scholarly journals Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region, USA

2021 ◽  
Author(s):  
Nawa Raj Pradhan
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Root Zone ◽  
Growing Season ◽  
Threshold Value ◽  
Reference Network ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Reynolds Creek ◽  
Dryness Index

A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km2 Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R2 value of 0.72 and average root mean square error (RMSE) of 0.042.

scholarly journals Does Earlier and Increased Spring Plant Growth Lead to Reduced Summer Soil Moisture and Plant Growth on Landscapes Typical of Tundra-Taiga Interface?

2019 ◽  
Vol 11 (17) ◽  
pp. 1989 ◽  
Author(s):  
Alemu Gonsamo ◽  
Michael T. Ter-Mikaelian ◽  
Jing M. Chen ◽  
Jiaxin Chen
Keyword(s):  
Soil Moisture ◽  
Plant Growth ◽  
High Latitude ◽  
Boreal Forests ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Root Zone ◽  
Terrestrial Ecosystems ◽  
Growing Season ◽  
Northern High Latitude

Over the past four decades, satellite observations have shown intensified global greening. At the same time, widespread browning and reversal of or stalled greening have been reported at high latitudes. One of the main reasons for this browning/lack of greening is thought to be warming-induced water stress, i.e., soil moisture depletion caused by earlier spring growth and increased summer evapotranspiration. To investigate these phenomena, we use MODIS collection 6, Global Inventory Modeling and Mapping Studies third-generation (GIMMS) normalized difference vegetation index (NDVI3g), and Global Land Evaporation Amsterdam Model (GLEAM) satellite-based root-zone soil moisture data. The study area was the Far North of Ontario (FNO), 453,788 km2 of heterogeneous landscape typical of the tundra-taiga interface, consisting of unmanaged boreal forests growing on mineral and peat soils, wetlands, and the most southerly area of tundra. The results indicate that the increased plant growth in spring leads to decreased summer growth. Lower summer soil moisture is related to increased spring plant growth in areas with lower soil moisture content. We also found that earlier start of growing season leads to decreased summer and peak season maximum plant growth. In conclusion, increased spring plant growth and earlier start of growing season deplete summer soil moisture and decrease the overall summer plant growth even in temperature-limited high latitude ecosystems. Our findings contribute to evolving understanding of changes in vegetation dynamics in relation to climate in northern high latitude terrestrial ecosystems.

scholarly journals A multitemporal remote sensing approach to parsimonious streamflow modeling in a southcentral Texas watershed, USA

2007 ◽  
Vol 4 (1) ◽  
pp. 1-33 ◽  
Author(s):  
B. P. Weissling ◽  
H. Xie ◽  
K. E. Murray
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Convective Precipitation ◽  
Precipitation Event ◽  
Streamflow Prediction ◽  
Remotely Sensed Data ◽  
Moisture Condition ◽  
Soil Moisture Condition

Abstract. Soil moisture condition plays a vital role in a watershed's hydrologic response to a precipitation event and is thus parameterized in most, if not all, rainfall-runoff models. Yet the soil moisture condition antecedent to an event has proven difficult to quantify both spatially and temporally. This study assesses the potential to parameterize a parsimonious streamflow prediction model solely utilizing precipitation records and multi-temporal remotely sensed biophysical variables (i.e.~from Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra satellite). This study is conducted on a 1420 km2 rural watershed in the Guadalupe River basin of southcentral Texas, a basin prone to catastrophic flooding from convective precipitation events. A multiple regression model, accounting for 78% of the variance of observed streamflow for calendar year 2004, was developed based on gauged precipitation, land surface temperature, and enhanced vegetation Index (EVI), on an 8-day interval. These results compared favorably with streamflow estimations utilizing the Natural Resources Conservation Service (NRCS) curve number method and the 5-day antecedent moisture model. This approach has great potential for developing near real-time predictive models for flood forecasting and can be used as a tool for flood management in any region for which similar remotely sensed data are available.

scholarly journals Multi-decadal analysis of root-zone soil moisture applying the exponential filter across CONUS

