Low-temperature disasters seriously affect the ecological environment and people's daily lives. In the research, the spatial distribution of low-temperature risk over 30 years has been obtained by collecting meteorological and socio-economic data from 1991 to 2020, and then a risk assessment model based on hazard, exposure, and vulnerability indicators is built. The results show that the high-risk areas are mainly concentrated in the central, eastern, and southern parts of Northeast China, and the overall risk shows an increasing trend; the regions at high risk are primarily found in Hunan Province and the central part of Jiangxi Province in south China. Further, the change in risk is most obvious between 1991-2005, and the overall risk value decreased about 41.28%. The Contribution to low-temperature risk in Northeast China comes mainly from vulnerability. While, In South China, the contribution of various factors to risk is more evenly distributed.

D-LinkNet has been a popular convolutional neural network. However, the scale of the model would be huge. Focused on the problem, this article made some improvements:(1) Replace the initial block with a stem block. (2) Add bottleneck layers before and after DBlock to form a new module called DBlockPlus. (3) Rebuild the entire network based on ResNet. The article also proposed a method named ESIPs to eliminate small independent patches and adopted a composite loss function called DF loss to better address the impact of extreme class imbalance in binary classification. And another composite loss function was introduced for comparative experiments. The experimental results show that the SBD-LinkNet not only has less parameters but also has a higher accuracy and the road extraction results presented a better visual effect after processing by ESIPs. And the model that uses DF loss also performs better in road extraction from complicated scene.

Tobacco is a critical cash crop in China, so its grow status has been attributed more and more attention. How to acquire the accurate plant area, row spacing and plant spacing have been the key points for its grow status monitoring and yield prediction. Currently, remote sensing has been a popular method for its speediness, large scale and costless, which could replace the traditional manual methods. We proposed a method to extract the planting information of tobacco at the rosette stage with UAV (Unmanned Aerial Vehicle) remote sensing images, and solved the following problems: the difficulty of detecting the small and densely planted tobacco objects, the scattered tobacco fields with different shapes in Sichuan Province. Four experimental areas were selected in Sichuan Province, and image processing and sample label production were carried out. The results indicate that the average accuracy of tobacco field area, row spacing and plant spacing extracted by this method reached 98.35%, 97.90% and 97.74%, respectively, which proved the extraction method of plant information are valuable.

Detecting semantic changes in high-resolution aerial images is a challenging task in remote sensing technology. Previous change detection methods have encountered difficulties in detecting subtle changes and have faced problems such as overfitting of image time series and unclear boundaries of the generated change maps. This paper proposes SEFNet, a multi-task change detection network based on symmetric enhancement fusion, to address the aforementioned issues. SEFNet is built on a decoupled three-branch change detection network framework, which allows each branch module to perform more specialized functions, facilitating more targeted training. Additionally, this study introduces the Symmetry-enhanced Fusion Change Detection unit (SFCD) module to extract multi-level change information. To highlight subtle changes while incorporating shallow detailed features of the network, the SFCD method employs feature enhancement methods and feature reuse strategies. Additionally, the proposed symmetric fusion module reduces the network's sensitivity to image time. A multitask classifier is also utilized to generate semantic change detection maps. The method's effectiveness is demonstrated on the SECOND dataset, achieving the best results in the current state-of-the-art dataset.

Semantic change detection (SCD) based on remote sensing images can provide an effective solution for large-scale land use monitoring. The existing change detection (CD) networks face limitations due to their limited receptive fields, which make it difficult to provide a comprehensive and consistent response to changes in similar large objects. Moreover, these limitations often cause the network to ignore weak changes localized in the images. To address these limitations, we propose change-guided similarity pyramid network (CG-SPNet), a decoupled multitask architecture that integrates three key components, including the multiscale positional similarity pyramid (MSSP), the symmetry-enhanced fusion CD unit (SFCD), and the change-guided feature interaction module (CGM). MSSP module captures positional correlation of semantic features at multiple scales by introducing an attention pyramid during pooling downsampling. SFCD utilizes a convolutional attention mechanism to enhance local features and highlight weak changes in dual images, and the symmetric fusion (SF) approach reduces the network’s sensitivity to timing. CGM is used to address the challenge of effective feature interaction, which combines cosine similarity loss to leverages cross-attention to compute the positional relevance of change features to embed a priori information for semantic features. Through rigorous experiments and analyses, we have successfully validated the essential components of CG-SPNet. It achieves the state-of-the-art performance on the SECOND dataset as well as our self-built CZWZ dataset.

