Glacier mapping based on Chinese high-resolution remote sensing GF-1 satellite and topographic data

The precise glacier boundary is a fundamental requirement for glacier inventory,the assessment of climate change and water management in remote mountain areas.However,some glaciers in mountain areas are covered by debris.The high spatial resolution images bring opportunities in mapping debris-covered glaciers.To discuss the capability of Chinese GaoFen-1 satellite lacking the short wave infrared band and thermal infrared band in mapping glaciers,this study distinguished supraglacial terrain from surrounding debris by combining GaoFen-1(GF-1)wide-field-view(WFV)images,the ratio of the thermal infrared imagery and morphometric parameters(DEM and slope)with 30 m resolution.The overall accuracy of 90.94%indicated that this method was effective for mapping supraglacial terrain in mountain areas.Comparing this result with the combination of GF-1 WFV and low-resolution morphometric parameters shows that a high-quality DEM and the thermal infrared band enhanced the accuracy of glacier mapping especially debris-covered ice in steep terrain.The user's and producer's accuracies of glacier area were also improved from 89.67%and 85.95%to 92.83%and 90.34%,respectively.GF data is recommended for mapping heavily debris-covered glaciers and will be combined with SAR data for future studies.

Extracting Lotus Fields Using the Spectral Characteristics of GF-1 Satellite Data

The lotus(Nelumbo nucifera Gaertn.)is an aquatic plant that grows in shallow water and has long been cultivated in South China.It can improve the incomes of farmers and plays an important role in alleviating poverty in rural China.However,a modern method is required to accurately estimate the area of lotus fields.Lotus has spectral characteristics similar to those of rice,grassland,and shrubs.The features surrounding areas where it is grown are complex,small,and fragmented.Few studies have examined the remote sensing extraction of lotus fields,and automatic extraction and mapping are still challenging methods.Here,we compared the spectral characteristics of lotus fields and other ground objects and devised a remote sensing method for the rapid extraction of lotus fields.Using this method,the extraction accuracy of lotus was 96.3%.The Kappa coefficient was 0.926,which is higher than those of the unsupervised K-means classification,Mahalanobis distance,and support vector machine supervised classification,and demonstrates the potential of this method for extracting and mapping lotus fields by remote sensing.

Inversion of Canopy Nitrogen Content in Apple Orchard Based on GF-1 Satellite Image

The apple orchard in Qixia City, Yantai City, Shandong Province was used as the research area. The nitrogen content inversion of apple canopy was studied by using the satellite remote sensing images of GF-1. On the basis of GF-1 satellite multispectral image preprocessing, vegetation index was extracted by band math. The nitrogen sensitive vegetation index of apple canopy was selected by correlation analysis of nitrogen content in apple canopy. The best inversion model for the nitrogen content of apple canopy was selected by establishing the regression model of univariate and multivariate factors. The nitrogen content of the canopy of apple orchard in the study area was inverted in space. The results showed that the 6 vegetation indices of RVI, NDVI, EVI, VARI, NPCI and NRI were better correlated with nitrogen content in the vegetation index based on GF-1 satellite multispectral imaging. The best inversion model of nitrogen content in apple canopy layer is the multivariate stepwise regression (MSR) model: Nc = 35.74– 41.978^*NPCI-10.78^*NDVI. The R^2 and RMSE of the model was 0.69 and 1.07. The spatial inversion of nitrogen content in apple orchard canopy was obtained. This study provided theoretical basis and technical support for large-area rapid monitoring of regional fruit tree nutrients.

Recent geomagnetic storms observed by Macao Science Satellite-1

Geomagnetic storms are rapid disturbances of the Earth’s magnetosphere.They are related to many geophysical phenomena and have large influences on human activities.Observing and studying geomagnetic storms is thus of great significance to both scientific research and geomagnetic hazards prevention.The Macao Science Satellite-1(MSS-1)project includes two high-precision Chinese geomagnetic satellites successfully launched on May 21,2023.The main purpose of MSS-1 is to accurately measure the Earth’s magnetic field.Here,we analyze early MSS-1 geomagnetic field measurements and report observations of two recent geomagnetic storms that occurred on March 24,2024 and May 11,2024.We also calculate the related geoelectric fields as an initial step towards a quantitative assessment of geomagnetic hazards.

