoinformation. 2021; 103 ():102483.
Chicago/Turabian StyleYue Shi; Binbin He; Zhanmang Liao. 2021. "An improved dual-baseline PolInSAR method for forest height inversion." International Journal of Applied Earth Observation and Geoinformation 103, no. : 102483.
Rice false smut (RFS), caused by Ustilaginoidea virens, is a significant grain disease in rice that can lead to reduced yield and quality. In order to obtain spatiotemporal change information, multitemporal hyperspectral UAV data were used in this study to determine the sensitive wavebands for RFS identification, 665–685 and 705–880 nm. Then, two methods were used for the extraction of rice false smut-infected areas, one based on spectral similarity analysis and one based on spectral and temporal characteristics. The final overall accuracy of the two methods was 74.23 and 85.19%, respectively, showing that the second method had better prediction accuracy. In addition, the classification results of the two methods show that the areas of rice false smut infection had an expanding trend over time, which is consistent with the natural development law of rice false smut, and also shows the scientific nature of the two methods.
Gangqiang An; Minfeng Xing; Binbin He; Haiqi Kang; Jiali Shang; Chunhua Liao; Xiaodong Huang; Hongguo Zhang. Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data. Remote Sensing 2021, 13, 3185 .
AMA StyleGangqiang An, Minfeng Xing, Binbin He, Haiqi Kang, Jiali Shang, Chunhua Liao, Xiaodong Huang, Hongguo Zhang. Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data. Remote Sensing. 2021; 13 (16):3185.
Chicago/Turabian StyleGangqiang An; Minfeng Xing; Binbin He; Haiqi Kang; Jiali Shang; Chunhua Liao; Xiaodong Huang; Hongguo Zhang. 2021. "Extraction of Areas of Rice False Smut Infection Using UAV Hyperspectral Data." Remote Sensing 13, no. 16: 3185.
Dead fuel moisture content (DFMC) is a key driver for fire occurrence and is often an important input to many fire simulation models. There are two main approaches to estimating DFMC: empirical and process-based models. The former mainly relies on empirical methods to build relationships between the input drivers (weather, fuel and site characteristics) and observed DFMC. The latter attempts to simulate the processes that occur in the fuel with energy and water balance conservation equations. However, empirical models lack explanations for physical processes, and process-based models may provide an incomplete representation of DFMC. To combine the benefits of empirical and process-based models, here we introduced the Long Short-Term Memory (LSTM) network and its combination with an effective physics process-based model fuel stick moisture model (FSMM) to estimate DFMC. The LSTM network showed its powerful ability in describing the temporal dynamic changes of DFMC with high R2 (0.91), low RMSE (3.24%) and MAE (1.97%). When combined with a FSMM model, the physics-guided model FSMM-LSTM showed betterperformance (R2 = 0.96, RMSE = 2.21% and MAE = 1.41%) compared with the other models. Therefore, the combination of the physics process and deep learning estimated 10-h DFMC more accurately, allowing the improvement of wildfire risk assessments and fire simulating.
Chunquan Fan; Binbin He. A Physics-Guided Deep Learning Model for 10-h Dead Fuel Moisture Content Estimation. Forests 2021, 12, 933 .
AMA StyleChunquan Fan, Binbin He. A Physics-Guided Deep Learning Model for 10-h Dead Fuel Moisture Content Estimation. Forests. 2021; 12 (7):933.
Chicago/Turabian StyleChunquan Fan; Binbin He. 2021. "A Physics-Guided Deep Learning Model for 10-h Dead Fuel Moisture Content Estimation." Forests 12, no. 7: 933.
Fuel moisture content (FMC) of live vegetation is a crucial wildfire risk and spread rate driver. This study presents the first daily FMC product at a global scale and 500 m pixel resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and radiative transfer models (RTMs) inversion techniques. Firstly, multi-source information parameterized the PROSPECT-5 (leaf level), 4SAIL (grass and shrub canopy level) and GeoSail (tree canopy level) RTMs to generate three look-up tables (LUTs). Each LUT contained the most realistic model inputs range and combination, and the corresponding simulated spectra. Secondly, for each date and location of interest, a global landcover map classified fuels into three classes: grassland, shrubland and forest. For each fuel class, the best LUT-based inversion strategy based on spectral information, cost function, percentage of solutions, and central tendency determined the optimal model for the global FMC product. Finally, 3,034 FMC measurements from 120 worldwide sites validated the statistically significant results (R2 = 0.62, RMSE = 34.57%, p < 0.01). Filtering out low quality field measurements achieved better accuracy (R2 = 0.71, RMSE = 32.36%, p < 0.01, n = 2008). It is anticipated that this global FMC product can assist in wildfire danger modeling, early prediction, suppression and response, as well as improve awareness of wildfire risk to life and property.
