Remote Sensing

Disease control programs are needed to identify the breeding sites of mosquitoes, which transmit malaria and other diseases, in order to target interventions and identify environmental risk factors. The increasing availability of very-high-resolution drone data provides new opportunities to find and characterize these vector breeding sites. Within this study, drone images from two malaria-endemic regions in Burkina Faso and Côte d’Ivoire were assembled and labeled using open-source tools. We developed and applied a workflow using region-of-interest-based and deep learning methods to identify land cover types associated with vector breeding sites from very-high-resolution natural color imagery. Analysis methods were assessed using cross-validation and achieved maximum Dice coefficients of 0.68 and 0.75 for vegetated and non-vegetated water bodies, respectively. This classifier consistently identified the presence of other land cover types associated with the breeding sites, obtaining Dice coefficients of 0.88 for tillage and crops, 0.87 for buildings and 0.71 for roads. This study establishes a framework for developing deep learning approaches to identify vector breeding sites and highlights the need to evaluate how results will be used by control programs. Full article

Playas, as the flattest landforms in semiarid and arid regions, are extremely sensitive to climate changes, such as changes in the hydrologic regime of the landscape. The changes in these landforms due to irrigation, anthropogenic activities, and climate change could be a source of disasters. In this study, we explored the spatial-temporal changes of the Abarkuh Playa in Central Iran using the time series of the Sentinel-1 backscatter dataset in the three scales. Our results showed that the western area of the Abarkuh Playa has been changed to other landforms with different characteristics, which is clear from all backscatter maps. The spatial-temporal analysis of the time series of backscatter data using the independent component analysis and time series of precipitation revealed that the backscatter variations were associated with direct rainfall across the playa and the surface was reacting to changes in the soil moisture content. The results of the power scale showed that the boundary of the playa could successfully be recognized as the oscillating pattern from other landforms in the study area. Moreover, the spatial-temporal analysis of backscatter in the power scale showed that different polarizations could reveal different patterns of surface changes for the playa. Full article

As an important food crop, summer maize is widely planted all over the world. Monitoring its growth and output is of great significance for world food security. With the trend of global warming and deterioration, the frequency of high temperature and heat damage affecting summer corn has been increasing in the past ten years. Therefore, there is an increasing demand for monitoring the high temperature and heat damage of summer maize. At present, there are nearly a hundred indices or methods for research on high temperature and heat damage. However, research based on the vegetation index cannot fully describe the damage caused by high-temperature thermal damage, and there is an obvious asynchrony effect. Research based on hyperspectral remote sensing has many inconveniences in data acquisition and complex physical model construction. Therefore, this study uses remote sensing data, including MODIS surface reflection data, MODIS land surface temperature products, as well as ground observation data and statistical data, combined with multiple remote sensing indices and land surface temperature, to construct a remote sensing index, LSHDI (land surface heat damage index). The LSHDI first searches for a location with the worst vegetation growth conditions in the three-dimensional feature space based on the LST (land surface temperature), the normalized difference vegetation index (NDVI), and the land surface water index (LSWI). Then, it calculates the distance between each point and this location to measure the degree of vegetation affected by high temperature and heat damage. Finally, because there is no reliable disaster verification dataset that has been published at present, this study uses soil moisture as a reference to explain the performance and stability of the LSHDI. The results showed that their coefficient of determination was above 0.5 and reached a significance level of 0.01. The LSHDI can well-reflect the high temperature and heat damage of land surface vegetation and can provide important data support and references for agricultural management departments. Full article

Efficient monitoring and early warning are the preconditions of realizing expansive soil landslide hazard prevention and control. Previous early warning of expansive soil landslides was evaluated through soil sampling experiments to analyze the stability coefficient. However, the existing methods lack timeliness and ignore the inconsistent deformation characteristics of different parts of the landslide mass. There are still difficulties in the dynamic numerical early warning of landslides at multiple points. Considering that the degradation of expansive soil landslides’ strength is directly reflected by surface displacement, for the Ningming expansive soil demonstration area and based on the GNSS shallow real-time displacement monitoring sequence, a landslide early-warning method based on the GNSS displacement rate combined with the GNSS displacement tangent angle model was proposed, and we thus designed early-warning thresholds for different warning levels. Combined with multi-source data such as soil moisture, soil pressure, and rainfall, the feasibility of accurate early warning of expansive soil landslides based on GNSS real-time surface displacement was verified. The proposed method does not require numerical calculation of internal stress and achieved two successful early warnings of landslides in the test area, which has a certain promotional value. Full article