On-orbit Geometric Calibration and Preliminary Accuracy Evaluation of GF-14 Satellite

GF-14 satellite is a new generation of sub-meter stereo surveying and mapping satellite in China,carrying dual-line array stereo mapping cameras to achieve 1∶10000 scale topographic mapping without Ground Control Points(GCPs).In fact,space-based high-precision mapping without GCPs is a challenging task that depends on the close cooperation of several payloads and links,of which on-orbit geometric calibration is one of the most critical links.In this paper,the on-orbit geometric calibration of the dual-line array cameras of GF-14 satellite was performed using the control points collected in the high-precision digital calibration field,and the calibration parameters of the dual-line array cameras were solved as a whole by alternate iterations of forward and backward intersection.On this basis,the location accuracy of the stereo images using the calibration parameters was preliminarily evaluated by using several test fields around the world.The evaluation result shows that the direct forward intersection accuracy of GF-14 satellite images without GCPs after on-orbit geometric calibration reaches 2.34 meters(RMS)in plane and 1.97 meters(RMS)in elevation.

基于GF-1数据的耕地土壤镉(Cd)含量遥感估算方法

本文采用多种光谱变换和回归分析方法探索了使用GF-1卫星影像监测耕地土壤镉(Cd)含量的可行性。首先针对获取的GF-1原始影像数据,在完成预处理及剔除植被信息后进行倒对数、平方根和反正弦平方根变换,生成4套光谱影像;然后分别用采样点5 m缓冲区内各套影像光谱统计值与Cd含量进行相关性分析和多种回归分析。选择模型决定系数最高(>95%)的反正弦平方根变换后的自适应重加权回归方法构建的线性回归模型作为遥感估算模型。遥感估算结果在稻田积水、边缘地带等出现了异常估算值;笔者分析原因后应用线性插值的方法得到最终估算结果。相关性分析和建模精度表明该方法是可行的,有望应用于实际土壤质量监测和土地管理中。

基于HJ-1星和GF-1号影像融合特征提取冬小麦种植面积

为提高基于国产环境与灾害监测预报卫星(HJ-1/CCD)影像大范围提取冬小麦种植面积的精度,以江苏省宿迁市沭阳县为研究区域,对冬小麦拔节期30 m×30 m的HJ-1/CCD多光谱影像和2 m×2 m的高分1号卫星全色影像(GF-1/PMS)进行融合与面向对象分类研究。将GF-1/PMS全色影像进行8、16和24 m重采样,得到4种空间分辨率(含2 m)的全色影像,分别与HJ-1/CCD多光谱影像利用光谱锐化法(Gram-Schmidt,GS)进行融合。通过对融合影像进行质量评价,选择适合研究区冬小麦种植田块格局的适宜尺度影像。将HJ-1/CCD多光谱影像重采样,得到与适宜尺度融合影像相同尺度的影像,在两景影像中分别选取包含光谱、纹理信息的训练融合影像样本(samples of fused image,SFI)和重采样影像样本(samples of resampling image,SRI),采用面向对象分类方法对适宜尺度融合影像(fused image,FI)和重采样影像(resampling image,RI)进行冬小麦种植面积提取。结果表明,16 m×16 m融合影像的效果优于2 m×2 m、8 m×8 m和24 m×24 m融合影像,其均值、标准差、平均梯度和相关系数分别为161.15、83.01、4.55和0.97。面向对象分类后,SFI对重采样影像RI16m分类的总体精度为92.22%,Kappa系数为0.90。SFI对融合影像FI16m分类的总体精度为94.44%,Kappa系数为0.93。SRI对重采样影像RI16m分类的总体精度为84.44%,Kappa系数为0.80。SFI对融合影像FI16m分类效果最好,说明基于融合影像和融合影像提取样本(SFI)结合的面向对象分类方法能准确提取冬小麦种植面积。另外,重采样影像和融合影像提取样本(SFI)相结合的面向对象分类方法也可较好提取冬小麦种植面积。为利用国产中空间分辨率HJ-1/CCD卫星和高分1号卫星融合影像有效提取大区域冬小麦种植面积信息提供了参考。

