A comparison between different vegetation water indices in the ability of monitoring water status of wheat in April

2008 ◽  
Author(s):  
Wang Pu ◽  
Kong Fanming ◽  
Ding Huiyan ◽  
Zhao Liuhui ◽  
Nie Jianliang
Keyword(s):  
Water Status ◽  
Vegetation Water ◽  
Water Indices

Detecting changes in root zone soil moisture from radar vegetation backscatter

2020 ◽  
Author(s):  
Coleen Carranza ◽  
Tim van Emmerik ◽  
Martine van der Ploeg
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Hydrological Cycle ◽  
Root Zone ◽  
Water Status ◽  
Roughness Parameter ◽  
Growing Season ◽  
Irrigation Scheduling ◽  
Vegetation Water Content ◽  
Vegetation Water

<p>Root zone soil moisture (θ<sub>rz</sub>) is a crucial component of the hydrological cycle and provides information for drought monitoring, irrigation scheduling, and carbon cycle modeling. During vegetation conditions, estimation of θ<sub>rz</sub> thru radar has so far only focused on retrieving surface soil moisture using the soil component of the total backscatter (σ<sub>soil</sub>), which is then assimilated into physical hydrological models. The utility of the vegetation component of the total backscatter (σ<sub>veg</sub>) has not been widely explored and is commonly corrected for in most soil moisture retrieval methods. However, σ<sub>veg </sub>provides information about vegetation water content. Furthermore, it has been known in agronomy that pre-dawn leaf water potential is in equilibrium with that of the soil. Therefore soil water status can be inferred by examining  the vegetation water status. In this study, our main goal is to determine whether changes in root zone soil moisture (Δθ<sub>rz</sub>) shows corresponding changes in vegetation backscatter (Δσ<sub>veg</sub>) at pre-dawn. We utilized Sentinel-1 (S1) descending pass and in situ soil moisture measurements from 2016-2018 at two soil moisture networks (Raam and Twente) in the Netherlands. We focused on corn and grass which are the most dominant crops at the sites and considered the depth-averaged θ<sub>rz</sub> up to 40 cm to capture the rooting depths for both crops. Dubois’ model formulation for VV-polarization was applied to estimate the surface roughness parameter (H<sub>rms</sub>) and σ<sub>soil </sub>during vegetated periods. Afterwards, the Water Cloud Model was used to derive σ<sub>veg</sub> by subtracting σ<sub>soil</sub> from S1 backscatter (σ<sub>tot</sub>). To ensure that S1 only measures vegetation water content, rainy days were excluded to remove the influence of intercepted rainfall on the backscatter. The slope of regression lines (β) fitted over plots of Δσ<sub>veg</sub> against Δθ<sub>rz</sub> were used investigate the dynamics over a growing season. Our main result indicates that Δσ<sub>veg </sub>- Δθ<sub>rz</sub> relation is influenced by crop growth stage and changes in water content in the root zone. For corn, changes in β’s over a growing season follow the trend in a crop coefficient (K<sub>c</sub>) curve, which is a measure of crop water requirements. Grasses, which are perennial crops, show trends corresponding to the mature crop stage. The correlation between soil moisture (Δθ) at specific soil depths (5, 10, 20, and 40 cm) and Δσ<sub>veg </sub> matches root growth for corn and known rooting depths for both corn and grass. Dry spells (e.g. July 2018) and a large increase in root zone water content in between two dry-day S1 overpass (e.g. from rainfall) result in a lower β, which indicates that Δσ<sub>veg</sub> does not match well with Δθ<sub>rz</sub>. The influence of vegetation on S1 backscatter is more pronounced for corn, which translated to a clearer Δσ<sub>veg</sub> - Δθ<sub>rz</sub> relation compared to grass. The sensitivity of Δσ<sub>veg</sub> to Δθ<sub>rz</sub> in corn means that the analysis may be applicable to other broad leaf crops or forested areas, with potential applications for monitoring  periods of water stress.</p>

Sensitivity studies of the moisture effects on MODIS SWIR reflectance and vegetation water indices

