Search Results (471)

The high resolution of the Synthetic Aperture Radar (SAR) imagery, in addition to its capability to see through clouds and rain, makes it a crucial remote sensing technique. However, SAR images are very sensitive to radar parameters, the observation geometry and the scene’s characteristics. Moreover, for a complex scene of interest with targets located on a rough soil, a composite scattering between the target and the surface occurs and creates distortions on the SAR image. These characteristics can make the SAR images difficult to analyse and process. To better understand the complex EM phenomena and their signature in the SAR image, we propose a methodology to generate raw SAR signals and SAR images for scenes of interest with a target located on a rough surface. With this prospect, the entire radar acquisition chain is considered: the sensor parameters, the atmospheric attenuation, the interactions between the incident EM field and the scene, and the SAR image formation. Simulation results are presented for a rough dielectric soil and a canonical target considered as a Perfect Electric Conductor (PEC). These results highlight the importance of the composite scattering signature between the target and the soil. Its power is 21 dB higher that that of the target for the target–soil configuration considered. Finally, these simulations allow for the retrieval of characteristics present in actual SAR images and show the potential of the presented model in investigating EM phenomena and their signatures in SAR images. Full article

To meet the requirements for radar echo acquisition and feature extraction from stratified media and rough-surfaced targets, a vehicle was geometrically modelled in CAD. Monte Carlo techniques were applied to generate the rough interfaces at air–snow and snow–soil boundaries and over the vehicle surface. Soil complex permittivity was characterized with a four-component mixture model, while snow permittivity was described using a mixed-media dielectric model. The composite electromagnetic scattering from a rough-surfaced vehicle on snow-covered soil was then analyzed with the finite-difference time-domain (FDTD) method. Parametric studies examined how incident angle and frequency, vehicle orientation, vehicle surface root mean square (RMS) height, snow liquid water content and depth, and soil moisture influence the composite scattering coefficient. Results indicate that the coefficient oscillates with scattering angle, producing specular reflection lobes; it increases monotonically with larger incident angles, higher frequencies, greater vehicle RMS roughness, and higher snow liquid water content. By contrast, its dependence on snow thickness, vehicle orientation, and soil moisture is complex and shows no clear trend. Full article

Hyperspectral technology has been widely applied to the retrieval of soil properties, such as soil organic matter (SOM) and particle size distribution (PSD). However, most previous studies have focused on hyperspectral data acquired from the nadir direction, and the influence of viewing geometry on hyperspectral-based soil property retrieval remains unclear. In this study, bidirectional reflectance factors (BRFs) were collected at 48 different viewing angles for 154 soil samples with varying SOM contents and PSDs. SOM and PSD were then retrieved using combinations of ten spectral preprocessing methods (raw reflectance, Savitzky–Golay filter (SG), first derivative (D1), second derivative (D2), standard normal variate (SNV), multiplicative scatter correction (MSC), SG + D1, SG + D2, SG + SNV, and SG + MSC), one sensitive wavelength selection method, and three retrieval algorithms (partial least squares regression (PLSR), support vector machine (SVM), and convolutional neural networks (CNNs)). The influence of viewing geometry on the selection of spectral preprocessing methods, retrieval algorithms, sensitive wavelengths, and retrieval accuracy was systematically analyzed. The results showed that soil BRFs are influenced by both soil properties and viewing angles. The viewing geometry had limited effects on the choice of preprocessing method and retrieval algorithm. Among the preprocessing methods, D1, SG + D1, and SG + D2 outperformed the others, while PLSR achieved a higher accuracy than SVM and CNN when retrieving soil properties. The selected sensitive wavelengths for both SOM and PSD varied slightly with viewing angle and were mainly located in the near-infrared region when using BRFs from multiple viewing angles. Compared with single-angle data, multi-angle BRFs significantly improved retrieval performance, with the R² increasing by 11% and 15%, and RMSE decreasing by 16% and 30% for SOM and PSD, respectively. The optimal viewing zenith angle ranged from 10° to 20° for SOM and around 40° for PSD. Additionally, backward viewing directions were more favorable than forward directions, with the optimal viewing azimuth angles being 0° for SOM and 90° for PSD. These findings provide useful insights for improving the accuracy of soil property retrieval using multi-angle hyperspectral observations. Full article

