Abstract
This article aims to explore the applicability of SMMI (soil moisture monitoring index), MSMMI (modified soil moisture monitoring index), PDI (perpendicular drought index), and MPDI (modified perpendicular drought index) in estimating soil moisture (SM) in farmland. The random forest classifier was used to obtain two-stage land cover types maps. The sensitivity of Sentinel-2 spectral bands to the measured SM at a depth of 0-5 cm was optimized by random forest regression. According to the sensitive bands, SMMI and PDI from different feature spaces were constructed to explore their feasibility for monitoring SM under different land cover types. Second, fractional vegetation cover (FVC) in the study area was estimated by nine kinds of FVC estimation models and compared with the measured FVC. The effects of different FVC methods on estimating SM by MSMMI and MPDI were evaluated. The results show that red edge and short-wave infrared (SWIR) bands of Sentinel-2 had irreplaceable effects on the land cover classification. In terms of monitoring SM in bare soil areas, the SM indices with SWIR bands had high correlations with measured SM. For vegetation-covered areas, MSMMI from the FVCgr model (dimidiate pixel model with red edge bands) and the Short wave infrared1-Short wave infrared2 feature space had the highest correlation with the measured 0-5 cm depth SM. Whether vegetation-covered areas or bare soil areas, the combination of red edge and SWIR bands can effectively improve the estimation accuracy of SM. MSMMI can be used as the best SMMI in the study area. Sentinel-2 images, with great potential, can effectively estimate SM at a depth of 0-5 cm in farmland with complex environments.
Highlights
Soil moisture (SM) plays an important role in climate change because it is a basic parameter in the process of formation, transformation, and consumption of land and water resources [1]
This study mainly focused on the following questions: 1) Which bands and derived vegetation indices of Sentinel-2 are more important for land cover classification in agricultural areas? (Part A of section IV) 2) For different land cover types, which bands of Sentinel-2 are more sensitive to the measured SM? Can red edge bands of Sentinel-2 improve the estimation accuracy of SM? (Part B and C of section IV) 3) Which of the four soil moisture indices (SMMI, Perpendicular Drought Index (PDI), Modified Soil Moisture Monitoring Index (MSMMI), and Modified Perpendicular Drought Index (MPDI)) can better accurately estimate SM at the regional scale? Which estimation model of fractional vegetation cover (FVC) is more suitable for monitoring SM by MSMMI and MPDI? (Part D and E of section IV)
The results showed that the correlations between MSMMI from SWIR1-SWIR2 space based on FVCg with red edge bands (FVCgr) model and measured SM was still the highest (Rall = -0.745, Rveg = -0.707, and Rsoil = -0.897)
Summary
Soil moisture (SM) plays an important role in climate change because it is a basic parameter in the process of formation, transformation, and consumption of land and water resources [1]. Insufficient water supply will hinder the growth of crops, indirectly affecting yield. Excessive water supply will lead to soil hypoxia, further hinder the respiration of crop roots, resulting in reduced physiological functions and even death. Remote sensing methods for monitoring SM are as follows: thermal inertia [2], vegetation water supply index [3], temperature vegetation dryness index [4], anomaly vegetation index [5], conditional vegetation index [6], vegetation temperature condition index [7], microwave [8,9,10] and spectral feature space method [11,12,13,14,15]. Methods derived from spectral feature spaces are widely applied to assess SM because of being simple and easy to operate
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