bluuebunny/crossref_metadata_2025 huggingface dataset:
Estimation of Chlorophyll-A Concentration with Remotely Sensed Data for the Nine Plateau Lakes in Yunnan Province
Authors: Wang Dong , Tang Bo-Hui , Fu Zhitao , Huang Liang , Li Menghua , Chen Guokun , Pan Xuejun
Published: October 2022

The quantitative retrieval of the chlorophyll-a concentration is an important remote sensing method that is used to monitor the nutritional status of water bodies. The high spatial resolution of the Sentinel-2 MSI and its subdivision in the red-edge band highlight the characteristics of water chlorophyll-a, which is an important detection tool for assessing water quality parameters in plateau lakes. In this study, the Nine Plateau Lakes in the Yunnan-Kweichow Plateau of China were selected as the study area. Using Sentinel-2 MSI transit images and in situ measured chlorophyll-a concentration as the data source, the chlorophyll-a concentrations of plateau lakes (CCAPLs) were investigated, and the surface temperatures of plateau lakes (STPLs) were retrieved to verify the hypothesis that the lake surface temperature could increase the chlorophyll-a concentration. By comparing feature importance using a random forest (RF), the Sentinel-2 MSI surface reflectance and in situ data were linearly fitted using four retrieval spectral indices with high feature importance, and the accuracy of the estimated concentration of chlorophyll-a was evaluated by monitoring station data in the same period. Then, Landsat-8 TIRS Band 10 data were used to retrieve the STPL with a single-channel temperature retrieval algorithm and to verify the correlation between the STPL and the CCAPL. The results showed that the retrievals of the CCAPL and the STPL were consistent with the actual situation. The root-mean-square error (RMSE) of the fifteenth normalized difference chlorophyll-a index (NDCI15) was 0.0249. When the CCAPL was greater than 0.05 mg/L and the STPL was within 28–34 °C, there was a positive linear correlation between the CCAPL and the STPL. These results will provide support for the remote sensing monitoring of eutrophication in plateau lakes and will contribute to the scientific and effective management of plateau lakes.
A Novel Hybrid Attention-Driven Multistream Hierarchical Graph Embedding Network for Remote Sensing Object Detection
Authors: Tian Shu , Cao Lin , Kang Lihong , Xing Xiangwei , Tian Jing , Du Kangning , Sun Ke , Fan Chunzhuo , Fu Yuzhe , Zhang Ye
Published: October 2022

Multiclass geospatial object detection in high-spatial-resolution remote-sensing images (HSRIs) has recently attracted considerable attention in many remote-sensing applications as a fundamental task. However, the complexity and uncertainty of spatial distribution among multiclass geospatial objects are still huge challenges for object detection in HSRIs. Most current remote-sensing object-detection approaches fall back on deep convolutional neural networks (CNNs). Nevertheless, most existing methods only focus on mining visual characteristics and lose sight of spatial or semantic relation discriminations, eventually degrading object-detection performance in HSRIs. To tackle these challenges, we propose a novel hybrid attention-driven multistream hierarchical graph embedding network (HA-MHGEN) to explore complementary spatial and semantic patterns for improving remote-sensing object-detection performance. Specifically, we first constructed hierarchical spatial graphs for multiscale spatial relation representation. Then, semantic graphs were also constructed by integrating them with the word embedding of object category labels on graph nodes. Afterwards, we developed a self-attention-aware multiscale graph convolutional network (GCN) to derive stronger for intra- and interobject hierarchical spatial relations and contextual semantic relations, respectively. These two relation networks were followed by a novel cross-attention-driven spatial- and semantic-feature fusion module that utilizes a multihead attention mechanism to learn associations between diverse spatial and semantic correlations, and guide them to endowing a more powerful discrimination ability. With the collaborative learning of the three relation networks, the proposed HA-MHGEN enables grasping explicit and implicit relations from spatial and semantic patterns, and boosts multiclass object-detection performance in HRSIs. Comprehensive and extensive experimental evaluation results on three benchmarks, namely, DOTA, DIOR, and NWPU VHR-10, demonstrate the effectiveness and superiority of our proposed method compared with that of other advanced remote-sensing object-detection methods.
Post-Flood Analysis for Damage and Restoration Assessment Using Drone Imagery
Authors: Whitehurst Daniel , Joshi Kunal , Kochersberger Kevin , Weeks James
Published: October 2022

With natural disasters continuing to become more prevalent in recent years, the need for effective disaster management efforts becomes even more critical. Specifically, flooding is an extremely common natural disaster which can cause significant damage to homes and other property. In this article, we look at an area in Hurley, Virginia which suffered a significant flood event in August 2021. A drone is used to capture aerial imagery of the area and reconstructed to produce 3-dimensional models, Digital Elevation Models, and stitched orthophotos for flood modeling and damage assessment. Pre-flood Digital Elevation Models and available weather data are used to perform simulations of the flood event using HEC-RAS software. These were validated with measured water height values and found to be very accurate. After this validation, simulations are performed using the Digital Elevation Models collected after the flood and we found that a similar rainfall event on the new terrain would cause even worse flooding, with water depths between 29% and 105% higher. These simulations could be used to guide recovery efforts as well as aid response efforts for any future events. Finally, we look at performing semantic segmentation on the collected aerial imagery to assess damage to property from the flood event. While our segmentation of debris needs more work, it has potential to help determine the extent of damage and aid disaster response. Based on our investigation, the combination of techniques presented in this article has significant potential to aid in preparation, response, and recovery efforts for natural disasters.
Comparative Analysis of Binhu and Cosmic-2 Radio Occultation Data
Authors: Zhang Hongjie , Huangfu Jingliang , Wang Xingbao , Chen Wen , Peng Wenwu , Tang Qi , Chu Yiqi , Xue Ziyue
Published: October 2022