Soybean (Glycine max) is a protein-rich legume crop that plays an important role in achieving food security. The aim of this study was to isolate soybean-nodulating rhizobia from Côte d’Ivoire soils and evaluate their potential as efficient strains in order to develop local bioinoculants. For this objective, 38 composite soil samples were collected from Côte d’Ivoire’s five major climatic zones. These soils were used as substrate to trap the nodulating rhizobia using the promiscuous soybean variety R2-231. A total of 110 bacterial strains were isolated and subsequently identified. The analysis of ITS (rDNA16S-23S), glnII and recA sequences revealed a relatively low genetic diversity of these native rhizobia. Moreover, the ITS phylogeny showed that these were scattered into two Bradyrhizobium clades dominated by the B. elkanii supergroup, with ca. 75% of all isolates. Concatenated glnII-recA sequence phylogeny confirmed that the isolates belong in the majority to ‘B. brasilense’, together with B. vignae and some putative genospecies of Bradyrhizobium that needs further elucidation. The core gene phylogeny was found to be incongruent with nodC and nifH phylogenies, probably due to lateral gene transfer influence on the symbiotic genes. The diversity and composition of the Bradyrhizobium species varied significantly among different sampling sites, and the key explanatory variables identified were carbon (C), magnesium (Mg), nitrogen (N), pH, and annual precipitation. Based on both shoot biomass and leaf relative chlorophyll content, three isolates consistently showed a higher symbiotic effectiveness than the exotic inoculant strain Bradyrhizobium IRAT-FA3, demonstrating their potential to serve as indigenous elite strains as bioinoculants. Full article

Validating the potential of multi-frequency synthetic aperture radar (SAR) data for multi-layer and profile soil moisture (SM) estimation modeling, we conducted an airborne multi-frequency SAR joint observation experiment (AMFSEX) over the Liao River Basin in China. The experiment simultaneously acquired airborne high spatial resolution quad-polarization (quad-pol) SAR data at five frequencies, including the Ka-, X-, C-, S-, and L-band. A preliminary “vegetation–soil” parameter estimation model based on the multi-frequency SAR data was established. Theoretical penetration depths of the multi-frequency SAR data were analyzed using the Dobson empirical model and the Hallikainen modified model. On this basis, a water cloud model (WCM) constrained by multi-polarization weighted and penetration depth weighted parameters was used to analyze the estimation accuracy of the multi-layer and profile SM (0–50 cm depth) under different vegetation types (grassland, farmland, and woodland). Overall, the estimation error (root mean square error, RMSE) of the surface SM (0–5 cm depth) ranged from 0.058 cm³/cm³ to 0.079 cm³/cm³, and increased with radar frequency. For multi-layer and profile SM (3 cm, 5 cm, 10 cm, 20 cm, 30 cm, 40 cm, 50 cm depth), the RMSE ranged from 0.040 cm³/cm³ to 0.069 cm³/cm³. Finally, a multi-input multi-output regression model (Gaussian process regression) was used to simultaneously estimate the multi-layer and profile SM. For surface SM, the overall RMSE was approximately 0.040 cm³/cm³. For multi-layer and profile SM, the overall RMSE ranged from 0.031 cm³/cm³ to 0.064 cm³/cm³. The estimation accuracy achieved by coupling the multi-source data (multi-frequency SAR data, multispectral data, and soil parameters) was superior to that obtained using the SAR data alone. The optimal SM penetration depth varied across different vegetation cover types, generally falling within the range of 10–30 cm, which holds true for both the scattering model and the regression model. This study provides methodological guidance for the development of multi-layer and profile SM estimation models based on the multi-frequency SAR data. Full article

Surface soil moisture (SM) is a critical factor in hydrological modeling, agricultural management, and numerical weather forecasting. This paper presents a highly effective soil moisture retrieval algorithm developed for the FY-3E (FengYun-3E) GNOS-R (GNSS Occultation Sounder II-Reflectometry) instrument. The algorithm incorporates a first-order vegetation model that considers vegetation density and volume scattering. Utilizing multi-angle GNOS-R observations, the algorithm derives surface reflectivity, which is combined with ancillary data on opacity, vegetation water content, and soil moisture from SMAP (Soil Moisture Active Passive) to optimize the retrieval process. The algorithm has been specifically tailored for different surface conditions, including bare soil, areas with low vegetation, and densely vegetated regions. The algorithm directly incorporates the angle-dependence of observations, leading to enhanced retrieval accuracy. Additionally, a new approach parameterizes surface roughness as a function of angle, allowing for refined corrections in reflectivity measurements. For vegetated areas, the algorithm effectively isolates the soil surface signal by eliminating volume scattering and vegetation effects, enabling the accurate estimation of soil moisture. By leveraging multi-angle data, the algorithm achieves significantly improved retrieval accuracy, with root mean square errors of 0.0235, 0.0264, and 0.0191 (g/cm³) for bare, low-vegetation, and dense-vegetation areas, respectively. This innovative methodology offers robust global soil moisture estimation capabilities using the GNOS-R instrument, surpassing the accuracy of previous techniques. Full article

