Remote sensing of vegetation water content using shortwave infrared reflectances

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.Commonly, to retrieve soil moisture and VOD from microwave remote sensing, VWC is considered to be vertically homogeneous and relatively static.&#160; 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.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.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.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.&#160; Furthermore, the sensitivity of VOD to various plant components (leaf, stem and ear) varies between phenological stages and depends on polarization.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.[1] M. Vreugdenhil,W. A. Dorigo,W.Wagner, R. A. De Jeu, S. Hahn, andM. J. VanMarle, &#8220;Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval,&#8221; IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 6, pp. 3513&#8211;3531, 2016.

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Assessment of vegetation water content in wheat using near and shortwave infrared SPOT-5 Data in an irrigated area

Revue des sciences de l eau ◽

10.7202/1036542ar ◽

2016 ◽

Vol 29 (2) ◽

pp. 97-107 ◽

Cited By ~ 1

Author(s):

Tarik Benabdelouahab ◽

Riad Balaghi ◽

Rachid Hadria ◽

Hayat Lionboui ◽

Bernard Tychon

Keyword(s):

Water Content ◽

Irrigation Management ◽

Grain Filling ◽

Irrigation Scheduling ◽

Stress Index ◽

Vegetation Water Content ◽

Irrigated Area ◽

Vegetation Water ◽

Shortwave Infrared ◽

Spot 5

In Morocco, water availability is becoming a national priority for the agricultural sector. In this context, the stakeholders try continuously to improve strategies of water irrigation management, on one hand, and to assess vegetation water content status, on the other hand, in order to improve irrigation scheduling and prevent water stress that affects yield adversely. The aim of this study was to evaluate the potential of two spectral indices, calculated from SPOT-5 high resolution visible (HRV) data, to retrieve the vegetation water content values of wheat in an irrigated area. These indices were the normalized difference water index (NDWI) and the moisture stress index (MSI). The values of these indices were compared with corresponding values of in situ-measured vegetation water content in 16 fields of wheat during the 2012-2013 cropping season. Good correlations were found between observed vegetation water content values and NDWI and MSI values during the crop growth period from anthesis to grain filling. These results were validated using the k-fold cross validation method and showed a good stability of the proposed regression models with a slight advantage for the NDWI. Based on these results, the NDWI was chosen to map the spatial variability of vegetation water content of wheat at the east of the Beni-Moussa irrigated perimeter. These results proved that the indices based on near and shortwave infrared band (NIR and SWIR) are able to monitor vegetation water content changes in wheat from anthesis to the grain filling stage. These indices could be used to improve irrigation and crop management of wheat at both the field and regional levels.

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

Remote Sensing of Environment ◽

10.1016/s0034-4257(02)00036-6 ◽

2002 ◽

Vol 82 (2-3) ◽

pp. 198-207 ◽

Cited By ~ 191

Author(s):

Pietro Ceccato ◽

Stéphane Flasse ◽

Jean-Marie Grégoire

Keyword(s):

Remote Sensing ◽

Water Content ◽

Spectral Index ◽

Remote Sensing Data ◽

Sensing Data ◽

Vegetation Water Content ◽

Vegetation Water

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Estimation of vegetation water content at leaf and canopy level using dual-wavelength commercial terrestrial laser scanners

Interface Focus ◽

10.1098/rsfs.2017.0041 ◽

2018 ◽

Vol 8 (2) ◽

pp. 20170041 ◽

Cited By ~ 6

Author(s):

Ahmed Elsherif ◽

Rachel Gaulton ◽

Jon Mills

Keyword(s):

Water Content ◽

Incidence Angle ◽

Three Dimensional ◽

Intensity Data ◽

Infrared Wavelength ◽

Vegetation Water Content ◽

Equivalent Water Thickness ◽

Vertical Heterogeneity ◽

Vegetation Water ◽

Shortwave Infrared

Vegetation water content, quantified as the leaf equivalent water thickness (EWT), can serve as an indicator of vegetation stress. The intensity data recorded by terrestrial laser scanning (TLS) instruments, operating at shortwave infrared wavelengths, can be used to estimate the three-dimensional distribution of EWT, after a full and rigorous calibration for the range and incidence angle effects. However, TLS instruments do not record the incidence angles automatically, making calibration challenging. In this study, intensity data from two commercially available TLS instruments (Leica P40, 1550 nm shortwave infrared wavelength, and Leica P20, 808 nm near-infrared wavelength) were combined in a normalized difference index (NDI). The NDI was found to minimize the incidence angle effects with no need for further calibration. A dry-down experiment was conducted using deciduous and conifer canopies. The NDI was found to be highly correlated to EWT at leaf level ( R 2 of 0.91 and 0.74) and at canopy level ( R 2 of 0.89 and 0.74) for the deciduous and conifer canopies, respectively. Three-dimensional distributions of EWT at canopy level were generated, which revealed some vertical heterogeneity.

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Remote sensing of grassland–shrubland vegetation water content in the shortwave domain

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2005.10.002 ◽

2006 ◽

Vol 8 (4) ◽

pp. 225-236 ◽

Cited By ~ 44

Author(s):

Andrew Davidson ◽

Shusen Wang ◽

John Wilmshurst

Keyword(s):

Remote Sensing ◽

Water Content ◽

Vegetation Water Content ◽

Vegetation Water

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.779-780.1571 ◽

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.

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