Accurate building extraction from very high-resolution (VHR) remote sensing images plays an important role in urban dynamic monitoring, planning, and management. However, it is still a challenging task to achieve building extraction with high accuracy and integrity due to diverse building appearances and more complex ground background in VHR remote sensing images. Recently, unity networking (UNet) has been proven to be capable of feature extraction and semantic segmentation of remote sensing images. However, UNet cannot achieve sufficient multiscale and multilevel features with larger receptive fields. To address these problems, an improved network based on UNet structure (Refine-UNet) is proposed for extracting buildings from the VHR images. The proposed Refine-UNet mainly consists of an encoder module, a decoder module, and a refine skip connection scheme. The refine skip connection scheme is composed of an atrous spatial convolutional pyramid pooling (ASPP) module and several improved depthwise separable convolution (IDSC) modules. Experimental results on the Jilin-1 VHR datasets with a spatial resolution of 0.75 m demonstrate that compared with UNet, pyramid scene parsing network (PSPNet), DeepLabV3+, and a deep convolutional encoder-decoder architecture for image segmentation (SegNet), the proposed Refine-UNet can obtain more accurate building extraction results and achieve the best precision of 95.1% and intersection over union (IoU) of 87.0%, indicating the great practical potential.

Farmland segmentation scenario from remote sensing images plays an important role in crop growth monitoring, precision agriculture and intelligent agriculture. To achieve high precision segmentation of farmland boundary, a Multi-task Deformable UNet combined Enhanced network (MDE-UNet) is proposed for farmland boundary segmentation. The network consists of two parts: a Multi-task Deformable UNet (MD-UNet) segmentation module with Deformable UNet (D-UNet) as the basic network and an enhancement module with a lightweight UNet improved by residual attention. In the MD-UNet segmentation module, three branches are used for precise segmentation of deterministic, fuzzy, and raw boundary, respectively. In the enhancement module, an improved lightweight UNet is designed, which can enhance the feature extraction ability of the MD-UNet segmentation module and further improve the segmentation accuracy. The accuracy and mIoU in the GF-2 farmland segmentation test dataset can reach 96.41% and 91.29% using the proposed model, respectively. The MDE-UNet method outperforms other representative deep learning methods such as DeepLab v3+, FCN-8s, SegFormer, and UTNet, and has potential for practical applications of farmland boundary segmentation.

Compared with most ship detection methods for synthetic aperture radar (SAR) images, ship contour extraction can provide more of the detailed shape and edge information of an observed ship and play a significant role in sea surface monitoring and marine transportation. In this study, a joint ship contour extraction method (faster region convolutional neural network (R-CNN), fast nonlocal mean (FNLM) filter and Chan–Vese model (FFCV) method) was proposed to obtain detailed ship information from SAR images, including ship detection in complex scenes and contour extraction in target slices. First, Faster R-CNN was employed to slice ships from large-scene SAR images. Then, FNLM filtering was applied to denoise and enhance the structural information of the target slices. Finally, an optimized Chan–Vese model was proposed in this article, which can not only accurately extract the contour of the observed ship but also reduce the computation time of the model. The SAR ship detection dataset (SSDD) was selected and finely relabeled to evaluate the contour extraction performance. An evaluation index$R_{N}$, including quantitative value and offset direction, was developed to evaluate the extraction accuracy of the target contour from the SAR images. Compared with the Mask R-CNN network, the average contour extraction accuracy index$R_{N}$of the proposed FFCV method reached −0.002 on all the images in the SSDD dataset, and its results were closer to the real ship contours while maintaining the applicability to complex scenes.