Effective soil salinity monitoring is crucial for sustainable land management in arid regions. Most current studies face limitations by relying solely on single-source data. This study presents a novel three-dimensional (3D) optical-radar feature space model combining Gaofen-3 polarimetric synthetic aperture radar (SAR) and Sentinel-2 multispectral data for China’s Yutian Oasis. The random forest (RF) feature selection algorithm identified three optimal parameters: Huynen_vol (volume scattering component), RVI_Freeman (radar vegetation index), and NDSI (normalized difference salinity index). Based on the interactions of these three optimal features within the 3D feature space, we constructed the Optical-Radar Salinity Inversion Model (ORSIM). Subsequent validation using measured soil electrical conductivity (EC) data (May–June 2023) demonstrated strong model performance, with ORSIM achieving R² = 0.75 and RMSE = 7.57 dS/m. Spatial analysis revealed distinct salinity distribution patterns: (1) Mildly salinized areas clustered in the central oasis region, and (2) severely salinized zones predominated in northern low-lying margins. This spatial heterogeneity strongly correlated with local topography-higher elevation (south) to desert depression (north) gradient. The 3D feature space approach advances soil salinity monitoring by overcoming traditional 2D limitations while providing an accurate, transferable framework for arid ecosystem management. Furthermore, this study significantly expands the application potential of SAR data in soil salinization research. Full article

The non-contact detection of foreign objects embedded in lossy dielectric media such as soil, vegetation, or ice remains a critical challenge in applications including environmental monitoring and agricultural safety. This communication presents the design and experimental validation of an array antenna system capable of accurately localizing foreign objects in such lossy mediums. The proposed array antenna is capable of focusing electromagnetic energy at the location of the foreign object, thereby enabling precise positioning. The main idea of the foreign object detection is to set some of the antenna elements as test receiving antennas and measure the scattering parameters between the transmitting antennas and the receiving antennas. The excitation distribution of the transmitting array is optimized by using the method of maximum power transmission efficiency based on the differential scattering parameter matrices with the absence and presence of the foreign object. To validate the proposed design, a 5 × 5 microstrip patch array antenna was fabricated and tested with colza oil as a lossy medium. A copper block immersed in the colza oil served as the foreign object for detection, demonstrating the feasibility of the non-contact detection scheme. Experimental results demonstrate that the radiated field can be effectively focused at the object location, confirming the feasibility and precision of the proposed non-contact detection approach. Full article

In this study, we investigate the impact of soil moisture at varying depths on the stability of Chinese ecosystems, with ecosystem stability assessed using the Enhanced Vegetation Index (EVI) and Gross Primary Productivity (GPP). A multi-perspective analysis is conducted across different climatic zones and land cover types. Sen’s Slope Estimation and the Mann–Kendall trend test, combined with linear regression and correlation analyses, are employed to analyze the long-term trends of EVI and GPP in different climatic zones and land cover types and to assess the effects of soil moisture changes on ecosystem stability. The research reveals the following findings: (1) On a national scale, both EVI and GPP exhibit positive growth trends, with more significant increases in humid areas and relatively slower growth in arid areas. In addition, EVI and GPP of different land cover types exhibit positive inter-annual variation trends, reflecting a gradual enhancement in ecosystem productivity. (2) Cluster analysis shows that EVI has strong spatial correlation, with a distribution pattern of low–low (L-L) clusters in the north and high–high (H-H) clusters in the south. L-H clusters are concentrated in the Huaihai, Southwest Rivers, and Pearl River basins, while H-L clusters are scattered along the eastern coast. The spatial correlation of GPP is mainly concentrated in the south and the northeast, with a distribution pattern of L-L in the northeast, L-H in the Yangtze River basin, and H-H in the south. H-L clusters are dispersed in the downstream area of the Yangtze River. Both EVI and GPP show a tendency for high-value aggregation in space, with high-value areas of EVI located in the south and low-value areas in the central and western regions. High-value areas of GPP are in the south, while low-value areas are in the northeast, particularly in the Yangtze River Delta. (3) The correlation between EVI, GPP, and soil moisture varies significantly across different climatic regions. Arid and semi-humid regions show significant correlations between specific soil moisture depths and EVI and GPP, while such correlations are not significant in humid regions. The EVI and GPP values of croplands and grasslands are significantly and negatively correlated with soil moisture at depths of 150–200 cm (SM4). Conversely, wetland GPP values increase significantly with increasing soil moisture. Other vegetation types do not show significant correlations with soil moisture. The results of this study provide an important basis for understanding the impact of climate change on ecosystem stability and offer scientific guidance for ecological protection and water resource management. Full article