Global Navigation Satellite System (GNSS) radio occultation (RO) technology has been widely used in Earth atmospheric detection and has a significant impact on numerical weather prediction (NWP), climate detection, and other fields. Cosmic-2 LEO-1 (C2E1) is a well-known RO data provider; however, its observations are confined to 45°S and 45°N. Recently, the Binhu meteorological observation test satellite (BH) has provided global coverage of RO data, including refractivity, specific humidity, and temperature data. In this study, RO data from BH and C2E1 are analyzed and compared from 8 February 2022 to 17 February 2022. Employing the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) data as a reference, both BH and C2E1 RO data agree with the ERA data, with the refractivity, temperature, and specific humidity profiles reflecting the real conditions of the natural atmosphere. In addition, BH data are comparable to C2E1 data at low and middle latitudes (0–45°), and BH data at middle and high latitudes (45–90°) are of better quality than those at low and middle latitudes (0–45°). For example, without considering the errors introduced by the interpolation of the ERA-5 data for comparative analysis, the BH refractivity profiles show a mean absolute bias of 0.73 N at low and middle latitudes and only 0.23 N at middle and high latitudes, and that for BH specific humidity profiles at middle and high latitudes is 0.015 g/kg, which is only half of that at low and middle latitudes. The BH temperature and specific humidity data show promising data accuracy. Therefore, BH RO data may provide important supplementary data at higher latitudes and may improve future NWPs through assimilation.
A Novel Lidar Signal-Denoising Algorithm Based on Sparrow Search Algorithm for Optimal Variational Modal Decomposition
Authors: Li Zhiyuan , Li Shun , Mao Jiandong , Li Juan , Wang Qiang , Zhang Yi
Published: October 2022

Atmospheric lidar is susceptible to the influence of light attenuation, sky background light, and detector dark currents during the detection process. This results in a large amount of noise in the lidar return signal. To reduce noise and extract a useful signal, a novel denoising method combined with variational modal decomposition (VMD), the sparrow search algorithm (SSA) and singular value decomposition (SVD) is proposed. The SSA is used to optimize the number of decomposition layers K and the quadratic penalty factor α values of the VMD algorithm. Some intrinsic mode function (IMF) components obtained from the VMD-SSA decomposition are grouped and reconstructed according to the interrelationship number selection criterion. Then, the reconstructed signal is further denoised by combining the strong noise-reduction ability of SVD to obtain a clean lidar return signal. To verify the effectiveness of the VMD-SSA-SVD method, the method is compared and analysed with wavelet packet decomposition, empirical modal decomposition (EMD), ensemble empirical modal decomposition (EEMD), and adaptive noise-complete ensemble empirical modal decomposition (CEEMD), and its noise-reduction effect is considerably improved over that of the other four methods. The method can eliminate the complex noise in the lidar return signal while retaining all the details of the signal. The signal is not distorted, the waveform is smoother, and far-field noise interference can be suppressed. The denoised signal is closer to the real signal with higher accuracy, which shows the feasibility and the practicality of the proposed method.
Desert Soil Salinity Inversion Models Based on Field In Situ Spectroscopy in Southern Xinjiang, China
Authors: Wang Yu , Xie Modong , Hu Bifeng , Jiang Qingsong , Shi Zhou , He Yinfeng , Peng Jie
Published: October 2022

Soil salinization is prominent environmental issue in arid and semi-arid regions, such as Xinjiang in Northwest China. Salinization severely restricts economic and agricultural development and would lead to ecosystem degradation. Finding a method of rapidly and accurately determining soil salinity (SS) is one of the main challenges in salinity evaluation, saline soil development, and utilization. In situ visible and near infrared (Vis-NIR) spectroscopy has proven to be a promising technique for detecting soil properties since it can realize real-time, rapid detection of SS. However, it still remains challenging whether Vis-NIR in situ spectroscopy can invert SS with high accuracy due to the interference of environmental factors (e.g., light, water vapor, solar altitude angle, etc.) on the spectral in the field. To fill this knowledge gap, we collected Vis-NIR in situ spectral and lab-measured SS data from 135 surface soil samples in the Kongterik Pasture Nature Reserve (KPNR) in the desert oasis ecotone of southern Xinjiang, China. We used genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithms to select the feature bands of SS. Subsequently, we combined extreme learning machines (ELM), back-propagation neural networks (BPNN), and convolutional neural networks (CNN) to build inversion models of SS. The results showed that different feature bands selection methods could improve the Vis-NIR in situ spectral prediction model accuracy. Either SS inversion models were built using full-band spectral data or feature-band spectral data. Compared with the full-band (401–2400 nm) spectral modeling, the validation set R2 of ELM, BPNN, and CNN models built selected feature bands selected by PSO, GA, and SA, respectively, were improved by more than 0.06. The accuracy of predicting SS varied widely among modeling methods. The accuracy of CNN model was obviously higher than that of BPNN and ELM models. The optimal hybrid model for predicting SS constructed in this study is SA-CNN model (R2 = 0.79, RMSE = 9.41 g kg−1, RPD = 1.81, RPIQ = 2.37). This study showed that the spectral feature bands selection methods can reduce the influence of environmental factors on in situ spectroscopy and significantly enhance the inversion accuracy of SS. The present study provided that estimating SS using in situ Vis-NIR spectral is feasible.
Monitoring Mesoscale to Submesoscale Processes in Large Lakes with Sentinel-1 SAR Imagery: The Case of Lake Geneva
Authors: Hamze-Ziabari Seyed Mahmood , Foroughan Mehrshad , Lemmin Ulrich , Barry David Andrew
Published: October 2022