Owing to the characteristics of cloud and snow, it is difficult to detect them when they coexist. Thus, we propose an end-to-end semantic segmentation network for cloud and snow detection based on hybrid feature (CSD-HFnet) to be applied to remote sensing images (RSIs) in which cloud and snow coexist. First, the local binary pattern (LBP), gray-level co-occurrence matrix (GLCM), and superpixel segmentation are combined as basic features to preserve the textural, spatial and shape information of the cloud and snow objects. We also propose deep learning feature extraction network to obtain the multi-scale deep learning features, which is utilized to better distinguish cloud from snow. The original spectral bands, the basic features, and the multi-scale deep learning features are input into the feature integration module simultaneously to form the primary multi-scale hybrid features (PMHF). Then, the PMHF are filtered, sorted and weighted by the feature filtering & sorting block and attention mechanism block to obtain the advanced multi-scale hybrid features (AMHF). Finally, the AMHF are input into the cloud and snow segmentation network, which consists of several memory-capable gate circuits for training and validation of cloud and snow detection. The results indicate that CSD-HFnet with AMHF can provide reliable detection under the condition of cloud and snow coexistence. CSD-HFnet can detect cloud and snow in multi-spectral RSIs of various spatial resolutions with an OA of 95.37%. Moreover, CSD-HFnet exhibits excellent cloud detection ability for red-green-blue (RGB) images with an OA of 95.88%, higher than other state-of-the-art cloud detection methods.

As one of the major components in the hydrological system, seasonal snow cover in Northeast China has drawn much attention recently. Because of the coarse spatial resolution of the passive microwave (PMW), heterogeneity of snowpack, and forest cover, it is difficult for existing snow products to achieve high precision snow parameters (e.g. snow depth (SD) or snow water equivalent (SWE)) assessment and hydrological research in fine scale. In this study, a novel SD retrieval algorithm that considered both the spatiotemporal dynamic of snow characteristics and forest attenuation was developed by combining the Calibrated Enhanced Resolution Brightness Temperature (TB) data and other auxiliary information, and produced a fine resolution (i.e., 6.25 km × 6.25 km) and high accuracy SD data in Northeast China. Instead of complex physical models, the machine learning was used to untangle the nonlinear complex relationship between SD and the enhanced resolution TB, forest fraction (FF), and snow characteristics. The verification results at ground weather stations showed that the retrieved SD by the proposed algorithm had high consistency with the observed SD, its RMSE, bias, and correlation coefficient (R) of 6.32 cm, -0.23 cm, and 0.63, respectively. Compared with the existing SD products (WESTDC and AMSR2), the developed model greatly improved both in spatial resolution and retrieval accuracy. In general, the fine-resolution SD inversion model achieved satisfactory accuracy and stability, and it will be used to generate long-term SD dataset service for climate change and hydrological research in the future.

Radar images with high spatial resolution are not affected by illumination or meteorological conditions, which effectively compensate for the shortcomings of optical images and passive microwave images. Thus, active microwave remote sensing technology has advantages in snow depth (SD) research. Machine learning algorithms (MLAs), which do not need to consider complex physical models, have increasingly been applied to SD research. Considering the snow conditions of different underlying surfaces, it is very important to select the appropriate parameter combinations (PC) reflecting the SD information for MLAs. In this study, C-band SAR data with 20 m spatial resolution, ground-based SD observation data from meteorological stations, and field measurement data in Northeast China were used to construct and validate the SD estimation method. Two parameter selection methods including the correlation coefficient method and the machine learning (ML) fusion method were proposed to discuss the influence of different PC on SD estimation. Then, XGBoost, random forest (RF), linear support vector regression (LSVR), and kernel support vector regression (KSVR) were applied to estimate SD based on the selected PC, and evaluate the accuracies of SD estimation using different MLAs. The results demonstrated that the PC selected using the correlation coefficient method and XGBoost algorithm could achieve the best SD results in the study area. Combining RPC-C with XGBoost algorithm, in cropland areas, the average values of mean absolute error (MAE) and root mean squared error (RMSE) were 1.75 and 2.58 cm, respectively. Combining RPC-F with XGBoost algorithm, in forest areas, the average values of MAE and RMSE were 3.12 and 5.07 cm, respectively. The research of this letter can select the optimal PC for MLAs and effectively improve the accuracy of SD estimation using C-band SAR data.

