Accurate identification and segmentation of individual tree points are crucial for assessing forest spatial distribution, understanding tree growth and structure, and managing forest resources. Traditional methods based on Canopy Height Models (CHM) are simple yet prone to over- and/or under-segmentation. To deal with this problem, this paper introduces a novel approach that combines marker-controlled watershed segmentation with a spectral clustering algorithm. Initially, we determined the local maxima within a series of variable windows according to the lower bound of the prediction interval of the regression equation between tree crown radius and tree height to preliminarily segment individual trees. Subsequently, using this geometric shape analysis method, the under-segmented trees were identified. For these trees, vertical tree crown profile analysis was performed in multiple directions to detect potential treetops which were then considered as inputs for spectral clustering optimization. Our experiments across six plots showed that our method markedly surpasses traditional approaches, achieving an average Recall of 0.854, a Precision of 0.937, and an F1-score of 0.892.

Global Navigation Satellite System interferometric reflectometry (GNSS-IR), a new and inexpensive technique, has become available to the broader scientific community for detecting surface environmental information, such as soil moisture, snow depth and vegetation growth. However, there have been limited experiments focusing on the potential of crop height retrieval, especially the performance evaluation of BeiDou Navigation Satellite System (BDS) with other GNSS. Accuracy and reliability are challenging to achieve with traditional methods utilizing a single GNSS, and few measured verification data. In this study, an improved method that includes segmentation processing and multi-GNSS fusion is proposed based on GPS/GLONASS/Galileo/BDS multi-frequency data. Furthermore, experiments were carried out on a farmland in Fengqiu County, Henan Province, China. The results show that the height retrievals from four GNSS were in good agreement with the in situ observations during the whole growth cycle of the wheat after overwintering. Meanwhile, the retrievals based on the proposed method exhibited greater correspondence than the single frequency results, the correlation coefficient was increased and the root-mean-square error (RMSE) was reduced, respectively. Therefore, this study illustrates the feasibility of the proposed method to precisely estimate wheat height and its potential for use in the early warning of wheat lodging based on GNSS-IR.

Soil moisture is an important geophysical parameter for studying terrestrial water and energy cycles. It has been proven that Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) can be applied to monitor soil moisture. Unlike the Global Positioning System (GPS) that has only medium earth orbit (MEO) satellites, the Beidou Navigation Satellite System (BDS) also has geosynchronous earth orbit (GEO) satellites and inclined geosynchronous satellite orbit (IGSO) satellites. Benefiting from the distribution of three different orbits, the BDS has better coverage in Asia than other satellite systems. Previous retrieval methods that have been confirmed on GPS cannot be directly applied to BDS MEO satellites due to different satellite orbits. The contribution of this study is a proposed multi-satellite soil moisture retrieval method for BDS MEO and IGSO satellites based on signal-to-noise ratio (SNR) observations. The method weakened the influence of environmental differences in different directions by considering satellite repeat period. A 30-day observation experiment was conducted in Fengqiu County, China and was used for verification. The satellite data collected were divided according to the satellite repeat period, and ensured the response data moved in the same direction. The experimental results showed that the BDS IGSO and MEO soil moisture estimation results had good correlations with the in situ soil moisture fluctuations. The BDS MEO B1I estimation results had the best performance; the estimation accuracy in terms of correlation coefficient was 0.9824, root mean square error (RMSE) was 0.0056 cm³cm⁻³, and mean absolute error (MAE) was 0.0040 cm³cm⁻³. The estimations of the BDS MEO B1I, MEO B2I, and IGSO B2I performed better than the GPS L1 and L2 estimations. For the BDS IGSO satellites, the B1I signal was more suitable for soil moisture retrieval than the B2I signal; the correlation coefficient was increased by 19.84%, RMSE was decreased by 42.64%, and MAE was decreased by 43.93%. In addition, the BDS MEO satellites could effectively capture sudden rainfall events.