As in oceans, large-scale coherent circulations such as gyres and eddies are ubiquitous features in large lakes that are subject to the Coriolis force. They play a crucial role in the horizontal and vertical distribution of biological, chemical and physical parameters that can affect water quality. In order to make coherent circulation patterns evident, representative field measurements of near-surface currents have to be taken. This, unfortunately, is difficult due to the high spatial and temporal variability of gyres/eddies. As a result, few complete field observations of coherent circulation in oceans/lakes have been reported. With the advent of high-resolution satellite imagery, the potential to unravel and improve the understanding of mesoscale and submesoscale processes has substantially increased. Features in the satellite images, however, must be verified by field measurements and numerical simulations. In the present study, Sentinel-1 SAR satellite imagery was used to detect gyres/eddies in a large lake (Lake Geneva). Comparing SAR images with realistic high-resolution numerical model results and in situ observations allowed for identification of distinct signatures of mesoscale gyres, which can be revealed through submesoscale current patterns. Under low wind conditions, cyclonic gyres manifest themselves in SAR images either through biogenic slicks that are entrained in submesoscale and mesoscale currents, or by pelagic upwelling that appears as smooth, dark elliptical areas in their centers. This unique combination of simultaneous SAR imagery, three-dimensional numerical simulations and field observations confirmed that SAR imagery can provide valuable insights into the spatial scales of thus far unresolved mesoscale and submesoscale processes in a lake. Understanding these processes is required for developing effective lake management concepts.
Simulation Calculation of Element Number Density in the Earth’s Atmosphere Based on X-ray Occultation Sounding
Authors: Yu Daochun , Li Baoquan
Published: October 2022

The number density profiles of elements N and O in the altitude range of 120–250 km are retrieved by simulation based on X-ray occultation. Based on the parameters of the NICER telescope, the energy spectrum forward model of the Crab Nebula in the energy range of 0.25–8 keV during the occultation is constructed, and the energy spectrum simulation data are obtained by adding noise to the energy spectrum forward model at different tangent point altitudes. The NICER energy band includes the K-shell absorption edges (0.4 keV, 0.53 keV for N, O), and there are significant differences in X-ray cross sections at the K-shell absorption edges, which provides an opportunity to retrieve the atmospheric density of each element. The MCMC algorithm is used to fit the energy spectrum forward model and simulation data, and the density profiles of elements N and O are retrieved. It is found that the retrieved error of O element in the altitude range of 120–140 km is large, which may be related to the low proportion of O in the line of sight and the low signal-to-noise ratio of simulation data. In the altitude range of 140–200 km, the retrieved error of elements N and O is small, but in the altitude range of 200–250 km, the retrieved error of elements N and O becomes larger, and the inconsistency between the retrieved results and NRLMSISE-00 model values increases. This is because the number of absorbed photons is reduced due to the thin atmospheric density at higher altitude, which introduces great uncertainty into the retrieved results. This study lays a foundation for element density retrieval based on X-ray occultation measured data in the future.
Features of the Extreme Fire Season of 2021 in Yakutia (Eastern Siberia) and Heavy Air Pollution Caused by Biomass Burning
Authors: Tomshin Oleg , Solovyev Vladimir
Published: October 2022

