scholarly journals Microwave Vegetation Index from Multi-Angular Observations and Its Application in Vegetation Properties Retrieval: Theoretical Modelling

2019 ◽  
Vol 11 (6) ◽  
pp. 730 ◽  
Author(s):  
Somayeh Talebiesfandarani ◽  
Tianjie Zhao ◽  
Jiancheng Shi ◽  
Paolo Ferrazzoli ◽  
Jean-Pierre Wigneron ◽  
...  
Keyword(s):  
Water Content ◽  
Vegetation Index ◽  
Soil Layer ◽  
Global Scale ◽  
Optical Observations ◽  
Theoretical Modelling ◽  
Vegetation Monitoring ◽  
L Band ◽  
Vegetation Water ◽  
Global Vegetation

Monitoring global vegetation dynamics is of great importance for many environmental applications. The vegetation optical depth (VOD), derived from passive microwave observation, is sensitive to the water content in all aboveground vegetation and could serve as complementary information to optical observations for global vegetation monitoring. The microwave vegetation index (MVI), which is originally derived from the zero-order model, is a potential approach to derive VOD and vegetation water content (VWC), however, it has limited application at dense vegetation in the global scale. In this study, we preferred to use a more complex vegetation model, the Tor Vergata model, which takes into account multi-scattering effects inside the vegetation and between the vegetation and soil layer. Validation with ground-based measurements proved this model is an efficient tool to describe the microwave emissions of corn and wheat. The MVI has been derived through two methods: (i) polarization independent ( MVI B P ) and (ii) time invariant ( MVI B T ), based on model simulations at the L band. Results show that the MVI B T has a stronger sensitivity to vegetation properties compared with MVI B P . MVI B T is used to retrieve VOD and VWC, and the results were compared to physical VOD and measured VWC. Comparisons indicated that MVI B T has a great potential to retrieve VOD and VWC. By using L band time-series information, the performance of MVIs could be enhanced and its application in a global scale could be improved while paying attention to vegetation structure and saturation effects.

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

scholarly journals THEORETICAL MODELLING STUDY ON THE RELATIONSHIP BETWEEN MULTI-FREQUENCY MICROWAVE VEGETATION INDEX AND VEGETATION PROPERTIES (OPTICAL DEPTH AND SINGLE SCATTERING ALBEDO)

2018 ◽  
Vol XLII-3 ◽  
pp. 1623-1627
Author(s):  
S. Talebi ◽  
J. Shi ◽  
T. Zhao
Keyword(s):  
Optical Depth ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Soil Surface ◽  
Single Scattering ◽  
Single Scattering Albedo ◽  
Theoretical Modelling ◽  
Vegetation Monitoring ◽  
Scattering Effects ◽  
The Relationship

This paper presents a theoretical study of derivation Microwave Vegetation Indices (MVIs) in different pairs of frequencies using two methods. In the first method calculating MVI in different frequencies based on Matrix Doubling Model (to take in to account multi scattering effects) has been done and analyzed in various soil properties. The second method was based on MVI theoretical basis and its independency to underlying soil surface signals. Comparing the results from two methods with vegetation properties (single scattering albedo and optical depth) indicated partial correlation between MVI from first method and optical depth, and full correlation between MVI from second method and vegetation properties. The second method to derive MVI can be used widely in global microwave vegetation monitoring.

scholarly journals Evaluation of a Global Vegetation Model using time series of satellite vegetation indices

2011 ◽  
Vol 4 (4) ◽  
pp. 1103-1114 ◽  
Author(s):  
F. Maignan ◽  
F.-M. Bréon ◽  
F. Chevallier ◽  
N. Viovy ◽  
P. Ciais ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Global Scale ◽  
Vegetation Dynamic ◽  
Rate Of Increase ◽  
Vegetation Models ◽  
Global Vegetation ◽  
The Impact

Abstract. Atmospheric CO2 drives most of the greenhouse effect increase. One major uncertainty on the future rate of increase of CO2 in the atmosphere is the impact of the anticipated climate change on the vegetation. Dynamic Global Vegetation Models (DGVM) are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetation Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR). A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the overall performance. We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them.

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 Preliminary Study on the Radar Vegetation Index (RVI) Application to Actual Paddy Fields by ALOS/PALSAR Full-polarimetry SAR Data

2015 ◽  
Vol XL-7/W3 ◽  
pp. 129-131 ◽  
Author(s):  
Y. Yamada
Keyword(s):  
Vegetation Index ◽  
Wave Length ◽  
Paddy Fields ◽  
Bragg Scattering ◽  
Growth Data ◽  
Alos Palsar ◽  
Rice Plants ◽  
L Band ◽  
Sar Data ◽  
Vegetation Water