Extracting the peak value of the X-ray signal in the original magnetopause detection method of soft X-ray imaging (SXI) for the SMILE satellite is problematic because of the unclear interface of the magnetosphere system under low solar wind density and the short integration time. Herein, we propose a segmentation algorithm for soft X-ray images based on depth learning, we construct an SXI simulation dataset, and we segment the magnetospheric system by learning the spatial structure characteristics of the magnetospheric system image. Then, we extract the maximum position of the X-ray signal and calculate the spatial configuration of the magnetopause using the tangent fitting approach. Under a uniform universe condition, we achieved a pixel accuracy of the maximum position of the photon number detected by the network as high as 90.94% and contained the position error of the sunset point of the 3D magnetopause below 0.2 RE. This result demonstrates that the proposed method can detect the peak photon number of magnetospheric soft X-ray images with low solar wind density. As such, its use improves the segmentation accuracy of magnetospheric soft X-ray images and reduces the imaging time requirements of the input image. Full article

Integrated development of urban agglomeration is emerging as the main pattern of China’s new modernization. Yet, atmospheric pollution continues to have an adverse impact on public health, challenging efforts to promote coordinated regional development. To better understand the interaction between atmospheric pollution-related health burdens and urbanization, this study employed deep learning technology to obtain high-resolution satellite-derived PM_2.5 concentration data across the Yangtze River Delta (YRD) region. Using the Global Exposure Mortality Model (GEMM), this study estimated premature mortality resulting from long-term exposure to PM_2.5 and innovatively incorporated exposure factors to improve accuracy. Results indicated that while PM_2.5 concentrations decreased by 16.13% from 2015 to 2019, the region still experienced 239,000 premature mortalities in 2019, with notable disparities among cities of different economic levels and sizes. Furthermore, it was found through correlation analysis that residential density and GDP per capita were highly associated with premature mortality. In conclusion, these findings highlight the continuing challenge of achieving equitable effectiveness of joint air pollution control across regions in the context of integrated development of urban agglomeration. Full article

Aerosol Optical Depth (AOD) is a crucial physical parameter used to measure the radiative and scattering properties of the atmosphere. Obtaining full-coverage AOD measurements is essential for a thorough understanding of its impact on climate and air quality. However, satellite-based AOD products can be affected by abnormal weather conditions and high reflectance surfaces, leading to gaps in spatial coverage. To address this issue, we propose a satellite-based AOD filling method based on hourly level-3 Himawari-8 AOD products. In this study, the optimal model with a mean bias error (MBE) less than 0.01 and a root-mean-square error (RMSE) less than 0.1 in most land cover types was selected to generate the full-coverage AOD. The generated full-coverage AOD was validated against in situ measurements from the AERONET sites and compared with the performance of Himawari-8 AOD and MERRA-2 AOD over the AERONET sites. The validation results indicate that the accuracy of full-coverage AOD is comparable to that of the Advanced Himawari Imager (AHI) AOD, and for other land cover types (excluding barren land), the accuracy of full-coverage AOD is superior to that of MERRA-2 AOD. To investigate the practical application of full-coverage AOD, we utilized it as an input parameter to perform radiative transfer simulations in northwestern and southern China. The validation results showed that the simulated at-sensor radiance based on full-coverage AOD was in good agreement with the at-sensor radiance observations from MODIS. These results indicate that complete and accurate measurements of AOD have considerable potential for application in the simulation of at-sensor radiance and other related topics. Full article

In the Northwest Pacific (NWP), where a unique monsoon climate exists and where both typhoons and extratropical storms occur frequently, hazardous waves pose a significant risk to maritime safety. To analyze the 20-year variability of hazardous waves in this region, this study utilized hourly reanalysis data from the ECMWF ERA5 dataset covering the period from 2001–2020, alongside the wave risk assessment method. The ERA5 data exhibits better consistency, in both the temporal and spatial dimensions, than satellite data. Although hazardous wind seas occur more frequently than hazardous swells, swells make hazardous waves travel further. Notably, the extreme wave height (EWH) shows an increasing trend in high- and low-latitude areas of the NWP. The change in meridional wind speeds is the primary reason for the change in the total wind speed in the NWP. Notably, the maximum annual increase rate of 0.013 m/year for EWH exists in the region of the Japanese Archipelago. This study elucidated the distributions of wave height intensity and wave risk levels, noting that the EWHs of the 50-year and 100-year return periods can reach 20.92 m and 23.07 m, respectively. Full article