Semantic layout synthesis based on Generative Adversarial Networks (GANs) has made significant advances, however, the generation of high-quality real remote sensing images through semantic layouts remains a significant challenge. In this paper, we propose a Multi-Task Generative Adversarial Network (MTGAN) that utilizes semantic layouts to generate remote sensing images featuring five common geographic objects. Our approach takes into account the unique characteristics of remote sensing images, such as multiple scales, multiple objects, low inter-class separability, and high intra-class heterogeneity. The MTGAN utilizes a global-local GAN structure, with the global generator producing global context information and the local generator generating specific class information. By combining global macro information and local detail information, the MTGAN is able to generate remote sensing images with close contextual connections and clear details of geographical objects.

Tobacco is one of the most important cash crops in China, and accurate acquisition of its planting information is of great significance for tobacco management and yield prediction. In this paper, a tobacco information extraction method based on UAV high-resolution images is proposed. The method uses the RtinaNet target detection model to achieve the identification and localization of tobacco in UAV high-resolution images, and generates tobacco coordinate information. Based on the tobacco coordinates, we designed two algorithms for extracting plant spacing, row spacing and area of tobacco fields, respectively. The experimental results showed that the accuracy of the method reached 98.49%-99.77%, 97.08%-99.98% and 94.48%-99.63% for area, row spacing and plant spacing extracted from the seven experimental tobacco fields, respectively.

Soil plant analysis development (SPAD) represents relative chlorophyll content, which directly affects tabacco health. Accurate monitoring of tobacco canopy SPAD is vital to guide field management. Due to low correlation, few number of features and single model, the existed inversion models have low accuracy and poor robustness. This paper expanded samples from hyperspectral images using PROSAIL, so as to avoid overfitting. In the aspect of feature extraction, the optimized vegetation index is added to increase the correlation between features and SPAD. The inversion model combines K-means and XGBoost to form a mixed model. The results show that the mixed model has better effect on the validation set than other models, R2=0.83, RMSE=3.9.

High-temperature extreme events are a major ecological and environmental problem faced by human beings. Focused on the risk assessments of such an extreme events, the research applied remote sensing, meteorological and socio-economic data from 2000 to 2020 with risk-exposure-vulnerability assessment framework to explore the spatial and temporal difference in the middle and lower reaches of the Yangtze River. Further, the spatial distribution of high-temperature risk has been obtained during three periods through analytic hierarchy process. The research results show that the high-risk areas are mainly concentrated in the provincial capital cities and around the Taihu Lake basin. Meanwhile, the high-risk presents an increasing trend, especially, the Taihu Lake basin shows the most obvious rising trend rate, as 7.33%.In addition, some higher-risk cities in the north tend to decrease, while the other in the south tend to increase, which show significant regional differences in the experiment area.

In recent years, many semantic segmentation methods based on convolutional neural networks (CNN) have been applied to road extraction, but objects with similar spectral characteristics to roads in RGB images and road occlusions cause the discontinuous output of road extraction. To ensure extraction performance, hyperspectral data is used as a supplement to RGB data to improve the ability of remote sensing image road extraction in this paper. This paper uses a multi-scale fusion attention network to combine RGB and multispectral imagery. First, band selection is used to select multispectral bands with high inter-class separability, and the Cross-Source Feature Recalibration Module (CSFR) is used to calibrate and fuse spectral features at different scales to achieve fusion of multi-source features at different scales. In addition, a multi-scale attention decoder is proposed to fuse multi-level road features and global context information. The proposed method was applied to the SpaceNet dataset and self-annotated images from Chongzhou, a representative city in China. Our method performs better over the baseline HRNet by a large margin of +6.38 IoU and +5.11 F1-score on the SpaceNet dataset, +3.61 IoU and +2.32 F1-score on the self-annotated dataset (ChongZhou dataset).