Kim and van Zyl (2001) proposed a kind of radar vegetation index (RVI). RVI = 4*min(λ1, λ2, λ3) / (λ1 + λ2 + λ3) They modified the equation as follows. (2009) RVI = 8 * σ<sup>0</sup>hv / (σ<sup>0</sup>hh + σ<sup>0</sup>vv +σ<sup>0</sup>hv ) by L-band full-polarimetric SAR data. They applied it into rice crop and soybean. (Y.Kim, T.Jackson et al., 2012) They compared RVI for L-, C- and X-bands to crop growth data, LAI and NDVI. They found L-band RVI was well correlated with Vegetation Water Content, LAI and NDVI. But the field data were collected by the multifrequency polarimetric scatterometer. The platform height was 4.16 meters from the ground. The author tried to apply the method to actual paddy fields near Tsukuba science city in Japan using ALOS/PALSAR, full-polarimetry L-band SAR data. The staple crop in Eastern Asia is rice and paddy fields are dominant land use. A rice-planting machine comes into wide use in this areas. The young rice plants were bedded regularly ridged line in the paddy fields by the machine. The space between two ridges of rice plants is about 30 cm and the wave length of PALSAR sensor is about 23 cm. Hence the Bragg scattering will appear depending upon the direction of the ridges of paddy fields. Once the Bragg scattering occurs, the backscattering values from the pixels should be very high comparing the surrounding region. Therefore the radar vegetation index (RVI) would be saturated. The RVI did not follow the increasing of vegetation anymore. Japan has launched ALOS-2 satellite and it has PALSAR-2, L-band SAR. Therefore RVI application product by PALSAR-2 will be watched with deep interest.

scholarly journals First Vegetation Optical Depth Mapping from Sentinel-1 C-band SAR Data over Crop Fields

2019 ◽  
Vol 11 (23) ◽  
pp. 2769 ◽  
Author(s):  
Mohammad El Hajj ◽  
Nicolas Baghdadi ◽  
Jean-Pierre Wigneron ◽  
Mehrez Zribi ◽  
Clément Albergel ◽  
...  
Keyword(s):  
Water Content ◽  
Optical Depth ◽  
Water Loss ◽  
Vegetation Index ◽  
Temporal Dynamics ◽  
Irrigation Management ◽  
Coefficient Of Determination ◽  
Vegetation Monitoring ◽  
Growth Cycles ◽  
Sar Data

Monitoring crop status at plot scale in agricultural areas is essential for crop and irrigation management and yield optimization. The Vegetation Optical Depth (VOD) of canopy is directly related to the canopy water content, and thus, it represents an effective tool for crop health monitoring. Currently, VOD is provided at low spatial resolution which makes these estimations useless for vegetation monitoring at plot scale. Therefore, the aim of this study is to provide the first approach to estimate VOD at plot scale for non-irrigated plots from C-band Sentinel-1 (S1) Synthetic Aperture Radar (SAR) data. The proposed approach was tested on a study site of 50 km × 50 km located in Catalonia, Spain. VOD estimates were provided for two crop growth cycles of non-irrigated crop types (barley, fallow, oat, wheat, and rapeseed). The relevance of VOD estimates was investigated for both growth cycles using temporal profiles of the Normalized Difference Vegetation Index (NDVI). It is shown that the temporal dynamics of VOD values computed from VV polarization fits that of NDVI with a medium to good coefficient of determination (R2 ranging from 0.39 to 0.61 for barley, fallow, oat, and wheat respectively). However, during the beginning of the senescence period in both cycles (mainly in May for winter crops), VOD decreases with the decrease in Vegetation Water Content (VWC) while NDVI keeps increasing as photosynthetic activity continues developing. This illustrates the importance of VOD in crop water loss (stress and/or transpiration) monitoring. The potential of VOD to spot water loss in vegetation is also demonstrated as the evening (18h00) VOD values are lower than those of morning (06h00) due to high daytime temperature that reduces water content in vegetation. Finally, it is shown that VOD values computed from VH polarization are not correlated with NDVI.

scholarly journals Using Long-Term SAR Backscatter Data to Monitor Post-Fire Vegetation Recovery in Tundra Environment

2019 ◽  
Vol 11 (19) ◽  
pp. 2230 ◽  
Author(s):  
Zhiwei Zhou ◽  
Lin Liu ◽  
Liming Jiang ◽  
Wanpeng Feng ◽  
Sergey V. Samsonov
Keyword(s):  
Vegetation Change ◽  
Vegetation Index ◽  
Arctic Tundra ◽  
Optical Observations ◽  
Vegetation Recovery ◽  
Vegetation Coverage ◽  
Burned Area ◽  
Strong Impact ◽  
L Band ◽  
Surface Vegetation

Wildfires could have a strong impact on tundra environment by combusting surface vegetation and soil organic matter. For surface vegetation, many years are required to recover to pre-fire level. In this paper, by using C-band (VV/HV polarization) and L-band (HH polarization) synthetic aperture radar (SAR) images acquired before and after fire from 2002 to 2016, we investigated vegetation change affected by the Anaktuvuk River Fire in Arctic tundra environment. Compared to the unburned areas, C- and L-band SAR backscatter coefficients increased by up to 5.5 and 4.4 dB in the severely burned areas after the fire. Then past 5 years following the fire, the C-band SAR backscatter differences decreased to pre-fire level between the burned and unburned areas, suggesting that vegetation coverage in burned sites had recovered to the unburned level. This duration is longer than the 3-year recovery suggested by optical-based Normalized Difference Vegetation Index (NDVI) observations. While for the L-band SAR backscatter after 10-year recovery, about 2 dB higher was still found in the severely burned area, compared to the unburned area. The increased roughness of the surface is probably the reason for such sustained differences. Our analysis implies that long records of space-borne SAR backscatter can monitor post-fire vegetation recovery in Arctic tundra environment and complement optical observations.

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