scholarly journals Relationships between GPP, Satellite Measures of Greenness and Canopy Water Content with Soil Moisture in Mediterranean-Climate Grassland and Oak Savanna

2011 ◽  
Vol 2011 ◽  
pp. 1-14 ◽  
Author(s):  
Shishi Liu ◽  
Oliver A. Chadwick ◽  
Dar A. Roberts ◽  
Chris J. Still
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Gross Primary Production ◽  
Resistance Index ◽  
Growing Season ◽  
Middle Part ◽  
Oak Savanna ◽  
Similar Response ◽  
The Impact ◽  
Canopy Water

We investigated the impact of soil moisture on gross primary production (GPP), chlorophyll content, and canopy water content represented by remotely sensed vegetation indices (VIs) in an open grassland and an oak savanna in California. We found for the annual grassland that GPP late in the growing season was controlled by the declining soil moisture, but there was a 10–20-day lag in the response of GPP to soil moisture. However, during the early and middle part of the growing season, solar radiation accounted for most of the variation in GPP. In the oak savanna, the grass understory exhibited a similar response, but oak trees were not sensitive to soil moisture in the upper 50 cm of the soil profile. Furthermore, while we found most VIs to be more or less related to soil moisture, the Visible Atmospherically Resistance Index (VARI) was the most sensitive to the change of soil moisture.

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>

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

<p>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’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.</p><p>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.</p><p>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.</p><p>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.</p>

Soil moisture variation drives canopy water content dynamics across the western U.S.

2021 ◽  
Vol 253 ◽  
pp. 112233
Author(s):  
Drew S. Lyons ◽  
Solomon Z. Dobrowski ◽  
Zachary A. Holden ◽  
Marco P. Maneta ◽  
Anna Sala
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Moisture Variation ◽  
Canopy Water ◽  
Soil Moisture Variation

scholarly journals Changes in surface hydrology, soil moisture and gross primary production in the Amazon during the 2015/2016 El Niño

2018 ◽  
Vol 373 (1760) ◽  
pp. 20180084 ◽  
Author(s):  
Erik van Schaik ◽  
Lars Killaars ◽  
Naomi E. Smith ◽  
Gerbrand Koren ◽  
L. P. H. van Beek ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Primary Production ◽  
El Niño ◽  
Amazon Basin ◽  
El Nino ◽  
Gross Primary Production ◽  
Surface Hydrology ◽  
The North ◽  
North Eastern ◽  
The Impact

The 2015/2016 El Niño event caused severe changes in precipitation across the tropics. This impacted surface hydrology, such as river run-off and soil moisture availability, thereby triggering reductions in gross primary production (GPP). Many biosphere models lack the detailed hydrological component required to accurately quantify anomalies in surface hydrology and GPP during droughts in tropical regions. Here, we take the novel approach of coupling the biosphere model SiBCASA with the advanced hydrological model PCR-GLOBWB to attempt such a quantification across the Amazon basin during the drought in 2015/2016. We calculate 30–40% reduced river discharge in the Amazon starting in October 2015, lagging behind the precipitation anomaly by approximately one month and in good agreement with river gauge observations. Soil moisture shows distinctly asymmetrical spatial anomalies with large reductions across the north-eastern part of the basin, which persisted into the following dry season. This added to drought stress in vegetation, already present owing to vapour pressure deficits at the leaf, resulting in a loss of GPP of 0.95 (0.69 to 1.20) PgC between October 2015 and March 2016 compared with the 2007–2014 average. Only 11% (10–12%) of the reduction in GPP was found in the (wetter) north-western part of the basin, whereas the north-eastern and southern regions were affected more strongly, with 56% (54–56%) and 33% (31–33%) of the total, respectively. Uncertainty on this anomaly mostly reflects the unknown rooting depths of vegetation. This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.

Effect of Crop Presence on Persistence of Atrazine, Metribuzin, and Clomazone in Surface Soil

Weed Science ◽  
1996 ◽  
Vol 44 (3) ◽  
pp. 698-703 ◽  
Author(s):  
Kent Gallaher ◽  
Thomas C. Mueller
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water Content ◽  
Half Life ◽  
Surface Soil ◽  
Growing Season ◽  
Soil Microbial Activity ◽  
Soil Microbial ◽  
Herbicide Concentration ◽  
The Difference

Atrazine, metribuzin, and clomazone half-lives averaged over treatments and seasons were approximately 27, 22, and 55 d, respectively. Clomazone dissipation was not affected by the presence or absence of a soybean crop. Atrazine and metribuzin dissipation was not affected by crops in 1992, but was more rapid in no-crop plots than in cropped plots in 1993. The difference may have been the result of higher soil water content with no-crop (a few weeds present) in 1993 than either corn or soybean. Lower soil moisture may have slowed soil microbial activity, thus suppressing atrazine and metribuzin degradation in the 1993 growing season. Few significant correlations were found between herbicide half-life or herbicide concentration and cocklebur growth, although one would expect these to be an index of activity.

scholarly journals Spatio-Temporal Variation of Drought within the Vegetation Growing Season in North Hemisphere (1982–2015)

Water ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 2146 ◽  
Author(s):  
Zhaoqi Zeng ◽  
Yamei Li ◽  
Wenxiang Wu ◽  
Yang Zhou ◽  
Xiaoyue Wang ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Gross Primary Production ◽  
Coniferous Forest ◽  
Critical Time ◽  
Growing Season ◽  
Well Being ◽  
Estimation Methods ◽  
Drought Risk ◽  
Drought Characteristics ◽  
The Impact