血清TGF-β1、Nesfatin-1、SFRP5与2型糖尿病胰岛素抵抗关系探究

目的:探讨血清转化生长因子-β1(TGF-β1)、Nesfatin-1、分泌型卷曲相关蛋白-5(SFRP5)与2型糖尿病胰岛素抵抗的关系。方法:选取我院2021年4月-2023年4月收治的148例2型糖尿病患者为研究对象,根据稳态模型胰岛素抵抗指数(HOMA-IR)检测结果将患者分为轻度胰岛素抵抗组(91例,HOMA-IR<2.5)和中重度胰岛素抵抗组(57例,HOMA-IR≥2.5)。比较两组入院时血清TGF-β1、Nesfatin-1、SFRP5水平,分析入院时血清TGF-β1、Nesfatin-1、SFRP5水平与HOMA-IR的相关性,分析发生中重度胰岛素抵抗的影响因素、联合检测的诊断价值及危险度。结果:入院时,中重度胰岛素抵抗组血清TGF-β1、Nesfatin-1水平高于轻度胰岛素抵抗组,SFRP5水平低于轻度胰岛素抵抗组(P<0.05);入院时,HOMA-IR与血清TGF-β1、Nesfatin-1水平呈正相关,与SFRP5水平呈负相关(P<0.05);入院时,血清TGF-β1、Nesfatin-1、SFRP5水平为2型糖尿病患者发生中重度胰岛素抵抗的影响因素(P<0.05);入院时血清TGF-β1、Nesfatin-1、SFRP5水平联合诊断中重度胰岛素抵抗的AUC为0.808,最佳诊断敏感度为91.23%,特异度为70.33%;入院时血清TGF-β1、Nesfatin-1、SFRP5高水平患者发生中重度胰岛素抵抗的危险度是低水平的3.560、2.134、0.341倍(P<0.05)。结论:血清TGF-β1、Nesfatin-1、SFRP5水平与2型糖尿病胰岛素抵抗密切相关,可作为临床诊断病情的参考依据。

基于GF-1卫星影像的青岛市冬小麦种植面积变化动态监测

为准确获取青岛市冬小麦种植面积及空间分布变化特点,帮助相关部门优化种植结构,合理安排农业生产,研究利用GF-1 WFV卫星遥感影像,选择青岛市2014—2021年冬小麦种植面积遥感提取的最佳时相,采用图像监督分类法提取冬小麦的种植面积,并通过分类后比较法分析小麦种植面积的动态变化。结果表明:2014—2021年青岛市冬小麦的种植面积为21.7万~24.8万hm^(2),2016年小麦种植面积最大,2020年最小,每年冬小麦种植面积波动范围为0.3万~2.7万hm^(2);遥感提取的种植面积与统计部门提供的种植面积误差较小,误差百分率不超过6.5%;小麦与其他地物类型之间相互转换的差异很小,2014—2021年逐年小麦转为其他地物的面积变化范围为8.0万~10.9万hm^(2),其他地物转为小麦的面积变化范围为7.9万~11.4万hm^(2)。

Preface to the Special Issue on the Macao Science Satellite-1 Mission

The Macao Science Satellite-1(MSS-1)mission(https://mss.must.edu.mo/)is marked by a new high-precision constellation of satellites that will survey the Earth’s geomagnetic and space environment.MSS-1 consists of two satellites that are to be launched in the near future.Since these two low Earth orbit(LEO)satellites will operate in circular orbits,with an inclination of about 41°,they are expected to provide essential measurements covering the Earth’s lower-latitude regions—including,especially,the South Atlantic Anomaly(SAA).This special issue presents 18 articles to provide the international scientific community with details regarding the mission’s goals,relevant scientific research,on-board payloads,and international collaborations.Contributors are members of the scientific and engineering groups involved in the mission.In this preface,we categorize the articles and give some brief comments or editor’s recommendations.