Yakutia (Eastern Siberia) is one of the most fire-prone regions of Russia, which is frequently affected by large-scale wildfires despite a relatively short warm period, which usually lasts from May to September. In 2021, Yakutia experienced the worst fire season over the last four decades. In this study, we investigate features of the extreme fire season, factors that promote extreme fire weather, and heavy air pollution caused by biomass burning in the region utilizing multiple satellite and ground-based observations along with reanalysis data and forward-trajectory modelling. The results demonstrate that the total number of hotspots (HS) in 2021 amounted to ~150,000, which is almost twice as much as the previous record year (2020). One of the main features of the 2021 fire season was the period of extensive growth of the number of HS, which occurred from 24 July to 12 August. High fire danger during the fire season was promoted by positive anomalies in monthly air temperature (August) and negative anomalies in monthly precipitation (May–July). August of 2021 in central Yakutia was the second most hot August (14.9 °C) during a 43-year NCEP-DOE Reanalysis record (1979–2021). In addition, the intensification of wildfires during August 2021 was associated with persistent high-pressure systems, which promoted dry weather conditions in the region by blocking the transport of moist air masses from the western part of Russia. The low wind speeds, observed in the center of a high-pressure system, led to the accumulation of wildfire emissions in the atmosphere, which significantly affect air quality in the region. The monthly mean aerosol optical depth values in July 2021 were 0.82 (MODIS MAIAC) and 1.37 (AERONET) which were 14.9 and 18.7 times higher than respective values of 2007 (the year with minimal wildfires in the Asian part of Russia and Yakutia). Based on aerosol index observations and forward trajectories, we demonstrate that smoke plumes originated from the study area were transported over long distances reaching the Ural Mountains in the west, Mongolia in the south, the North Pole in the north, and Alaska in the east, traveling the distances of ~2000–7000 km. Maximum spatial extent of the smoke plumes reached ~10–12 mln. km2.
A Random Forest Model for Drought: Monitoring and Validation for Grassland Drought Based on Multi-Source Remote Sensing Data
Authors: Wang Qian , Zhao Lin , Wang Mali , Wu Jinjia , Zhou Wei , Zhang Qipeng , Deng Meie
Published: October 2022

The accuracy of drought monitoring models is crucial for drought monitoring and early warning. Random forest (RF) is being used widely in the field of artificial intelligence. Nonetheless, the application of a random forest model in grassland drought monitoring research is yet to be further explored. In this study, various drought hazard factors were integrated based on remote sensing data, including from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Global Precipitation Measurement (GPM), as multisource remote sensing data. Based on the RF, a comprehensive grassland drought monitoring model was constructed and tested in Inner Mongolia, China, as an example. The critical issue addressed is the construction of a grassland drought disaster monitoring model based on meteorological data and multisource remote sensing data by using an RF model, and the verification of the accuracy and reliability of its monitoring results. The results show that the grassland drought monitoring model could quantitatively monitor the drought situation in Inner Mongolia grasslands. There was a significantly positive correlation between the drought indicators output by the model and the standardized precipitation evapotranspiration index (SPEI) measured in the field. The correlation coefficients (R) between the drought degree were 0.9706 and 0.6387 for the training set and test set, respectively. The consistent rate between the model drought index and the SPEI reached 87.90%. Drought events in Inner Mongolia were monitored from April to September in wet years, normal years, and dry years using the constructed model. The monitoring results of the model constructed in this study were in accordance with the actual drought conditions, reflecting the development and spatial evolution of drought conditions. This study provides a new application method for the comprehensive assessment of grassland drought.
Detection of Peanut Leaf Spot Disease Based on Leaf-, Plant-, and Field-Scale Hyperspectral Reflectance
Authors: Guan Qiang , Song Kai , Feng Shuai , Yu Fenghua , Xu Tongyu
Published: October 2022

Leaf spot (LS) caused by Cercosporidium personatum is one of the most harmful peanut diseases in the late growth stage and severely affects the yield of peanuts. Hyperspectral disease detection technology is efficient, objective, and accurate and is suitable for large-scale crop management practices. To establish a multi-scale spectral index (SI) with high accuracy and stability for the detection of peanut LS disease, the spectral reflectance of different disease severity levels at leaf, plant, and field scales was collected, and the difference in wavelength caused by disease severity was analyzed using the mean, variance, and dispersion matrix of hyperspectral reflectance. Meanwhile, the feature weights at different scales were obtained using Relief-F, and the average feature weights identified 540, 660, and 770 nm as multi-scale sensitive wavelengths. Three new SIs were constructed by combining single, ratiometric, and normalized wavelengths. The new SIs were compared and analyzed with 35 commonly used SIs by correlation analysis and M-statistic values, and 6 SIs were significantly correlated with disease severity levels and had good separability. Finally, k-nearest neighbor (KNN) and multinomial logistic regression (MLR) were used to evaluate the ability of the above SIs to detect LS severity. The results showed that the leaf spot multi-scale spectral index (LS-MSSI) constructed in this study was superior to the other SIs and obtained high accuracy at different scales simultaneously. At the leaf and plant scales, the MLR obtained high accuracy, with the overall accuracy (OA) reaching 93.77% and 92.50% and Kappa reaching 91.59% and 89.97%, respectively. At the field scale, the KNN obtained high accuracy, with the OA and Kappa reaching 90.29% and 87.04%, respectively. The LS-MSSI proposed in this study has high accuracy, stability, and robustness in the detection of LS severity at multiple scales, providing a technical basis and scientific guidance for the detection and precise management of peanuts.
The Quantitative Impact of the Arable Land Protection Policy on the Landscape of Farmland Abandonment in Guangdong Province
Authors: Li Le , Zheng Siyan , Zhao Kefei , Shen Kejian , Yan Xiaolu , Zhao Yaolong
Published: October 2022

