Suffering from speckle noise and complex scattering phenomena, classification results of SAR images are usually noisy and shattered, which makes them difficult to use in practical applications. Deep-learning-based semantic segmentation realizes segmentation and categorization at the same time, and thus can obtain smooth and fine-grained classification maps. However, this kind of methods require large data sets with pixel-wise categorical annotations, which are time consuming and tedious to retrieve. Compared with photographs and optical remote sensing images, manually annotating SAR data is even harder, which results in a delay of using relevant techniques in this field. In this letter, a new data set is proposed to support semantic segmentation for high-resolution PolSAR images. Limited by the aforementioned problems, the data set is only a small one with 50 image patches. Therefore, two transfer learning strategies are proposed, which adopt the fully convolutional network (FCN) and U-net architecture, respectively, and use distinct pretraining data sets to adapt to different situations. The experiments demonstrate the good performance of both methods and a promising applicability of using small training sets. Moreover, although trained with small patches, both networks can perfectly apply on large images. The new data set and methods are hopeful to support various PolSAR applications as baselines.

The spatial distribution of forest stands is one of the fundamental properties of forests. Timely and accurately obtained stand distribution can help people better understand, manage, and utilize forests. The development of remote sensing technology has made it possible to map the distribution of tree species in a timely and accurate manner. At present, a large amount of remote sensing data have been accumulated, including high-spatial-resolution images, time-series images, light detection and ranging (LiDAR) data, etc. However, these data have not been fully utilized. To accurately identify the tree species of forest stands, various and complementary data need to be synthesized for classification. A curve matching based method called the fusion of spectral image and point data (FSP) algorithm was developed to fuse high-spatial-resolution images, time-series images, and LiDAR data for forest stand classification. In this method, the multispectral Sentinel-2 image and high-spatial-resolution aerial images were first fused. Then, the fused images were segmented to derive forest stands, which are the basic unit for classification. To extract features from forest stands, the gray histogram of each band was extracted from the aerial images. The average reflectance in each stand was calculated and stacked for the time-series images. The profile curve of forest structure was generated from the LiDAR data. Finally, the features of forest stands were compared with training samples using curve matching methods to derive the tree species. The developed method was tested in a forest farm to classify 11 tree species. The average accuracy of the FSP method for ten performances was between 0.900 and 0.913, and the maximum accuracy was 0.945. The experiments demonstrate that the FSP method is more accurate and stable than traditional machine learning classification methods.

Drought is one of the most damaging environmental hazards and a naturally occurring phenomenon in Central Asia that is accompanied by crucial consequences for the agriculture sector. This research aimed at understanding the nature and extent of drought over the cropland regions of Central Asia with the help of spatiotemporal information from the region. We assessed drought occurrence using the vegetation health index (VHI). An algorithm was developed to reduce the noise of heterogeneous land surfaces by adjusting the vegetation index and brightness temperature. The vegetation condition index (VCI) and temperature condition index (TCI) were calculated using Moderate Resolution Imaging Spectroradiometer (MODIS) products for the growing season (April–September) from 2000 to 2015. The intense drought years were identified and a drought map (drought probability occurrence) was generated. The findings of this research indicated regional heterogeneity in the cropland areas having experienced droughts, observed through spatiotemporal variations. Some of the rain-fed and irrigated croplands of Kazakhstan demonstrated a higher vulnerability to annual drought occurrences and climate change impacts, while other cropland regions were found to be more resistant to such changes. The development of policy tools is required to support informed decision-making and planning processes to adapt to the occurrence of droughts. This could be achieved by the timely assessment, monitoring, and evaluation of the spatiotemporal distribution trends and variabilities of drought occurrences in this region. The results from this study focus on the spatiotemporal variations in drought to reveal the bigger picture in order to better understand the regional capacity for sustainable land management and agricultural activities within a changing environment.

In recent decades, an amplification of warming in Arctic and high-elevation regions has been widely observed, along with a general enhancement of vegetation growth. However, driven by variability in controlling factors and complex mechanisms, climate and vegetation changes can be highly heterogeneous in space and time. In this study, an analysis is performed separating Arctic and Tibetan Plateau (TP) vegetated areas into various units according to a map of terrestrial ecoregions. The most recent variations of heat, moisture, and vegetation growth (MODIS Normalized Difference Vegetation Index) are evaluated over 2001–2018. Relationships among the climate and vegetation variables are assessed. Six distinct change patterns are identified: (1) synchronized increase of day and night temperature and precipitation during April to October coinciding with strong vegetation greening, (2) profound warming with no change in precipitation and vegetation, (3) an increase of summer temperature and vegetation with a negative latitudinal gradient, (4) greening under increased precipitation without warming, (5) browning not likely being driven by climate, (6) warming only during the nighttime and moderately enhanced vegetation growth. It is demonstrated that vegetation growth in the Arctic and TP is largely controlled by nighttime temperature and precipitation, as opposed to daytime temperature. The exception is the Canadian Arctic, where greening is directly related to summer daytime warming, and a contrasting relationship is observed on the TP. The underlying causes of these patterns are discussed, relating them to multiple mechanisms reported in the literature. These findings may help to further understand the changing Arctic and high-elevation climate and its effects on vegetation growth.

Information containing in the complex data is seldom considered by researchers when dealing with single synthetic aperture radar (SAR) image processing. In 2015, the statistical noncircularity, which indicates the distribution consistency between the real and imaginary parts, has been found to be surprisingly effective when analyzing the ultrahigh-resolution (UHR) millimeter-wave (MMW) SAR data set, particularly for man-made structures. However, the proposed parameter can only measure the overall noncircular level, which is inadequate to separate different noncircular behaviors. Moreover, its extraction method based on the complex generalized Gaussian distribution is very time consuming and thus makes it hard to be applied. Therefore, in this letter, we present a much simpler and more universal way to compute the noncircularity level and propose three specific parameters to specifically denote different noncircular behaviors. We also give two examples based on the real Chinese UHR MMW SAR (CUM-SAR) data set to show the abilities of the noncircularity level parameter and one example to show the preliminary ability of the specific noncircular parameters. Finally, the potentials and future application directions of these parameters are discussed. We believe that noncircularity parameters will benefit various research areas and promote the applications of UHR MMW SAR systems.

Synthetic aperture radar (SAR) tomography (TomoSAR) estimates scene reflectivity along elevation coordinates, based on multi-baseline measurements. Common TomoSAR approaches are based on every single range-azimuth cell or the cell’s neighborhood. By using an additional synthetic aperture for elevation, these techniques have higher resolution power for elevation, to discriminate scatterers with differences in location in the same range-azimuth cell. However, they cannot provide sufficient range resolution power to discriminate these scatterers by joining different spectra to a wider range spectrum, which limits the resolution power of TomoSAR. Therefore, in this paper, we proposed using a compressive sensing (CS) technique to reconstruct range and elevation signals from multi-baseline SAR images. This TomoSAR method not only retrieves the vertical signals but also improves the resolution in the range, which helps improve the resolution power of the TomoSAR technique. We present the theory of the CS-based two-dimensional TomoSAR technique and compare it to the CS-based one-dimensional TomoSAR technique. The excellent resolution power and scatterer localization accuracy of this novel technique are demonstrated using simulations and real data obtained by RADARSAT-2 in Lanzhou, China.