Accurate identification of mattic epipedon degradation is critically important for addressing ecological issues such as the weakening of alpine grassland carbon sink capacity and reduced soil and water conservation. However, efficient and rapid methods for its detection remain limited. This study aimed to clarify the hyperspectral response mechanisms of mattic epipedon degradation and, based on hyperspectral technology, to construct models for identifying degraded mattic epipedon and screen preprocessing methods suitable for different moisture conditions. The results showed the following: (1) The XGBoost model with preprocessing using multiplicative scatter correction combined with second derivative transformation (MSC+SD) performed best, achieving an identification accuracy and Kappa coefficient of 0.85 and 0.82, respectively. The characteristic bands were concentrated in the visible light range (446–450 nm) and short-wave infrared range (2134 nm, 2267–2269 nm), which are closely related to the spectral responses of organic carbon and mineral components. (2) Spectral reflectance was significantly negatively correlated with moisture content, and model accuracy decreased as moisture content increased. (3) After correction using the EPO algorithm, the model accuracy for the high-moisture group improved by 13.2–16.7%, whereas that for the low-moisture group (<15%) decreased by 7.5%, verifying 15% moisture content as the critical threshold for water interference. This study elucidated the impact mechanism of moisture on the hyperspectral characteristics of the mattic epipedon. The established MSC+SD-XGBoost model adapts to varying moisture conditions, providing technical support for the rapid monitoring of mattic epipedon degradation and holding significant practical value for carbon management in alpine ecosystems. Full article

Rice is a globally important food crop, and phosphorus is an essential nutrient element for rice growth. In many of China’s arable lands, there is a deficiency in available phosphorus content. Therefore, screening and breeding rice germplasm resources that are tolerant to low phosphorus can enhance the growth capability of rice in low-phosphorus soils. This study set up treatments with two phosphorus concentrations: H₂PO₄⁻ at 0.18 mmol/L, referred to as normal phosphorus (NP), and H₂PO₄⁻ at 0.009 mmol/L, referred to as low phosphorus (LP). Using hydroponic methods, 156 different genotype rice germplasms were treated for 35 days, after which the morpho-physiological traits of the rice seedling shoots, root morphology, and material content were measured. An analysis of the coefficient of variation (CV) for low phosphorus tolerance coefficients across different rice germplasm resources revealed that 16 indicators had CVs greater than 10%, which can be used as criteria for screening rice varieties with low phosphorus tolerance at the seedling stage. The relevant indicators and low-phosphorus resistance characteristics of different rice varieties were comprehensively evaluated using principal component analysis, correlation analysis, membership function, and cluster analysis methods. The results indicate that the principal component analysis transformed 23 indicators into 5 comprehensive indicators, with a cumulative contribution rate of 86.947%. The D value was evaluated in a comprehensive evaluation of low-phosphorus resistance, and 156 rice germplasm resources were divided into four types by cluster analysis. A scatter plot was created using the comprehensive phosphorus efficiency values of different rice germplasms under normal phosphorus and low phosphorus conditions. Through further verification, the germplasms with strong low-phosphorus tolerance finally selected through comprehensive screening were Y3-14, Y3-35, Y3-21, Jinnongda 705, Changjing 625, and Jinnongda 873. The germplasms with poor low-phosphorus tolerance were Jijing 338, Jingu 981, Tong 35, Y3-31, and Longdao 20. Full article

The presence of vegetation in agricultural fields affects the accuracy of soil moisture retrieval using synthetic aperture radar (SAR) data. As a result, the estimation of soil moisture using the existing Oh model produces high error values. The magnitude of this error primarily depends upon the nature of crops, crop coverage, and the roughness of the field. Hence, in this study, along with the Oh model, we proposed a novel approach using model-based decomposition to reduce the volume contribution of the vegetation. This proposed method is employed on fallow as well as different crop fields in the summer of 2023 in the Kharagpur region of India using the Sentinel-1 dual polarimetric SAR data. The Root Mean Square Error (RMSE) of the proposed method is ≈25% to 52% lower over different crop types as compared to the existing Oh model. Moreover, the proposed method is also compared with the Chang model, designed to estimate soil moisture in vegetative fields. The proposed method exhibits RMSE that is around ≈10% to 17% lower across various crop kinds, in comparison to the Chang model. Thus, the proposed novel approach, with the advantage of not requiring in situ plant descriptors, will simplify the application of dual polarimetric SAR data for soil moisture estimation in a variety of land-use scenarios. Full article

