最新刊期
卷 24 ， 期 10 ， 2020
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摘要：Atmospheric ultraspectral sounder (AIUS) of GF-5 satellite was successfully launched in May 2018. AIUS is the first very high-resolution infrared-band occultation observation sensor developed in China that provides strong data support for investigating atmospheric distribution state. Air temperature is an important parameter that characterizes the thermodynamic state of the atmosphere. Its distribution state directly affects the interaction between long- and short-wave radiation of the earth-atmosphere system and subsequently influences global balance of radiation energy. Direct inversion of temperature using hyperspectral data is limited by its large size, inconvenient storage, and requirement of channel selection due to the correlation between different spectral information. The strong influence of interfering components on the accuracy of actual air temperature inversion results in low inversion accuracy. Channel selection algorithm based on information entropy is combined with sensitivity analysis of target and interfering components in this study to carry out experiments, complete the channel selection of AIUS occultation observation data, and provide a theoretical foundation for future AIUS temperature inversion investigations. First, the sensitivity analysis of target and interference components based on the RFM model is conducted to explore the feasibility of channel selection and perform preliminary channel selection. Second, channel selection is carried out on the basis of information entropy theory, and the results are analyzed and discussed. Finally, the temperature inversion effect is verified using optimization algorithm on the basis of the channel selection results. Occultation observation is highly sensitive to temperature changes, and channel selection is feasible in the occultation observation mode. The increase in number of channels logarithmically increases the amount of information and is close to saturation when the number of channels is equal to 1000. One hundred AIUS channels are selected for temperature inversion to improve operational efficiency and meet precision requirements.关键词：remote sensing;GF-5;AIUS;occultation detection;temperature;sensitivity;channel;information entropy- 682
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发布时间：2020-12-01 -
摘要：The application of red edge features, which are sensitive bands of vegetation, is a high-technology method for remote sensing to identify crops and realize precision agriculture. Multispectral GF-6 image of the study area in the northern region of Songnen Plain in Heilongjiang Province pioneers the use red edge bands in China. A total of 82859 crop samples of corn, soybean, and rice were used as research objects. The classification accuracy of crops was evaluated and the performance of red edge features in crop identification, such as red edge bands and vegetation index, was discussed from the following aspects. (1) Statistical characteristics of radiance values of crop samples initially showed that discrimination is better at Band 5-0.710 μm and Band 6-0.750 μm in the two red edge bands than findings of other GF-6 bands. (2) Traditional normalized (NDVI) and red-edge normalized difference vegetation indexes, namely, NDVI710 and NDVI750, are constructed. Results showed that the two indexes are more significant than the traditional NDVI in the classification of crop samples characterized using J-M distance. (3) Effective bands are screened using various methods, and classification strategies for the four types of crops are formulated using Support Vector Machine (SVM). Crop classification in the study area is completed using five sets of random sample segmentation schemes, namely, 5∶5, 6∶4, 7∶3, 8∶2, and 9∶1. Twenty types of classification accuracy demonstrated a kappa coefficient higher than 0.9609 and overall accuracy higher than 0.9742. The 5∶5 and 8∶2 segmentation schemes in the column direction exhibited the highest and lowest accuracy, respectively. The sorting accuracy in the horizontal direction demonstrated the following order: SVM-RFE > SVM-RF > SVM with red edge bands > SVM without red edge band, which also showed that the participation of red edge vegetation index and red edge band significantly improves the recognition precision of crops. (4) SVM-RFE and SVM-RF both obtained minimal misclassifications due to the lack of other samples, such as waters. However, SVM-RFE is superior to SVM-RF in terms of classification accuracy and image detail display with a kappa coefficient and overall accuracy of 0.8060 and 0.8743, respectively, in the cross-validation of two classified images. Hence, the red edge feature of GF-6 is superior in crop recognition with its significantly improved recognition accuracy. Subsequent investigations can focus on developing additional red edge-related vegetation indexes and optimize the role of red edge characteristics in precision agriculture.关键词：remote sensing;GF-6 satellite;the red-edge band;Support Vector Machine (SVM);Random Forest method (RF);Recursive Feature Elimination method (RFE)- 1332