Considering the tradeoff between spatial resolution and temporal resolution, spatiotemporal fusion has become a promising technique to monitor snow cover dynamics with both high spatial and temporal resolutions. The representative spatiotemporal fusion methods, e.g. Spatial Temporal Data Fusion Approach (STDFA), usually exist obvious phenomenon of spectral distortion when the surface reflectance changes nonlinearly, which affects the quality of the spatiotemporal fusion image. To address this issue, an effective STDFA-Matching-Pix2pix-Generative Adversarial Network (SMPG) algorithm combining the unmixing-based method, deep learning method, pre-matching and post-matching module is proposed to reduce the spectral distortion of STDFA fusion image. The high-temporal-low-spatial (HTLS) resolution MOD09GA data and high-spatial-low-temporal resolution (HSLT) Landsat 8 data are selected in this study. SMPG algorithm is firstly employed to obtain daily high-spatial-high-temporal (HSHT) images, and then daily snow cover results with a spatial resolution of 30 m are obtained by calculating the normalized difference snow index (NDSI). SMPG algorithm is further compared with STDFA, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), Swin SpatioTemporal Fusion Model (SwinSTFM), and Generative Adversarial Network-based SpatioTemporal Fusion Model (GAN-STFM). The experimental results indicate that the proposed algorithm yields better overall performance in daily spatiotemporal fusion image and snow cover result with a spatial resolution of 30 m. The mean correlation coefficient (CC) of SMPG can achieve 0.962, which is 0.06-0.36 higher than that of other spatiotemporal fusion methods. The error between the percentage of snow cover area obtained through SMPG and validation data is within 0.84%.

Accurate and timely crop mapping is essential for agricultural applications, and deep-learning methods have been applied on a range of remotely sensed data sources to classify crops. In this article, we develop a novel crop classification method based on spatiotemporal deep-learning fusion technology. However, for crop mapping, the selection and labeling of training samples is expensive and time consuming. Therefore, we propose a fully automated training-sample-selection method. First, we design the method according to image processing algorithms and the concept of a sliding window. Second, we develop the Geo-3D convolutional neural network (CNN) and Geo-Conv1D for crop classification using time-series Sentinel-2 imagery. Specifically, we integrate geographic information of crops into the structure of deep-learning networks. Finally, we apply an active learning strategy to integrate the classification advantages of Geo-3D CNN and Geo-Conv1D. Experiments conducted in Northeast China show that the proposed sampling method can reliably provide and label a large number of samples and achieve satisfactory results for different deep-learning networks. Based on the automatic selection and labeling of training samples, the crop classification method based on spatiotemporal deep-learning fusion technology can achieve the highest overall accuracy (OA) with approximately 92.50% as compared with Geo-Conv1D (91.89%) and Geo-3D CNN (91.27%) in the three study areas, indicating that the proposed method is effective and efficient in multi-temporal crop classification.

During the saline-alkali land improvement process, the soil conductivities throughout the whole land area are usually investigated in advance to grade the soil salinization. As a new soil investigating technique, ground penetrating radar (GPR) has been widely used in geographical and agricultural applications. Nevertheless, there are still challenges in applying GPR to saline-alkali soil conductivity predictions, because it is very difficult to separate the direct path wave (DPW) and the land surface reflecting echoes under near-field condition. To realize fast soil conductivity predicting, a novel waveform correlation analyzing method based on the pulsed GPR technique is proposed in this article. By this method, the saline-alkali soil conductivities can be estimated directly by waveform comparison instead of complex equation solving. Experiments are performed on specific saline-alkali land with different surface morphologies located in the western Jilin province, northeast China. An empirical relationship between GPR echoes and the saline-alkali soil conductivities is established by the measurements in the first survey line. Then, this empirical formula is used to predict the soil conductivities in the second survey line to verify the methodology. Analyzing results show that the estimated conductivities are consistent with the WET (water content, electrical conductivity, and temperature) sensor measurements and the soil sample measuring results. Based on the nondestructive GPR technique, the proposed method can provide a fast and efficient way to estimate the conductivities of the saline-alkali land.