Velocity estimation of space moving targets is a key part of space situational awareness. However, most of the existing methods do not consider the satellite observation process, and the performance mainly depends on the preset target motion state, which has great limitations. To accurately obtain the motion characteristics of space infrared dim targets in space-based infrared detection, a velocity estimation method based on multi-satellite observation and robust locally weighted regression is proposed. Firstly, according to parameters such as satellite position, satellite attitude angle, and sensor line of sight, the overall target observation model from the sensor coordinate frame to the Earth-centered inertial coordinate frame is established, and the pixel coordinates of the target imaging point are extracted using the gray-weighted centroid method. Then, combined with the least squares criterion, the position sequence of the space target is obtained. Finally, a robust locally weighted regression operation is performed on the target position sequence to estimate the velocity. This study verified the feasibility of the proposed method through simulation examples, with the results showing that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the method were only 0.0733 m/s and 1.6640 m/s without measurement error. Moreover, the velocity estimation accuracy was better than that of other methods in most scenarios. In addition, the estimation accuracy under the impact of various measurement errors was analyzed, and it was found that the pixel coordinate extraction error had the greatest impact on velocity estimation accuracy. The proposed method provides a technical basis for the recognition of space infrared dim moving targets. Full article

Woody vegetation landscape features, such as hedges, tree patches, and riparian vegetation, are important elements of landscape and biotic diversity. For the reason that biodiversity loss is one of the major ecological problems in the EU, it is necessary to establish efficient workflows for the registration and monitoring of woody vegetation landscape features. In the paper, we propose and evaluate a methodology for automated detection of changes in woody vegetation landscape features from a digital orthophoto (DOP). We demonstrate its ability to capture most of the actual changes in the field and thereby provide valuable support for more efficient maintenance of landscape feature layers, which is important for the shaping of future environmental policies. While the most reliable source for vegetation cover mapping is a combination of LiDAR and high-resolution imagery, it can be prohibitively expensive for continuous updates. The DOP from cyclic aerial photography presents an alternative source of up-to-date information for tracking woody vegetation landscape features in-between LiDAR recordings. The proposed methodology uses a segmentation neural network, which is trained with the latest DOP against the last known ground truth as the target. The output is a layer of detected changes, which are validated by the user before being used to update the woody vegetation landscape feature layer. The methodology was tested using the data of a typical traditional Central European cultural landscape, Goričko, in north-eastern Slovenia. The achieved $F_1$ of per-pixel segmentation was 83.5% and 77.1% for two- and five-year differences between the LiDAR-based reference and the DOP, respectively. The validation of the proposed changes at a minimum area threshold of 100 m² and a minimum area percentage threshold of 20% showed that the model achieved recall close to 90%. Full article

The LuTan-1(LT-1), known as the L-band differential interferometric synthetic aperture radar (SAR) satellite system, is an essential piece of civil infrastructure in China, providing extensive applications such as surface deformation monitoring and topographic mapping. To achieve high-resolution and wide-swath (HRWS) observation abilities, the LT-1 takes the dual receiving antenna (DRA) imaging mode as its working mode. However, amplitude and phase errors between channels lead to a mismatch between the reconstruction filter and the multichannel echo signal, worsen the reconstructed azimuth spectrum, and introduce ambiguity targets in the final imaging results, seriously affecting the final imaging quality. In order to better evaluate the channel error and azimuth ambiguity performance of the LT-1 system, this paper proposed an advanced channel consistency correction method and conducted many measured data experiments. The experimental results show that the proposed method is effective, and the LT-1 system has excellent channel error control and azimuth ambiguity performance. Full article

Despite numerous studies, the impact of global warming on the tropical cyclone (TC) track and intensity by reasons of data inhomogeneity in remote sensing and large natural variability over a relatively short period of observation is still controversial. Three carbon-emission sensitivity experiments are conducted to investigate how TC track and intensity respond to changes in the oceanic and atmospheric environment under global warming. The results show a high sensitivity of the simulated TC track and intensity to global warming. On one hand, with increase in carbon emissions, the western Pacific subtropical high expands notably, increasing the poleward steering flow and eventually leading to a poleward shift of TC. On the other hand, the underlying sea-surface temperature and surface-entropy flux increase and, thus, favor the convections near the eyewall. Moreover, the TC structure becomes more upright, which is closely related to the larger pressure gradient near the eyewall. As a result, TC intensity increases with carbon emissions. However, this increase is notably smaller than the maximum potential intensity theory as the TC intensity can reach a threshold if carbon emission still increases in the future. The involved mechanisms on the changes of TC track and intensity are also revealed. Full article