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发布时间：2020-12-01
Chinese Satellites
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摘要：Traditional methods of aerosol remote sensing inversion can achieve high accuracy in areas with low surface reflectance, homogeneous structures, and dense vegetation while facing serious challenges in highly bright and spatially heterogeneous areas, such as cities and mining areas. High land surface reflectance causes insufficient aerosol information acquired by sensors that can lead to difficulties in aerosol inversion. A remote sensing inversion method of Aerosol Optical Depth (AOD) using deep learning algorithm is proposed in this study to extract aerosol identification information from satellite signals further and apply it to Landsat 8 OLI data. Aerosol-measured data of Aerosol Robotic Network (AERONET) sites in different regions of the world and the corresponding geometric angle and apparent reflectance of the Landsat 8 OLI data are selected to construct sample data sets according to the reasonable space-time matching method. The Deep Belief Network (DBN) is applied to implement the proposed method. The network is trained and tested on the basis of the reasonable setting of training batches and times. An AOD fitted network model on satellite remote sensing information is then generated to achieve aerosol inversion. Inversions are verified using independent AERONET-measured data. The verification showed that the proposed method can realize AOD inversion with continuous spatial coverage over different surface types and high accuracy (R = 0.8745, RMSE = 0.0391, MAE = 0.0616, and EE = 87.94%). Compared with traditional approaches, the proposed method can achieve high-accuracy aerosol inversions using only single-phase satellite remote sensing data, simplify the steps of aerosol inversion, and improve the stability and time–space adaptability of the results.关键词：remote sensing;aerosol optical depth;remote sensing inversion;Landsat 8 OLI;deep learning;Deep Belief Network- 911
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发布时间：2020-12-01 -
摘要：Sea Surface Salinity (SSS) is a key element of marine dynamic environment that plays an important role in many sea gas interactions and ocean processes. At present, large-scale analysis of Indian and Pacific Oceans are limited and the majority of investigations are based on in situ observation or reanalysis data. As an SSS observation satellite, Aquarius has a unique advantage in analyzing the characteristics of SSS distribution. Therefore, this study aims to analyze distribution characteristics of surface salinity in the Indian and Pacific Oceans based on Aquarius and Argo data.New high-accuracy monthly- 661
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发布时间：2020-12-01 -
摘要：Extensive, timely, and accurate mapping of yield losses is critical and prerequisite in disaster prevention and reduction, agricultural insurance, and food security. Given the coarse resolution, poor generalization ability, low timeliness, and weak operability of traditional loss assessment method, we propose a new approach called Multiscale Disaster Loss Assessment (MDLA) by coupling crop model with remote sensing to assess yield loss rapidly with satellite images. A series of disaster scenarios was simulated using a calibrated crop model. Related results, including final yield and crop growing state variable LAI, were inputted into disaster datasets. A susceptibility model of disaster was then constructed. Finally, pixel-by-pixel yield loss was evaluated on the basis of the susceptibility model combined with high-resolution image with gridded disaster indices within the Google Earth Engine (GEE) platform.The new method was used to assess the impacts of chilling injury on maize by applying carefully calibrated CERES-Maize in Oroqen, Inner Mongolia Autonomous Region. We constructed the cold susceptibility model, which properly characterized the cold damage on maize yield, including three independent variables, LAI in two growing season windows and a cold index (cold degree days), and yield loss. We further mapped pixel-based maize yield losses together with Sentinel-2 data. Mapping results showed that CERES-Maize, once calibrated, can appropriately simulate the growth and development state of maize under various management and weather conditions with a phenology bias of < 3.3% and yield NRMSE of < 8.9%. Furthermore, impacts of chilling injury varies in cold type and occurring time due to the high susceptibility of maize at the peak growing period (emergence-silking and silking-graining filling). The MDLA method successfully estimated significant losses during cold years with an accuracy of 11.4%. Moreover, the recent cold event (occurred at 2018/08/09) reduced the maize yield by 23.7% and affected 1.86 × 104 ha of growing areas. The occurrence of more than 25% yield loss in high-altitude regions indicated that low