In the past two decades, the Ministry of Agriculture and Rural Affairs of China (MARA) has issued a series of strict cultivated land protection policies to prevent the spread of farmland abandonment and maintain a dynamic balance between the quantity and quality of arable land. However, high-speed economic development, strict arable land protection policies, and ecological security and sustainable development strategies interacting with human activities have brought challenges to quantifying the effectiveness of arable land protection policies. In this study, we proposed a method to quantify the impacts of the arable land protection policies and evaluate the quantitative impacts on farmland abandonment in Guangdong Province after 2014 from the perspective of landscape ecology. The results illustrated that the landscape fragmentation of farmland abandonment in Guangdong Province decreased after the new arable land policies were issued. More annual farmland abandonment (AFA) shifted to seasonal farmland abandonment (SFA), revealing the considerable pronounced effects of farmland abandonment management. The new policies effectively restrained the area increase for AFA in the regions with lower rural population (RPOP) and lower gross domestic product (GDP), and reduced the fragmentation of AFA in the regions with the highest RPOP and lower GDP. Additionally, the new policies effectively restrained the fragmentation increase for SFA in the regions with lower RPOP and lower GDP, and reduced the area increase for SFA in the regions with the highest RPOP and lower GDP. The management effect was not that significant in the regions with higher RPOP and higher GDP. These findings will provide important data references for arable land decision making in southern China.
Phenological Changes and Driving Forces of Lake Ice in Central Asia from 2002 to 2020
Authors: Hou Guanyu , Yuan Xiuliang , Wu Shixin , Ma Xiaofei , Zhang Zihui , Cao Xingwen , Xie Conghui , Ling Qing , Long Weiyi , Luo Geping
Published: October 2022

Lake ice phenology is an indicator of past and present climate, it is sensitive to regional and global climate change. In the past few decades, the climate of Central Asia has changed significantly due to global warming and anthropogenic activities. However, there are few studies on the lake ice phenology in Central Asia. In this study, the lake ice phenology of 53 lakes in Central Asia were extracted using MODIS daily LST products from 2002 to 2020. The results show that MODIS-extracted lake ice phenology is generally consistent with Landsat-extracted and AVHRR-extracted lake ice phenology. Generally, lakes in Central Asia start to freeze from October to December. The trends in the lake ice phenology show strong regional differences. Lakes distributed along the Kunlun Mountains show overall delayed trends in all lake ice phenology variables, while lakes located in southwestern Central Asia show clear advancing trends in the freeze-up start dates (7.06 days) and breakup end dates (6.81 days). Correlations between the phenology of lake ice and local and climatic factors suggest that the ice breakup process and the duration of its complete coverage depend more on heat, while precipitation mainly affects the freezing time of the ice. Wind speed mainly affects the time of completely frozen of ice. In general, the breakup process is more susceptible to climatic factors, while local factors have strong influences on the freeze-up process.
Influence of the Accuracy of Chlorophyll-Retrieval Algorithms on the Estimation of Solar Radiation Absorbed in the Barents Sea
Authors: Glukhovets Dmitry , Sheberstov Sergey , Vazyulya Svetlana , Yushmanova Anna , Salyuk Pavel , Sahling Inna , Aglova Evgeniia
Published: October 2022

The aim of this work is to study the influence of errors on the accuracy of estimation of absorbed solar energy in the visible spectral range in the water column of the Barents Sea occurring due to the use of various satellite ocean color chlorophyll-retrieval algorithms. The estimates of absorbed energy are based on data obtained during field experiments conducted in various parts of the Barents Sea in June-August 2021, as well as on modeling results. A new regional algorithm for evaluation of chlorophyll concentration in the Barents Sea (B22) is presented as a data source. This algorithm provides more accurate estimates compared to the standard MODIS algorithm under conditions of coccolithophore bloom. Comparing of the results of hydro-optical modeling to the data of shipboard hydro-optical measurements made it possible to validate the obtained data and confirmed the correctness of the selected models. Furthermore, we calculate the parameters of underwater light fields and the absorbed thermal energy in the visible spectral range in the seawater column, showing that differences in the chlorophyll concentration due to the accuracy of satellite bio-optical algorithms (30–50%) have little effect on the vertical distribution of solar energy absorbed in the seawater column.
Regional Monitoring of Fall Armyworm (FAW) Using Early Warning Systems
Authors: Buchaillot Ma. Luisa , Cairns Jill , Hamadziripi Esnath , Wilson Kenneth , Hughes David , Chelal John , McCloskey Peter , Kehs Annalyse , Clinton Nicholas , Araus José Luis , Kefauver Shawn C.
Published: October 2022

The second United Nations Sustainable Development Goal (SDG2), zero hunger, aims to improve the productivity, food security, nutrition, and sustainability of small-scale farmers. The fall armyworm (FAW, Spodoptera frugiperda) has been devasting to smallholder farmer food security since it spread to sub-Saharan Africa in 2016, who have suffered massive crop losses, particularly maize, an important staple for basic sustenance. Since the FAW mainly devours green leaf biomass during the maize vegetative growth stage, the implementation of remote sensing technologies offers opportunities for monitoring the FAW. Here, we developed and tested a Sentinel 2 a+b satellite-based monitoring algorithm based on optimized first-derivative NDVI time series analysis using Google Earth Engine. For validation, we first employed the FAO Fall Armyworm Monitoring and Early Warning System (FAMEWS) mobile app data from Kenya, and then subsequently conducted field validation campaigns in Zimbabwe, Kenya, and Tanzania. Additionally, we directly observed loss of green biomass during maize vegetative growth stages caused by the FAW, confirming the observed signals of loss of the leaf area index (LAI) and the total green biomass (via the NDVI). Preliminary analyses suggested that satellite monitoring of small-scale farmer fields at the regional level may be possible with an NDVI first-derivative time series anomaly analysis using ESA Sentinel 2 a+b (R2 = 0.81). Commercial nanosatellite constellations, such as PlanetScope, were also explored, which may offer benefits from greater spatial resolution and return interval frequency. Due to other confounding factors, such as clouds, intercropping, weeds, abiotic stresses, or even other biotic pests (e.g., locusts), validation results were mixed. Still, maize biomass anomaly detection for monitoring the FAW using satellite data could help confirm the presence of the FAW with the help of expanded field-based monitoring through the FAO FAMEWS app.
Multi-Sensor Remote Sensing of Intertidal Flat Habitats for Migratory Shorebird Conservation
Authors: Lathrop Richard G. , Merchant Daniel , Niles Larry , Paludo Danielle , Santos Carlos David , Larrain Carmen Espoz , Feigin Stephanie , Smith Joseph , Dey Amanda
Published: October 2022

