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

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.

Download Full-text

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

10.5194/egusphere-egu21-6461 ◽

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

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.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.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.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.The methodology of Vreugdenhil et al.&#160; [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.&#160;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.<ul><li>[1] 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, pp. 3513&#8211;3531, 2016</li> </ul>

Download Full-text

Detecting changes in root zone soil moisture from radar vegetation backscatter

10.5194/egusphere-egu2020-1005 ◽

2020 ◽

Author(s):

Coleen Carranza ◽

Tim van Emmerik ◽

Martine van der Ploeg

Keyword(s):

Soil Moisture ◽

Water Content ◽

Hydrological Cycle ◽

Root Zone ◽

Water Status ◽

Roughness Parameter ◽

Growing Season ◽

Irrigation Scheduling ◽

Vegetation Water Content ◽

Vegetation Water

Root zone soil moisture (&#952;rz) is a crucial component of the hydrological cycle and provides information for drought monitoring, irrigation scheduling, and carbon cycle modeling. During vegetation conditions, estimation of &#952;rz thru radar has so far only focused on retrieving surface soil moisture using the soil component of the total backscatter (&#963;soil), which is then assimilated into physical hydrological models. The utility of the vegetation component of the total backscatter (&#963;veg) has not been widely explored and is commonly corrected for in most soil moisture retrieval methods. However, &#963;veg provides information about vegetation water content. Furthermore, it has been known in agronomy that pre-dawn leaf water potential is in equilibrium with that of the soil. Therefore soil water status can be inferred by examining&#160; the vegetation water status. In this study, our main goal is to determine whether changes in root zone soil moisture (&#916;&#952;rz) shows corresponding changes in vegetation backscatter (&#916;&#963;veg) at pre-dawn. We utilized Sentinel-1 (S1) descending pass and in situ soil moisture measurements from 2016-2018 at two soil moisture networks (Raam and Twente) in the Netherlands. We focused on corn and grass which are the most dominant crops at the sites and considered the depth-averaged &#952;rz up to 40 cm to capture the rooting depths for both crops. Dubois&#8217; model formulation for VV-polarization was applied to estimate the surface roughness parameter (Hrms) and &#963;soil during vegetated periods. Afterwards, the Water Cloud Model was used to derive &#963;veg by subtracting &#963;soil from S1 backscatter (&#963;tot). To ensure that S1 only measures vegetation water content, rainy days were excluded to remove the influence of intercepted rainfall on the backscatter. The slope of regression lines (&#946;) fitted over plots of &#916;&#963;veg against &#916;&#952;rz were used investigate the dynamics over a growing season. Our main result indicates that &#916;&#963;veg - &#916;&#952;rz relation is influenced by crop growth stage and changes in water content in the root zone. For corn, changes in &#946;&#8217;s over a growing season follow the trend in a crop coefficient (Kc) curve, which is a measure of crop water requirements. Grasses, which are perennial crops, show trends corresponding to the mature crop stage. The correlation between soil moisture (&#916;&#952;) at specific soil depths (5, 10, 20, and 40 cm) and &#916;&#963;veg matches root growth for corn and known rooting depths for both corn and grass. Dry spells (e.g. July 2018) and a large increase in root zone water content in between two dry-day S1 overpass (e.g. from rainfall) result in a lower &#946;, which indicates that &#916;&#963;veg does not match well with &#916;&#952;rz. The influence of vegetation on S1 backscatter is more pronounced for corn, which translated to a clearer &#916;&#963;veg - &#916;&#952;rz relation compared to grass. The sensitivity of &#916;&#963;veg to &#916;&#952;rz in corn means that the analysis may be applicable to other broad leaf crops or forested areas, with potential applications for monitoring&#160; periods of water stress.