Xingwen Quan; Marta Yebra; David Riaño; Binbin He; Gengke Lai; Xiangzhuo Liu. Global fuel moisture content mapping from MODIS. International Journal of Applied Earth Observation and Geoinformation 2021, 101, 102354 .
AMA StyleXingwen Quan, Marta Yebra, David Riaño, Binbin He, Gengke Lai, Xiangzhuo Liu. Global fuel moisture content mapping from MODIS. International Journal of Applied Earth Observation and Geoinformation. 2021; 101 ():102354.
Chicago/Turabian StyleXingwen Quan; Marta Yebra; David Riaño; Binbin He; Gengke Lai; Xiangzhuo Liu. 2021. "Global fuel moisture content mapping from MODIS." International Journal of Applied Earth Observation and Geoinformation 101, no. : 102354.
Object detection is challenging in HSR remote sensing images which have a complex background and irregular object locations. To minimize manual annotation cost in supervised learning methods and achieve advanced detection performance, we proposed a point-based weakly supervised learning method to address the object detection challenge in HSR remote sensing images. In the study, point labels are introduced to guide candidate bounding box mining and generate pseudo bounding boxes for objects. Then, pseudo bounding boxes are applied to train the detection model. A progressive candidate bounding box mining strategy is proposed to refine object detection. Experiments are conducted on a comprehensive HSR data set which contains four categories. Results indicate the proposed method achieves competitive performance compared to YOLOv5 which is trained on manual bounding box annotations. In comparison to the state-of-the-art weakly supervised learning method, our method outperforms WSDDN method with 0.62 mAP score.
Youyou Li; Binbin He; Farid Melgani; Teng Long. Point-Based Weakly Supervised Learning for Object Detection in High Spatial Resolution Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 5361 -5371.
AMA StyleYouyou Li, Binbin He, Farid Melgani, Teng Long. Point-Based Weakly Supervised Learning for Object Detection in High Spatial Resolution Remote Sensing Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):5361-5371.
Chicago/Turabian StyleYouyou Li; Binbin He; Farid Melgani; Teng Long. 2021. "Point-Based Weakly Supervised Learning for Object Detection in High Spatial Resolution Remote Sensing Images." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 5361-5371.
Soil moisture is vital for the crop growth and directly affects the crop yield. Conventional SAR based soil moisture monitoring is often influenced by vegetation cover and surface roughness. Machine-learning methods are not constrained by physical parameters and have high nonlinear fitting capabilities. In this study, machine-learning methods were applied to estimate soil moisture over winter wheat fields during its growing season. RADARSAT-2 data with quad polarizations and 240 sample plots in the study area were acquired and collected, respectively. In addition to the four linear polarization channels, polarimetric decomposition parameters were extracted to expand the SAR feature space. Three advanced machine-learning models were selected and compared which were Support Vector Regression (SVR), Random Forests (RF), and Gradient Boosting Regression Tree (GBRT). To improve the performances of the models, three feature selection methods were compared, which were based on Pearson correlation, SVM Recursive Feature Elimination (SVM-RFE), and RF respectively. The coefficient of determination (R2) and root-mean-square error (RMSE) were used to compare and assess the performances of those models. The results revealed that polarimetric decomposition parameters were effective in estimating soil moisture, and RF model obtained the highest prediction accuracy (training set: RMSE = 2.44 vol.%, and R2 = 0.94; validation set: RMSE = 4.03 vol.%, and R2 = 0.79). This study finally concluded that using polarimetric decomposition parameters combined with machine learning and feature selection methods could effectively estimate soil moisture at a high accuracy, which helps monitor soil moisture across the agricultural field during its growing season.
Lin Chen; Minfeng Xing; Binbin He; Jinfei Wang; Jiali Shang; Xiaodong Huang; Min Xu. Estimating Soil Moisture Over Winter Wheat Fields During Growing Season Using Machine-Learning Methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, 14, 3706 -3718.
AMA StyleLin Chen, Minfeng Xing, Binbin He, Jinfei Wang, Jiali Shang, Xiaodong Huang, Min Xu. Estimating Soil Moisture Over Winter Wheat Fields During Growing Season Using Machine-Learning Methods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; 14 (99):3706-3718.
Chicago/Turabian StyleLin Chen; Minfeng Xing; Binbin He; Jinfei Wang; Jiali Shang; Xiaodong Huang; Min Xu. 2021. "Estimating Soil Moisture Over Winter Wheat Fields During Growing Season Using Machine-Learning Methods." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 3706-3718.
Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports the light detection and ranging (LiDAR), optical and airborne synthetic aperture radar (SAR) data for fuel load estimation, but the optical and SAR data are generally individually explored. Optical and SAR data are expected to be sensitive to different types of fuel loads because of their different imaging mechanisms. Optical data mainly captures the characteristics of leaf and forest canopy, while the latter is more sensitive to forest vertical structures due to its strong penetrability. This study aims to explore the performance of Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data as well as their combination on estimating three different types of fuel load—stem fuel load (SFL), branch fuel load (BFL) and foliage fuel load (FFL). We first analyzed the correlation between the three types of fuel load and optical and SAR data. Then, the partial least squares regression (PLSR) was used to build the fuel load estimation models based on the fuel load measurements from Vindeln, Sweden, and variables derived from optical and SAR data. Based on the leave-one-out cross-validation (LOOCV) method, results show that L-band SAR data performed well on all three types of fuel load (R2 = 0.72, 0.70, 0.72). The optical data performed best for FFL estimation (R2 = 0.66), followed by BFL (R2 = 0.56) and SFL (R2 = 0.37). Further improvements were found for the SFL, BFL and FFL estimation when integrating optical and SAR data (R2 = 0.76, 0.81, 0.82), highlighting the importance of data selection and combination for fuel load estimation.
Yanxi Li; Xingwen Quan; Zhanmang Liao; Binbin He. Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data. Remote Sensing 2021, 13, 1189 .
AMA StyleYanxi Li, Xingwen Quan, Zhanmang Liao, Binbin He. Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data. Remote Sensing. 2021; 13 (6):1189.
Chicago/Turabian StyleYanxi Li; Xingwen Quan; Zhanmang Liao; Binbin He. 2021. "Forest Fuel Loads Estimation from Landsat ETM+ and ALOS PALSAR Data." Remote Sensing 13, no. 6: 1189.
Foliage fuel load (FFL) is a critical factor affecting crown fire intensity and rate of spread. Satellite observations provide the potential for monitoring FFL dynamics across large areas. Previous studies commonly used empirical methods to estimate FFL, which potentially lacks reproducibility. This study applied Landsat 7 ETM+ and 8 OLI data for FFL retrieval using radiative transfer model (RTM) and machine learning method. To this end, the GeoSail, SAIL and PROSPECT RTMs were firstly coupled together to model the near realistic scenario of a two-layered forest structure. Secondly, available ecological information was applied to constrain the coupled RTM modeling phases, in order to decrease the probability of generating unrealistic simulations. Thirdly, the coupled RTMs were linked to three machine learning models - random forest, support vector machine and multi-layer perceptron - as well as a traditional look-up table. Finally, the performance of each method was validated by FFL measurements from Southwest China and Sweden. The resulting multi-layer perceptron (R2 = 0.77, RMSE = 0.13 and rRMSE = 0.43) outperformed the other three methods. Evaluation of applicability of the FFL estimates was conducted in a southwest China forest where two forest fires occurred in 2014 and 2020. The FFL dynamics from 2013 through 2020 showed that fire was likely to occur when the FFL accumulated to a critical point (around 27106 kg), highlighting the relevance of remote sensing-derived FFL estimates for understanding potential fire occurrence.
Xingwen Quan; Yanxi Li; Binbin He; Geoffrey Cary; Gengke Lai. Application of Landsat ETM+ and OLI Data for Foliage Fuel Load Monitoring Using Radiative Transfer Model and Machine Learning Method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2021, PP, 1 -1.
AMA StyleXingwen Quan, Yanxi Li, Binbin He, Geoffrey Cary, Gengke Lai. Application of Landsat ETM+ and OLI Data for Foliage Fuel Load Monitoring Using Radiative Transfer Model and Machine Learning Method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021; PP (99):1-1.
Chicago/Turabian StyleXingwen Quan; Yanxi Li; Binbin He; Geoffrey Cary; Gengke Lai. 2021. "Application of Landsat ETM+ and OLI Data for Foliage Fuel Load Monitoring Using Radiative Transfer Model and Machine Learning Method." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP, no. 99: 1-1.