2017 ◽  
Vol 21 (9) ◽  
pp. 4403-4417 ◽  
Author(s):  
Kenneth J. Tobin ◽  
Roberto Torres ◽  
Wade T. Crow ◽  
Marvin E. Bennett
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Root Zone ◽  
European Space Agency ◽  
Department Of Energy ◽  
Reference Network ◽  
Optimization Approach ◽  
Moisture Variation ◽  
The Us

Abstract. This study applied the exponential filter to produce an estimate of root-zone soil moisture (RZSM). Four types of microwave-based, surface satellite soil moisture were used. The core remotely sensed data for this study came from NASA's long-lasting AMSR-E mission. Additionally, three other products were obtained from the European Space Agency Climate Change Initiative (CCI). These datasets were blended based on all available satellite observations (CCI-active, CCI-passive, and CCI-combined). All of these products were 0.25° and taken daily. We applied the filter to produce a soil moisture index (SWI) that others have successfully used to estimate RZSM. The only unknown in this approach was the characteristic time of soil moisture variation (T). We examined five different eras (1997–2002; 2002–2005; 2005–2008; 2008–2011; 2011–2014) that represented periods with different satellite data sensors. SWI values were compared with in situ soil moisture data from the International Soil Moisture Network at a depth ranging from 20 to 25 cm. Selected networks included the US Department of Energy Atmospheric Radiation Measurement (ARM) program (25 cm), Soil Climate Analysis Network (SCAN; 20.32 cm), SNOwpack TELemetry (SNOTEL; 20.32 cm), and the US Climate Reference Network (USCRN; 20 cm). We selected in situ stations that had reasonable completeness. These datasets were used to filter out periods with freezing temperatures and rainfall using data from the Parameter elevation Regression on Independent Slopes Model (PRISM). Additionally, we only examined sites where surface and root-zone soil moisture had a reasonably high lagged r value (r > 0. 5). The unknown T value was constrained based on two approaches: optimization of root mean square error (RMSE) and calculation based on the normalized difference vegetation index (NDVI) value. Both approaches yielded comparable results; although, as to be expected, the optimization approach generally outperformed NDVI-based estimates. The best results were noted at stations that had an absolute bias within 10 %. SWI estimates were more impacted by the in situ network than the surface satellite product used to drive the exponential filter. The average Nash–Sutcliffe coefficients (NSs) for ARM ranged from −0. 1 to 0.3 and were similar to the results obtained from the USCRN network (0.2–0.3). NS values from the SCAN and SNOTEL networks were slightly higher (0.1–0.5). These results indicated that this approach had some skill in providing an estimate of RZSM. In terms of RMSE (in volumetric soil moisture), ARM values actually outperformed those from other networks (0.02–0.04). SCAN and USCRN RMSE average values ranged from 0.04 to 0.06 and SNOTEL average RMSE values were higher (0.05–0.07). These values were close to 0.04, which is the baseline value for accuracy designated for many satellite soil moisture missions.

The influence of antecedent soil moisture conditions on the rainfall–runoff threshold value of a roaded catchment used for water harvesting

2013 ◽  
Vol 13 (5) ◽  
pp. 1202-1208
Author(s):  
C. W. Baek ◽  
N. Coles
Keyword(s):  
Soil Moisture ◽  
Rainwater Harvesting ◽  
Threshold Value ◽  
Rainfall Runoff ◽  
Antecedent Soil Moisture ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Antecedent Moisture ◽  
Moisture Conditions ◽  
The Relationship

A roaded catchment (RC) is a representative type of artificial catchment for rainwater harvesting. The rainfall–runoff threshold value of the RC is the main factor which influences the system efficiency and cost. Antecedent soil moisture condition is an important factor which impacts on the determination of the rainfall–runoff threshold value. In this study, rainfall–antecedent soil moisture condition–runoff relationships and the potential efficiency of RCs are presented. Rainfall and runoff data monitored at research sites in Merredin and Mount Barker are used to determine this relationship. Two antecedent moisture criteria; Antecedent Moisture Conditions (AMC) and Average Antecedent Precipitation (AAP) are used to analyse the relationship between previous rainfall and soil moisture for each RC. Monitored results show that AMC is not that suitable to show the relationship between rainfall and antecedent soil moisture condition of the RC in the dryland of Western Australia and it is recommended to use AAP to determine this relationship.

