Optical Remote Sensing of Vegetation Water Content

2018 ◽  
pp. 183-200
Author(s):  
Colombo Roberto ◽  
Busetto Lorenzo ◽  
Meroni Michele ◽  
Rossini Micol ◽  
Panigada Cinzia
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Optical Remote Sensing ◽  
Vegetation Water Content ◽  
Vegetation Water

Optical Remote Sensing of Vegetation Water Content

2011 ◽  
pp. 227-244 ◽  
Author(s):  
Colombo Roberto ◽  
Busetto Lorenzo ◽  
Meroni Michele ◽  
Rossini Micol ◽  
Panigada Cinzia
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Optical Remote Sensing ◽  
Vegetation Water Content ◽  
Vegetation Water

Interpreting Vegetation Optical Depth (VOD) using detailed destructive vegetation sampling of a corn canopy

2020 ◽  
Author(s):  
Saeed Khabbazan ◽  
Ge Gao ◽  
Paul Vermunt ◽  
Susan Steele-Dunne ◽  
Jasmeet Judge ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Vertical Distribution ◽  
Water Content ◽  
Optical Depth ◽  
Microwave Remote Sensing ◽  
Growing Season ◽  
Vegetation Water Content ◽  
Wide Range ◽  
Vegetation Water

<p>Vegetation Optical Depth (VOD) is directly related to Vegetation Water Content (VWC), which can be used in different applications including crop health monitoring, water resources management and drought detection. Moreover, VOD is used to account for the attenuating effect of vegetation in soil moisture retrieval using microwave remote sensing.</p><p>Commonly, to retrieve soil moisture and VOD from microwave remote sensing, VWC is considered to be vertically homogeneous and relatively static.  However, nonuniform vertical distribution of water inside the vegetation may lead to unrealistic retrievals in agricultural areas. Therefore, it is important to improve the understanding of the relation between vegetation optical depth and distribution of bulk vegetation water content during the entire growing season.</p><p>The goal of this study is to investigate the effect of different factors such as phenological stage, different crop elements and nonuniform distribution of internal vegetation water content on VOD. Backscatter data were collected every 15 minutes using a tower-based, fully polarimetric, L-band radar. The methodology of Vreugdenhil et al. [1] was adapted to estimate VOD from single-incidence angle backscatter data in each polarization.</p><p>In order to characterize the vertical distribution of VWC, pre-dawn destructive sampling was conducted three times a week for a full growing season. VWC could therefore be analyzed by constituent (leaf, stem, ear) or by height.</p><p>A temporal correlation analysis showed that the relation between VOD and VWC during the growing season is not constant. The assumed linear relationship is only valid during the vegetative growth stages for corn.  Furthermore, the sensitivity of VOD to various plant components (leaf, stem and ear) varies between phenological stages and depends on polarization.</p><p>Improved understanding of VOD can contribute to improved consideration of vegetation in soil moisture retrieval algorithms. More importantly, it is essential for the interpretation of VOD data in a wide range of vegetation monitoring applications.</p><p>[1] M. Vreugdenhil,W. A. Dorigo,W.Wagner, R. A. De Jeu, S. Hahn, andM. J. VanMarle, “Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 6, pp. 3513–3531, 2016.</p>

Canopy Water Content Estimation for Typical Emerged Plant Community from Simulation Worldview-2 Data: A Case Study in Wild Duck Lake Wetland, Beijing

2013 ◽  
Vol 779-780 ◽  
pp. 1571-1575
Author(s):  
Chuan Lin ◽  
Zhao Ning Gong ◽  
Wen Ji Zhao
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Quantitative Estimation ◽  
Physiological Status ◽  
Electromagnetic Spectrum ◽  
Growth Trend ◽  
Wild Duck ◽  
Vegetation Water Content ◽  
Vegetation Water ◽  
Canopy Water

Quantitative estimation of vegetation water content with remote sensing technique is of great significance for vegetation physiological status and growth trend monitoring. It also provides a theoretical foundation for actual application of vegetation water content diagnosis using remote sensing images in Wild Duck Lake wetland. In this paper the NDVIs and SRs calculated from simulation WorldVeiw-2 curves can be used to model and predict canopy water content of typical emerged plant. The NDVIs and SRs involving the additional spectral bands of WorldView-2, such as the red-edge and near infrared regions of the electromagnetic spectrum, improve the prediction accuracy compared with the traditional NDVIs and SRs.

scholarly journals Monitoring Cotton (Gossypium sps.) Crop Condition through Synergy of Optical and Radar Remote Sensing

2018 ◽  
Author(s):  
Dipanwita Haldar ◽  
Rojalin Tripathy ◽  
Viral Dave ◽  
Rucha Dave ◽  
Bimal Bhattacharya ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Spatial Variability ◽  
Water Content ◽  
Vegetation Index ◽  
Broad Band ◽  
Plant Water ◽  
Area Index ◽  
Vegetation Water Content ◽  
Vegetation Water ◽  
Crop Condition

Morphological parameters like cotton height, branches, Leaf Area Index and biomass are mainly affected by the vegetation water content (VWC). Periodical assessment of the VWC and crop parameters is required for timely management of the crop for maximizing yield. The study aimed at using both optical and microwave remotely sensed data to assess cotton crop condition based on the above mentioned traits. Vegetation indices (VI) derived from ground based measurements (5 narrow band and 2 broad band VIs) as well as satellite derived reflectance (2 broad band VIs) were assessed. Regression models were derived for estimating LAI, biomass and plant water content using the ground based indices and applied to the satellite derived spectral index (from LISS-III) map to estimate the respective parameters. HH and HV polarization from RISAT-1 were used to derive Radar Vegetation Index (RVI). The coefficient of determination of the model for estimating LAI, biomass and vegetation water content of cotton with optical vegetation index as input parameter were found to be 0.42, 0.51 and 0.52, respectively. The correlation between RVI and plant height, date of planting in terms of the age of the crop and vegetation water content were found to range between 0.4 to 0.6. The fresh biomass from RVI showed spatial variability from 100 gm-2 to 4000 gm-2 while the dry biomass map derived from NDVI showed spatial variability of 50 to 950 g m-2 for the study area. Plant water content in the district varied from 65 to 85%. The correlation between optical vegetation index and RVI was not significant. Hence a multiple linear regression model using both optical index (NDVI and LSWI) and SAR index (RVI) was developed to assess the LAI, biomass and plant water content. The model showed a R2 of 0.5 for LAI estimation but not significant for biomass and water content. This study show cased the use of combined optical and microwave (C band) remote sensing for cotton condition assessment.

Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1

2002 ◽  
Vol 82 (2-3) ◽  
pp. 188-197 ◽  
Author(s):  
Pietro Ceccato ◽  
Nadine Gobron ◽  
Stéphane Flasse ◽  
Bernard Pinty ◽  
Stefano Tarantola
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Spectral Index ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Vegetation Water Content ◽  
Vegetation Water
Sign in / Sign up

Export Citation Format

Share Document