Remote Sensing (Mar 2019)

Effective Band Ratio of Landsat 8 Images Based on VNIR-SWIR Reflectance Spectra of Topsoils for Soil Moisture Mapping in a Tropical Region

  • Dinh Ngo Thi,
  • Nguyen Thi Thu Ha,
  • Quy Tran Dang,
  • Katsuaki Koike,
  • Nhuan Mai Trong

DOI
https://doi.org/10.3390/rs11060716
Journal volume & issue
Vol. 11, no. 6
p. 716

Abstract

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Effective mapping and monitoring of soil moisture content (SMC) in space and time is an expected application of remote sensing for agricultural development and drought mitigation, particularly in the context of global climate change impact, given that agricultural drought is occurring more frequently and severely worldwide. This study aims to develop a regional algorithm for estimating SMC by using Landsat 8 (L8) imagery, based on analyses of the response of soil reflectance, by corresponding L8 bands with the change of SMC from dry to saturated states, in all 103 soil samples taken in the central region of Vietnam. The L8 spectral band ratio of the near-infrared band (NIR: 850–880 nm, band 5) versus the short-wave infrared 2 band (SWIR2: 2110 to 2290 nm, band 7) shows the strongest correlation to SMC by a logarithm function (R2 = 0.73 and the root mean square error, RMSE ~ 12%) demonstrating the high applicability of this band ratio for estimating SMC. The resultant maps of SMC estimated from the L8 images were acquired over the northern part of the Central Highlands of Vietnam in March 2015 and March 2016 showed an agreement with the pattern of severe droughts that occurred in the region. Further discussions on the relationship between the estimated SMC and the satellite-based retrieved drought index, the Normal Different Drought Index, from the L8 image acquired in March 2016, showed a strong correlation between these two variables within an area with less than 20% dense vegetation (R2 = 0.78 to 0.95), and co-confirms the bad effect of drought on almost all areas of the northern part of the Central Highlands of Vietnam. Directly estimating SMC from L8 imagery provides more information for irrigation management and better drought mitigation than by using the remotely sensed drought index. Further investigations on various soil types and optical sensors (i.e., Sentinel 2A, 2B) need to be carried out, to extend and promote the applicability of the prosed algorithm, towards better serving agricultural management and drought mitigation.

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