CAJ | 학술논문

近年来，中国遥感事业已取得长足进步，高分一号（GF-1）卫星首次实现了中国自主研发的高分辨率对地观测。为探讨国产GF-1卫星影像在农业遥感长势监测中的适应性，以许昌地区为研究对象，选取同期Landsat-8卫星影像，结合地面采样数据LAI，从传感器光谱响应特征、经验回归模型监测精度以及LAI空间一致性等3方面进行2类遥感数据的对比评价。结果表明，GF-1影像近红外、红、蓝波段光谱响应与 Landsat-8有差异，与绿波段光谱响应非常吻合，各波段光谱反射率与Landsat-8影像同类光谱间均存在显著线性关系。通过各波段组合多种归一化植被指数，采用经验回归模型反演LAI发现，GF-1影像反演的最优模型为NDVI的指数模型，R2为0.848，Landsat-8影像反演的最优模型为蓝红组合的归一化植被指数（blue-red NDVI，BRNDVI）的指数模型，R2为0.687，2类影像反演LAI与地面实测值均呈现较为一致的线性关系。由许昌地区玉米LAI值空间分布可见，GF-1影像反演的玉米LAI值与Landsat-8影像反演值过渡趋势一致，在许昌西部种植结构复杂地区，GF-1影像以其空间分辨率优势更能凸显 LAI 分布差异。通过该文研究表明， GF-1卫星的高时间分辨率以及高空间分辨率特征能够代替传统中分辨率数据成为农业遥感长势监测中的重要数据源，该数据在农业遥感其他领域的应用是今后研究的重点。
근년래，중국요감사업이취득장족진보，고분일호（GF-1）위성수차실현료중국자주연발적고분변솔대지관측。위탐토국산GF-1위성영상재농업요감장세감측중적괄응성，이허창지구위연구대상，선취동기Landsat-8위성영상，결합지면채양수거LAI，종전감기광보향응특정、경험회귀모형감측정도이급LAI공간일치성등3방면진행2류요감수거적대비평개。결과표명，GF-1영상근홍외、홍、람파단광보향응여 Landsat-8유차이，여록파단광보향응비상문합，각파단광보반사솔여Landsat-8영상동류광보간균존재현저선성관계。통과각파단조합다충귀일화식피지수，채용경험회귀모형반연LAI발현，GF-1영상반연적최우모형위NDVI적지수모형，R2위0.848，Landsat-8영상반연적최우모형위람홍조합적귀일화식피지수（blue-red NDVI，BRNDVI）적지수모형，R2위0.687，2류영상반연LAI여지면실측치균정현교위일치적선성관계。유허창지구옥미LAI치공간분포가견，GF-1영상반연적옥미LAI치여Landsat-8영상반연치과도추세일치，재허창서부충식결구복잡지구，GF-1영상이기공간분변솔우세경능철현 LAI 분포차이。통과해문연구표명， GF-1위성적고시간분변솔이급고공간분변솔특정능구대체전통중분변솔수거성위농업요감장세감측중적중요수거원，해수거재농업요감기타영역적응용시금후연구적중점。
With China Remote Sensing career advancement, a large number of independent researches and development of satellite have launched. Among a new generation of high-resolution satellites, GF-1 stands out. It sets high spatial resolution, multi-spectral and high temporal resolution in a fusion technology with strategic significance. To explore Chinese GF-1 satellite images’ adaptability of agricultural growth monitoring, its images for the region of Xuchange China for maize growth were compared with the same period of Landsat-8 satellite images in three aspects of sensor spectral response characteristics, the accuracy of empirical regression model and LAI space consistency. There were a total of 24 sampling points for the study. First, graphs described the sample located pixels’ spectral reflectance of near-infrared band, red band, green band and blue band of the two types of sensors. It directly reflected the spectral reflectance differences between sensors in the same place, and differences between maize in different area. The reflectance of near-infrared and red band of Landsat-8 was higher compared with GF-1. The blue and green band’s reflectance of GF-1 was similar to that of Landsat-8. The linear correlation of two sensors’ reflectivity could be calculated at the same time. Second, four bands of two types of images were separately combined into seven kinds of normalized difference vegetation index to further eliminate the influence of atmospheric correction process. Like NDVI, the red band was replaced by blue or green or three visible bands’ combination of two by two or sum of them. Then, the empirical regression models were used to calculate the ability of inversing LAI among the vegetation index. Based on comparison ofR2 and RMSE among models, high fitting models were selected. The optimal model for Landsat-8 was based on BRNDVI, it was an index model. The best model for GF-1was based on NDVI, and model type was an index model. The reserved samples were used to test model’s fitting accuracy. The final result showed a good correlation between inversed LAI and measured LAI for all images. Third, LAI distribution of Xuchang district was reversed by the optimal model of two images, due to the variance in spatial resolution, GF-1 data did downscale process by resampling to 30 m scale. In total, maize LAI spatial distribution in two images was more consistent, and had a west to east high transition trend. For further research, the range for LAI unified into <3.0,≥3.0-4.0,≥4.0-5.0,≥5.0 pixels is needed in a visual display. High values greater than 5.0 were concentrated in Xuchang county, Yanling county and the eastern half of Changge city, the two distributions were more consistent; <3.0 pixels were rarely low in both. There were difference in the distribution of Yuzhou, western Changge city and Xiangcheng County,≥4.0-5.0 range had a wider distribution in Landsat-8 product of LAI, and≥3.0-4.0 pixels were more in GF-1 LAI product. In this paper, the application indicated that GF-1 satellite's high time resolution provides more chances to get cloudless data, and high spatial and spectral resolution features and it can replace the traditional medium resolution remote sensing of agricultural growth monitoring data to a certain extent. This research shows that GF-1is an important data source and the data’s application in other areas of agriculture is the focus of future research.