Research on the Estimation Model of Vegetation Water Content in Halophyte Leaves Based on the Newly Developed Vegetation Indices

2018 ◽  
Vol 84 (9) ◽  
pp. 538-548
Author(s):  
Zhe Li ◽  
Fei Zhang ◽  
Lihua Chen ◽  
Haiwei Zhang ◽  
Hsiang-Te-Kung
Keyword(s):  
Water Content ◽  
Vegetation Indices ◽  
Estimation Model ◽  
Vegetation Water Content ◽  
Vegetation Water

Land Surface Temperature and Miombo forest canopy phenophases: what induces leaf fall and leaf flush?

2020 ◽  
Author(s):  
Henry Zimba ◽  
Miriam Coenders-Gerrits ◽  
Banda Kawawa ◽  
Imasiku Nyambe ◽  
Hubert Savenije ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Water Content ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Indices ◽  
Leaf Fall ◽  
Vegetation Water Content ◽  
Vegetation Water ◽  
Leaf Flush ◽  
Photosynthetic Activities

<p>Miombo woodland is the most widespread tropical seasonal woodland and dry forest formation in Africa covering between 2.7 and 3.6 million km<sup>2</sup> in eleven countries. Leaf fall and leaf flush during the dry season is a major characteristic feature of the various Miombo species. However, the question on what induces the leaf fall process is by far inconclusive. Different studies indicate either moisture or temperature or both elements as inducers for leaf fall. Knowing what induces leaf fall is important for studying the consequence of e.g., climate change on the Miombo forest. To better understand the driver of leaf fall in Miombo forest we employed a simple remote sensing and statistical analysis approach using long term averages (2009 – 2018) of Land Surface Temperature (LST) of the Miombo forest, various vegetation indices (VI), actual evaporation (E<sub>a</sub>), and root zone soil moisture (SM). The vegetation indices (VI) included the Normalised Difference Water Index (NDWI) as indicator of vegetation water content and the Normalised Difference Vegetation Index (NDVI) as indicator of plant photosynthetic activities and leaf cover. Results showed that the NDWI, NDVI, E<sub>a</sub> and SM begun to decline immediately following the end of the rainy season in early April while the LST remained relatively constant before it began to decline in May when leaf fall in some Miombo species begins. Hysteresis graphs revealed that vegetation water content (i.e. NDWI) responded quicker to changes in both LST and SM. Furthermore, high rates of decrease in NDWI and NDVI values were observed between July and September the same period when LST increased. This is also the same period when leaf fall intensifies in Miombo forest. Correlation analysis revealed strong season-dependent LST relationship with VI and SM with the rainy season exhibiting strong negative linear correlations (R<sup>2</sup> = 0.77, 0.91, 0.88; for the NDWI, NDVI and SM respectively). In the dry season relatively weaker negative correlations (R<sup>2</sup> = 0.52, 0.60, 0.55; for NDWI, NDVI and SM respectively) were observed. On the other hand SM showed strong positive linear correlations (R<sup>2</sup> > 0.6) with NDWI and NDVI (for the rainy and dry seasons respectively). The correlations imply that in Miombo forest soil water content (i.e. SM), vegetation water content (i.e. NDWI) and the photosynthetic activities and leaf cover (i.e. NDVI) declines with increase in LST. These relationships show the possibility of land surface temperature being a major inducing element of leaf fall and changes in canopy structure in the Miombo woodland.</p>

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

Inferring the effects of surface canopy water on VOD estimation from L-band backscatter 

2021 ◽  
Author(s):  
Saeed Khabbazan ◽  
Paul.C. Vermunt ◽  
Susan.C. Steele Dunne ◽  
Ge Gao ◽  
Mariette Vreugdenhil ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Linear Relationship ◽  
Water Content ◽  
Growing Season ◽  
Empirical Parameter ◽  
Vegetation Water Content ◽  
L Band ◽  
Vegetation Water ◽  
Canopy Water ◽  
Effect Of Surface

<p>Quantification of vegetation parameters such as Vegetation Optical Depth (VOD) and Vegetation Water Content (VWC) can be used for better irrigation management, yield forecasting, and soil moisture estimation. Since VOD is directly related to vegetation water content and canopy structure, it can be used as an indicator for VWC. Over the past few decades, optical and passive microwave satellite data have mostly been used to monitor VWC. However, recent research is using active data to monitor VOD and VWC benefitting from their high spatial and temporal resolution.</p><p>Attenuation of the microwave signal through the vegetation layer is parametrized by the VOD. VOD is assumed to be linearly related to VWC with the proportionality constant being an empirical parameter b. For a given wavelength and polarization, b is assumed static and only parametrized as a function of vegetation type. The hypothesis of this study is that the VOD is not similar for dry and wet vegetation and the static linear relationship between attenuation and vegetation water content is a simplification of reality.</p><p>The aim of this research is to understand the effect of surface canopy water on VOD estimation and the relationship between VOD and vegetation water content during the growing season of a corn canopy. In addition to studying the dependence of VOD on bulk VWC for dry and wet vegetation, the effect of different factors, such as different growth stages and internal vegetation water content is investigated using time series analysis.</p><p>A field experiment was conducted in Florida, USA, for a full growing season of sweet corn. The corn field was scanned every 30 minutes with a truck-mounted, fully polarimetric, L-band radar. Pre-dawn vegetation water content was measured using destructive sampling three times a week for a full growing season. VWC could therefore be analyzed by constituent (leaf, stem, ear) or by height. Meteorological data, surface canopy water (dew or interception), and soil moisture were measured every 15 minutes for the entire growing season.</p><p>The methodology of Vreugdenhil et al.  [1], developed by TU Wien for ASCAT data, was adapted to present a new technique to estimate VOD from single-incidence angle backscatter data in each polarization. The results showed that the effect of surface canopy water on the VOD estimation increased by vegetation biomass accumulation and the effect was higher in the VOD estimated from the co-pol compared with the VOD estimated from the cross-pol. Moreover, the surface canopy water considerably affected the regression coefficient values (b-factor) of the linear relationship between VOD and VWC from dry and wet vegetation. This finding suggests that considering a similar b-factor for the dry and the wet vegetation will introduce errors in soil moisture retrievals. Furthermore, it highlights the importance of considering canopy wetness conditions when using tau-omega.</p><ul><li>[1] 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, pp. 3513–3531, 2016</li> </ul>

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