农业工程学报
農業工程學報
농업공정학보
Transactions of the Chinese Society of Agricultural Engineering
2015年
19期
159-168
,共10页
刘佳%王利民%杨玲波%滕飞%邵杰%杨福刚%富长虹
劉佳%王利民%楊玲波%滕飛%邵傑%楊福剛%富長虹
류가%왕이민%양령파%등비%소걸%양복강%부장홍
卫星%遥感%反射%高分一号%6S模型%大气校正%地表反射率
衛星%遙感%反射%高分一號%6S模型%大氣校正%地錶反射率
위성%요감%반사%고분일호%6S모형%대기교정%지표반사솔
satellites%remote sensing%reflection%GF-1%6S model%atmospheric correction%surface reflectance
高分一号（GF-1）卫星是中国高分系列卫星的首发星，自2013年4月成功发射以来，在中国农业遥感业务工作中得到了广泛应用，已成为中国大宗农作物种植面积遥感监测的主要数据源。该文基于6S（second simulation of a satellite signal in the solar spectrum）辐射传输模型原理，设计并实现了适合于GF-1卫星数据大气校正算法与程序。算法以GF-1卫星1级数据、元数据及传感器公开参数为输入数据，不需要其他外源辅助数据，经过辐射定标，计算各波段平均太阳辐射值、表观反射率，通过选择大气模式，驱动6S模型获取表观反射率转换为地表反射率的参数，逐像元计算影像地表反射率。在算法研制的基础上，应用Fortran和IDL语言编写了大气校正批处理程序，实现了大气校正过程的批处理。该文采用2014年4月3日、6月28日、11月2日，以及2015年1月19日4个时相北京地区GF1卫星WFV（wide field view）数据，分别代表春夏秋冬4个季节，通过与ENVI软件的FLAASH（fast line-of-sight atmospheric analysis of spectral hypercubes）大气校正结果对比进行评估。2种方法4个时相各波段全年相对偏差为3.26%，蓝光波段偏差最大为11.21%，其次是红、近红和绿光波段，分别为1.19%、0.73%和0.24%。作物覆盖区平均相对误差为12.99%，冬季最高为17.40%，秋季和春季分别为15.02%和14.15%，夏季相对差异最小为8.31%。各波段地表反射率的整体校正情况并未有太大差异，但6S校正后各波段反射率普遍比FLAASH校正结果略微偏高。2种校正结果计算的NDVI也基本一致，相对偏差0.64%；除水体外，绝对值差值的平均值均在0.0548以内。从计算效率来分析，6S模块实现了商用软件FLAASH模块中未提供的批量计算，在相同硬件环境下计算效率提高了75.0%以上。研究结果表明了该文开发的大气校正程序能够稳定批量处理GF-1卫星数据，可以作为农业遥感监测业务流程的组成部分。
高分一號（GF-1）衛星是中國高分繫列衛星的首髮星，自2013年4月成功髮射以來，在中國農業遙感業務工作中得到瞭廣汎應用，已成為中國大宗農作物種植麵積遙感鑑測的主要數據源。該文基于6S（second simulation of a satellite signal in the solar spectrum）輻射傳輸模型原理，設計併實現瞭適閤于GF-1衛星數據大氣校正算法與程序。算法以GF-1衛星1級數據、元數據及傳感器公開參數為輸入數據，不需要其他外源輔助數據，經過輻射定標，計算各波段平均太暘輻射值、錶觀反射率，通過選擇大氣模式，驅動6S模型穫取錶觀反射率轉換為地錶反射率的參數，逐像元計算影像地錶反射率。在算法研製的基礎上，應用Fortran和IDL語言編寫瞭大氣校正批處理程序，實現瞭大氣校正過程的批處理。該文採用2014年4月3日、6月28日、11月2日，以及2015年1月19日4箇時相北京地區GF1衛星WFV（wide field view）數據，分彆代錶春夏鞦鼕4箇季節，通過與ENVI軟件的FLAASH（fast line-of-sight atmospheric analysis of spectral hypercubes）大氣校正結果對比進行評估。2種方法4箇時相各波段全年相對偏差為3.26%，藍光波段偏差最大為11.21%，其次是紅、近紅和綠光波段，分彆為1.19%、0.73%和0.24%。作物覆蓋區平均相對誤差為12.99%，鼕季最高為17.40%，鞦季和春季分彆為15.02%和14.15%，夏季相對差異最小為8.31%。各波段地錶反射率的整體校正情況併未有太大差異，但6S校正後各波段反射率普遍比FLAASH校正結果略微偏高。2種校正結果計算的NDVI也基本一緻，相對偏差0.64%；除水體外，絕對值差值的平均值均在0.0548以內。從計算效率來分析，6S模塊實現瞭商用軟件FLAASH模塊中未提供的批量計算，在相同硬件環境下計算效率提高瞭75.0%以上。研究結果錶明瞭該文開髮的大氣校正程序能夠穩定批量處理GF-1衛星數據，可以作為農業遙感鑑測業務流程的組成部分。
고분일호（GF-1）위성시중국고분계렬위성적수발성，자2013년4월성공발사이래，재중국농업요감업무공작중득도료엄범응용，이성위중국대종농작물충식면적요감감측적주요수거원。해문기우6S（second simulation of a satellite signal in the solar spectrum）복사전수모형원리，설계병실현료괄합우GF-1위성수거대기교정산법여정서。산법이GF-1위성1급수거、원수거급전감기공개삼수위수입수거，불수요기타외원보조수거，경과복사정표，계산각파단평균태양복사치、표관반사솔，통과선택대기모식，구동6S모형획취표관반사솔전환위지표반사솔적삼수，축상원계산영상지표반사솔。재산법연제적기출상，응용Fortran화IDL어언편사료대기교정비처리정서，실현료대기교정과정적비처리。해문채용2014년4월3일、6월28일、11월2일，이급2015년1월19일4개시상북경지구GF1위성WFV（wide field view）수거，분별대표춘하추동4개계절，통과여ENVI연건적FLAASH（fast line-of-sight atmospheric analysis of spectral hypercubes）대기교정결과대비진행평고。2충방법4개시상각파단전년상대편차위3.26%，람광파단편차최대위11.21%，기차시홍、근홍화록광파단，분별위1.19%、0.73%화0.24%。작물복개구평균상대오차위12.99%，동계최고위17.40%，추계화춘계분별위15.02%화14.15%，하계상대차이최소위8.31%。각파단지표반사솔적정체교정정황병미유태대차이，단6S교정후각파단반사솔보편비FLAASH교정결과략미편고。2충교정결과계산적NDVI야기본일치，상대편차0.64%；제수체외，절대치차치적평균치균재0.0548이내。종계산효솔래분석，6S모괴실현료상용연건FLAASH모괴중미제공적비량계산，재상동경건배경하계산효솔제고료75.0%이상。연구결과표명료해문개발적대기교정정서능구은정비량처리GF-1위성수거，가이작위농업요감감측업무류정적조성부분。
GF-1 satellite is the first satellite of the high resolution satellite series in China. Since its successful launch on April 26 2013, GF-1 satellite has been widely applied in agricultural remote sensing monitoring practice in China, and it has become a major data source of agricultural remote sensing dynamic monitoring. Based on the principle of radioactive transfer model of 6S (second simulation of a satellite signal in the solar spectrum), this paper designed and realized the algorithm and program suitable for GF-1 satellite data atmospheric correction. By using the 6S model, the algorithm obtains the parameters for the conversion from reflectivity (or irradiance) of Top Of Atmosphere (TOA) to surface reflectance, and then calculates the surface reflectance of each pixel of each image according to the conversion parameter. The algorithm takes GF-1 satellite first