2008 ◽  
Vol 29 (24) ◽  
pp. 7065-7075 ◽  
Author(s):  
Lingli Wang ◽  
John J. Qu ◽  
Xianjun Hao ◽  
Qingping Zhu
Keyword(s):  
Moisture Effects ◽  
Sensitivity Studies ◽  
Vegetation Water ◽  
Water Indices

A new Remote Sensing-based vegetation water stress index: -Temperature Vegetation Water Stress Index (TVWSI)

2020 ◽  
Author(s):  
Rakesh Chandra Joshi ◽  
Dongryeol Ryu ◽  
Gary J. Sheridan ◽  
Patrick N.J. Lane
Keyword(s):  
Water Stress ◽  
Water Status ◽  
Canopy Temperature ◽  
Stress Index ◽  
Spectral Space ◽  
Water Stress Index ◽  
Water Stress Condition ◽  
Forested Areas ◽  
Vegetation Water ◽  
Canopy Water

<p>Remote sensing techniques are widely used to evaluate the biophysical status of vegetation, including water stress caused by soil water deficit. Based on the nominal links between water stress condition, transpiration and canopy temperature in the vegetation, numerous studies have used a trapezoidal relationship between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) over vegetated surfaces to develop the water stress metric, in which the level of stress could be identified by the spatial location of the pixels on the spectral space (Goetz and Goetz 1997; Lambin, Lambin, and Ehrlich 1996; Nemani et al. 1993; Nemani and Running 1989; Price 1990; Sandholt, Rasmussen, and Andersen 2002). However, the amount of change in canopy temperature could also vary spatially by the canopy water status at that time. Thus, LST-NDVI alone cannot construct an efficient metric to see the spatial patterns of water stress at ecosystem level unless they are coupled with water status of vegetation at that moment. This study hypothesizes that a metric which can combine LST-NDVI information with an indicator for canopy water status could give more accurate estimations of the real-time vegetation water stress. The remotely sensed plant canopy water status indicator (a metric based on canopy reflection in the Short-Wave Infrared region (SWIR)) could add the canopy water status information to the LST-NDVI based indices, which may better explain spatial/temporal water stress condition in the plants especially in densely forested areas where signal saturation is a major issue. In this study, the third-dimensional information of SWIR has been combined with LST-NDVI spectral space to create a new remotely sensed vegetation water stress index, TVWSI (Temperature Vegetation Water Stress Index) which seems to be more realistic to capture stress dynamics at large scale. </p><p>Sixty grids (2 km X 2 km) each containing 16 pixels of daily MODIS-reflectance (band 1 – band 7, 500 m spatial resolution) and 4 pixels of daily MODIS-LST (1 km spatial resolution) were chosen over forested areas in Victoria representing most of the bioregions as classified by the Interim Biogeographic Regionalisation for Australia (IBRA7). From 2002 to 2018 daily TVWSI values of each grid were evaluated against the modelled daily available soil moisture content in the top 1 m of the soil profile, and rainfall data, from the Australian Bureau of Meteorology (BOM). TVWSI performed better than other dryness indices mentioned in the literature. A high correlation was obtained between TVWSI vs. soil moisture and TVWSI vs. rainfall with a coefficient of determination value of 0.6 (p<0.001) and 0.61 (p<0.001) respectively when data were combined spatially and temporally. Even improved correlations ranging (0.4-0.7, p<0.001) were obtained for individual grids over the mentioned period. While correlation ranging (0.15-0.48, p<0.001) were obtained using dryness indices like Perpendicular Drought Index (PDI), Modified PDI (MPDI), Temperature Vegetation Dryness Index (TVDI) and Vegetation Supply Water Index (VSWI). The result shows that the TVWSI can capture real-time ecosystem water stress well and the metric could be an efficient input parameter for many hydrological, drought and fire prediction models.</p><p> </p>

Retrieving Leaf and Canopy Water Content of Winter Wheat Using Vegetation Water Indices