Download Full-text

Effects of sub-daily internal and external canopy water fluctuations on radar backscatter

10.5194/egusphere-egu2020-13128 ◽

2020 ◽

Author(s):

Paul Vermunt ◽

Susan Steele-Dunne ◽

Saeed Khabbazan ◽

Jasmeet Judge ◽

Leila Guerriero

Keyword(s):

Soil Moisture ◽

The Netherlands ◽

Water Content ◽

Growing Season ◽

Agricultural Crop ◽

Radar Backscatter ◽

Vegetation Water Content ◽

Vegetation Water ◽

Canopy Water ◽

Daily Patterns

Radar observations of vegetated surfaces are highly affected by water in the soil and canopy. Consequently, radar has been used to monitor surface soil moisture for decades now. In addition, radar has been proven a useful tool for monitoring agricultural crop growth and development and forest fuel load estimation, as a result of the sensitivity of backscatter to vegetation water content (VWC). These current applications are based on satellite revisit periods of days to weeks. However, with future satellite constellations and geosynchronous radar missions, such as ESA&#8217;s Earth Explorer candidate mission HydroTerra, we will be able to monitor soil and vegetation multiple times per day. This opens up opportunities for new applications.Examples could be (1) early detection of water stress in vegetation through anomalies in daily cycles of VWC, and (2) spatio-temporal estimations of rainfall interception, an important part of the water balance. However, currently, we lack the knowledge to physically understand sub-daily patterns in backscatter. Hence, the aim of our research is to understand the effect of water-related factors on sub-daily patterns of radar backscatter of a growing corn canopy.Two intensive field campaigns were conducted in Florida (2018) and The Netherlands (2019). During both campaigns, soil moisture, external canopy water (dew, interception), soil water potential, and weather conditions were monitored every 15 minutes for the entire growing season. In addition, regular destructive sampling was performed to measure seasonal and sub-daily variations of vegetation water content. In Florida, hourly field scans were made with a truck-mounted polarimetric L-band scatterometer. In The Netherlands, these measurements were extended with X- and C-band frequencies.Here, results will be presented from both campaigns. Different periods in the growing season will be highlighted. In particular, we will elaborate on the effects of variations in internal and external canopy water, and soil moisture on diurnal backscatter patterns.

Download Full-text

Derivation of Vegetation Optical Depth and Water Content in the Source Region of the Yellow River using the FY-3B Microwave Data

Remote Sensing ◽

10.3390/rs11131536 ◽

2019 ◽

Vol 11 (13) ◽

pp. 1536 ◽

Cited By ~ 2

Author(s):

Rong Liu ◽

Jun Wen ◽

Xin Wang ◽

Zuoliang Wang ◽

Zhenchao Li ◽

...

Keyword(s):

Remote Sensing ◽

Water Content ◽

Optical Depth ◽

Yellow River ◽

Source Region ◽

Microwave Remote Sensing ◽

Microwave Frequency ◽

The Yellow River ◽

Growing Period ◽

Vegetation Water

This study uses the brightness temperature at the given microwave frequency (18.7 GHz) from the Microwave Radiation Imager (MWRI) on-board the Fengyun-3B (FY-3B) satellite to improve the τ-ω model by considering the radiative contribution from waterbody in the pixels over the wetland of the Yellow River source region, China. In order to retrieve vegetation optical depth (VOD), a dual-polarization slope parameter is defined to express the surface emissivity in the τ-ω model as the sum of soil emissivity and waterbody emissivity. In the regions with no waterbody, the original τ-ω model without considering waterbody impact is used to derive VOD. With use of the field observed vegetation water content (VWC) in the source region of the Yellow River during the summer of 2012, a regression relationship between VOD and VWC is established and then the vegetation parameter b is estimated. The relationship is employed to derive the spatial VWC during the entire vegetation growing period. The VOD retrieved is invalid and failed in some part of the study area by using the previous τ-ω model, while the results from the improved τ-ω model indicate that the VOD is in the range of 0.20 to 1.20 and the VWC is in the range of 0.20kg/m2 to 1.40kg/m2 in the entire source region of the Yellow River in 2012. Both VOD and VWC exhibit a pattern of low values in the west part and high values in the east part. The largest regional variations appear along the Yellow River. The comparison between the remote-sensing-estimated VWC and the ground-measured VWC gives the root mean square error of 0.12kg/m2. These assessments reveal that with considering the fractional seasonal wetlands in the source region of the Yellow River, the microwave remote sensing measurements from the FY-3B MWRI can be successfully used to retrieve the VWC in the source region of the Yellow River.

Download Full-text