As regulated by the ‘fire environment triangle’, three major forces are essential for understanding wildfire danger: (1) topography, (2) weather and (3) fuel. Within this concept, this study aimed to assess the wildfire danger for China based on a set of topography, weather and fuel variables. Among these variables, two remotely sensed key fuel variables, fuel moisture content (FMC) and foliage fuel load (FFL), were integrated into the assessment. These fuel variables were retrieved using radiative transfer models from the MODIS reflectance products. The random forest model identified the relationships between these variables and historical wildfires and then produced a daily updated and moderate-high spatial resolution (500 m) dataset of wildfire danger for China from 2001 to 2020. Results showed that this dataset performed well in assessing wildfire danger for China in terms of the ‘Area Under the Curve’ value, the fire density within each wildfire danger level, and the visualisation of spatial patterns. Further analysis showed that when the FMC and FFL were excluded from the assessment, the accuracy decreased, revealing the reasonability of the remotely sensed FMC and FFL in the assessment. As regulated by the ‘fire environment triangle’, three major forces are essential for understanding wildfire danger: (1) topography, (2) weather and (3) fuel. Within this concept, this study aimed to assess the wildfire danger for China based on a set of topography, weather and fuel variables. Among these variables, two remotely sensed key fuel variables, fuel moisture content (FMC) and foliage fuel load (FFL), were integrated into the assessment. These fuel variables were retrieved using radiative transfer models from the MODIS reflectance products. The random forest model identified the relationships between these variables and historical wildfires and then produced a daily updated and moderate-high spatial resolution (500 m) dataset of wildfire danger for China from 2001 to 2020. Results showed that this dataset performed well in assessing wildfire danger for China in terms of the ‘Area Under the Curve’ value, the fire density within each wildfire danger level, and the visualisation of spatial patterns. Further analysis showed that when the FMC and FFL were excluded from the assessment, the accuracy decreased, revealing the reasonability of the remotely sensed FMC and FFL in the assessment.
Xingwen Quan; Qian Xie; Binbin He; Kaiwei Luo; Xiangzhuo Liu. Integrating remotely sensed fuel variables into wildfire danger assessment for China. International Journal of Wildland Fire 2021, 1 .
AMA StyleXingwen Quan, Qian Xie, Binbin He, Kaiwei Luo, Xiangzhuo Liu. Integrating remotely sensed fuel variables into wildfire danger assessment for China. International Journal of Wildland Fire. 2021; ():1.
Chicago/Turabian StyleXingwen Quan; Qian Xie; Binbin He; Kaiwei Luo; Xiangzhuo Liu. 2021. "Integrating remotely sensed fuel variables into wildfire danger assessment for China." International Journal of Wildland Fire , no. : 1.
Soil moisture (Mv) estimation and monitoring over agricultural areas using Synthetic Aperture Radar (SAR) are often affected by vegetation cover during the growing season. Volume scattering and vegetation attenuation can complicate the received SAR backscatter signal when microwave interacts with the vegetation canopy. To address the existing problems, this paper employed the model-based polarimetric decomposition method considering the two-way attenuation to remove the volume scattering and vegetation attenuation. A de-orientation process of SAR data was applied to remove the influence of randomly distributed target orientation angles before the polarimetric decomposition. To parameterize the two-way attenuation, Radar Vegetation Index (RVI) derived from the SAR intensity images was adopted. The Dubois model was used to describe backscattering from the underlying bare soil. Since the soil roughness parameters are difficult to measure under vegetation cover, the optimum surface roughness method was used to parameterize the Dubois model. This soil moisture retrieval algorithm was applied to the polarimetric multi-temporal RADARSAT-2 SAR data over soybean fields. The validation indicates the root-mean-square error of 9.2 vol.% and 8.2 vol.% at HH and VV polarization respectively over the entire soybean growing period, suggesting that the proposed method is capable of reducing the effect of vegetation cover for soil moisture monitoring over the soybean field.
Tengfei Xiao; Minfeng Xing; Binbin He; Jinfei Wang; Jiali Shang; Xiaodong Huang; Xiliang Ni. Retrieving Soil Moisture Over Soybean Fields During Growing Season Through Polarimetric Decomposition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 1132 -1145.
AMA StyleTengfei Xiao, Minfeng Xing, Binbin He, Jinfei Wang, Jiali Shang, Xiaodong Huang, Xiliang Ni. Retrieving Soil Moisture Over Soybean Fields During Growing Season Through Polarimetric Decomposition. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):1132-1145.
Chicago/Turabian StyleTengfei Xiao; Minfeng Xing; Binbin He; Jinfei Wang; Jiali Shang; Xiaodong Huang; Xiliang Ni. 2020. "Retrieving Soil Moisture Over Soybean Fields During Growing Season Through Polarimetric Decomposition." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 1132-1145.
