As an important parameter in recent and numerous environmental studies, soil moisture (SM) influences the exchange of water and energy at the interface between the land surface and atmosphere. Accurate estimate of the spatio-temporal variations of SM is critical for numerous large-scale terrestrial studies. Although microwave remote sensing provides many algorithms to obtain SM at large scale, such as SMOS and SMAP etc., resulting in many data products, they are almost low resolution and not applicable in small catchment or field scale. Estimations of SM from optical and thermal remote sensing have been studied for many years and significant progress has been made. In contrast to previous reviews, this paper presents a new, comprehensive and systematic review of using optical and thermal remote sensing for estimating SM. The physical basis and status of the estimation methods are analyzed and summarized in detail. The most important and latest advances in soil moisture estimation using temporal information have been shown in this paper. SM estimation from optical and thermal remote sensing mainly depends on the relationship between SM and the surface reflectance or vegetation index. The thermal infrared remote sensing methods uses the relationship between SM and the surface temperature or variations of surface temperature/vegetation index. These approaches often have complex derivation processes and many approximations. Therefore, combinations of optical and thermal infrared remotely sensed data can provide more valuable information for SM estimation. Moreover, the advantages and weaknesses of different approaches are compared and applicable conditions as well as key issues in current soil moisture estimation algorithms are discussed. Finally, key problems and suggested solutions are proposed for future research.

There has been little investigation for the automatic extraction of power lines from aerial images due to the low resolution of aerial images in the past decades. With increasing aerial photogrammetric technology and sensor technology, it is possible for photogrammetrists to monitor the status of power lines. This letter analyzes the property of imaged power lines and presents an algorithm to automatically extract the power line from aerial images acquired by an aerial digital camera onboard a helicopter. This algorithm first uses a Radon transform to extract line segments of the power line, then uses the grouping method to link each segment, and finally applies the Kalman filter technology to connect the segments into an entire line. We compared our algorithm with the line mask detector method and the ratio line detector, and evaluated their performances. The experimental results demonstrated that our algorithm can successfully extract the power lines from aerial images regardless of background complexity. This presented method has successfully been applied in China National 863 project for power line surveillance, 3-D reconstruction, and modeling.

Oceanic LiDAR (hereafter referred to as O-LiDAR) is an important remote sensing device for measuring the near-coastal water depth and for studying the optical properties of water bodies. With the commercialization of LiDAR, the theoretical research on the underwater transmission characteristics of LiDAR has been intensified worldwide. Primary research interests include the simulation and modeling of LiDAR underwater echo signals and the inversion of optical parameters using LiDAR water echo signals. This article provides an overview of the principle of LiDAR echo signal formation, and comprehensively summarizes the LiDAR echo signal simulation modeling methods and the corresponding factors that affect modeling accuracy by focusing on the characteristics of different methods. We found that the current simulation methods of LiDAR underwater transmission echo signals primarily include an analytical method based on the radiation transfer equation and a statistical method based on the Monte Carlo (MC) model. The radiation transport equation needs to be appropriately simplified using the analytical method, usually using the quasi-single-small-angle approximation principle. The analytical method has high calculation efficiency but its accuracy is dependent to the quasi-single small-angle approximation. The statistical method can analyze the influence of various factors on echo signals by controlling the variables, but it has poor calculation efficiency. Finally, the semianalytical MC model was used to quantitatively analyze the three main factors (LiDAR system parameters, water body optical parameters, and environmental parameters) affecting underwater LiDAR transmission characteristics, and summarizes the mechanism and results of different factors.

The existing buffers algorithms cannot effectively to meet the demands of high accuracy of buffer analysis in practice although many efforts have been made in the past 60 years. A generalized buffering algorithm (GBA) is presented, which considers the geometric distance and the attribute characteristics of all instances within buffer zone. The proposed algorithm includes three major steps: (1) select and initialize target instance; (2) determine buffer boundary points through mining homogeneous pattern; (3) “smoothly” connect buffer boundary points to generate the generalized buffer zone. The details for the generations of the generalized point buffer (GPIB) zone, the generalized line buffer (GLB) zone, and the generalized polygon buffer (GPLB) zone are discussed. Two dataset are used to validate the performances of the proposed GBA. Six parameters are applied as indexes to evaluate the proposed algorithm. The experimental results discovered that (1) the GBA is close to the tradition buffering algorithm (TBA) when the angle increment ( $\Delta \varphi$ ) in GPIB, line increment ( $\Delta L$ ) in GLB, and arc length increment ( $\Delta S$ ) in GPLB approach to zero, respectively; (2) the proposed GBA can accurately reflect the real situation of the buffering zone, and improve the deficiency and accuracy of TBA in real application.

In recent years, several models based on fully convolutional neural networks have been proposed. These models mainly focused on improving accuracy but ignored computational efficiency. For this reason, this research proposes an innovative deep learning model, entitled “ ECA-MobileNetV3(large)+Seg-Net model ” for simultaneously concerning both. In the encoder, the structure-reduced MobileNetV3(large) is selected as the backbone network, which uses 11 network layers to replace 20 network layers of MobileNetV3(large). The hybrid dilated convolution with dilation rates of 1, 2, and 4 is introduced in the depthwise separable convolution to expand the local receptive field and to enhance the connection of contextual information. Finally, the ECA module is fused with the bneck structure. In the decoder, the 18 network layers of SegNet’s decoder are replaced with 9 layers to achieve a lightweight network with small parameters. When compared with the original SegNet, the overall accuracy (OA) of the model proposed in this research is averagely improved by 5.97%, 5.56%, and 4.13%, and the sugarcane identification accuracy is averagely improved by 7.16%, 4.01%, and 9.13%, respectively in the three tested areas. Additionally, the memory size, the number of parameters, and FLOPs are all reduced by 6/7.