temperature is a major threat on crop production in northeastern China.Our results indicated that MDLA is consistent with statistical regression, crop model simulation, and assimilation technology. Moreover, the advantages of MDLA are presented as follows: (1) The impact of disaster is appropriately characterized by combining remote sensing observation with simulated physiological states in crop models. (2) Processing the satellite image within the GEE platform significantly reduces the computing time of loss assessment. (3) Multiscale losses are mapped in a dynamic and operable way. This type of mapping can be performed not only in large-scale areas but also the county- or even field-scale regions. Our study can help decision-makers in reasonably preventing agricultural disasters and maintaining steady grain production while providing a more practical means for operational agricultural insurance.关键词：remote sensing;Multiscale Disaster Loss Assessment (MDLA);Google Earth Engine（GEE）;crop model;chilling injury index;maize- 706
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发布时间：2020-12-01
Doctor's Voice
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摘要：As a large agricultural country, China faces large-scale burning of crop stubble in the field during harvesting, post-harvesting, and pre-harvesting periods. In recent decades, Crop Residue Burning (CRB) played a noticeable role in the sudden and extreme haze episodes, which resulted in reducing atmospheric visibility and harming human health.On the regional or global scale, satellite remote sensing technology can offer a reliable fire data source that can resolve the significant in situ data gap. In this study, the MODerate Resolution Imaging Spectroradiometer active fire products, MOD14/MYD14, were employed from 2013 to 2017. Land cover data were used to extract crop residue burning spots by selecting active fires over farmlands in the daytime.Results showed that CRB spots were mainly distributed in the northeast, Sanjiang, Huang–Huai–Hai, middle-lower Yangtze, and Hetao plains, as well as Sichuan basin. As the largest contributor, the average annual CRB spot number in Northeast China reached 47.55% of that in China. From 2013 to 2017, the variation of the annual CRB spot number showed an “up-down-up” trend. The annual CRB spot number of China increased from 2013 to 2014, dramatically dropping from 2014 to 2016, and then slightly increased from 2016 to 2017. Compared with the figure of 2014, the total number of CRB spot in China in 2016 decreased by 34.48%. In the recent five years, the major regions over China with serious CRB were fixed because of rare changes in agricultural areas and activities. By calculating the differences in the annual CRB spot number between 2017 and 2013, the results indicated that the area of regions with negative difference was approximately two times of that with the positive difference. The regions with negative difference are in Henan and Anhui provinces, whereas the regions with negative difference are in Northeast China.Official policies played a heavy role in inhibiting crop residue burning. Henan Province in the Huang–Huai–Hai region and Anhui and Hubei provinces in the mid-low reaches of Yangtze River region are three typical provinces whose decrease in CRB numbers was significantly influenced by prohibitions of straw burning. In Henan province, the CRB was not improved before 2015 due to the limitation of policy implementation. However, the monthly CRB numbers in June and October of 2016 declined to 86.66% and 98.93% in June and October of 2015, respectively. This effect can be attributed to the combination of CRB prohibition, accountability mechanism, economic punishment, and increasing use of crop residue. Heilongjiang Province shows negative feedback in the prohibitions of straw burning. Although the monthly CRB number in October 2016 decreased to 40.9% compared with that in October 2015, the monthly CRB number rebounded remarkably in April 2017 because of the large yield of crop residue and the lack of crop residue use. Therefore, further strengthening of prohibitions of straw burning in major regions and in post- and pre-harvesting periods is necessary. To enhance the comprehensive utilization rate of crop residue, a sustainable strategy on crop residue recycling should be expanded in most regions of China. Moreover, more attention should be given to turn disposable crop residue into farmland as a replacement for fertilizers. The policy of “banning” and “using” is a long-term effective measure to control CRB.关键词：remote sensing;MODIS;Terra/Aqua;crop residue burning;spatio-temporal variations;policy effect;variance analysis;China- 778
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发布时间：2020-12-01 -