Burn severity mapping is critical to quantifying fire impact on key ecological processes and post-fire forest management. Satellite remote sensing has the advantages of high spatial-temporal resolution and large-scale monitoring and provides a more efficient way to evaluate forest fire burn severity than traditional field or aerial surveys. However, the proportion of tree canopy cover (TCC) affects the spectral signal received by remote sensing sensors from the background charcoal and ash. Consequently, not considering this factor normally leads a spectral confusion in burn severity retrieval. In this study, the burn severity of two Qinyuan forest fires was estimated using a coupled Radiative Transfer Model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) reflectance data. A two-layer Canopy Reflectance Model (ACRM) RTM was coupled with the GeoSail RTM by replacing the spectra of the background input of GeoSail RTM to simulate the spectra of the three-layered forests for burn severity retrieval measured as the Composite Burn Index (CBI). The TCC data was then served to RTM parameterization and constrain the backward inversion procedure of the coupled RTM to alleviate spectral confusion. Finally, the inversion retrievals were evaluated using 163 field measured CBI. The coupled RTM can simulate the radiative transfer characteristics of three-layer vegetation and has greater potential to accurately estimate burn severity worldwide. To evaluate the merit of our proposed method, the CBI was estimated through coupled RTM inversion with TCC constraint (CP_RTM+TCC), coupled RTM inversion with global optimal search (CP-RTM+GOS), Forest Reflectance and Transmittance (FRT) RTM inversion with TCC constraint (FRT+TCC), and random forest (RF) algorithm. The results showed that the method proposed in this study (CP_RTM+TCC) yielded the highest estimation accuracy (R2 = 0.92, RMSE = 0.2) among the four methods used as benchmark, indicating its reasonable ability to assist forest managers to better understand post-fire vegetation regeneration and forest management.
Changming Yin; Binbin He; Xingwen Quan; Marta Yebra; Gengke Lai. Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires. Remote Sensing 2020, 12, 3590 .
AMA StyleChangming Yin, Binbin He, Xingwen Quan, Marta Yebra, Gengke Lai. Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires. Remote Sensing. 2020; 12 (21):3590.
Chicago/Turabian StyleChangming Yin; Binbin He; Xingwen Quan; Marta Yebra; Gengke Lai. 2020. "Remote Sensing of Burn Severity Using Coupled Radiative Transfer Model: A Case Study on Chinese Qinyuan Pine Fires." Remote Sensing 12, no. 21: 3590.
Chlorophyll is an essential pigment for photosynthesis in crops, and leaf chlorophyll content can be used as an indicator for crop growth status and help guide nitrogen fertilizer applications. Estimating crop chlorophyll content plays an important role in precision agriculture. In this study, a variable, rate of change in reflectance between wavelengths ‘a’ and ‘b’ (RCRWa-b), derived from in situ hyperspectral remote sensing data combined with four advanced machine learning techniques, Gaussian process regression (GPR), random forest regression (RFR), support vector regression (SVR), and gradient boosting regression tree (GBRT), were used to estimate the chlorophyll content (measured by a portable soil–plant analysis development meter) of rice. The performances of the four machine learning models were assessed and compared using root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The results revealed that four features of RCRWa-b, RCRW551.0–565.6, RCRW739.5–743.5, RCRW684.4–687.1 and RCRW667.9–672.0, were effective in estimating the chlorophyll content of rice, and the RFR model generated the highest prediction accuracy (training set: RMSE = 1.54, MAE =1.23 and R2 = 0.95; validation set: RMSE = 2.64, MAE = 1.99 and R2 = 0.80). The GPR model was found to have the strongest generalization (training set: RMSE = 2.83, MAE = 2.16 and R2 = 0.77; validation set: RMSE = 2.97, MAE = 2.30 and R2 = 0.76). We conclude that RCRWa-b is a useful variable to estimate chlorophyll content of rice, and RFR and GPR are powerful machine learning algorithms for estimating the chlorophyll content of rice.
Gangqiang An; Minfeng Xing; Binbin He; Chunhua Liao; Xiaodong Huang; Jiali Shang; Haiqi Kang. Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data. Remote Sensing 2020, 12, 3104 .
AMA StyleGangqiang An, Minfeng Xing, Binbin He, Chunhua Liao, Xiaodong Huang, Jiali Shang, Haiqi Kang. Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data. Remote Sensing. 2020; 12 (18):3104.
Chicago/Turabian StyleGangqiang An; Minfeng Xing; Binbin He; Chunhua Liao; Xiaodong Huang; Jiali Shang; Haiqi Kang. 2020. "Using Machine Learning for Estimating Rice Chlorophyll Content from In Situ Hyperspectral Data." Remote Sensing 12, no. 18: 3104.