This paper proposes an encoding–decoding network with a pyramidal representation module, which will be referred to as EDPNet, and is designed for efficient semantic image segmentation. On the one hand, during the encoding process of the proposed EDPNet, the enhancement of the Xception network, i.e., Xception+ is employed as a backbone to learn the discriminative feature maps. The obtained discriminative features are then fed into the pyramidal representation module, from which the context-augmented features are learned and optimized by leveraging a multi-level feature representation and aggregation process. On the other hand, during the image restoration decoding process, the encoded semantic-rich features are progressively recovered with the assistance of a simplified skip connection mechanism, which performs channel concatenation between high-level encoded features with rich semantic information and low-level features with spatial detail information. The proposed hybrid representation employing the proposed encoding–decoding and pyramidal structures has a global-aware perception and captures fine-grained contours of various geographical objects very well with high computational efficiency. The performance of the proposed EDPNet has been compared against PSPNet, DeepLabv3, and U-Net, employing four benchmark datasets, namely eTRIMS, Cityscapes, PASCAL VOC2012, and CamVid. EDPNet acquired the highest accuracy of 83.6% and 73.8% mIoUs on eTRIMS and PASCAL VOC2012 datasets, while its accuracy on the other two datasets was comparable to that of PSPNet, DeepLabv3, and U-Net models. EDPNet achieved the highest efficiency among the compared models on all datasets.

Soil moisture is an important geophysical quantity for evaluating soil conditions. Traditional measurement methods such as remote sensing and field measurements cannot meet the requirements with high spatial and temporal resolution. Soil moisture retrieval based on Global Navigation Satellite System- Interferometry and Reflectometry (GNSS-IR) can be applied on the existing GNSS networks to solve these problems and has the great potential to complement existing soil moisture monitoring networks. Parameters (satellite elevation ranges, priori reflector height, and signal frequency) play a very important role in soil moisture retrieval using GNSS-IR and different selections could have great influence on the results. Previous research often relied on experience in the parameter selections, which was not conducive to the application and promotion of the technology. This study aims to investigate the effects of soil moisture retrieval using GNSS-IR based on different parameter selections. After describing the basic theory of soil moisture retrieval, the influence of the satellite elevation ranges, the priori reflector height, and the chosen signal frequency are investigated with National Science Foundation's (NSF) Plate Boundary Observatory (PBO) data and experimental data obtained in Nanjing, China. The results show that using data within an elevation range of [5°, 30°] and arc lengths greater than 20° can reduce the mean RMSE and MAE by 1%-31.5% and 2%-31%. The selection of the priori reflector height does not have a significant influence. L2 signals (compared to L1 signals) can improve the accuracy of soil moisture retrieval and can reduce the mean RMSE and MAE by 20.9% and 20.4%.

The near-surface air temperature lapse rate (SATLR) is a crucial parameter in climate, hydrology, and ecology research conducted in mountainous regions. However, existing research has difficulty characterizing its dynamic changes on an hourly scale. Obtaining data with high spatiotemporal resolution in complex terrains using existing methods poses challenges. This study introduces a hierarchical method for estimating SATLR at high spatiotemporal resolutions based on Fengyun-4A (FY-4A) Advanced Geostationary Radiation Imager (AGRI) land surface temperature (LST) data and machine learning techniques. Based on reconstructed FY-4A AGRI LST data, this study downscales the 4 km resolution data to a 1 km resolution using machine learning. It then estimates the spatial distribution of near-surface air temperature (SAT) and normalized near-surface air temperature (nSAT) by integrating station observations. Subsequently, high spatiotemporal resolution SATLRs are estimated, and their spatial and temporal distribution characteristics in the Beijing–Tianjin–Hebei mountainous region are analyzed. The results indicate that the SATLR exhibits a predominant distribution of 2~6 °C/km annually across the study area. However, in specific regions such as Taihang Mountains in the southwest, Damajun Mountain in the northwest, and certain areas of central Beijing City, the SATLR exceeds 6 °C/km in depth. Conversely, in Chengde City in the northeast and Huapiling in Damajun Mountain in the northwest, the SATLR is shallower than 2 °C/km. Seasonally, the average SATLR displays significant variation, with 3~5 °C/km being prevalent in spring, summer, and autumn, and 2~4 °C/km in winter. Moreover, the diurnal SATLR patterns from the second to fifth altitude grades exhibit consistency throughout the year and across seasons, albeit with varying overall values at different altitudes. Notably, the SATLR of the first altitude grade demonstrates stability within a day at lower elevations.