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Acta Agronomica Sinica ›› 2019, Vol. 45 ›› Issue (7): 1099-1110.doi: 10.3724/SP.J.1006.2019.81065

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Generation and application of high temporal and spatial resolution images of regional farmland based on ESTARFM model

CHEN Meng-Lu1,2,LI Cun-Jun1,*(),GUAN Yun-Lan2,ZHOU Jing-Ping1,WANG Dao-Yun2,LUO Zheng-Qian3   

  1. 1 Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
    2 East China University of Technology, Nanchang 330013, Jiangxi, China
    3 Xinjiang Academy of Agricultural Sciences Comprehensive Test Site, Urumqi 830091, Inner Mongolia, China
  • Received:2018-09-17 Accepted:2019-01-19 Online:2019-07-12 Published:2019-02-27
  • Contact: Cun-Jun LI E-mail:licj@nercita.org.cn
  • Supported by:
    This study was supported by the National Natural Science Foundation of China(41671435)

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Abstract:

Multi-temporal remote sensing images are important data sources for agricultural phenology, growth, and yield monitoring. However, visible light images are vulnerable to cloud and rain, and there is a lack of high temporal and spatial resolution data in reality, the remote sensing image fusion methods have become particularly important. ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) is used to synthesize high spatial-temporal resolution images in small areas. The adaptability and application of the algorithm in different agricultural growing areas in China have not yet fully developed. In this paper, the large area application test analysis was performed in the Hebei, Heilongjiang, and Xinjiang. Based on MODIS and Landsat images, we used ESTARFM to generate Landsat images with high spatial-temporal characteristics, which were compared with the real Landsat images. The application of ESTARFM algorithm in NDVI was performed for crop growth monitoring in Xinjiang. In conclusion ESTARFM can perform better image prediction in three different regional conditions, generate 30 m multi-temporal NDVI with good spatial resolution in Xinjiang, and monitor the growth of crops.

Key words: high spatiotemporal resolution, ESTARFM, fusion data, NDVI, growth monitoring

Fig. 1

Location of study area"

Table 1

Landsat-8 OLI image date"

研究区域
Research area		 中心经纬度
Center latitude and longitude		 日期(年积日)
Date (DOY)
河北研究区 Hebei research area 38°42′5″N,115°9′13″E 2017-03-27(86), 2017-04-12(102), 2017-05-14(134)
黑龙江研究区 Heilongjiang research area 46°13′16″N,126°43′27″E 2017-07-07(188), 2017-09-09(252), 2017-09-25(268)
新疆研究区 Xinjiang research area 38°29′55″N,77°21′14″E 2017-06-07(158), 2017-06-23(174), 2017-08-10(222)

Table 2

Band settings of MODIS and Landsat-8 images"

Landsat-8 OLI波段
Landsat-8 OLI band		 波长范围
Wavelength range
(nm)		 空间分辨率
Spatial resolution
(m)		 MCD43A4波段
MCD43A4 band		 波长范围
Wavelength range
(nm)		 空间分辨率
Spatial resolution
(m)
2 450-515 30 3 459-479 500
3 525-600 30 4 545-565 500
4 630-680 30 1 620-670 250
5 845-885 30 2 841-876 250
6 1560-1660 30 6 1628-1652 500
7 2100-2300 30 7 2105-2155 500

Fig. 2

Structure of technical flow"

Fig. 3

Comparison of original Landsat-8 OLI image with ESTARFM A, D, and G: true Landsat images in Hebei, Heilongjiang and Xinjiang area; C, F, and I: predicted images from Hebei, Heilongjiang and Xinjiang area via ESTARFM; B, E, and H: comparison of real image and predicted image detail."

Fig. 4

Comparison of reflectance values between real Landsat images (abscissa) and ESTARFM fusion images (ordinate) A1-A6: correlation between the true Landsat image of Hebei and the predicted bands; B1-B6: correlation between real Landsat images of Heilongjiang and predicted bands; C1-C6: correlation between true Landsat images in Xinjiang and predicted bands."

Fig. 5

Comparison of NDVI results between real and fusion images A: Hebei agricultural area (2017-04-12); B: Heilongjiang agricultural area (2017-09-09); C: Xinjiang agricultural area (2017-06-23)."

Table 3

Correlation analysis of original Landsat-8 OLI image and ESTARFM fusion"

区域与影像获取日期
Region and acquisition date		 波段
Band		 决定系数
R2		 均方根误差
RMSE		 方差
Variance		 平均绝对偏差
MAD
河北Hebei Blue 0.8684 0.0451 0.0020 0.0355
2017-04-12 Green 0.8810 0.0566 0.0032 0.0447
Red 0.9446 0.0767 0.0059 0.0614
NIR 0.8551 0.1010 0.0102 0.0741
SWIR1 0.9309 0.1029 0.0106 0.0807
SWIR2 0.9081 0.1106 0.0122 0.0901
黑龙江Heilongjiang Blue 0.8913 0.0171 0.0003 0.0171
2017-09-09 Green 0.8906 0.0190 0.0004 0.0190
Red 0.9017 0.0252 0.0006 0.0252
NIR 0.7013 0.0278 0.0008 0.0278
SWIR1 0.7775 0.0332 0.0011 0.0332
SWIR2 0.7236 0.0443 0.0019 0.0443
新疆Xinjiang Blue 0.7842 0.0407 0.0017 0.0349
2017-06-23 Green 0.8084 0.0496 0.0024 0.0420
Red 0.8613 0.0679 0.0046 0.0594
NIR 0.8682 0.0932 0.0087 0.0765
SWIR1 0.8817 0.0836 0.0069 0.0589
SWIR2 0.9123 0.0973 0.0095 0.0839

Fig. 6

Acquisition date of Landsat-8 OLI and MODIS data (DOY)"

Fig. 7

High spatial and temporal resolution of 8-day intervals in Akesu, Xinjiang"

Fig. 8

Variations of NDVI over time in images after fusion A: vegetation; B: desert; C: building; D: water."

Fig. 9

Classification map of crop remote sensing"

Fig. 10

Crop grading and growth monitoring map A: crop growth monitoring map; B: cotton growth grade map; C: rice growth grade map."

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