The deception jamming method based on Digital Radio Frequency Memory (DRFM) for Inverse Synthetic Aperture Radar (ISAR) has been a widely studied topic in recent decades. Typically, jamming signals generated using two-dimensional or three-dimensional false target models can create realistic false targets on the ISAR image. However, existing deception jamming methods cannot eliminate or revise the original echo, which can be retained by multiple anti-jamming methods once the radar judges out receiving the jamming signal. Additionally, these methods require large storage space for the models. Otherwise, the false targets cannot be generated realistically. To address these issues, this paper proposes a jamming signal generation algorithm based on two-dimensional template multiplication modulation and template time delay. The frequency shift and time delay relationship between the signals intercepted by the jammer and the real target echo is analyzed and derived in detail. With the use of these detailed derivations, it is possible to add and remove scatters by precisely locating the false scatter on the real ISAR image. The real target’s shape naturally changes as a result of the addition and removal of scatters. Furthermore, this method can adaptively change the resolution of the false target’s ISAR image with the radar pulse width and the accumulated pulse number. Meanwhile, the false target size on the ISAR image can be adjusted adaptively by altering the false template resolution. These features of the proposed method offer increased flexibility and efficiency for deception jamming. By accurately determining the position of false scatter on the ISAR image, this method offers improved performance compared with the existing techniques. Simulation results demonstrate the effectiveness of the proposed deception jamming method. Full article

Recent studies have demonstrated the potential of using bidirectional reflectance distribution function (BRDF) signatures captured by multi-angle observation data to enhance land cover classification and retrieve vegetation architectures. Considering the diversity of crop architectures, we proposed that crop mapping precision may be enhanced by using BRDF signatures. We compared the accuracy of four supervised machine learning classifiers provided by the Google Earth Engine (GEE), namely random forest (RF), classification and regression trees (CART), support vector machine (SVM), and Naïve Bayes (NB), using the moderate resolution imaging spectroradiometer (MODIS) nadir BRDF-adjusted reflectance data (MCD43A4 V6) and BRDF and albedo model parameter data (MCD43A1 V6) as input. Our results indicated that using BRDF signatures leads to a moderate improvement in classification results in most cases, compared to using reflectance data from a single nadir observation direction. Specifically, the overall validation accuracy increased by up to 4.9%, and the validation kappa coefficients increased by up to 0.092. Furthermore, the classifiers were ranked in order of accuracy, from highest to lowest: RF, CART, SVM, and NB. Our study contributes to the development of crop mapping and the application of multi-angle observation satellites. Full article

Assimilating the proper amount of water vapor into a numerical weather prediction (NWP) model is essential in accurately forecasting a heavy rainfall. Radar data assimilation can effectively initialize the three-dimensional structure, intensity, and movement of precipitation fields to an NWP at a high resolution (±250 m). However, the in-cloud water vapor amount estimated from radar reflectivity is empirical and assumes that the air is saturated when the reflectivity exceeds a certain threshold. Previous studies show that this assumption tends to overpredict the rainfall intensity in the early hours of the prediction. The purpose of this study is to reduce the initial value error associated with the amount of water vapor in radar reflectivity by introducing advanced remote sensing data. The ongoing research shows that errors can be largely solved by assimilating satellite all-sky radiances and global positioning system radio occultation (GPSRO) refractivity to enhance the moisture analysis during the cycling period. The impacts of assimilating moisture variables from satellite all-sky radiances and GPSRO refractivity in addition to hydrometeor variables from radar reflectivity generate proper amounts of moisture and hydrometeors at all levels of the initial state. Additionally, the assimilation of satellite atmospheric motion vectors (AMVs) improves wind information and the atmospheric dynamics driving the moisture field which, in turn, increase the accuracy of the moisture convergence and fluxes at the core of the convection. As a result, the accuracy of the timing and intensity of a heavy rainfall prediction is improved, and the hourly and accumulated forecast errors are reduced. Full article

