A method for canopy water content estimation for highly vegetated surfaces-shortwave infrared perpendicular water stress index

2007 ◽  
Vol 50 (9) ◽  
pp. 1359-1368 ◽  
Author(s):  
Abduwasit Ghulam ◽  
Zhao-Liang Li ◽  
QiMing Qin ◽  
QingXi Tong ◽  
JiHua Wang ◽  
...  
Keyword(s):  
Water Stress ◽  
Water Content ◽  
Stress Index ◽  
Water Stress Index ◽  
Shortwave Infrared ◽  
Canopy Water

scholarly journals Modeling Vegetation Water Stress over the Forest from Space: Temperature Vegetation Water Stress Index (TVWSI)

2021 ◽  
Vol 13 (22) ◽  
pp. 4635
Author(s):  
Rakesh Chandra Joshi ◽  
Dongryeol Ryu ◽  
Gary J. Sheridan ◽  
Patrick N. J. Lane
Keyword(s):  
Soil Moisture ◽  
Water Stress ◽  
Water Content ◽  
Stress Index ◽  
Australian Water ◽  
Environmental Models ◽  
Water Stress Index ◽  
Vegetation Water ◽  
Canopy Water ◽  
Content Information

The conventional Land Surface Temperature (LST)–Normalized Difference Vegetation Index (NDVI) trapezoid model has been widely used to retrieve vegetation water stress. However, it has two inherent limitations: (1) its complex and computationally intensive parameterization for multi-temporal observations and (2) deficiency in canopy water content information. We tested the hypothesis that an improved water stress index could be constructed by the representation of canopy water content information to the LST–NDVI trapezoid model. Therefore, this study proposes a new index that combines three indicators associated with vegetation water stress: canopy temperature through LST, canopy water content through Surface Water Content Index (SWCI), and canopy fractional cover through NDVI in one temporally transferrable index. Firstly, a new optical space of SWCI–NDVI was conceptualized based on the linear physical relationship between shortwave infrared (SWIR) and soil moisture. Secondly, the SWCI–NDVI feature space was parameterized, and an index d(SWCI, NDVI) was computed based on the distribution of the observations in the SWCI–NDVI spectral space. Finally, standardized LST (LST/long term mean of LST) was combined to d(SWCI, NDVI) to give a new water stress index, Temperature Vegetation Water Stress Index (TVWSI). The modeled soil moisture from the Australian Water Resource Assessment—Landscape (AWRA-L) and Soil Water Fraction (SWF) from four FLUXNET sites across Victoria and New South Wales were used to evaluate TVWSI. The index TVWSI exhibited a high correlation with AWRA-L soil moisture (R2 of 0.71 with p < 0.001) and the ground-based SWF (R2 of 0.25–0.51 with p < 0.001). TVWSI predicted soil moisture more accurately with RMSE of 21.82 mm (AWRA-L) and 0.02–0.04 (SWF) compared to the RMSE ranging 28.98–36.68 mm (AWRA-L) and 0.03–0.05 (SWF) were obtained for some widely used water stress indices. The TVWSI could also be a useful input parameter for other environmental models.

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

&lt;p&gt;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.&amp;#160;&lt;/p&gt;&lt;p&gt;Sixty grids (2 km X 2 km) each containing 16 pixels of daily MODIS-reflectance (band 1 &amp;#8211; 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&lt;0.001) and 0.61 (p&lt;0.001) respectively when data were combined spatially and temporally. Even improved correlations ranging (0.4-0.7, p&lt;0.001) were obtained for individual grids over the mentioned period. While correlation ranging (0.15-0.48, p&lt;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.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Modified Shortwave Infrared Perpendicular Water Stress Index: A Farmland Water Stress Monitoring Method

2013 ◽  
Vol 52 (9) ◽  
pp. 2024-2032 ◽  
Author(s):  
Haixia Feng ◽  
Chao Chen ◽  
Heng Dong ◽  
Jinliang Wang ◽  
Qingye Meng
Keyword(s):  
Water Stress ◽  
Bare Soil ◽  
Stress Index ◽  
Vegetation Coverage ◽  
Crop Water ◽  
Stress Monitoring ◽  
Water Stress Index ◽  
Different Depths ◽  
Vegetation Water ◽  
Shortwave Infrared

AbstractCrop water stress monitoring by remote sensing has been the focus of numerous studies. In this paper, specifically red (630–690 nm) and shortwave infrared (SWIR; 1550–1750 nm) wavelength bands are identified to monitor farmland water stress, and a method [modified shortwave infrared perpendicular water stress index (MSPSI)] is developed that is based on the spectral space constructed by SWIR − Red (Rd) and SWIR + Red (Rs). The MSPSI stayed at mostly the same water stress level for full vegetation coverage cases with high vegetation water content and saturated bare soil as well as full vegetation coverage with extremely low vegetation water and dry bare soil in the Rs–Rd spectral feature space. This approach makes the water stress conditions between different covers comparable and the MSPSI applicable to farmland water stress monitoring in different vegetation covers throughout the growing season. To validate the proposed index, the MSPSI calculated from Thematic Mapper images and Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m reflectance products (from March to October) in the Ningxia Hui Autonomous Region was compared with the ground-measured soil moisture content at different depths. It is evident from the results that the MSPSI derived from satellite imageries is highly correlated with ground-measured soil moisture at different depths (7.6 and 10 cm), with coefficients of determination R2 of 0.666, 0.512, 0.576, 0.361, 0.383, 0.391, 0.357, 0.410, and 0.418. The paper concludes that MSPSI is a promising index for crop water stress monitoring throughout the growing season.

Monitoring Crop Water Status and Irrigation Needs Using Multispectral Airborne Sensors

HortScience ◽  
1995 ◽  
Vol 30 (4) ◽  
pp. 905D-905
Author(s):  
Thomas R. Clarke ◽  
M. Susan Moran
Keyword(s):  
Water Stress ◽  
Near Infrared ◽  
Water Status ◽  
Canopy Cover ◽  
Stress Index ◽  
Crop Water ◽  
Vegetative Cover ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Subsurface Drip

Water application efficiency can be improved by directly monitoring plant water status rather than depending on soil moisture measurements or modeled ET estimates. Plants receiving sufficient water through their roots have cooler leaves than those that are water-stressed, leading to the development of the Crop Water Stress Index based on hand-held infrared thermometry. Substantial error can occur in partial canopies, however, as exposed hot soil contributes to deceptively warm temperature readings. Mathematically comparing red and near-infrared reflectances provides a measure of vegetative cover, and this information was combined with thermal radiance to give a two-dimensional index capable of detecting water stress even with a low percentage of canopy cover. Thermal, red, and near-infrared images acquired over subsurface drip-irrigated cantaloupe fields demonstrated the method's ability to detect areas with clogged emitters, insufficient irrigation rate, and system water leaks.

An insight to the performance of crop water stress index for olive trees

2013 ◽  
Vol 118 ◽  
pp. 79-86 ◽  
Author(s):  
N. Agam ◽  
Y. Cohen ◽  
J.A.J. Berni ◽  
V. Alchanatis ◽  
D. Kool ◽  
...  
Keyword(s):  
Water Stress ◽  
Stress Index ◽  
Crop Water ◽  
Crop Water Stress Index ◽  
Water Stress Index ◽  
Olive Trees ◽  
Crop Water Stress
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