Design and analysis of the Macao Science Satellite-1's laser retro-reflector array

The origin and spatial-temporal variation of the Earth’s magnetic field(EMF)is one of the important scientific problems that has long been unsolved.The Macao Science Satellite-1(MSS-1)under construction is China’s first high-precision EMF measurement satellite.To satisfy the highly precise requirements of the MSS-1 orbit measurement,a light,high-precision,four-prism laser retroreflector array was designed.It weighs approximately 285 g,its effective reflection area is greater than 1.77 cm^(2),and its size is 100×100×41 mm.The laser retro-reflector array has excellent performance,and it can achieve a ranging precision at the subcentimeter level for satellite laser ranging.It will be developed and installed on the MSS-1 as a power-free load for high-precision orbit measurement and accurate orbit calibration.The MSS-1 is planned to be brought into the International Laser Ranging Service observations.More than 31satellite laser ranging stations in the International Laser Ranging Service around the world will be able to measure the MSS-1 with long arcs,which will support the scientific mission of high-precision EMF exploration.

GF-1影像地物最优分割尺度确定方法与评价

GF-1卫星是中国高分辨率对地观测系统的第一颗卫星,在土地利用变更调查、土地利用动态监测等方面发挥重要作用。在面向对象的信息提取研究中,传统最优分割尺度方法得到的往往是某一个确定数值。以GF-1影像为实验影像,分别利用均值方差法、最大面积法、面积比均值法等方法,对影像常见地物进行最优分割尺度研究,得到最优分割尺度区间,采用eCognition软件ESP工具进行分割结果评价,在这个区间都能得到较好的分割效果。

Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring

High-resolution satellite data have been playing an important role in agricultural remote sensing monitoring. However, the major data sources of high-resolution images are not owned by China. The cost of large scale use of high resolution imagery data becomes prohibitive. In pace of the launch of the Chinese ＂High Resolution Earth Observation Systems＂, China is able to receive superb high-resolution remotely sensed images （GF series） that equalizes or even surpasses foreign similar satellites in respect of spatial resolution, scanning width and revisit period. This paper provides a perspective of using high resolution remote sensing data from satellite GF-1 for agriculture monitoring. It also assesses the applicability of GF-1 data for agricultural monitoring, and identifies potential applications from regional to national scales. GF-1＇s high resolution （i.e., 2 m/8 m）, high revisit cycle （i.e., 4 days）, and its visible and near-infrared （VNIR） spectral bands enable a continuous, efficient and effective agricultural dynamics monitoring. Thus, it has gradually substituted the foreign data sources for mapping crop planting areas, monitoring crop growth, estimating crop yield, monitoring natural disasters, and supporting precision and facility agriculture in China agricultural remote sensing monitoring system （CHARMS）. However, it is still at the initial stage of GF-1 data application in agricultural remote sensing monitoring. Advanced algorithms for estimating agronomic parameters and soil quality with GF-1 data need to be further investigated, especially for improving the performance of remote sensing monitoring in the fragmented landscapes. In addition, the thematic product series in terms of land cover, crop allocation, crop growth and production are required to be developed in association with other data sources at multiple spatial scales. Despite the advantages, the issues such as low spectrum resolution and image distortion associated with high spatial resolution and wide swath width, might pose challenges for GF-1 data applications and need to be addressed in future agricultural monitoring.

Chinese GF-1 and GF-3 satellites observed the giant iceberg calving off the Larsen C Ice Shelf

A massive iceberg, named A-68 by National Ice Center （NIC） officially, calved away from the Larsen C Ice Shelf in Antarctica on luly 12, 2017. The iceberg A-68 is about 5 800 km2, weighs more than a trillion tons and it is one of the biggest ever recorded icebergs. Chinese satellites Gaofen-1 （GF-1） and Gaofen-3 （GF-3） data was used to monitoring the propagation of the rift and the iceberg by National Satellite Ocean Application Service （NSOAS）.

