scholarly journals Validation of SMAP Soil Moisture at Terrestrial National Ecological Observatory Network (NEON) Sites Show Potential for Soil Moisture Retrieval in Forested Areas

2021 ◽  
Author(s):  
Edward Ayres ◽  
Andreas Colliander ◽  
Michael Cosh ◽  
Joshua A. Roberti ◽  
Sam Simkin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Measurement Accuracy ◽  
Forest Health ◽  
Mean Difference ◽  
Vegetation Water Content ◽  
The Us ◽  
Forested Areas ◽  
Vegetation Water

Soil moisture influences forest health, fire occurrence and extent, and insect and pathogen impacts, creating a need for regular, globally extensive soil moisture measurements that can only be achieved by satellite-based sensors, such as NASA’s Soil Moisture Active Passive (SMAP). However, SMAP data for forested regions, which account for ~20% of land cover globally, are flagged as unreliable due to interference from vegetation water content, and forests were underrepresented in previous validation efforts, preventing an assessment of measurement accuracy in these biomes. Here we compare over twelve thousand SMAP soil moisture measurements, representing 88 site-years, to in-situ soil moisture measurements from forty National Ecological Observatory Network (NEON) sites throughout the US, half of which are forested. At unforested NEON sites, agreement with SMAP soil moisture (unbiased RMSD: 0.046 m<sup>3</sup> m<sup>-3</sup>) was similar to previous sparse network validations (which include inflation of the metric due to spatial representativeness errors). For the forested sites, SMAP achieved a reasonable level of accuracy (unbiased RMSD: 0.06 m<sup>3</sup> m<sup>-3</sup> or 0.053 m<sup>3</sup> m<sup>-3</sup> after accounting for random representativeness errors) indicating SMAP is sensitive to changes in soil moisture in forest ecosystems. Moreover, we identified that both an index of vegetation water content and canopy height were related to mean difference, which incorporates measurement bias and representativeness bias, and suggests a potential approach to improve SMAP algorithm parameterization for forested regions. In addition, expanding the number and extent of soil moisture measurements at forested validation sites would likely further reduce mean difference by minimizing representativeness errors.

scholarly journals Validation of SMAP Soil Moisture at Terrestrial National Ecological Observatory Network (NEON) Sites Show Potential for Soil Moisture Retrieval in Forested Areas

2021 ◽  
Author(s):  
Edward Ayres ◽  
Andreas Colliander ◽  
Michael Cosh ◽  
Joshua A. Roberti ◽  
Sam Simkin ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Measurement Accuracy ◽  
Forest Health ◽  
Mean Difference ◽  
Vegetation Water Content ◽  
The Us ◽  
Forested Areas ◽  
Vegetation Water

Soil moisture influences forest health, fire occurrence and extent, and insect and pathogen impacts, creating a need for regular, globally extensive soil moisture measurements that can only be achieved by satellite-based sensors, such as NASA’s Soil Moisture Active Passive (SMAP). However, SMAP data for forested regions, which account for ~20% of land cover globally, are flagged as unreliable due to interference from vegetation water content, and forests were underrepresented in previous validation efforts, preventing an assessment of measurement accuracy in these biomes. Here we compare over twelve thousand SMAP soil moisture measurements, representing 88 site-years, to in-situ soil moisture measurements from forty National Ecological Observatory Network (NEON) sites throughout the US, half of which are forested. At unforested NEON sites, agreement with SMAP soil moisture (unbiased RMSD: 0.046 m<sup>3</sup> m<sup>-3</sup>) was similar to previous sparse network validations (which include inflation of the metric due to spatial representativeness errors). For the forested sites, SMAP achieved a reasonable level of accuracy (unbiased RMSD: 0.06 m<sup>3</sup> m<sup>-3</sup> or 0.053 m<sup>3</sup> m<sup>-3</sup> after accounting for random representativeness errors) indicating SMAP is sensitive to changes in soil moisture in forest ecosystems. Moreover, we identified that both an index of vegetation water content and canopy height were related to mean difference, which incorporates measurement bias and representativeness bias, and suggests a potential approach to improve SMAP algorithm parameterization for forested regions. In addition, expanding the number and extent of soil moisture measurements at forested validation sites would likely further reduce mean difference by minimizing representativeness errors.

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

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;The methodology of Vreugdenhil et al.&amp;#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.&amp;#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.&lt;/p&gt;&lt;ul&gt;&lt;li&gt;[1] Vreugdenhil,W. A. Dorigo,W.Wagner, R. A. De Jeu, S. Hahn, andM. J. VanMarle, &amp;#8220;Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval,&amp;#8221; IEEE Transactions on Geoscience and Remote Sensing, vol. 54, pp. 3513&amp;#8211;3531, 2016&lt;/li&gt; &lt;/ul&gt;