Typhoons are frequent natural disasters in the southern coastal areas of China, which resulted in severe economic losses and casualties in affected areas. Therefore, typhoon risk assessment is critical for typhoon prevention and reduction. The research takes Guangdong as the experiment area and utilizes multi-source data from historical typhoon disasters for risk assessment. The indexes, such as Hazard, vulnerability, and sensitivity indicators, are selected to build index-based typhoon risk assessment model with Analytic Hierarchy Process (AHP) and entropy weight method. The research results show that the overall typhoon risk in Guangdong presents a trend from initially lowerto higher. The lowest point was reached in 2005 with low-risk areas accounting for 60.6%, while the highest point was reached in 2020 with high-risk areas accounting for 8.24%. The average proportion of low-risk areas over the 25-year period is 29.98%, while high-risk areas account for 4.8%. Typhoon risk shows a trend of low in the south and high in the north.

Ensemble learning has been used to solve classification and regression problems in many fields, but its application in remote sensing is rarely explored. In this study, an ensemble machine learning algorithm named stacked generalization (or stacking) was developed to forecast surface soil moisture over the Tibetan plateau. The algorithm combined multiple learning techniques including Random Forests (RF), Extreme Gradient Boosting (XGBoost) and linear regression (LR). Normalized difference vegetation index (NDVI), modified soil-adjusted vegetation index (MSAVI), shortwave angle normalized index (SANI) and Shortwave Infrared Transformed Reflectance (STR) derived from Landsat-8 were used as the input variables for the soil moisture retrieving model. The experimental results show that the stacking algorithm is able to achieve an acceptable accuracy, the average root mean squared error (RMSE), mean absolute error (MAE) and coefficient of correlation (r) were 0.052 cm3cm−3, 0.039 cm3cm−3and 0.82 respectively, which is much better than its base models.

Sichuan region as an important hub of western China population, its phenological change has a great influence on the western economic construction and social development, so the phenological response under the background of global warming and change, the ecological balance, scientific research and agricultural production is of great significance [1]. Taking MODIS remote sensing satellite images of Sichuan province as the data set, aiming at the inversion problem of vegetation phenology, the machine learning method--Extreme gradient boosting(XGBoost) was used to build the vegetation phenology prediction model, and the results were compared with the traditional methods. The results show that the prediction model of machine learning method has a certain accuracy. The experimental results show that the XGBoost is able to achieve an acceptable accuracy, the average root mean squared error (RMSE), mean absolute error (MAE) and coefficient of correlation (R) were 4.684/4.413, 4.353/4.297, and 0.7725/0.7812 respectively.

Semantic segmentation in high resolution aerial images is a challenging task in remote sensing fields. Compared with other scenarios, semantic segmentation of remote sensing images requires larger receptive fields and more global information. The attention mechanism is one of the most effective way to integrate local features. In this paper, a Triple Attention Network (TANet) is proposed to get more global features. In specific, the paper introduce two self-attention module to get position attention and channel attention. And a label attention module, which generated the attention probability map by introducing spatial context information in the label. The experimental results shows that the proposed network has a higher FWIoU and PA scores than other networks.

Land Surface Temperature (LST) is an indicator of the thermal condition at the ground-atmosphere interface. The MODIS LST products from Terra and Aqua satellites provide spatially continuous monitoring ground temperature. But it still has limitations at local scale due to atmospheric disturbances and the spatiotemporal heterogeneity of land surface. In this study, a novel algorithm based on Random Forest (RF) is proposed to reconstruct MODIS LST. The RF was trained using MOD11A1, MOD09A1 and MOD15A2 from Terra MODIS and digital elevation data from ASTER DEM as inputs. LST observations from The Tibetan Plateau Soil Moisture and Temperature Monitoring Network (TP-SMTMN) are used as target data. Experimental results indicate the algorithm can improve the estimation MODIS LST products from the aspects of accuracy and data availability.

The paper described multi-temporal measurements of wheat using ground-based radar scatterometer (GBRS) system and investigated S-band scattering character of wheat parameters to ranks distribution during an entire growth cycle. S-band was chosen for its distinct behavior from popular L and C bands. The difference of backscatter for ranks distribution (row and column) were analyzed as the function of the wheat temporal variations parameters, such as wheat age, biomass, canopy height, LAI at six experimental acquisitions. The research showed the S-band scattering character of wheat parameters to rank distribution has different formulation mechanism and influence on total backscatter. Moreover, the relation between backscatter and ground data had also been analyzed. The research results could be helpful to provide a reference for modeling and cereal monitoring by remote sensing technology.

