Abstract
Abstract. Water extraction from remote sensing images can be applied in water resources monitoring, as well as an essential preprocessing for water quality parameter retrieval. China’s new generation Gaofen-5 satellite carries a hyperspectral imager named AHSI, which is suitable for water quality monitoring. In this paper, to determine the spectral index most suitable for AHSI data’s water extraction, we selected a large lake and a reservoir in China as the study areas, and used corresponding AHSI data for water extraction experiment. In the experiment, NDWI, MNDWI and AWEI were all tested on the AHSI reflectance data, and major water bodies were delineated by implementing the histogram bimodal method on the water index images. Based on analyses with water index images and water extraction results, NDWI is shown to be the optimal index for water extraction of AHSI data among these three widely-used indexes, as it can accurately extract water bodies and also eliminate pixels of water plants or shadows between land and water. In addition, MNDWI can be used on AHSI data since it is most robust in terms of providing a bimodal histogram. AWEI is, however, not suggested to be used for water extraction of the AHSI data.
Highlights
As an important part of water resources, land surface water, including lakes, rivers and reservoirs, changes dynamically in time and space
modified NDWI (MNDWI) shows obvious strip noise in water areas, this is due to the strip noise existing in the short-wave infrared (SWIR) bands of Advanced Hyperspectral Imager (AHSI) data
Comparing these water masks with its reflectance image, we have found that Normalized Difference Water Index (NDWI) image gives the best extraction results, as it is the only one that distinguish pixels with water plant or flooded farmland from water area
Summary
As an important part of water resources, land surface water, including lakes, rivers and reservoirs, changes dynamically in time and space. Existing methods could be divided into four categories: (1) thresholding based on spectral characteristics (Jain et al, 2005, Xu, 2006, Feyisa et al, 2014, Zhang et al, 2018), (2) image segmentation based on local features (He et al, 2016, Kaplan, Avdan, 2017), (3) machine learning classification method (Isikdogan et al, 2017, Chen et al, 2018, Wang et al, 2019), (4) mathematical-morphology driven approach (Daya Sagar et al, 1995, Rishikeshan, Ramesh, 2018) Among these methods, the first category is most widely-used, especially the threshold segmentation based on water spectral indices, because it is easy to understand and calculate. Compared with MNDWI and NDWI applied on Landsat imagery, AWEI uses the Blue band (band 1) and one more SWIR band (band 7)
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