Detecting changes in root zone soil moisture from radar vegetation backscatter

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

&lt;p&gt;Root zone soil moisture (&amp;#952;&lt;sub&gt;rz&lt;/sub&gt;) 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 &amp;#952;&lt;sub&gt;rz&lt;/sub&gt; thru radar has so far only focused on retrieving surface soil moisture using the soil component of the total backscatter (&amp;#963;&lt;sub&gt;soil&lt;/sub&gt;), which is then assimilated into physical hydrological models. The utility of the vegetation component of the total backscatter (&amp;#963;&lt;sub&gt;veg&lt;/sub&gt;) has not been widely explored and is commonly corrected for in most soil moisture retrieval methods. However, &amp;#963;&lt;sub&gt;veg &lt;/sub&gt;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&amp;#160; the vegetation water status. In this study, our main goal is to determine whether changes in root zone soil moisture (&amp;#916;&amp;#952;&lt;sub&gt;rz&lt;/sub&gt;) shows corresponding changes in vegetation backscatter (&amp;#916;&amp;#963;&lt;sub&gt;veg&lt;/sub&gt;) 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 &amp;#952;&lt;sub&gt;rz&lt;/sub&gt; up to 40 cm to capture the rooting depths for both crops. Dubois&amp;#8217; model formulation for VV-polarization was applied to estimate the surface roughness parameter (H&lt;sub&gt;rms&lt;/sub&gt;) and &amp;#963;&lt;sub&gt;soil &lt;/sub&gt;during vegetated periods. Afterwards, the Water Cloud Model was used to derive &amp;#963;&lt;sub&gt;veg&lt;/sub&gt; by subtracting &amp;#963;&lt;sub&gt;soil&lt;/sub&gt; from S1 backscatter (&amp;#963;&lt;sub&gt;tot&lt;/sub&gt;). 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 (&amp;#946;) fitted over plots of &amp;#916;&amp;#963;&lt;sub&gt;veg&lt;/sub&gt; against &amp;#916;&amp;#952;&lt;sub&gt;rz&lt;/sub&gt; were used investigate the dynamics over a growing season. Our main result indicates that &amp;#916;&amp;#963;&lt;sub&gt;veg &lt;/sub&gt;- &amp;#916;&amp;#952;&lt;sub&gt;rz&lt;/sub&gt; relation is influenced by crop growth stage and changes in water content in the root zone. For corn, changes in &amp;#946;&amp;#8217;s over a growing season follow the trend in a crop coefficient (K&lt;sub&gt;c&lt;/sub&gt;) 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 (&amp;#916;&amp;#952;) at specific soil depths (5, 10, 20, and 40 cm) and &amp;#916;&amp;#963;&lt;sub&gt;veg &lt;/sub&gt; 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 &amp;#946;, which indicates that &amp;#916;&amp;#963;&lt;sub&gt;veg&lt;/sub&gt; does not match well with &amp;#916;&amp;#952;&lt;sub&gt;rz&lt;/sub&gt;. The influence of vegetation on S1 backscatter is more pronounced for corn, which translated to a clearer &amp;#916;&amp;#963;&lt;sub&gt;veg&lt;/sub&gt; - &amp;#916;&amp;#952;&lt;sub&gt;rz&lt;/sub&gt; relation compared to grass. The sensitivity of &amp;#916;&amp;#963;&lt;sub&gt;veg&lt;/sub&gt; to &amp;#916;&amp;#952;&lt;sub&gt;rz&lt;/sub&gt; in corn means that the analysis may be applicable to other broad leaf crops or forested areas, with potential applications for monitoring&amp;#160; periods of water stress.&lt;/p&gt;

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

2020 ◽  
Author(s):  
Saeed Khabbazan ◽  
Ge Gao ◽  
Paul Vermunt ◽  
Susan Steele-Dunne ◽  
Jasmeet Judge ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Vertical Distribution ◽  
Water Content ◽  
Optical Depth ◽  
Microwave Remote Sensing ◽  
Growing Season ◽  
Vegetation Water Content ◽  
Wide Range ◽  
Vegetation Water

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

scholarly journals Reconstructing Continuous Vegetation Water Content To Understand Sub-daily Backscatter Variations

2021 ◽  
Author(s):  
Paul C. Vermunt ◽  
Susan C. Steele-Dunne ◽  
Saeed Khabbazan ◽  
Jasmeet Judge ◽  
Nick C. van de Giesen
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Water Cycle ◽  
Water Dynamics ◽  
High Temporal Resolution ◽  
Daily Variations ◽  
Vegetation Water Content ◽  
Dew Formation ◽  
Vegetation Water ◽  
Canopy Water

Abstract. Microwave observations are sensitive to vegetation water content (VWC). Consequently, the increasing temporal and spatial resolution of spaceborne microwave observations creates a unique opportunity to study vegetation water dynamics and its role in the diurnal water cycle. However, we currently have a limited understanding of sub-daily variations in VWC and how they affect passive and active microwave observations. This is partly due to the challenges associated with measuring internal VWC for validation, particularly non-destructively and at timescales of less than a day. In this study, we aimed to (1) use field sensors to reconstruct diurnal and continuous records of internal VWC of corn, and (2) use these records to interpret the sub-daily behaviour of a 10-day time series of polarimetric L-band backscatter with high temporal resolution. Sub-daily variations of internal VWC were calculated based on the cumulative difference between estimated transpiration and sap flow rates at the base of the stems. Destructive samples were used to constrain the estimates and for validation. The inclusion of continuous surface canopy water estimates (dew or interception) and surface soil moisture allowed us to attribute hour-to-hour backscatter dynamics to either internal VWC, surface canopy water or soil moisture variations. Our results showed that internal VWC varied with 10–20 % during the day in non-stressed conditions, and the effect on backscatter was significant. Diurnal variations of internal VWC and nocturnal dew formation affected vertically polarized backscatter most. Moreover, on a typical dry day, backscatter variations were 1.5 (HH-pol) to 3 (VV-pol) times more sensitive to VWC than to soil moisture. These results demonstrate that radar observations have the potential to provide unprecedented insight into the role of vegetation water dynamics in land-atmosphere interactions at sub-daily timescales.

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

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

&lt;p&gt;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&amp;#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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;

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