Quickly and accurately obtaining fuel moisture content information is of great significance for diagnosing vegetation growth, improving agricultural irrigation efficiency, guiding agricultural production, monitoring the drought conditions of natural communities, and forecasting forest fires. Used the measured fuel moisture content in the southern California sample points and various vegetation indices extracted from MODIS remote sensing satellite images as the dataset for the fuel moisture content retrieving model. In this study, three machine learning methods--extreme learning machine (ELM), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost) were used for the fuel moisture content retrieving model. The results show that these three methods can achieve better accuracy than the traditional machine learning method support vector machine (SVM). The experimental results show that the XGBoost is able to achieve an acceptable accuracy, the average root mean squared error (RMSE), mean absolute error (MAE) and coefficient of correlation (R) were 0.1552, 0.1243, and 0.7423 respectively, which is much better than the other models.

Extracting building information from remote sensing (RS) images have always played an important role in civil and military. In recent years, many efficient approaches are proposed to detect building in remote sensing images. CNN (Convolutional Neural Networks) has proven to be an effective way of this problem. In this paper, to learn building features better, we propose a convolutional network based on Unet++ and containing dense connections. Our contributions are as follows:(1)Rebuild Unet++ by applying DenseNet as its backbone; (2)Add 1×1 convolution layers that can be introduced as bottleneck layer before DenseBlock to reduce the number of input feature-maps, so as reduce the parameters and improve computational efficiency. The accuracy of building extraction results from the new network which was trained with our training dataset after data augmentation was evaluated with IoU scores. The experimental results show the proposed network has higher IoU scores than Unet++ with fewer parameters.

This paper described the soil scattering measurements from drought status (water content 16.56%) to submerged status (99.92%) at L-, S-, C-, and X-bands. The sensitivity of backscatter to soil water content at multiband was analyzed. Furthermore, the sensitivity of electromagnetic wave to soil water content was discussed in semi-flooded area. The research can be helpful to soil moisture retrieval and supply a reference to soil status monitoring on large scales.

Natural vegetation plays an important role in the ecological environment, because it has become an important indicator to evaluate the status of natural ecosystem. PUE (Precipitation Use Efficiency) has been widely used to evaluate ecosystem by utilizing the ratio of NPP (Net Primary Productivity) to precipitation. Focused on the status of Chinese natural vegetation growth, the precipitation data of 2423 meteorological stations in China from 2000 to 2015 were used to obtain the distribution of regional precipitation by spatial interpolation method. Meanwhile, the NPP spatial distribution data was extracted from MODIS remote sensing data from 2000 to 2015. So, the PUE of national vegetation distribution maps were obtained, and the PUE change trend of six typical natural vegetations was analyzed according to the location in China, which could be applied to evaluate the vegetation ecological status in long time series. The research could be helpful to provide references and evaluation criteria for the spatial-temporal response characteristics of ecological environment.

Recently, D-LinkNet has become a popular convolutional neural network for its high IoU scores in road extraction. Actually, D-LinkNet could have higher IoU scores if it uses ResNet that has deeper network as its encoder part, but the scale of the model would be very huge because of the center part design called DBlock in this paper. Focused on the problem, this paper made some improvements: (1) Add a 1 × 1 convolution that can be introduced as bottleneck layer before DBlock to reduce the number of input feature-maps, so as reduce the parameters and improve computational efficiency. Then add another 1 × 1 convolution after DBlock to recover the required output channels; (2) Rebuild the entire network based on ResNet units with new structure instead of the original one. The new network named as D-LinkNetPlus. Except for reducing parameters, this paper also proposed a method named ESIPs to eliminate small independent patches for the accuracy improvement of road extraction. The experimental results show that the D-LinkNetPlus not only has less parameters but also has a higher accuracy than the results gotten by original structure of D-LinkNet. In addition, the road extraction results presented a better visual effect after processing by ESIPs proposed in the paper.