Many species of shorebirds migrate long distances from their overwintering grounds in the southern hemisphere to breeding grounds in the northern hemisphere. The coastal intertidal zone, consisting of sand and mud flats exposed at low tide and covered at high tide, is heavily used as a migratory stopover or overwintering habitat. Understanding the spatial distribution of sediment types at these stopover sites is a critical step for understanding habitat use by shorebird species. Due to their importance as overwintering and stopover habitat for the imperiled western Atlantic subpopulation of the shorebird, the red knot (Calidris canutus rufa), as well as other migratory shorebirds, the northern coast of Brazil between Pará and Maranhão, and Bahía Lomas in northern Tierra del Fuego, Chile, were selected for further investigation as to the applicability of remotely sensed characterization of the intertidal flat habitats. Examination of the Landsat 8 multispectral reflectance and Sentinel-1 SAR backscatter reveals that sand and mud represent endmembers at opposite ends of a continuous gradient in feature space. While remotely sensed data can be used to discriminate between mud and sand intertidal types, the spectral relationships varied between the two very different geographic locations. The inclusion of both multispectral and radar sensing imagery can lead to important insights about the physical properties of the sediment that would be omitted by using one data source alone. Spectral unmixing techniques in Google Earth Engine were used to map the intertidal zone into general sediment classes spanning the gradient (i.e., mud, sandy mud, muddy sand, and sand). Comparison of the mapped outputs with field reference data suggests that mapping of mud- vs. sand-dominated areas can be accomplished with reasonable accuracy (overall accuracy of 75%).
Radar and Communication Spectral Coexistence on Moving Platform with Interference Suppression
Authors: Qian Junhui , Liu Ziyu , Lu Yuanyuan , Zheng Le , Zhang Ailing , Han Fengxia
Published: October 2022

With the development of intelligent transportation, radar and communication on moving platforms are competing for the spectrum. In this paper, we propose and demonstrate a new algorithmic framework for radar-communication spectral coexistence system on moving platform with mutual interference suppression, in which communication rate and the radar signal-to-interference-plus-noise ratio (SINR) are simultaneously optimized, under the energy constraints for the two systems and the radar constant modulus constraint. The radar spatial-temporal filter at the receiver and transmitting waveform are optimized, while the codebook matrix is optimized for the communication system. To cope with the established non-convex problem with triplet variables, we decouple the original problem into multiple subproblems, for which an alternating algorithm based on iterative procedures is derived with lower computational complexity. Specifically, the subproblems of communication codebook and radar filter design are convex and the closed-form solutions can be easily obtained, while the radar waveform optimization is non-convex. Then we propose a novel scheme by exploiting the alternating direction method of multipliers (ADMM) based on minorization-maximization (MM) framework. Finally, to reveal the effectiveness of the proposed algorithm in different scenarios, numerical results are provided.
Field Reflectance Measurements at Night of Beach and Desert Sands within a Particulate BRDF Model
Authors: Wise John E. , Mars John C.
Published: October 2022

The Bidirectional Reflectance Distribution Function (BRDF) of soils is frequently assumed to be Lambertian in remote sensing applications, even though many soils are observed to have a distinct backscatter. This is largely due to the difficulty of obtaining accurate in-situ BRDF of these materials over the entire hemisphere. The objective of the work presented here is to describe a new technique that combines the measurement of field BRDF data on desert and beach sands measured at night with a model that reproduces the backscatter except in a small region around zero phase angle. It is found that this combination produces parameterized BRDF of sufficient accuracy to allow its inclusion in a remote sensing materials database. The measurement technique was developed in the laboratory and transitioned to perform in-plane BRDF field measurements of sands at night. Field measurements were obtained using a portable light source and field spectrometer over the range of 0.4–2.4 micrometers. The spectrometer sensor optics and light source were mounted on a portable, manually operated goniometer allowing in-plane measurements to within 3 degrees of backscatter. Unlike daytime measurements, corrections for background were unnecessary, and BRDF was obtained by referencing to a Spectralon calibration standard. Analysis of the spectrometer field data used a variant of the Shadow Hiding Opposition Effect (SHOE) wherein a series expansion of Legendre Polynomials was substituted for the angularly dependent backscatter function. It is shown that when the out-of-plane scattering and the backscatter peak are small, this model provides an adequate description of the BRDF. The combination of the nocturnal in-situ measurements with an analysis using a model that can describe the backscatter produces parameterized BRDF for these sands of sufficient accuracy to allow inclusion in the NEFDS materials database. Measurement and analysis techniques are assessed, sample results for both the laboratory and field data are shown, and the extension to future soils measurements are discussed.
Spatial Representativeness of Eddy Covariance Measurements in a Coniferous Plantation Mixed with Cropland in Southeastern China
Authors: Xiang Wei , Rong Xingxing , Yan Wei , Qi Xiaowen , Wang Hesong , Jin Shaofei , Ai Jinlong
Published: October 2022