As one of the important coastal cities in China, Tianjin has been urbanized dramatically over the past 40 years, and the urbanization rate has been up to 83.15% by 2018. In this study, we used the Continuous Change Detection and Classification (CCDC) algorithm to comprehensively understand the urban expansion processes in Tianjin based on the Landsat Time Series (LTS) from 1985 to 2018 with 30-meter resolution. Specially, we applied the c-factor approach with the RossThick-LiSparse-R model to correct the Bidirectional Reflectance Distribution Function (BRDF) effect for each Landsat image and calculated a spatial line-density feature for improving the change detection and the classification. Based on the study in Tianjin, we found that BRDF correction can substantially improve the change detection (9.00% higher overall accuracy) and classification (1.08% higher overall accuracy); and the line-density is also beneficial to classification (0.48% higher overall accuracy), especially for impervious surface (1.70% less commission errors and 1.49% less omission errors). By analyzing the imperious surface change processes, we observed that Tianjin has undergone rapid urban expansion in the past decades, and the urban area was mainly transformed from cropland around the central area before 2005 and later from the coast.
Yuwei Guan; Yanru Zhou; Binbin He; Xiangzhuo Liu; Hongguo Zhang; Shilei Feng. Improving Land Cover Change Detection and Classification With BRDF Correction and Spatial Feature Extraction Using Landsat Time Series: A Case of Urbanization in Tianjin, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 4166 -4177.
AMA StyleYuwei Guan, Yanru Zhou, Binbin He, Xiangzhuo Liu, Hongguo Zhang, Shilei Feng. Improving Land Cover Change Detection and Classification With BRDF Correction and Spatial Feature Extraction Using Landsat Time Series: A Case of Urbanization in Tianjin, China. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):4166-4177.
Chicago/Turabian StyleYuwei Guan; Yanru Zhou; Binbin He; Xiangzhuo Liu; Hongguo Zhang; Shilei Feng. 2020. "Improving Land Cover Change Detection and Classification With BRDF Correction and Spatial Feature Extraction Using Landsat Time Series: A Case of Urbanization in Tianjin, China." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 4166-4177.
Youyou Li; Farid Melgani; Binbin He. CSVM Architectures for Pixel-Wise Object Detection in High-Resolution Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 6059 -6070.
AMA StyleYouyou Li, Farid Melgani, Binbin He. CSVM Architectures for Pixel-Wise Object Detection in High-Resolution Remote Sensing Images. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (9):6059-6070.
Chicago/Turabian StyleYouyou Li; Farid Melgani; Binbin He. 2020. "CSVM Architectures for Pixel-Wise Object Detection in High-Resolution Remote Sensing Images." IEEE Transactions on Geoscience and Remote Sensing 58, no. 9: 6059-6070.
Synthetic Aperture Radar (SAR) texture has been demonstrated to have the potential to improve forest biomass estimation using backscatter. However, forests are 3D objects with a vertical structure. The strong penetration of SAR signals means that each pixel contains the contributions of all the scatterers inside the forest canopy, especially for the P-band. Consequently, the traditional texture derived from SAR images is affected by forest vertical heterogeneity, although the influence on texture-based biomass estimation has not yet been explicitly explored. To separate and explore the influence of forest vertical heterogeneity, we introduced the SAR tomography technique into the traditional texture analysis, aiming to explore whether TomoSAR could improve the performance of texture-based aboveground biomass (AGB) estimation and whether texture plus tomographic backscatter could further improve the TomoSAR-based AGB estimation. Based on the P-band TomoSAR dataset from TropiSAR 2009 at two different sites, the results show that ground backscatter variance dominated the texture features of the original SAR image and reduced the biomass estimation accuracy. The texture from upper vegetation layers presented a stronger correlation with forest biomass. Texture successfully improved tomographic backscatter-based biomass estimation, and the texture from upper vegetation layers made AGB models much more transferable between different sites. In addition, the correlation between texture indices varied greatly among different tomographic heights. The texture from the 10 to 30 m layers was able to provide more independent information than the other layers and the original images, which helped to improve the backscatter-based AGB estimation.
Zhanmang Liao; Binbin He; Xingwen Quan. Potential of texture from SAR tomographic images for forest aboveground biomass estimation. International Journal of Applied Earth Observation and Geoinformation 2020, 88, 102049 .
AMA StyleZhanmang Liao, Binbin He, Xingwen Quan. Potential of texture from SAR tomographic images for forest aboveground biomass estimation. International Journal of Applied Earth Observation and Geoinformation. 2020; 88 ():102049.
Chicago/Turabian StyleZhanmang Liao; Binbin He; Xingwen Quan. 2020. "Potential of texture from SAR tomographic images for forest aboveground biomass estimation." International Journal of Applied Earth Observation and Geoinformation 88, no. : 102049.
The authors wish to make the following corrections to this paper [1]: 1
Long Wang; Xingwen Quan; Binbin He; Marta Yebra; Minfeng Xing; Xiangzhuo Liu. Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568. Remote Sensing 2020, 12, 206 .