Land use/cover change (LUCC) accompanied by climate change and human activities will have unpredictable impacts on watershed ecosystems. However, the extent to which these land use changes affect the spatial and temporal distribution of ecosystem services (ESs) in different regions remains unclear. The impact of LUCC on ESs in the Qingjiang Watershed (QJW), an ecologically sensitive area, and LUCC’s role in future ESs under different land use scenarios are crucial to promoting ecological conservation and land use management. This paper assessed water yield (WY), soil conservation (SC), carbon storage (CS) and habitat quality (HQ) using the InVEST model, and their responses to LUCC in the QJW from 1990 to 2018 using the geodetector and multiscale geographically weighted regression. We predicted land use patterns using the Logistic–CA–Markov model and their effects on ESs in 2034 under business as usual (BAU), ecological land protection (ELP), arable land protection (ALP) and ecological economic construction (EEC) scenarios. From 1990 to 2018, the area of cropland and woodland decreased by 28.3 and 138.17 km², respectively, while the built-up land increased by 96.65 km². The WY increased by 18.92%, while the SC, CS and HQ decreased by 26.94%, 1.05% and 0.4%, respectively. The increase in the arable land area led to a increase in WY, and the decrease in forest land and the increase in construction land led to a decrease in SC, CS and HQ. In addition to being influenced by land use patterns, WY and SC were influenced mainly by meteorological and topographical factors, respectively. In 2034, there was an obvious spatial growth conflict between cropland and construction land, especially in the area centered on Lichuan, Enshi and Yidu counties. Under four scenarios, WY and SC were ranked ALP > BAU > EEC > ELP, while CS and HQ were ranked ELP > EEC > BAU > ALP. Considering the sustainable eco-socio-economic development of the QJW, the EEC scenario can be chosen as a future development plan. These results can indicate how to rationally improve the supply of watershed ESs through land resource allocation, promoting sustainable regional development in mountainous watershed areas. Full article

Maturity is an important trait in dry pea breeding programs, but the conventional process predominately used to measure this trait can be time-consuming, labor-intensive, and prone to errors. Therefore, a more efficient and accurate approach would be desirable to support dry pea breeding programs. This study presents a novel approach for measuring dry pea maturity using machine learning algorithms and unmanned aerial systems (UASs)-collected data. We evaluated the abilities of five machine learning algorithms (random forest, artificial neural network, support vector machine, K-nearest neighbor, and naïve Bayes) to accurately predict dry pea maturity on field plots. The machine learning algorithms considered a range of variables, including crop height metrics, narrow spectral bands, and 18 distinct color and spectral vegetation indices. Backward feature elimination was used to select the most important features by iteratively removing insignificant ones until the model’s predictive performance was optimized. The study’s findings reveal that the most effective approach for assessing dry pea maturity involved a combination of narrow spectral bands, red-edge, near-infrared (NIR), and RGB-based vegetation indices, along with image textural metrics and crop height metrics. The implementation of a random forest model further enhanced the accuracy of the results, exhibiting the highest level of accuracy with a 0.99 value for all three metrics precision, recall, and f1 scores. The sensitivity analysis revealed that spectral features outperformed structural features when predicting pea maturity. While multispectral cameras achieved the highest accuracy, the use of RGB cameras may still result in relatively high accuracy, making them a practical option for use in scenarios where cost is a limiting factor. In summary, this study demonstrated the effectiveness of coupling machine learning algorithms, UASs-borne LIDAR, and multispectral data to accurately assess maturity in peas. Full article

In this paper, a method for joint sea surface rainfall intensity (RI), wind speed, and wave height retrieval based on spaceborne global navigation satellite system reflectometry (GNSS-R) data is proposed, which especially considers the effects between these two parameters. A method of rainfall detection (RD) according to different wind speed ranges is also proposed by mitigating the impact of swell and wind speed. The results, with data collected over the oceans near Southeast Asia, show that the RD method has a detection accuracy of up to 81.74%. The RI retrieval accuracy can reach about 2 mm/h by simultaneously correcting the effects of wind speed and swell. The accuracy of wind speed retrieval is improved by about 5% after removing rainfall interference through RD in advance. After considering the influence of wind speed and eliminating rainfall interference, the retrieval accuracy of significant wave height (SWH) is improved by about 18%. Finally, the deep convolutional neural network (DCNN) model is built to estimate the SWH of the swell. The results show that the retrieval accuracy of the swell height is better than 0.20 m after excluding rainfall interference. The proposed joint retrieval method provides an important reference for the future acquisition of multiple high-precision marine geophysical parameters by spaceborne GNSS-R technology. Full article