摘要：Land subsidence disasters in excessively exploited cultivation areas are caused by groundwater-dependent irrigation, which has a strong influence on the rapid decline of water table, especially in arid and semiarid regions. Many depression cones appeared in the agricultural districts in Junggar Basin, due to groundwater overexploitation, over developed crop production, and large water consumption. However, the land subsidence information in this basin is limited.Interferometric Synthetic Aperture Radar (InSAR) technology, which has emerged in recent years, has been widely used in monitoring agricultural irrigation settlement, benefiting from its high precision, high resolution and wide temporal and spatial coverage. Small Baseline Subset (SBAS) algorithm selects high-quality image pairs obtained via D-InSAR after setting the threshold of time base and space baseline. In order to investigate the characteristics and evolution of the surface deformation induced by groundwater-dependent irrigation, we obtained the deformation information on the southern margin of the Junggar Basin and northern foothills of the Tianshan Mountains using ENVISAT/ASAR data (descending and ascending tracks) covering Hutubi County from 2003 to 2010. Data were processed utilizing time series InSAR technology based on GAMMA software and SBAS-InSAR technology.Results from two different tracks are consistent, which show two subsidence funnels were formed in the field area after 2007. In the study area, the traditional irrigation method and clockwise irrigation system may cause average subsidence rates of 50 and 30 mm/y, respectively. The subsidence rate of the traditional irrigation method shows a linear trend over time, while that of the clockwise irrigation system is closely related to the season. In winter, the ground uplift in the area with clockwise irrigation is approximately 40 mm, which is significantly larger than that of the field area with the traditional irrigation method. In summer, the land subsidence rate caused by the clockwise irrigation system was 200 mm/y.Therefore, groundwater overexploitation in agricultural irrigation is the major factor of land subsidence. Moreover, surface deformation during non-irrigation period is caused by groundwater recharge. Long-term extraction of groundwater for agricultural irrigation induces not only ground displacement but also serious soil salinization, which are unfavorable for crop growth and soil self-repair. Therefore, changing irrigation methods and improving the utilization of water resources are crucial. Spatial and temporal variation characteristics of land subsidence can provide a theoretical reference for the sustainable development of water resources and agriculture.关键词：remote sensing;agricultural irrigation;surface deformation;InSAR;groundwater;Junggar Basin- 775
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发布时间：2020-12-01 -
摘要：Impervious surface information is an important indicator for monitoring urban expansion and regional ecological environment change. Extracting impervious surface information based on remote sensing technology is of great importance. The traditional large-scale impervious surface percentage estimation model is estimated by a single-factor regression model based on the correlation between the specific remote sensing information and the impervious surface ratio. Given the influence of the amount of information and universality of specific remote sensing information, these methods have considerable limitations in large-scale impervious surface extraction, and their regional adaptability of estimation results is slightly different. To address this problem, this study proposes a method for estimating the impervious surface based on multiple features remote sensing information to compensate for the uncertainty of single feature in large-scale impervious surface extraction. First, MOD13Q1, MOD09A1 products, nighttime lighting data (NPP-VIIRS), and Landsat 8 OLI are used as remote sensing data sources to construct multiple index features of impervious surface information from different angles. On this basis, a multi-regression model is used to establish a multifactor impervious surface percentage estimation model to realize remote sensing estimation of large-scale impervious surface percentage. This study selects 13 typical cities across the country as the main research area to verify the proposed model. Results show that the method can adapt to the estimation of impervious surface percentage in different regions and that it is better than traditional methods in complex urban areas. The effect has significantly improved the accuracy of the estimation of impervious surface percentage within the city.关键词：remote sensing;impervious surface;multi-features;night lights;Landsat 8 OLI;MODIS- 838
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发布时间：2020-12-01 -
