Abstract
Among the indirect estimation approaches of soil water content in the upper layer of the soil, the “triangle method” is one of the most common that relies on the simple relationship between the optical and thermal features sensed via Earth Observation. These features are controlled by water content at the surface and within the root zone but also by meteorological forcing including air temperature and humidity, as well as solar radiation. Night- and day-time MODIS composites of land-surface temperature (LST) allowed applying a version of the triangle method that takes into account the temporal admittance of the soil. In this study, it has been applied to a long time-series of pair images to analyze the seasonal influence of the meteorological forcing on a triangle method index (or temperature–vegetation index, TVX), as well as to discuss extra challenges of the diachronic approach including seasonality effects and the variability of environmental forcing. The Imera Meridionale basin (Sicily, Italy) has been chosen to analyze the method over a time-series of 12 years. The analysis reveals that, under these specific environmental and climatic conditions (strong seasonality and rainfall out of phase with vegetation growth), Normalized Difference Vegetation Index (NDVI) and LST pairs move circularly in time within the optical vs. thermal feature space. Concordantly, the boundaries of the triangle move during the seasons. Results showed a strong correlation between TVX and rainfall normalized amplitudes of the power spectra (r2 ~0.8) over the range of frequencies of the main harmonics.
Highlights
Soil water content plays an important role in the vegetated natural and agricultural ecosystems.The accurate assessment of the spatiotemporal traits of this fundamental variable is crucial for agricultural practices and, in general, for the detection of stress conditions over vegetated lands.Among the indirect estimations of soil water content, θ, in the upper soil layer, the “triangle method” is a methodology based solely on the joint analysis of the optical and the thermal features sensed via EarthObservation, EO [1].The traditional single acquisition formulation of the triangle method is based on the hypothesis that meteorological conditions are fairly constant across the area
To other methods, it requires the use of a single pair of VI/land-surface temperature (LST) images over a scene where a wide range of θ, covering both dry and wet extremes under a full range of vegetation coverage
The soil water content of a generic pixel is determined by looking at two variables: One representing a vegetation index (VI, e.g., Normalized Difference Vegetation Index (NDVI) or the vegetation fractional cover) and the land representing a vegetation index (VI, e.g., NDVI or the vegetation fractional cover) and the land surface temperature
Summary
Soil water content plays an important role in the vegetated natural and agricultural ecosystems.The accurate assessment of the spatiotemporal traits of this fundamental variable is crucial for agricultural practices (e.g., irrigation) and, in general, for the detection of stress conditions over vegetated lands.Among the indirect estimations of soil water content, θ, in the upper soil layer, the “triangle method” is a methodology based solely on the joint analysis of the optical and the thermal features (i.e., vegetation indices, VIs, and Land Surface Temperature, LST, respectively) sensed via EarthObservation, EO [1].The traditional single acquisition formulation of the triangle method is based on the hypothesis that meteorological conditions are fairly constant across the area. Soil water content plays an important role in the vegetated natural and agricultural ecosystems. The accurate assessment of the spatiotemporal traits of this fundamental variable is crucial for agricultural practices (e.g., irrigation) and, in general, for the detection of stress conditions over vegetated lands. Among the indirect estimations of soil water content, θ, in the upper soil layer, the “triangle method” is a methodology based solely on the joint analysis of the optical and the thermal features (i.e., vegetation indices, VIs, and Land Surface Temperature, LST, respectively) sensed via Earth. To other methods (i.e., the thermal inertia method [2,3]), it requires the use of a single pair of VI/LST images over a scene where a wide range of θ, covering both dry and wet extremes under a full range of vegetation coverage.
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