Abstract
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.
Highlights
Soil moisture content (SMC) is a key parameter that needs be monitored in order to provide information for plant growth and crop management, as well as to understand water-associated hazards such as flood and drought [1,2]
Remote Sens. 2019, 11, 716 (Results and Discussion) and divided into three parts: (1) the response of soil reflectance to various SMCs, based on the change in the spectral features of the soil samples at 10 various SMCs, from 0% to the saturated state; (2) the regional Landsat 8 (L8) band-based SMC estimation model that was developed using the curve-fit regression of SMC, and reflectance corresponding to the L8 spectral bands; (3) a discussion is made on the relationship of the estimated SMC and the Normal Difference Drought Index (NDDI) [26] in drought assessment and mapping
This study demonstrates the appropriateness of the spectral ratio of the L8 band near-infrared band (NIR)-to-short-wave infrared 2 band (SWIR2) for estimating the SMC, L8RSM, through analyses of the spectral response corresponding L8 band data, with various SMC over various soil types, which are obtained from in-laboratory measurements of Rrs(λ) and SMC (R2 = 0.73 and RMSE ~ 12%)
Summary
Soil moisture content (SMC) is a key parameter that needs be monitored in order to provide information for plant growth and crop management, as well as to understand water-associated hazards such as flood and drought [1,2]. Remote sensing has been used in many studies for rapidly measuring and mapping the surface soil moisture at large spatial scales [4,5,6,7]. Both optical and microwave remote sensing have been used to estimate SMC, microwave remote sensing is favored, because of its independence of solar illumination and cloud cover conditions [8]. Active microwave data, which provides even better spatial resolutions (10–100 m), are still not suitable for monitoring the SMC during the short dry season in tropical regions, due to its poor temporal resolution and high cost requirements [9,10]. Finding suitable optical remote sensing data to couple with microwave remote sensing data for effective monitoring of SMC has been encouraged and carried out
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