摘要：Satellite observation data are increasingly used to derive and predict thermohaline information within the ocean due to the development of satellite technology. However, the effective improvement of prediction accuracy in satellite remote sensing is still challenging. A model with strong spatiotemporal applicability and high robustness for subsurface thermohaline estimation is necessary due to the complex and highly dynamic processes in the ocean.In this study, a new LightGBM method combined with random forest algorithm is used to predict global subsurface temperature and salinity anomalies in the upper 1000 m depth based on remote sensing and Argo float data. The proposed method uses multisource sea surface parameters, including Sea Surface Height Anomaly (SSHA), Sea Ssurface Temperature Anomaly (SSTA), Sea Surface Salinity Anomaly (SSSA), and northward and eastward components of sea surface wind anomaly (USSWA and VSSWA), combined with longitude and latitude data (LON and LAT) as predictor variables and Argo gridded data as training and testing labels for model construction and prediction. This study creates five-parameter model (SSTA, SSHA, SSSA, USSWA, and VSSWA), six-parameter model with latitude (LAT, SSTA, SSHA, SSSA, USSWA, and VSSWA), six-parameter model with longitude (LON, SSTA, SSHA, SSSA, USSWA, and VSSWA), and seven-parameter model with longitude and latitude (LON, LAT, SSTA, SSHA, SSSA, USSWA, and VSSWA) to analyze and evaluate the role of LON + LAT in STA and SSA prediction using LightGBM and RF models.Using the monotemporal LightGBM model to predict STA, the average R2 of the seven-parameter model, six-parameter model with latitude, six-parameter model with longitude, five-parameter model is 0.980, 0.922, 0.937, 0.776 and the average RMSE is 0.072 ℃, 0.141 ℃, 0.127 ℃, 0.240 ℃. The average R2 in the SSA prediction is 0.963, 0.846, 0.872, 0.545, and the average RMSE is 0.012 psu, 0.025 psu, 0.022 psu, 0.042 psu. The average R2 of the seven-parameter model, six-parameter model with latitude, six-parameter model with longitude, five-parameter model when using time-series LightGBM model to predict STA is 0.703, 0.655, 0.585, 0.523, and the average RMSE is 0.298 ℃, 0.317 ℃, 0.356 ℃, 0.378 ℃. The average R2 in the SSA prediction is 0.426, 0.277, 0.197, 0.103, and the average RMSE is 0.050 psu, 0.057 psu, 0.059 psu, 0.064 psu. Hence, the seven-parameter model demonstrated the best performance. The maximum R2 and minimum RMSE in the seven-parameter LightGBM model are 0.992, 0.981 and 0.022 ℃, 0.004 psu in the monotemporal STA, SSA prediction. Meanwhile, the maximum R2 and minimum RMSE are 0.817, 0.574 and 0.092 ℃, 0.013 psu in the time-series STA, SSA prediction. The prediction accuracy of the model decreases gradually with increasing depth.This study suggested that LON + LAT significantly contribute to both STA and SSA prediction, but differently impact on respective STA and SSA prediction. The contribution of LON + LAT to the model increases with depth in the monotemporal and time-series STA prediction while maintaining a large contribution to the model at different depths in the monotemporal and time-series SSA prediction. LON makes a larger contribution than LAT in the monotemporal STA and SSA prediction, while LAT plays a more significant role than LON in the time-series STA and SSA prediction. Furthermore, LightGBM outperforms RF and is more robust in the subsurface thermohaline prediction.关键词：subsurface ocean;thermohaline anomaly;LightGBM;remote sensing prediction;longitude and latitude- 706
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发布时间：2020-12-01
Remote Sensing Applications
- 摘要：Vegetation systems worldwide are facing a growing challenge of locust threats, including Desert Locust (Schistocerca gregaria) invasion in African and Asian countries, Australian Plague Locust (Chortoicetes terminifera), and Oriental Migratory Locust (Locusta migratoria manilensis) in China. The traditional single-point hand-check monitoring method could obtain information on the occurrence and development of locust at the point level, which could not meet the needs of monitoring and timely prevention and control of locust at the area level. It is urgent to conduct large-scale locust remote sensing monitoring and early warning to support timely prevention and control of locust, to ensure the safety of agricultural production, and furthermore to promote the realization of the “Zero Hunger” goal. We reviewed the current research of locust from three aspects, i.e. pest habitat monitoring, pest occurrence early warning, and loss assessment. We found that, the locust monitoring and early warning normally has a coarse spatial and temporal resolution, which makes it impossible to accurately locate the hazard hotspots; and the loose coupling of remote sensing pest response mechanism and pest biological diffusion model leads to a poor temporal and spatial universality and prediction accuracy; also we lack of timely, quantitative and visualized remote sensing monitoring and early warning locust service products to promote effective pest prevention. Therefore, there is an urgent need to develop a multi-scale, long-term, high-precision locust monitoring and early warning platform in global, intercontinental, national, and regional levels, to establish spatial and temporal continuous pest monitoring and early warning indexes, to develop pest monitoring and early warning models by deeply coupling of remote sensing mechanism and pest