level data, metadata, and open parameter of sensor as the input data, without auxiliary data from other sources. The specific process includes 3 steps, i.e. radiometric calibration, running parameters settings and atmospheric correction. Radiometric calibration is to convert the DN (digital number) value of the original GF-1 satellite first level image into radiation brightness, and then calculate apparent reflectance by combining the reflectivity (or irradiance) of TOA. Either reflectivity (or irradiance) of TOA or apparent reflectance can be taken as the input of atmospheric correction program. Precondition for realizing the algorithm is to calculate the average solar irradiance parameters of each wave band of satellite sensor atmospheric top according to spectral response function of GF-1 satellite sensor and WRC (world radiation center) sun spectrum function. Operation parameters include 2 types: 1) input of satellite images, including satellite zenith angle, satellite azimuth angle, solar zenith angle, solar azimuth, sensor height, ground elevation, radiation calibration coefficient and spectral response functions of various loads, which can be acquired from the metadata of the images; 2) atmospheric model parameters, such as atmospheric model, atmospheric aerosol model, visibility, solar spectrum function. The default value will be set by the system according to the data conditions, and it can be adjusted according to the real situation. Spectral response function of GF-1 satellite is provided by the satellite producer, and the re-sampling is the spectral response curve with the resolution of 2.5 nm and it is input into the 6S model. Atmospheric correction is to convert the apparent reflectance image (or radiation brightness) into ground reflectance. Now, the input is the GF-1 apparent reflectance image (or radiation brightness) which needs atmospheric correction and the output is the ground reflectance image. On the basis of the development of the algorithm, the Fortran and interface description language are applied to compile atmospheric correction batch processing programs, so as to realize the batch processing during atmospheric correction process. This paper used the data of GF1 WFV (wide field view) of Beijing region on April 3, June 28, and November 2, 2014, and January 19, 2015, which 4 phases represented 4 seasons, i.e. spring, summer, autumn and winter. By using the atmospheric correction result of FLAASH (fast line-of-sight atmospheric analysis of spectral hypercubes) of ENVI software, the evaluation was conducted. The relative deviation of the whole year for 4 phases between the 2 methods was 3.26%. Blue light band had the highest deviation of 11.21%, followed by red, near-infrared, and green light bands, which were 1.19%, 0.73% and 0.24% respectively. The average relative error in the areas covered by crops was 12.99%, the highest was in winter which was 17.40% and those in autumn and spring were 15.02% and 14.15% respectively, and summer had the lowest value of 8.31%. Whole correction of ground reflectance of various bands didn’t show significant difference, but the reflectance of various bands after 6S correction was usually slightly higher than the correction result of FLAASH. The calculation results of the NDVI (normalized difference vegetation index) based on 2 correction results were basically same with the relative deviation of 0.64%, and the absolute difference was within 0.0548 except water body. In terms of calculation efficiency, the 6S model has realized the batch calculation which was not provided in the commercial software of FLAASH module. Under the same hardware environment, the calculation efficiency was improved by more than 75.0%. The research result shows that the atmospheric correction program developed by this paper can stably process GF-1 satellite data in batch, and it can be used as a component of agricultural remote sensing monitoring operation.