The eddy covariance (EC) technique has been widely used as a micrometeorological tool to measure carbon, water and energy exchanges. When utilizing the EC measurements, it is critical to be aware of the long-term information on source areas. In China, large-scale forest plantations have become a dominant driver of greening and carbon sinks on the planet. However, the spatial representativeness of EC measurements on forest plantations is still not well understood. Here, an EC flux site of a coniferous plantation mixed with cropland in a subtropical monsoon climate was selected to evaluate the spatial representativeness of the two approaches. One is the fraction of target vegetation type (FTVT), which was used to detect to what degree the flux is related to the target vegetation. The other is the sensor location bias calculated from the enhanced vegetation index (EVI), which was used to detect to what spatial extent the flux can be upscaled. The results showed that the monthly footprint climatologies changed intensely throughout the year. The source area is biased toward the southeast in summer and northwest in winter. The study area was mainly a composite of coniferous plantations (70.08%) and double-cropped rice (27.83%). The double-cropped rice, with a higher seasonal variation of EVI than the coniferous plantation, was mainly distributed in the eastern areas of the study site. As a result of spatial heterogeneity and footprint variation, the FTVT was 0.89 when the wind direction was southwest; however, this reduced to 0.65 when the wind direction changed to the northeast and exhibited a single-peak seasonal variation during a year. The sensor location bias of the EVI also showed a significant monthly variation and ranged from −14.21% to 19.04% in a circular window with an increasing size from 250 to 3000 m. The overlap index between daytime and nighttime (Oday_night) can potentially be a quality flag for the GPP derived from the EC flux data. These findings demonstrate the joint effects of the monsoon climate and underlying surface heterogeneity on the spatial representativeness of the EC measurements. Our study highlights the importance of having footprint awareness in utilizing EC measurements for calibration and validation in monsoon areas.
Coastal Waveform Retracking for HY-2B Altimeter Data by Determining the Effective Trailing Edge and the Low Noise Leading Edge
Authors: Hong Zhiheng , Yang Jungang , Liu Shanwei , Jia Yongjun , Fan Chenqing , Cui Wei
Published: October 2022

As an important remote sensing technology, satellite altimetry provides a large amount of observations of sea surface height over the global ocean. In coastal areas, the accuracy of satellite altimetry data decreases greatly due to issues arise in the vicinity of land, related to poorer geophysical corrections and artifacts in the altimeter reflected signals linked to the presence of land within the instrument footprint. To improve the application of HY-2B altimetry data in coastal areas, this study proposes a coastal waveform retracking strategy for HY-2B altimetry mission, which depends on the effective trailing edge and the leading edge, which are less affected by coastal ‘contamination’, to retrieve accurate waveform information. The HY-2B pass 323 and pass 196 data are reprocessed, and the accuracy of the reprocessing results in the range of 0–40 km offshore is validated against the tide gauge data and compared with the HY-2B standard SGDR data. According to the analysis conclusion, the accuracy of the reprocessed data is higher than that of the SGDR data and has good performance within 15 km offshore. For the pass 323, the mean value of correlation coefficient and RMS of the reprocessed data against the corresponding tide gauge data are 0.893 and 45.1 cm, respectively, in the range within 0–15 km offshore, and are 0.86 and 33.6 cm, respectively, in the range beyond 15 km offshore. For the pass 196, the mean value of correlation coefficient and RMS of the reprocessed data against the corresponding tide gauge data in the range within 0–12 km offshore are 0.84 and 33.0 cm, respectively, and in the range within 0–5 km offshore to the island are 0.90 and 29.3 cm, respectively, and in the range beyond 5 km offshore to the island are 0.92 and 36.2 cm, respectively, which are all better than the corresponding values of the SGDR data, especially in the range closed to the land. The results indicate that the proposed coastal waveform retracking strategy for HY-2B altimetry greatly improves the quality of HY-2B altimetry data in coastal areas.
CNN-Enhanced Heterogeneous Graph Convolutional Network: Inferring Land Use from Land Cover with a Case Study of Park Segmentation
Authors: Liu Zhi-Qiang , Tang Ping , Zhang Weixiong , Zhang Zheng
Published: October 2022