CNN (Convolutional Neural Networks) has been proved to be an effective method for road extraction in remote sensing fields recently. D-LinkNet based on LinkNet adopted consecutive dilation convolution with different expanding rate to enlarge the receptive field without reducing the resolution of the feature-maps so that had an outstanding performance in high resolution satellite imagery road extraction. However, too many parameters make some inadequacies for D-LinkNet, which introduced LinkNet as its backbone with ResNet construction. Focused on this problem, this paper proposed a new network to improve D-LinkNet: (1) Rebuild D-LinkNet by applying DenseNet as its backbone instead of ResNet; (2) Replacing initial block with stem block in the beginning of the network. The accuracy of road extraction results from the new network which was trained with our own training dataset after data augmentation was evaluated with IoU scores. The experimental results show that the proposed network has a higher IoU scores than D-LinkNet with less parameters.

Cereal crops, contrary to natural vegetation, have the different characteristics for their regular planting. Further, the random assumption of the radiative transfer theory is not suitable for cereal canopy. The paper aimed to present a modified scattering model of row wheat at X-band (center frequency 3.2GHz). The modified scattering model considered both the surface scattering of soil and the volume scattering of wheat canopy. In different wheat growth stage, the weights of the two kinds of scattering phenomenon were set up based on an empirical growth model because of their visible area. A series of data including wheat growth parameters and backscatter coefficients, related to the interaction, were collected for the analyses of the model. The research results showed the model could better reflect the scattering phenomenon of regulate planting, which is helpful to agriculture remote sensing fields.

The paper uses the PSO-SVR algorithm to inverse the water quality parameters based on GF-1 remote sensing image in Longquan lake where is located in Chengdu, Sichuan Province. Longquan Lake is a key drinking water source in Chengdu, so its water quality is very critical. Particle swarm optimization (PSO) optimizes the parameters of the support vector regression (SVR) inversion model to establish the new PSO-SVR inversion model, and PSO can effectively improve the efficiency and the accuracy of the SVR inversion model. At the same, the empirical inversion model was established by using the measured hyperspectral data and concentration of water quality parameters. Comparing with SVR inversion model, PSO-SVR inversion model achieves a better result in the application of suspended solids and Chlorophyll concentration inversion.

Semantic segmentation using adversarial networks has been approved to produce the better artificial results in image processing fields. Focused on current Deep Convolutional Neural Networks (DCNNs), since the convolutional kernel size has been fixed in every convolutional operation, the small objects would be ignored with large convolutional kernel size, and the segmentation result of large objects is not continuous with small convolutional kernel size. The paper developed a semantic segmentation model that combined the adversarial networks with multi-scale context aggregation. Further, the model was applied to road segmentation of UAV RS images. The experimental results of this semantic segmentation model with multi-scale context aggregation has a better performance for road segmentation and fit well with the reference standard results. It can improve the road segmentation accuracy obviously in the situation where there are other small regions whose shape or color is similar to road regions in UAV RS images.

The paper aimed to investigate interaction between electromagnetic wave and cereal parameters at column and row directions. S-band (center frequency 3.2GHz) and wheat had been selected as the research targets. During an entire wheat growth cycle, a series of data related to the interaction were collected for the analyses. Backscatter at row and column directions were analyzed as the function of the wheat temporal variations parameters. The research results show the dominated contribution determined the influence of wheat at row and column directions on radar backscatter. Moreover, a simple model has been presented to minimize the influence of column and row directions on radar backscatter. The research can be helpful to modeling and cereal monitoring by microwave remote sensing technology.

Soil Moisture Content (SMC) plays an important role in different environmental studies. In this study, to assess SMC, a method based on reflectance values in red and NIR bands from HJ-1B is introduced. This model divides SNIR-R into three separate regions based on the pixels NDVI values and fits three different regression equations to each region. All the achieved results showed that as NDVI values get larger, the accuracy of the proposed models decreases. Furthermore, in this study, the model was applied for assessment of SMC. It was concluded that satellite estimated SMC is highly correlated with field measured data.

UAVRS (unmanned aerial vehicles remote sensing) is widely used in various fields such as resource exploration, disaster monitoring. The paper designed a geometric distortion correction algorithm of UAV low altitude remote sensing image for emergency disaster, which mainly consisted of the following steps: eliminating rotational error, extracting the control points effectively and correcting geometric distortion for overlapping regions. Based on comprehensive experiments and applications, the algorithm is proved effectively to reduce the UAV image distortion. Furthermore, the algorithm can also improve the processing quality and reduce the processing speed of the image in emergency disaster situations.