AMA StyleLong Wang, Xingwen Quan, Binbin He, Marta Yebra, Minfeng Xing, Xiangzhuo Liu. Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568. Remote Sensing. 2020; 12 (2):206.
Chicago/Turabian StyleLong Wang; Xingwen Quan; Binbin He; Marta Yebra; Minfeng Xing; Xiangzhuo Liu. 2020. "Correction: Wang, L., et al. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11(13), 1568." Remote Sensing 12, no. 2: 206.
Burn severity mapping greatly informs fire management and can be used to predict post-fire vegetation recovery. Satellite remote sensing is a cost-effective method for estimating burn severity, providing a comprehensive spatially explicit view of whole landscapes. However, the proportion of tree canopy cover (TCC) affects the reflectance signal, obscuring background char and ash. Consequently, traditional optical satellite remote sensing methods that do not account for variation in TCC misclassify burn severity, especially in areas with extremely low or high TCC. In this study, TCC data served to parameterize and constrain the inversion of the Forest Reflectance and Transmittance (FRT) radiative transfer model (RTM) to alleviate spectral confusion when retrieving burn severity. The methodology was evaluated using field measurements of burn severity for a series of wildfires in the fire-prone tropical savannas of northern Australia and the western United States. Burn severity classes were used for Australia while the Composite Burn Index (CBI) for US. Reflectance data from Sentinel-2A Multi-Spectral Instrument (MSI) and Landsat-5 Thematic Mapper (TM) corresponding to post-fire field survey dates were used to retrieve burn severity using FRT RTM (with and without using TCC information in its parameterization and inversion) and two standard empirical burn indices, dNBR and RdNBR, for comparison. Using FRT RTM without TCC constraint produced an overestimation for low burn severity in regions with low TCC and an underestimation for moderate and high burn severity in regions with high TCC. Burn severity estimation accuracy significantly improved by integrating TCC in the parameterization and inversion of FRT RTM. The overall accuracy in northern Australia increased from 65% to 81%, and the kappa coefficient increased from 0.35 to 0.55. In the western United States, R2 between estimated and observed CBI, increased from 0.33 to 0.54, root mean square error (RMSE) reduced from 0.53 to 0.43, and in all instances, the method performed better than dNBR and RdNBR. The method used in this study achieved more accurate burn severity mapping, thus assisting land managers to better understand post-fire vegetation resilience and forest management.
Changming Yin; Binbin He; Marta Yebra; Xingwen Quan; Andrew C. Edwards; Xiangzhuo Liu; Zhanmang Liao. Improving burn severity retrieval by integrating tree canopy cover into radiative transfer model simulation. Remote Sensing of Environment 2019, 236, 111454 .
AMA StyleChangming Yin, Binbin He, Marta Yebra, Xingwen Quan, Andrew C. Edwards, Xiangzhuo Liu, Zhanmang Liao. Improving burn severity retrieval by integrating tree canopy cover into radiative transfer model simulation. Remote Sensing of Environment. 2019; 236 ():111454.
Chicago/Turabian StyleChangming Yin; Binbin He; Marta Yebra; Xingwen Quan; Andrew C. Edwards; Xiangzhuo Liu; Zhanmang Liao. 2019. "Improving burn severity retrieval by integrating tree canopy cover into radiative transfer model simulation." Remote Sensing of Environment 236, no. : 111454.
Previous studies have shown that Live Fuel Moisture Content (LFMC) is a crucial driver affecting wildfire occurrence worldwide, but the effect of LFMC in driving wildfire occurrence still remains unexplored over the southwest China ecosystem, an area historically vulnerable to wildfires. To this end, we took 10-years of LFMC dynamics retrieved from Moderate Resolution Imaging Spectrometer (MODIS) reflectance product using the physical Radiative Transfer Model (RTM) and the wildfire events extracted from the MODIS Burned Area (BA) product to explore the relations between LFMC and forest/grassland fire occurrence across the subtropical highland zone (Cwa) and humid subtropical zone (Cwb) over southwest China. The statistical results of pre-fire LFMC and cumulative burned area show that distinct pre-fire LFMC critical thresholds were identified for Cwa (151.3%, 123.1%, and 51.4% for forest, and 138.1%, 72.8%, and 13.1% for grassland) and Cwb (115.0% and 54.4% for forest, and 137.5%, 69.0%, and 10.6% for grassland) zones. Below these thresholds, the fire occurrence and the burned area increased significantly. Additionally, a significant decreasing trend on LFMC dynamics was found during the days prior to two large fire events, Qiubei forest fire and Lantern Mountain grassland fire that broke during the 2009/2010 and 2015/2016 fire seasons, respectively. The minimum LFMC values reached prior to the fires (49.8% and 17.3%) were close to the lowest critical LFMC thresholds we reported for forest (51.4%) and grassland (13.1%). Further LFMC trend analysis revealed that the regional median LFMC dynamics for the 2009/2010 and 2015/2016 fire seasons were also significantly lower than the 10-year LFMC of the region. Hence, this study demonstrated that the LFMC dynamics explained wildfire occurrence in these fire-prone regions over southwest China, allowing the possibility to develop a new operational wildfire danger forecasting model over this area by considering the satellite-derived LFMC product.
