Accurate estimation and monitoring of rice phenology is necessary for the management and yield prediction of rice. The radar backscattering coefficient, one of the most direct and accessible parameters has been proved to be capable of retrieving rice growth parameters. This paper aims to investigate the possibility of monitoring the rice phenology (i.e., transplanting, vegetative, reproductive, and maturity) using the backscattering coefficients or their simple combinations of multi-temporal RADARSAT-2 datasets only. Four RADARSAT-2 datasets were analyzed at 30 sample plots in Meishan City, Sichuan Province, China. By exploiting the relationships of the backscattering coefficients and their combinations versus the phenology of rice, HH/VV, VV/VH, and HH/VH ratios were found to have the greatest potential for phenology monitoring. A decision tree classifier was applied to distinguish the four phenological phases, and the classifier was effective. The validation of the classifier indicated an overall accuracy level of 86.2%. Most of the errors occurred in the vegetative and reproductive phases. The corresponding errors were 21.4% and 16.7%, respectively.

Change detection (CD) is an essential aspect of urban planning and resource management. Deep learning (DL) has the potential to detect complex changes from massive data more automatically than traditional methods. However, current DL-based CD methods have limited abilities to efficiently extract bitemporal features, emphasize real changes, detect weak edges and ultimately integrate multi-scale change information in detail. To address these challenges, we propose an edge and flow guided iterative convolutional neural network (EFICNN). Our network introduces an innovative and efficient iterative backbone (IB) as the feature extractor through pre-trained fine-tuning. By embedding the IB into a Siamese architecture, it is possible to extract richer bi-temporal features from the input RS images. On this basis, we design a 3D difference enhancement module (3D-DEM) that utilizes a parameter-free 3D attention mechanism to emphasize and connect bi-temporal differential and concatenated change features. Additionally, an edge-guided attention module (EGAM) is developed to enhance weak edges. This module combines reverse attention and edge attention based on the Laplacian pyramid to capture change backgrounds and high-frequency change edges, respectively. Inspired by the optical flow alignment between adjacent frames, we ultimately employ a flow-guided fusion module (FGFM) to promote the propagation of fine-grained features from deep to shallow and dynamically optimize the multi-scale fusion process. Qualitative and quantitative experiments on three publicly available datasets demonstrate that our method outperforms seventeen SOTA methods in terms of real change recognition and edge refinement. Our code is available at https://github.com/LYT-work/EFICNN.

China is suffering from serious soil and water loss due to improper land use, leading to flood and drought disasters, and industrial and agricultural reduction. The commonly used monitoring methods of soil and water loss rely on the visual interpretation of remote sensing images, which is time-consuming and laborious. In this study, deep learning semantic segmentation technology was applied to monitoring land use for soil and water conservation. Manual visual interpretation mark samples and land use products collected in recent years were combined to construct training samples for deep learning. Deep convolution neural network model based on ResNet152 and DeeplabV3+ was selected for performing land use classification. Then the soil and water loss were quantified using the Chinese Soil Loss Equation (CSLE) based on the hydrological, geographic, vegetation cover and land use information. The experimental results show that the deep learning model can quickly extract robust edge features from remote sensing images and has high pixel accuracy (83.4%). Although the model accuracy was affected by the land cover types and image quality in different study area, it can still achieve an accuracy higher than 70% in other counties through our experiment. It can further improve the information service level of soil and water conservation product, and provide a useful guideline for automatic land use interpretation and identification of soil and water conservation status based on high resolution remote sensing images.

Tree information in urban areas plays a significant role in many fields of study, such as ecology and environmental management. Airborne LiDAR scanning (ALS) excels at the fast and efficient acquisition of spatial information in urban-scale areas. Tree extraction from ALS data is an essential part of tree structural studies. Current raster-based methods that use canopy height models (CHMs) suffer from the loss of 3D structure information, whereas the existing point-based methods are non-robust in complex environments. Aiming at making full use of the canopy’s 3D structure information that is provided by point cloud data, and ensuring the method’s suitability in complex scenes, this paper proposes a new point-based method for tree extraction that is based on 3D morphological features. Considering the elevation deviations of the ALS data, we propose a neighborhood search method to filter out the ground and flat-roof points. A coarse extraction method, combining planar projection with a point density-filtering algorithm is applied to filter out distracting objects, such as utility poles and cars. After that, a Euclidean cluster extraction (ECE) algorithm is used as an optimization strategy for coarse extraction. In order to verify the robustness and accuracy of the method, airborne LiDAR data from Zhangye, Gansu, China and unmanned aircraft vehicle (UAV) LiDAR data from Xinyang, Henan, China were tested in this study. The experimental results demonstrated that our method was suitable for extracting trees in complex urban scenes with either high or low point densities. The extraction accuracy obtained for the airborne LiDAR data and UAV LiDAR data were 99.4% and 99.2%, respectively. In addition, a further study found that the aberrant vertical structure of the artificially pruned canopy was the main cause of the error. Our method achieved desirable results in different scenes, with only one adjustable parameter, making it an easy-to-use method for urban area studies.

Rice-crop intensity is the annual number of rice growth cycles in a field. Monitoring the intensity on a large scale is vital in evaluating grain production and its ecological impact. Synthetic aperture radar (SAR) has an all-weather imaging capability. However, the existing SAR-based rice-crop intensity mapping methods mostly focus on small regions due to the diversity of rice backscatter patterns, the inefficiency of the time-series feature extraction, and the unavailability of rice phenological information on a large scale. In this study, a harmonic-based method is proposed to identify the essential backscatter periodicities. It also suppresses short-term disturbance in time-series Sentinel-1 SAR data without setting filtering windows or assuming profile shapes. The method detects backscatter troughs, eliminating the requirement for point-by-point traversal mathematical operations. Annual temperature profiles are derived from time-series ERA5-Land data to identify troughs related to rice growth cycles under various agro-climatic conditions. Then, the single (135 537 km2), double (19 036 km2), and triple (259 km2) rice-crop intensities covering the entire Southern China in 2020 are mapped in a 10-m resolution, without relying on region-specific prior phenological information. The method achieves an overall accuracy of 82.26% and can potentially support the continental or global mapping task.

C-band polarimetric synthetic aperture radar (PolSAR) data has been previously explored for estimating the leaf area index (LAI) of rice. Although the rice-growing cycle was partially covered in most of the studies, details for each phenological phase need to be further characterized. Additionally, the selection and exploration of polarimetric parameters are not comprehensive. This study evaluates the potential of a set of polarimetric parameters derived from multi-temporal RADARSAT-2 datasets for rice LAI estimation. The relationships of rice LAI with backscattering coefficients and polarimetric decomposition parameters have been examined in a complete phenological cycle. Most polarimetric parameters had weak relationships (R² < 0.30) with LAI at the transplanting, reproductive, and maturity phase. Stronger relationships (R² > 0.50) were observed at the vegetative phase. HV/VV and ${\mathrm{RVI}}_{\mathrm{FD}}$ had significant relationships (R² > 0.80) with rice LAI for the whole growth period. They were utilized to develop empirical models. The best LAI inversion performance (RMSE = 0.81) was obtained when ${\mathrm{RVI}}_{\mathrm{FD}}$ was used. The acceptable error demonstrated the possibility to use the decomposition parameters for rice LAI estimation. The HV/VV-based model had a slightly lower estimation accuracy (RMSE = 1.29) but can be a practical alternative considering the wide availability of dual-polarized datasets.