UAV image registration is a key in the process of UAV image matching and application. Based on a large number of images, overlapping irregular and lack of ground control point, paper put forward the image registration algorithm based on overlapping area detection. Firstly the algorithm uses the improved distributed search method based on phase correlation method to search overlapping area and get the translation and rotation parameters of the original image, which is very effective for rotating image. Then a new angular point matching method combined with the offset and rotation angle is used, which is based on Harris. The experimental results show that the method can be applied to UAV remote sensing image registration properly, and has the characteristics of efficiency, accuracy and robustness.

Persistent scatterers synthetic aperture radar interferometry (PSI) is a powerful remote sensing technique to detect the subsidence and landslides with an accuracy of millimeters. Distributed scatterers (DS) can be extracted to increase the measurement points with preserved Persistent scatterers (PSs), especially in non-urban areas covered with vegetation. In the experiment, we selected a set of SAR images of Radarsat-2 satellite, covered Mao country area, to detect the subsidence during the six months. An improved method has been presented to optimize measurement points for applying the InSAR technique to monitor the deformation of mountain areas through processing small scales full-polarization SAR Images.

Traditional DINSAR method is just available to obtain the deformation of no layover areas. While, for some line-shape objects located vertically over the ground, such as power transmission tower, traditional DINSAR technique can not be used to obtain the inclination displacement of it. This paper proposes an improved DINSAR (IM-DINSAR) method which can solve this problem by removing the top-down vertical height phase and obtaining the differential phase just caused by inclination displacement. The Radarsat-2 high resolution spotlight mode data are used for experiment and we obtain the inclination displacement of the towers in the study area based on this new method.

In this paper, a method for road detection based on Duda and path operators has been presented. The roads are represented as slender dark regions with constant width and reflectance in the high-resolution SAR images. The path operators (path openings and closings) were performed as morphological filters in retaining linear structures. However, the filters were not sensitive to the width of linear feature. Focused on the limitation of the method, a preprocessing procedure using Duda operators was introduced before adopting the method of morphological profiles with path operators. When the modified method was applied on RADARSAT-2 datasets for different areas, the research results show that the completeness and correctness are over 70% for road detection from SAR images.

Flood is one of serious natural disasters in the word. Synthetic aperture radar (SAR) has become a popular tool to detect the flood disaster for its distinct benefits such as retrieval of surface information, penetrability, and availability in all weathers. This paper aims to analyze microwave scattering characteristics of soil from low water content to semi-flooded status based on ground-scatterometer radar measurement. The research can demonstrate the scattering characteristic of soil at different status and be helpful to retrieve and monitor flood areas in the disaster. A regressive model combined with the radar data and the submerged proportion of soil has been presented.

The detection of ocean oil spill based on synthetic aperture radar (SAR) image has been a hot topic attracting extensive attention. In this paper, a hybrid scheme, in which we extract feature parameters and then achieve classification as follows, is presented. Two-dimensional (2-D) Otsu algorithm is applied in image segmentation process, and neural network is applied in classification course. Before image segmentation, a sort of universal processing is used, and it enables 2-D Otsu algorithm to be more applicable to SAR images of ocean oil spill.

This letter investigates the influence of row wheat for azimuthal look angles on radar backscatter at L-, S-, C-, and X-bands. The radar backscatter was collected with full polarization (HH, HV, VH, and VV) and incidence angles (10°-70°) at different wheat phenological stages. Simultaneously, wheat parameters (biomass, canopy height, stem density, leaf inclination, etc.) and soil parameters (moisture and roughness) were measured to explain the influence based on radiative transfer theory. The research results show that, when the contribution of soil scattering dominates in total backscatter, the influence of row wheat on radar backscatter is varied with the electromagnetic wavelength and the visible soil contained in radar footprint area. When volume scattering contributes more in total backscatter, the influence of row wheat on radar backscatter mainly comes from leaf parameters and wheat geometric shape. Moreover, research results also show that azimuthal look angles affect radar backscatter much mainly for variable scattering cross section and leaf parameters. The research is helpful for cereal monitoring, modeling, and cereal parameter inversion from synthetic aperture radar images.