Kaiwei Luo; Xingwen Quan; Binbin He; Marta Yebra. Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China. Forests 2019, 10, 887 .
AMA StyleKaiwei Luo, Xingwen Quan, Binbin He, Marta Yebra. Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China. Forests. 2019; 10 (10):887.
Chicago/Turabian StyleKaiwei Luo; Xingwen Quan; Binbin He; Marta Yebra. 2019. "Effects of Live Fuel Moisture Content on Wildfire Occurrence in Fire-Prone Regions over Southwest China." Forests 10, no. 10: 887.
Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R2 = 0.71, RMSE = 4.43 vol.%, p < 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season.
Minfeng Xing; Binbin He; Xiliang Ni; Jinfei Wang; Gangqiang An; Jiali Shang; Xiaodong Huang. Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data. Remote Sensing 2019, 11, 1956 .
AMA StyleMinfeng Xing, Binbin He, Xiliang Ni, Jinfei Wang, Gangqiang An, Jiali Shang, Xiaodong Huang. Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data. Remote Sensing. 2019; 11 (16):1956.
Chicago/Turabian StyleMinfeng Xing; Binbin He; Xiliang Ni; Jinfei Wang; Gangqiang An; Jiali Shang; Xiaodong Huang. 2019. "Retrieving Surface Soil Moisture over Wheat and Soybean Fields during Growing Season Using Modified Water Cloud Model from Radarsat-2 SAR Data." Remote Sensing 11, no. 16: 1956.
Fuel moisture content (FMC) is a crucial variable affecting fuel ignition and rate of fire spread. Much work so far has focused on the usage of remote sensing data from multiple sensors to derive FMC; however, little attention has been devoted to the usage of the C-band Sentinel-1A data. In this study, we aimed to test the performance of C-band Sentinel-1A data for multi-temporal retrieval of forest FMC by coupling the bare soil backscatter linear model with the vegetation backscatter water cloud model (WCM). This coupled model that linked the observed backscatter directly to FMC, was firstly calibrated using field FMC measurements and corresponding synthetic aperture radar (SAR) backscatters (VV and VH), and then a look-up table (LUT) comprising of the modelled VH backscatter and FMC was built by running the calibrated model forwardly. The absolute difference (MAEr) of modelled and observed VH backscatters was selected as the cost function to search the optimal FMC from the LUT. The performance of the presented methodology was verified using the three-fold cross-validation method by dividing the whole samples into equal three parts. Two parts were used for the model calibration and the other one for the validation, and this was repeated three times. The results showed that the estimated and measured forest FMC were consistent across the three validation samples, with the root mean square error (RMSE) of 19.53% (Sample 1), 12.64% (Sample 2) and 15.45% (Sample 3). To further test the performance of the C-band Sentinel-1A data for forest FMC estimation, our results were compared to those obtained using the optical Landsat 8 Operational Land Imager (OLI) data and the empirical partial least squares regression (PLSR) method. The latter resulted in higher RMSE between estimated and measured forest FMC with 20.11% (Sample 1), 26.21% (Sample 2) and 26.73% (Sample 3) than the presented Sentinel-1A data-based method. Hence, this study demonstrated that the good capability of C-band Sentinel-1A data for forest FMC retrieval, opening the possibility of developing a new operational SAR data-based methodology for forest FMC estimation.
Long Wang; Xingwen Quan; Binbin He; Marta Yebra; Minfeng Xing; Xiangzhuo Liu. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing 2019, 11, 1568 .
AMA StyleLong Wang, Xingwen Quan, Binbin He, Marta Yebra, Minfeng Xing, Xiangzhuo Liu. Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation. Remote Sensing. 2019; 11 (13):1568.
Chicago/Turabian StyleLong Wang; Xingwen Quan; Binbin He; Marta Yebra; Minfeng Xing; Xiangzhuo Liu. 2019. "Assessment of the Dual Polarimetric Sentinel-1A Data for Forest Fuel Moisture Content Estimation." Remote Sensing 11, no. 13: 1568.