Evaluating satellite hyperspectral (Orbita) and multispectral (Landsat 8 and Sentinel-2) imagery for identifying cotton acreage

ABSTRACT

Hyperspectral remote sensing demonstrates a great potential for identifying crops. Orbita hyperspectral image satellite (OHS) is a new hyperspectral satellite in orbit with the highest spectral and spatial resolutions and relatively short spectral range. In this study, the OHS, along with Land Remote-Sensing Satellite (System, Landsat) eight operational land imager (OLI) images (assigned as L8) and Sentinel-2B (S2B) images, was used to identify cotton and estimate its acreage in Beiquan town, Shihezi city, Xinjiang Uygur Autonomous Region of China based on spectral reflectance, sample separability, and accuracy. And Two Class Support vector machine was adapted to map cotton acreage. For the spectral reflectance of cotton, OHS is slightly higher than the analytical spectral device (ASD) in the visible range and is similar in the near-infrared range. Both the S2B and L8 spectrum curves of cotton fit well with the ASD spectrum’s. For the sample separability, the pair values between cotton and non-cotton (e.g. impervious surface, water, maize, grape, etc.) of OHS and S2B are better than that of L8 in general, indicating that OHS and S2B are more reliable than L8 for cotton identification using OHS, L8, and S2B. The results show that the overall accuracy of these three sensors is comparable: an overall accuracy of 0.96 from OHS, 0.95 from S2B, and 0.94 from L8. Moreover, the cotton identification accuracy is not only related to the spatial resolution but also to the spectral resolution and band position, and more importantly to each sensor specifications, even though each satellite hyperspectral and multispectral sensor has its own unique advantages in being complimentary for identifying cotton and other crops.

Acknowledgements

The OHS data were obtained from the Orbita Aerospace Science & Technology Co., Ltd. The S2B data were downloaded freely through the web portal of the Copernicus project (https://scihub.copernicus.eu). The L8 data were obtained from the United States Geological Survey (USGS) Earth Explore website (http://earthexplorer.usgs.gov).

Disclosure statement

The authors declare no conflicts of interest.

Additional information

Funding

This research was funded by National Natural Science Foundation of China (42004013), the Promoting Economic Development of Guangdong Province Special Foundation in 2018 (GDME-2018E005), the National Modern Agricultural Technology System Cotton Industry System Field Management Mechanization Post-Expert Project (CARS-15-22), Cloud Service Platform and Applications based on “ZHUHAI No.1” Constellation & Remote Sensing Big Data (ZH0111-0405-170027-P-WC), and the Foundation of Young Creative Talents in Higher Education of Guangdong Province (2019KQNCX009) and the open fund of Guangxi Key Laboratory of Spatial Information and Geomatics (19-050-11-03).