The traditional methods for coherence optimization in the framework of multibaseline (MB) polarimetric differential interferometric synthetic aperture radar (DInSAR) applications, such as Best, MB1 equal scattering mechanism (MB1-ESM), suboptimum scattering mechanism (SOM), and exhaustive search polarimetric optimization (ESPO), all have some disadvantages. The MB2-ESM method just can be preferred for its normal accuracy and fast computing time. Signal-noise ratio optimum (SNR-OPT) in the coarse grid followed by the conjugated gradient method (SNR-OPT-CG-CGM) can be selected because of its higher accuracy and acceptable cost time. SNR-OPT has higher computational efficiency compared with ESPO because it makes the 4-D coherence optimization problem transform into two independent 2-D optimization problems (“2 + 2” optimization problem). However, SNR-OPT still costs much time. In this letter, we propose a new method which can further make this “2 + 2” optimization problem transform into one 2-D and two independent 1-D optimization problems (“ 2+1+1” optimization problem). Thus, the computational efficiency will be improved much more compared with SNR-OPT, and meanwhile, the accuracy just decreases a little. Seven full polarimetric RADARSAT-2 images are taken for experiment, and the results also show that the improved SNR-OPT-CG-CGM method is a better method considering the tradeoff between computation time and accuracy compared with other methods for DINSAR applications.

The research made an adaptation for Michigan Microwave Canopy Scattering (MIMICS) model at four bands (L, S, C, X) after taking wheat ears as the first layer. When wheat ears appears in heading stage, they locate on the top of the whole wheat and owes the different dielectric constant and water content, which should be considered the important scattering elements. The scattering character of wheat ears are fully considered in two growth stage (heading stage and ripening stage). In the process of adaptation, the stem and leaves layer was taken as the second layer instead of the trunk layer, which is different from forested environment to cereal condition. A new contribution to backscatter was inserted to compensate the total backscatter. The results after comparing the measured data and the predicted data by adaptation of MIMICS model showed the inclusion of wheat ears as one of the model components is feasible in modeling the wheat backscatter.

A new extension method in the equal scattering mechanism (ESM) from single baseline to multibaseline (MB) for polarimetric synthetic aperture radar interferometry (PolInSAR) coherence optimization is proposed in this letter. However, despite this, this new method and the traditional available methods such as Best, ESM, MB-ESM, and suboptimum scattering mechanism have their disadvantages in the framework of differential interferometric SAR (DInSAR) applications. The ESM method cannot guarantee the global maximum in theory and just optimizes an approximation formula of the original coherence definition, which leads to a deviation. The method using exhaustive search polarimetric optimization (ESPO) must search four parameters one by one in a defined step size and cost the main computational drawback. Focusing on the disadvantage of these methods, this letter proposes another new method to transform the 4-D optimization problem into two independent 2-D problems, which costs less time than that by ESPO with basically the same accuracy. Seven full polarimetric RADARSAT-2 images are taken for experiment, and the results show that this new method has a better effect in computation time and accuracy for DInSAR applications.

The paper proposed a soil moisture retrieval model with S-band field experimental data and volumetric water content measured in field. Focused on the problem that antenna irradiated region may not meet the minimum radar resolution pixel, the research applied the multiple independent samples measuring method and analyzed the relevance between soil moisture and backscattering coefficient of S-band VV polarization. The inversion equation obtained was applied to inverse soil moisture from SAR (Synthetic Aperture Radar) S-band images of HJ-1C (a satellite designed for environment and disaster monitoring). The inversion results were verified by the multiple independent samples data measured and agreed well with the experimental data, which shows the S-band VV polarization data can be used to monitor the soil moisture in a large scale.

The paper studied the model relation of soil moisture and spectral index by using the data field measured spectral reflectance and soil moisture, and analyzed the correlation between the soil moisture and spectral index. Furthermore, based on analyzing the data measured, the research selected the sensitive band and built the optimal inversion model. The soil moisture of the studied area (Maoergai area of Minjiang upriver) was inversed, and then the inversed results were analyzed and evaluated. By analyzing the different inversed results accuracy, the research obtained the optimal method of soil moisture inversion for ecological water information index remote sensing quantitative model.