Monitoring vegetation water content by using optical vegetation index and microwave vegetation index: Field experiments and applications

2011 ◽  
Author(s):  
Hui Lu ◽  
Toshio Koike ◽  
Hiroyuki Tsutsui ◽  
Hedeyuki Fujii
Keyword(s):  
Water Content ◽  
Vegetation Index ◽  
Field Experiments ◽  
Vegetation Water Content ◽  
Vegetation Water

Radar Vegetation Index for Estimating the Vegetation Water Content of Rice and Soybean

2012 ◽  
Vol 9 (4) ◽  
pp. 564-568 ◽  
Author(s):  
Yihyun Kim ◽  
T. Jackson ◽  
R. Bindlish ◽  
Hoonyol Lee ◽  
Sukyoung Hong
Keyword(s):  
Water Content ◽  
Vegetation Index ◽  
Vegetation Water Content ◽  
Vegetation Water

scholarly journals Relationship between Vegetation Biophysical Properties and Surface Temperature Using Multisensor Satellite Data

2007 ◽  
Vol 20 (22) ◽  
pp. 5593-5606 ◽  
Author(s):  
Seungbum Hong ◽  
Venkat Lakshmi ◽  
Eric E. Small
Keyword(s):  
Surface Temperature ◽  
Water Content ◽  
Land Surface ◽  
Vegetation Index ◽  
Climatic Variability ◽  
Regional Distribution ◽  
Spatial And Temporal Variability ◽  
Area Index ◽  
Vegetation Water Content ◽  
Vegetation Water

Abstract Vegetation is an important factor in global climatic variability and plays a key role in the complex interactions between the land surface and the atmosphere. This study focuses on the spatial and temporal variability of vegetation and its relationship with land–atmosphere interactions. The authors have analyzed the vegetation water content (VegWC) from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E), the leaf area index (LAI), the normalized difference vegetation index (NDVI), the land surface temperature (Ts), and the Moderate Resolution Imaging Spectroradiometer (MODIS). Three regions, which have climatically differing characteristics, have been selected: the North America Monsoon System (NAMS) region, the Southern Great Plains (SGP) region, and the Little River Watershed in Tifton, Georgia. Temporal analyses were performed by comparing satellite observations from 2003 and 2004. The introduction of the normalized vegetation water content (NVegWC) derived as the ratio of VegWC and LAI corresponding to the amount of water in individual leaves has been estimated and this yields significant correlation with NDVI and Ts. The analysis of the NVegWC–NDVI relationship in the above listed three regions displays a negative exponential relation, and the Ts–NDVI relationship (TvX relationship) is inversely proportional. The correlation between these variables is higher in arid areas such as the NAMS region, and becomes less correlated in the more humid and more vegetated regions such as the area of eastern Georgia. A land-cover map is used to examine the influence of vegetation types on the vegetation biophysical and surface temperature relationships. The regional distribution of vegetation reflects the relationship between the vegetation biological characteristics of water and the growing environment.

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.

Estimation of Vegetation Water Content From the Radar Vegetation Index at L-Band

2016 ◽  
Vol 54 (2) ◽  
pp. 981-989 ◽  
Author(s):  
Yuancheng Huang ◽  
Jeffrey P. Walker ◽  
Ying Gao ◽  
Xiaoling Wu ◽  
Alessandra Monerris
Keyword(s):  
Water Content ◽  
Vegetation Index ◽  
Vegetation Water Content ◽  
L Band ◽  
Vegetation Water

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>

scholarly journals Estimating Grassland Curing with Remotely Sensed Data

2017 ◽  
Author(s):  
Wasin Chaivaranont ◽  
Jason P. Evans ◽  
Yi Y. Liu ◽  
Jason J. Sharples
Keyword(s):  
Vegetation Index ◽  
Field Measurements ◽  
Fire Danger ◽  
Remotely Sensed Data ◽  
Vegetation Water Content ◽  
Grassland Ecosystems ◽  
Normalised Difference Vegetation Index ◽  
Dead Material ◽  
Degree Of Curing ◽  
Vegetation Water

Abstract. Wildfire can become a catastrophic natural hazard, especially during dry summer seasons in Australia. Severity is influenced by various meteorological, geographical, and fuel characteristics. Modified Mark 4 McArthur's Grassland Fire 10 Danger Index (GFDI) is a commonly used approach to determine the fire danger level in grassland ecosystems. The degree of curing (DOC, i.e. proportion of dead material) of the grass is one key ingredient in determining the fire danger. It is difficult to collect accurate DOC information in the field, therefore, ground observed measurements are rather limited. In this study, we used satellite observed vegetation greenness (Normalised Difference Vegetation Index, NDVI) and vegetation water content (Vegetation Optical Depth, VOD) information to improve the accuracy of the DOC estimation. First, a statistically 15 significant relationship is established between selected ground observed DOC and satellite observed vegetation datasets (NDVI and VOD) with an r2 of 0.67. DOC levels estimated using satellite observations were then evaluated using field measurements with an r2 of 0.55. Results suggest that satellite based DOC estimation can reasonably reproduce ground based observations in space and time. Comparison with currently available satellite based DOC products shows that our model has a comparable and arguably more balanced performance.

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