GF-1与GF-6 WFV影像在滇池悬浮物浓度反演中的对比分析

总悬浮物(TSM)是水环境评价的重要参数之一,也是遥感水质反演的重要指标。GF-1/WFV和GF-6/WFV作为高分系列对外免费开放的卫星数据,在遥感监测中的应用较为广泛,但目前针对两种数据的对比分析以及GF-6/WFV新增波段在水体水质参数反演中的适用性研究较少。以云南滇池水域为研究区域,对与水体实测数据同步过境(或时相相近)的GF-1/WFV和GF-6/WFV遥感影像采用统计分析的方法进行相同波段(蓝、绿、红、近红外)一致性分析,在此基础上运用经验回归方法分别构建两种数据的TSM反演模型,并将加入GF-6/WFV新增波段的模型与GF-1/WFV构建的模型进行对比分析,选择最优模型应用于滇池2020年的6幅GF-6/WFV图像得到滇池TSM分布图。结果表明:GF-1/WFV与GF-6/WFV的蓝、绿、红、近红外波段的相关系数分别为0.98,0.98,0.97和0.99,两种数据的表观反射率具有很高的一致性。GF-1/WFV基于蓝、绿、近红外波段构建的差值模型“B2+B4-B1”反演精度较高,模型反演的均方根误差为6.35 mg·L^(-1),平均绝对百分比误差为23.60%。GF-6/WFV基于近红外、红边1和红边2波段构建的比值模型“1/B5+B6”反演精度较高,模型反演的均方根误差(RMSE)为3.07 mg·L^(-1),平均绝对百分比误差(MAPE)为20.65%,以GF-1/WFV构建的差值模型“B1-B4”与GF-6/WFV构建的“B5-B4”对比发现后者均方根误差减小了2.61 mg·L^(-1),平均绝对百分比降低了32.33%,实验表明加入红边波段的模型反演效果较其他模型更好。采用建模公式得到了2020年滇池TSM分布图,滇池TSM的变化范围在4~45 mg·L^(-1),均值为18.23 mg·L^(-1),总体空间分布呈现北重南轻的分布态势,滇池TSM时间分布表现为上升-下降趋势。该研究不仅可以为湖泊水质监测传感器波段设置提供参考和借鉴,也为滇池水资源监管部门进行水质遥感监测提供了技术支撑。

A global mantle conductivity model derived from 8 years of Swarm satellite magnetic data

Mantle conductivity imaging is one of the scientific goals of the forthcoming Macao Science Satellite-1(MSS-1).To achieve this goal,we develop a data analysis and inversion scheme for satellite magnetic data to probe global one-dimensional(1D)mantle conductivity structures.Using this scheme,we present a new global mantle conductivity model by analyzing over 8 years of Swarm satellite magnetic data.First,after sophisticated data selection procedures and the removal of core and crustal fields,the inducing and induced spherical harmonic coefficients of magnetic potential due to the magnetospheric ring current are derived.Second,satellite Cresponses are estimated from the time series of these coefficients.Finally,the observed responses are inverted for both smooth and threejump conductivity models using a quasi-Newton algorithm.The obtained conductivity models are in general agreement with previous global mantle conductivity models.A comparison of our conductivity model with the laboratory conductivity model suggests the mean state of the upper mantle and transition zone is relatively dry.This scheme can be used to process the forthcoming Macao Science Satellite-1 magnetic data.

The HY-1 satellite and ground system in China

INTRODUCTIONWith the coming of the 21st century, we are faced the problems of the environment, re-sources and population. The ocean is regarded as supplier of resources such as food, minerals,energy and space, and plays an important role in sustainable economic development. At thesame time the ocean has a very important effect on the worldwide environmental changes.