Drought disasters jeopardize the production of vegetation and are expected to exert impacts on human well-being in the context of global climate change. However, spatiotemporal variations in drought characteristics (including the drought duration, intensity, and frequency), specifically for vegetation areas within a growing season, remain largely unknown. Here, we first constructed a normalized difference vegetation index to estimate the length of the growing season for each pixel (8 km) by four widely used phenology estimation methods; second, we analyzed the temporal and spatial patterns of climate factors and drought characteristics (in terms of the Standardized Precipitation Evapotranspiration Index (SPEI)), within a growing season over vegetation areas of the northern hemisphere before and after the critical time point of 1998, which was marked by the onset of a global warming hiatus. Finally, we extracted the highly drought-vulnerable areas of vegetation by examining the sensitivity of the gross primary production to the SPEI to explore the underlying effects of drought variation on vegetation. The results revealed, first, that significant (p < 0.05) increases in precipitation, temperature, and the SPEI (a wetting trend) occurred from 1982 to 2015. The growing season temperature increased even more statistically significant after 1998 than before. Second, the duration and frequency of droughts changed abruptly and decreased considerably from 1998 to 2015; and this wetting trend was located mainly in high-latitude areas. Third, at the biome level, the wetting areas occurred mainly in the tundra, boreal forest or taiga, and temperate coniferous forest biomes, whereas the highly drought-vulnerable areas were mainly located in the desert and xeric shrubland (43.5%) biomes. Our results highlight the fact that although the drought events within a growing season decreased significantly in the northern hemisphere from 1998 to 2015, the very existence of a mismatch between a reduction in drought areas and an increase in highly drought-vulnerable areas makes the impact of drought on vegetation nonnegligible. This work provides valuable information for designing coping measures to reduce the vegetative drought risk in the Northern Hemisphere.

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;

Impact of revegetation of the Loess Plateau of China on the regional growing season water balance

2020 ◽  
Author(s):  
Weidong Guo ◽  
Andrew Pitman ◽  
Jun Ge ◽  
Beilei Zan ◽  
Congbin Fu
Keyword(s):  
Soil Moisture ◽  
Loess Plateau ◽  
Wrf Model ◽  
Growing Season ◽  
Weather Research And Forecasting ◽  
The Loess Plateau ◽  
Deep Soil ◽  
Bare Land ◽  
Biophysical Changes ◽  
The Impact

&lt;p&gt;To resolve a series of ecological and environmental problems over the Loess Plateau, the was initiated at the end of 1990s. Following the conversion of croplands and bare land on hillslopes to forests, the Loess Plateau has displayed a significant greening trend with soil erosion being reduced. However, the GFGP has also affected the hydrology of the Loess Plateau which has raised questions whether the GFGP should be continued in the future. We investigated the impact of revegetation on the hydrology of the Loess Plateau using high resolution simulations and multiple realisations with the Weather Research and Forecasting (WRF) model. Results suggests that land cover change since the launch of the GFGP has reduced runoff and soil moisture due to enhanced evapotranspiration. Further revegetation associated with the GFGP policy is likely to increase evapotranspiration further, and thereby reduce runoff and soil moisture. The increase in evapotranspiration is associated with biophysical changes, including deeper roots that deplete deep soil moisture stores. However, despite the increase in evapotranspiration our results show no impact on rainfall. Our study cautions against further revegetation over the Loess Plateau given the reduction in water available for agriculture and human settlements, without any significant compensation from rainfall.&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;

Analysis of Soil Moisture Changes in Europe during a Single Growing Season in a New ECMWF Soil Moisture Assimilation System

2008 ◽  
Vol 9 (1) ◽  
pp. 116-131 ◽  
Author(s):  
Bart van den Hurk ◽  
Janneke Ettema ◽  
Pedro Viterbo
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Growing Season ◽  
Atmospheric Model ◽  
Surface Model ◽  
Soil Moisture Data ◽  
The Impact ◽  
Assimilation System

Abstract This study aims at stimulating the development of soil moisture data assimilation systems in a direction where they can provide both the necessary control of slow drift in operational NWP applications and support the physical insight in the performance of the land surface component. It addresses four topics concerning the systematic nature of soil moisture data assimilation experiments over Europe during the growing season of 2000 involving the European Centre for Medium-Range Weather Forecasts (ECMWF) model infrastructure. In the first topic the effect of the (spinup related) bias in 40-yr ECMWF Re-Analysis (ERA-40) precipitation on the data assimilation is analyzed. From results averaged over 36 European locations, it appears that about half of the soil moisture increments in the 2000 growing season are attributable to the precipitation bias. A second topic considers a new soil moisture data assimilation system, demonstrated in a coupled single-column model (SCM) setup, where precipitation and radiation are derived from observations instead of from atmospheric model fields. For many of the considered locations in this new system, the accumulated soil moisture increments still exceed the interannual variability estimated from a multiyear offline land surface model run. A third topic examines the soil water budget in response to these systematic increments. For a number of Mediterranean locations the increments successfully increase the surface evaporation, as is expected from the fact that atmospheric moisture deficit information is the key driver of soil moisture adjustment. In many other locations, however, evaporation is constrained by the experimental SCM setup and is hardly affected by the data assimilation. Instead, a major portion of the increments eventually leave the soil as runoff. In the fourth topic observed evaporation is used to evaluate the impact of the data assimilation on the forecast quality. In most cases, the difference between the control and data assimilation runs is considerably smaller than the (positive) difference between any of the simulations and the observations.

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