The planting pattern (e.g. row orientation, width and spacing) has an important effect on the photosynthesis and yields of maize. UAV remote sensing offers a potential way to extract these pattern parameters conveniently and fast. A method based on the imagery acquired with an unmanned aerial vehicle (UAV) is proposed to extract row-structure parameters of the maize. A revised parallel-beam radon transform (RPRT) is performed and row orientation angle, row width and space distance are determined in Radon transform domain. The validation results using the in-situ measurements data show that the extraction results have high accuracy. These parameters obtained by UAV imageries can be as the input ones to maize growth and yield estimation model and later used in decision support tools.

As a result of vital role of soil moisture in governing water and energy cycles of land-atmosphere, the remote sensing of soil moisture has become a key component of the observation and research programs involving water and energy cycles on the earth's surface [1] - [2] . In addition, the accurate monitoring and prediction of soil moisture plays a crucial role in crop growth, flood and drought monitoring and prediction, research of hydrological and land surface process and global water cycle [3] - [5] . The microwave is the optimal mean to obtain soil moisture in large scale due to its strong penetration capability [6] and sensitivity to the change of surface soil moisture. And the L band microwave is considered to be the best band for monitoring soil moisture [7] - [10] . The Soil Moisture Active Passive (SMAP) satellite with an L-band (1.26 GHz) radar and an L-band radiometer (1.41 GHz) was launched on January 31, 2015 by the NASA [4] . The baseline science requirement for SMAP is to provide estimates of soil moisture in the top 5 cm of soil with an error of no greater than 0.04 cm 3 /cm 3 at 10 km spatial resolution and 3-day average intervals over the global land area [12] . The soil moisture baseline algorithm of SMAP is single-channel algorithm using horizontally polarized TB (SCA-H). In SCA-H, the emissivity model of bare land uses a semi-empirical Hp model and the value of H is determined by using empirical method for different land cover types; The vegetation model with zero-order radiative transfer model to describe the influence of vegetation on the surface emissivity; The dielectric constant model is one of the three models of Mironov model, Dobson model and Wang model.

As a result of vital role of soil moisture in governing water and energy cycles of land-atmosphere, the remote sensing of soil moisture has become a key component of the observation and research programs involving water and energy cycles on the earth's surface [1]-[2]. In addition, the accurate monitoring and prediction of soil moisture plays a crucial role in crop growth, flood and drought monitoring and prediction, research of hydrological and land surface process and global water cycle. [3]-[5] The microwave is the optimal mean to obtain soil moisture in large scale due to its strong penetration capability[6] and sensitivity to the change of surface soil moisture. And the L band microwave is considered to be the best band for monitoring soil moisture [7]-[10]. The Soil Moisture Active Passive (SMAP) satellite with an L-band (1.26 GHz) radar and an L-band radiometer (1.41 GHz) was launched on January 31, 2015 by the NASA [4]. The baseline science requirement for SMAP is to provide estimates of soil moisture in the top 5 cm of soil with an error of no greater than 0.04 cmvcnr at 10 km spatial resolution and 3-day average intervals over the global land area [12]. The soil moisture baseline algorithm of SMAP is single-channel algorithm using horizontally polarized TB (SCA-H). In SCA-H, the emissivity model of bare land uses a semi-empirical Hp model and the value of H is determined by using empirical method for different land cover types; The vegetation model with zero-order radiative transfer model to describe the influence of vegetation on the surface emissivity; The dielectric constant model is one of the three models of Mironov model, Dobson model and Wang model.

Fractional vegetation cover (FVC) is an important ground cover component in terrestrial ecosystems. The objective of this study is to evaluate four FVC estimating methods using in-situ measurements and canopy reflectances simulated by the PROSAIL model over different soil backgrounds. Comparison with in-situ measurements from croplands in Northeast China showed that the model proposed by Baret et al., which was based on NDVI, predicted FVC most accurately, with a RMSE of 0.05. The simulated RMSEs derived from Baret et al., Carlson and Ripley, Gutman and Ignatove models decreased with increasing soil reflectances at 850 nm. However, the RMSEs of SDVI model increased with increasing soil reflectances. Results indicated that the accuracies of these FVC models are sensitive to soil backgrounds.