Prediction of chlorophyll a concentration using HJ-1 satellite imagery for Xiangxi Bay in Three Gorges Reservoir

Since the impoundment of the Three Gorges Reservoir in 2003, algal blooms have frequently been observed in it. The chlorophyll a concentration is an important parameter for evaluating algal blooms. In this study, the chlorophyll a concentration in Xiangxi Bay, in the Three Gorges Reservoir, was predicted using HJ-1 satellite imagery. Several models were established based on a correlation analysis between in situ measurements of the chlorophyll a concentration and the values obtained from satellite images of the study area from January 2010 to December 2011. Chlorophyll a concentrations in Xiangxi Bay were predicted based on the established models. The results show that the maximum correlation is between the reflectance of the band combination of B4/（B2＋B3） and in situ measurements of chlorophyll a concentration. The root mean square errors of the predicted values using the linear and quadratic models are 18.49 mg/m3 and 18.52 mg/m3, respectively, and the average relative errors are 37.79% and 36.79%, respectively. The results provide a reference for water bloom prediction in typical tributaries of the Three Gorges Reservoir and contribute to large-scale remote sensing monitoring and water quality management.

GF-6/WFV与GF-1/WFV传感器数据对比

目的:利用GF-6和GF-1卫星高时空分辨率、覆盖范围广等特点,组网协同研究二者宽幅(WFV)传感器之间的差异。方法:基于同期过境的2对GF-6/WFV与GF-1/WFV影像数据,采用样区均值法进行地表反射率层面的交互对比分析,探究G F-6/WFV与GF-1/WFV波段光谱数据之间的定量关系,并构建线性转换方程。结果:GF-6/WFV与GF-1/WFV的观测能力具有较高的一致性,对应波段间线性回归方程的决定系数(R 2)均高于0.942。GF-1/WFV1、GF-1/WFV2与GF-6/WFV数据间的均方根误差(RMSE)在蓝光波段较小,但在绿光、红光和近红外波段的差异逐渐增大;总体上,GF-6/WFV的信号强度比GF-1/WFV低9%~14%,其低于GF-1/WFV1约9.327%,低于GF-1/WFV2约13.573%。经线性转换后的GF-1/WFV数据与G F-6/WFV数据的差异明显降低,GF-1/WFV1数据对应波段的RMSE降低56.435%,相对偏差率(ME)降低8.351%;GF-1/WFV2数据RMSE的降低率达60%以上,ME降低11.119%。结论:G F-6/WFV与GF-1/WFV的转换方程具有较高的准确性,可有效提升2颗卫星组网观测的一致性,对监测地表变化、采集地面信息等具有重要意义。本研究可为GF-6与GF-1卫星数据的协同观测和组网应用提供科学参考。

2018-2022年青海省主要湖泊蓄水量时空变化数据集

青海省是中国西北部重要的生态功能区,水资源总量丰富,但时空分布极不均匀,且由于气候、地理环境等因素的影响,大部分湖泊长期缺乏监测。而湖泊作为地表水水资源的重要载体,是研究气候变化和人类活动对水循环过程影响的重要指示器。国产高分一号、六号卫星宽幅相机空间分辨率为16 m,组网运行可实现2天重复观测,可以高频次获取地表水体面积;ICESat-2激光测高数据具备厘米级的测高精度,可以获取高精度的地表水体测高水位。本数据集基于GF-1/6WFV水体面积和ICESat-2测高水位构建青海省湖泊蓄水量遥感监测模型,通过遥感手段对青海省湖泊蓄水量进行时空变化监测。经验证,本数据集中湖泊蓄变量监测精度总体优于93%,满足湖泊蓄水量时空变化遥感监测需求。本数据集在时间尺度上涵盖2018-2022年,共计60期月度监测结果;空间尺度上覆盖了青海省面积大于30 km^(2)的33个湖泊,主要集中分布在青海省西部、北部、东南部,可为青海省湖泊、水文与气候变化过程监测、水资源动态评价、优化水资源管理与调度提供基础数据支撑。