Identifying the spatiotemporal variations in and driving factors of trade-offs and synergies among ESs in the plain area forms a critical foundation for the effective management of ecosystems and regulation. It is also crucial for effectively distributing the management of natural assets and the formulation of effective ecological policy. This research utilized correlation analysis, GWR and OPGD to examine the trade-offs and synergies among Net Primary Production, Soil Carbon, Water Conservation, and Habitat Quality in the Beijing Plain from 2001 to 2020. The results revealed that from 2001 to 2020, HQ and SC showed a declining trend, while NPP and WC exhibited an increasing trend. The trade-off intensities among NPP-SC, NPP-WC, and WC-HQ increased, whereas the trade-off intensities among NPP-HQ, SC-HQ, and SC-WC decreased. High-synergy areas for NPP-HQ, SC-HQ, and SC-WC were focused in the central urban area, with scattered distribution in the southeast and northwest. NPP-SC displayed a fragmented spatial distribution with significant variations. The spatiotemporal distributions of NPP-WC and WC-HQ were highly similar, both exhibiting strong synergy. However, NPP-WC demonstrated strong trade-offs in the northern plain area but weaker trade-offs elsewhere, while WC-HQ exhibited strong trade-offs outside the central urban area. The kind of land use was the primary element affecting the trade-off intensities of NPP-HQ, SC-HQ, and WC-HQ. NDVI and precipitation significantly influenced NPP-SC. The key factors influencing the spatial variation in NPP-WC were the land use type, temperature, and precipitation. Temperature was the primary determinant affecting SC-WC. The trade-off intensity among ESs is not determined by a single factor but is driven by the interactions between services or shared influencing factors, exhibiting high spatial heterogeneity. These findings provide valuable guidance for developing strategies for land-use planning and ecological restoration. Full article

This study investigated spatial distribution features and ecological risks of eight potential toxic elements (Cr, Ni, Cu, Zn, Pb, As, Cd, and Hg) in surface soil samples (0–20 cm) collected from farmland in the Nanyang Basin, China. This research also aimed to analyze the sources of these elements. Its findings revealed that the mean contents of Cr, Ni, Cu, Zn, Pb, As, Cd, and Hg were 54.35, 26.57, 25.20, 82.09, 22.17, 8.27, 0.17, and 0.13 mg·kg⁻¹, respectively, all of which were lower than their corresponding risk screening values. However, the mean contents of Cu, Zn, Cd, and Hg exceeded the background values of Henan Province. Spatial distribution analysis revealed that Cr and Ni exhibited similar patterns, with high contents primarily observed in the western part of the research area. Generally speaking, Cu, Zn, and Pb contents were higher in the south and lower in the north, whereas Hg, As, and Cd displayed a scattered distribution of high-value areas. Ecological risk assessment indicated that Hg and Cd posed relatively high risks, with their comprehensive ecological risk indexes (RIs) predominantly classified as moderate. Source identification suggested that As primarily originates from agriculture, Cd from industry sources, Hg from coal combustion, and the remaining elements from mixed sources, including parent material, transportation, and agriculture. Full article

The estimation and correction of antenna patterns are essential for ensuring the relative radiometric quality of SAR images. Traditional methods for antenna pattern estimation rely on artificial calibrators or specific stable natural scenes like the Amazon rainforest, which are limited by cost, complexity, and geographic constraints, making them unsuitable for frequent imaging demands. Meanwhile, general natural scenes are imaged frequently using SAR systems, but their true scattering characteristics are unknown, posing a challenge for direct antenna pattern estimation. Therefore, it is considered to use the calibrated SAR to obtain the scattering characteristics of these general scenarios; that is, introducing the concept of cross-calibration. Accordingly, this paper proposes a novel method for estimating the SAR range antenna pattern based on cross-calibration. The method addresses three key challenges: (1) Identifying pseudo-invariant natural scenes suitable as reference targets through spatial uniformity and temporal stability assessments using standard deviation and amplitude correlation analyses; (2) Achieving pixel-level registration of heterogeneous SAR images with an iterative method despite radiometric imbalances; (3) Extracting stable power values by segmenting images and applying differential screening to minimize outlier effects. The proposed method is validated using Gaofen-3 SAR data and shows robust performance in bare soil, grassland, and forest scenarios. Comparing the results of the proposed method with the tropical forest-based calibration method, the maximum shape deviation between the range antenna patterns of the two methods is less than 0.2 dB. Full article