Land use segmentation is a fundamental yet challenging task in remote sensing. Most current methods mainly take images as input and sometimes cannot achieve satisfactory results due to limited information. Inspired by the inherent relations between land cover and land use, we investigate land use segmentation using additional land cover data. The topological relations among land cover objects are beneficial for bridging the semantic gap between land cover and land use. Specifically, these relations are usually depicted by a geo-object-based graph structure. Deep convolutional neural networks (CNNs) are capable of extracting local patterns but fail to efficiently explore topological relations. In contrast, contextual relations among objects can be easily captured by graph convolutional networks (GCNs). In this study, we integrated CNNs and GCNs and proposed the CNN-enhanced HEterogeneous Graph Convolutional Network (CHeGCN) to incorporate local spectral-spatial features and long-range dependencies. We represent topological relations by heterogeneous graphs which are constructed with images and land cover data. Afterwards, we employed GCNs to build topological relations by graph reasoning. Finally, we fused CNN and GCN features to accomplish the inference from land cover to land use. Compared with other homogeneous graph-based models, the land cover data provide more sufficient information for graph reasoning. The proposed method can achieve the transformation from land cover to land use. Extensive experiments showed the competitive performance of CHeGCN and demonstrated the positive effects of land cover data. On the IoU metric over two datasets, CHeGCN outperforms CNNs and GCNs by nearly 3.5% and 5%, respectively. In contrast to homogeneous graphs, heterogeneous graphs have an IoU improvement of approximately 2.5% in the ablation experiments. Furthermore, the generated visualizations help explore the underlying mechanism of CHeGCN. It is worth noting that CHeGCN can be easily degenerated to scenarios where no land cover information is available and achieves satisfactory performance.
Investigation of Absorption Bands around 3.3 μm in CRISM Data
Authors: Manzari Paola , Marzo Cosimo , Ammannito Eleonora
Published: October 2022

Absorptions in the range 3.1 μm to 3.6 μm are under the spotlight in the context of planetary research, because hydrocarbon molecules show absorption bands in this range. Consequently, even knowing that the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) was designed for the detection of mineralogical features on Mars’s surface, we exploited CRISM data in the range 3.2 μm to 3.4 μm to search for potential hydrocarbon compounds. To date, methane has been the only hydrocarbon detected on Mars. Therefore, we began our investigation into CRISM data in locations in which methane had been detected and where it could form due to the mineralogy of the specific site. The datasets chosen for this study included observation sites in the Oxia Planum, the Gale Crater, and Nili Fossae areas. We mapped the modified Gaussian model (MGM) to fit the CRISM data in order to extract the band parameters of the absorptions in the 3.3 μm spectral region. As a result, we found clusters of pixels with spectra that exhibited band centers between approximately 3.28 and 3.35 μm. The hydrocarbons showing absorptions in this range included polycyclic aromatic compounds as well as methane, ethane, and aliphatic compounds. We speculated that some absorptions of approximately 3.3 μm could be related to methane, so we calculated a theoretical lower limit of detection for each observation in the selected CRISM datasets. This was performed by simulating the CRISM spectra for the different sites, with diverse concentrations of CH4, using NASA’s Planetary Spectrum Generator online tool. These simulations established the relationship between the concentration and methane band depths, as detected by the CRISM. Methane band depths exceeding the thresholds varied from one observation to another, in the range of 0.0136 to 0.0237, which corresponded to a range of theoretically lower limits of concentration between 180 and 600 ppbv. Although we could not confirm or deny the occurrences of methane seepages or hydrocarbons in the investigated datasets, we demonstrated a possible method for searching for hydrocarbons in other CRISM data and for assessing a confidence limit in the detection of the methane band in CRISM data.
Research on the Measurement Accuracy of Shipborne Rayleigh Scattering Lidar
Authors: Chen Zhifang , Yan Zhaoai , Zhang Bingyan , Hu Xiong , Cheng Xuan , Guo Wenjie
Published: October 2022

This paper aims to study the measurement accuracy of Rayleigh scattering lidar (light detection and ranging) based on a ship platform and analyze the influence of the laser beam uncertainty on the temperature inversion results. Taking the ship platform roll data as a reference, the Rayleigh scattering lidar oscillating model is simplified to a sine function, and the inversion accuracy of atmospheric temperature is analyzed under different settled observation angles and different roll angles. When the settled observation angle is 0° and the roll angle amplitudes are 10°, 20°, and 30°, the maximum deviations of the temperature within the height range of 30–80 km are 3.47 K, 13.73 K, and 22.78 K, respectively, and the average deviations are 2.35 K, 9.09 K, and 12.95 K, respectively. When the observation angle is set to 30° and the roll angle amplitudes are 10°, 20°, and 30°, the maximum deviations of the temperature within the height range of 30–80 km are 11.75 K, 27.49 K, and 53.50 K, respectively, and the average deviations are 11.05 K, 13.88 K, and 16.12 K, respectively. The results of this paper show that ship platform rolling greatly influences the measurement of atmospheric temperature, which provides a certain data reference for the construction and use of Rayleigh scattering lidar in the ship platform.
Correction: Chen et al. High-Precision Stand Age Data Facilitate the Estimation of Rubber Plantation Biomass: A Case Study of Hainan Island, China. Remote Sens. 2020, 12, 3853
Authors: Chen Bangqian , Yun Ting , Ma Jun , Kou Weili , Li Hailiang , Yang Chuan , Xiao Xiangming , Zhang Xian , Sun Rui , Xie Guishui , Wu Zhixiang
Published: October 2022

In the original article [...]