2018 ◽  
Vol 11 (1) ◽  
pp. 112-126 ◽  
Author(s):  
Chao Zhang ◽  
Elizabeth Pattey ◽  
Jiangui Liu ◽  
Huanjie Cai ◽  
Jiali Shang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Water Content ◽  
Vegetation Water ◽  
Canopy Water ◽  
Water Indices

scholarly journals Atmospherically resistant vegetation water indices using the 970-nm water absorption feature

2020 ◽  
Vol 14 (03) ◽  
pp. 1
Author(s):  
Quanjun Jiao ◽  
Liangyun Liu ◽  
Jiangui Liu ◽  
Hao Zhang ◽  
Bing Zhang
Keyword(s):  
Water Absorption ◽  
Absorption Feature ◽  
Vegetation Water ◽  
Water Indices

scholarly journals Estimating Gravimetric Water Content of a Winter Wheat Field from L-Band Vegetation Optical Depth

2019 ◽  
Vol 11 (20) ◽  
pp. 2353
Author(s):  
Thomas Meyer ◽  
Thomas Jagdhuber ◽  
María Piles ◽  
Anita Fink ◽  
Jennifer Grant ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Water Content ◽  
Water Status ◽  
Growth Cycle ◽  
Wheat Field ◽  
Vegetation Height ◽  
Vegetation Water Content ◽  
L Band ◽  
Vegetation Water

A considerable amount of water is stored in vegetation, especially in regions with high precipitation rates. Knowledge of the vegetation water status is essential to monitor changes in ecosystem health and to assess the vegetation influence on the water budget. In this study, we develop and validate an approach to estimate the gravimetric vegetation water content (mg), defined as the amount of water [kg] per wet biomass [kg], based on the attenuation of microwave radiation through vegetation. mg is expected to be more closely related to the actual water status of a plant than the area-based vegetation water content (VWC), which expresses the amount of water [kg] per unit area [m2]. We conducted the study at the field scale over an entire growth cycle of a winter wheat field. Tower-based L-band microwave measurements together with in situ measurements of vegetation properties (i.e., vegetation height, and mg for validation) were performed. The results indicated a strong agreement between the in situ measured and retrieved mg (R2 of 0.89), with mean and standard deviation (STD) values of 0.55 and 0.26 for the in situ measured mg and 0.57 and 0.19 for the retrieved mg, respectively. Phenological changes in crop water content were captured, with the highest values of mg obtained during the growth phase of the vegetation (i.e., when the water content of the plants and the biomass were increasing) and the lowest values when the vegetation turned fully senescent (i.e., when the water content of the plant was the lowest). Comparing in situ measured mg and VWC, we found their highest agreement with an R2 of 0.95 after flowering (i.e., when the vegetation started to lose water) and their main differences with an R2 of 0.21 during the vegetative growth of the wheat vegetation (i.e., where the mg was constant and VWC increased due to structural changes in vegetation). In addition, we performed a sensitivity analysis on the vegetation volume fraction (δ), an input parameter to the proposed approach which represents the volume percentage of solid plant material in air. This δ-parameter is shown to have a distinct impact on the thermal emission at L-band, but keeping δ constant during the growth cycle of the winter wheat appeared to be valid for these mg retrievals.

scholarly journals Vegetation Water Content Prediction: Towards More Relevant Explicatory Waveband Variables

2017 ◽  
Author(s):  
Eric Ariel L. Salas
Keyword(s):  
Water Content ◽  
Field Data ◽  
Water Status ◽  
Individual Performance ◽  
Absorption Feature ◽  
Water Index ◽  
Vegetation Water Content ◽  
Vegetation Water ◽  
The Individual ◽  
Canopy Water

Although the water absorption feature (WAF) at 970 nm is not very well-defined, it may be used alongside other indices to estimate the canopy water content.  The individual performance of a number of existing vegetation water content (VWC) indices against the WAF is assessed using linear regression model.  We developed a new Combined Vegetation Water Index (CVWI) by merging indices to boost the weak absorption feature. CVWI showed a promise in assessing the vegetation water status derived from the 970 nm absorption wavelength.  CVWI was able to differentiate two groups of dataset when regressed against the absorption feature.  CVWI could be seen as an easy and robust method for vegetation water content studies using hyperspectral field data.

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