biological mechanism, and to release multi-scale, high-time-frequency pest products and services. On the one hand, we need to bring together and produce cutting edge research to provide information for locust monitoring and early warning, by integrating multi-source data, such as Earth Observation-EO, meteorological, entomological and plant pathological, etc. On the other hand, multi-models, including vegetation radiation transfer model, vegetation parameter inversion model, pest diffusion model, loss assessment model, are needed to be coupled with each other to provide temporal and spatial continuously pest monitoring, forecasting and loss assessment results. Besides, an intelligent platform, including storage module, calculation module, product module, is needed to be constructed, to integrating big data intelligent analysis, conducting high-performance model computing, realizing online locust product production and service push. The future trend of pest remote sensing system is realizing automatic storage and intelligent storage of massive data, fast calling of multi-level models and high-performance computing, and online producing of pest products and visualization. It will fully open up the entire link from data to models to product services, to effectively improve the global level of intelligence to deal with migratory pests, and to provide scientific and technological support for ensuring food security and maintaining regional stability. Furthermore, with locust now a world migratory pest, China and other countries, together with each other to discuss joint monitoring, collaborative scientific research and development of new coordinated integrated pest management mechanisms to provide economic, effective and ecologically-friendly management solutions.关键词：locust;remote sensing;monitoring;early warning;platform
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发布时间：2020-12-01 -
摘要：Forest fire is one of the major forest disturbances, which directly affects forest ecosystem structure, carbon cycle, even global climate change. In recent years, the technical progresses of aerial platforms and onboard sensors have effectively improved the capability of airborne remote sensing to detect and monitor forest fires. As a result, many forest fire applications have been carried out with airborne data, including the forest fuel assessment, evaluation of forest fire potential and prevention, forest fire monitoring and situation evaluation during firefighting, post-fire damage assessment, ecosystem management and restoration.This paper introduces an integrated airborne remote sensing system, i.e., Chinese Academy of Forestry's LiDAR, Thermal, CCD and Hyperspectral (CAF-LiTCHy) airborne observation system. The CAF-LiTCHy airborne observation system is equipped with Light Detection and Ranging (LiDAR) scanner, thermal infrared camera, CCD camera and hyperspectral scanner and can obtain the corresponding data, simultaneously. The parameters of these sensors and the data processing scheme were described to show the advanced and scientific nature of CAF-LiTCHy airborne observation system. Moreover, the detailed “image - spectral - temperature - height” information of forest benefits the aforementioned applications. Here, the Xichang ‘3.30 Forest Fire’ in Sichuan Province was selected as a case study to test the capability of CAF-LiTCHy airborne observation system in post-fire survey and disaster assessment.The burn severity levels can be interpreted from the high spatial resolution CCD image directly. Meanwhile, the LiDAR-derived Canopy Height Model (CHM) shows the different burn severities of the canopy in spatial and vertical structural level. The vegetation indices calculated from hyperspectral image indicate that the pigment content and water content of canopy decrease simultaneously. The thermal information shows a higher temperature in high burn severity areas than other areas due to lots of charcoals from fire. Finally, with the combination of the CCD, CHM, spectral and thermal temperature information, the distribution of burn severity levels of the study area was mapped. The CAF-LiTCHy airborne observation system can obtain forest fire information and offer fire-related parameters effectively. The design of CAF-LiTCHy airborne observation system is advanced and scientific, and the acquired data are in high resolution, multiple dimensions and good geo-location consistency. The efficiency and stability for airborne data collection provide rapid response for fire events. Furthermore, the CAF-LiTCHy airborne observation system has the potential to provide support for fire prevention, fire risk forecast and early warning, firefighting decision-making, post-fire damage assessment and ecological restoration.关键词：remote sensing;airborne observation system;optical full spectrum remote sensing;CAF-LiTCHy;Xichang ‘3.30 Forest Fire’;burn severity;thermal image- 790
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发布时间：2020-12-01
Research Progress
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