Impact of climate change on soil moisture condition in Nova Scotia

2012 ◽  
Author(s):  
Raheleh Malekian ◽  
Robert Gordon ◽  
Ali Madani ASABE Member ◽  
Seyyed Ebrahim Hashemi
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Nova Scotia ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Impact Of Climate Change

scholarly journals Effect of soil moisture condition on the conversion rate of oxamyl.

1979 ◽  
Vol 27 (3) ◽  
pp. 191-198
Author(s):  
J.H. Smelt ◽  
A. Dekker ◽  
M. Leistra
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Conversion Rate ◽  
Soil Moisture Content ◽  
Loamy Sand ◽  
Clay Loam ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Incubation Experiments ◽  
Wilting Point

The decomposition of oxamyl in four soils under moist conditions was measured in incubation experiments at 15 deg C. Half-lives of oxamyl in soils with moisture tensions of approx. -9.8 X 103 Pa were 13 days in a clay loam, 14 days in a loamy sand, 34 days in a peaty sand and 39 days in a humic loamy sand. The rate of oxamyl decomposition in the clay loam decreased with decreasing soil moisture content down to values for below wilting point. Oxamyl decomposition in the humic loamy sand decreased with decreasing soil moisture content, but increased sharply in the very dry range. (Abstract retrieved from CAB Abstracts by CABI’s permission)

scholarly journals Soil moisture obtained through remote sensing to assess drought events

2020 ◽  
Vol 24 (9) ◽  
pp. 575-580 ◽  
Author(s):  
Tiago de M. Inocêncio ◽  
Alfredo Ribeiro Neto ◽  
Alzira G. S. S. Souza
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Condition Index ◽  
Semiarid Region ◽  
Moisture Index ◽  
Moisture Condition ◽  
Soil Moisture Condition ◽  
Northeast Region ◽  
Promising Tool ◽  
Soil Moisture Index

ABSTRACT The sequence of drought events in the Northeast of Brazil in recent decades raises attention to the importance of studying this phenomenon. The objective of this study was to evaluate the duration and severity of drought events from 1988 to 2018 in hydrographic basins of the state of Pernambuco, Brazil, using two drought indexes: Standardized Soil Moisture Index and Soil Moisture Condition Index, calculated based on data of the Soil Moisture Project of the European Space Agency’s Climate Change Initiative. The duration of the droughts was determined considering the months between their beginning and end, and their severity was based on the area formed in the graph between the curve of the index and the x-axis. The soil moisture database showed to be a promising tool for the analysis and monitoring of drought events in the Northeast region of Brazil, mainly for analysis and monitoring of drought events. The indexes allowed the evaluation of the drought phenomenon over the 30-year period, showing increases from 2012, which were more pronounced in the Semiarid region. The hydrographic basins responded differently to a same event, depending on the climate characteristics of the region in which they are located. Consecutive years with rainfall below the historical mean increased the magnitude of the droughts, as found for the 2012-2017 period, in which the indexes presented delays to return to more favorable values, showing the effect that one drought year has on the following year.

Changing sensitivity of global vegetation productivity to hydro-climate drivers

2021 ◽  
Author(s):  
Wantong Li ◽  
Matthias Forkel ◽  
Mirco Migliavacca ◽  
Markus Reichstein ◽  
Sophia Walther ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Growing Season ◽  
Climatic Conditions ◽  
Terrestrial Vegetation ◽  
Spatial And Temporal Patterns ◽  
Vegetation Productivity ◽  
Area Index ◽  
Global Vegetation

<p>Terrestrial vegetation couples the global water, energy and carbon exchange between the atmosphere and the land surface. Thereby, vegetation productivity is determined by a multitude of energy- and water-related variables. While the emergent sensitivity of productivity to these variables has been inferred from Earth observations, its temporal evolution during the last decades is unclear, as well as potential changes in response to trends in hydro-climatic conditions. In this study, we analyze the changing sensitivity of global vegetation productivity to hydro-climate conditions by using satellite-observed vegetation indices (i.e. NDVI) at the monthly timescale from 1982–2015. Further, we repeat the analysis with simulated leaf area index and gross primary productivity from the TRENDY vegetation models, and contrast the findings with the observation-based results. We train a random forest model to predict anomalies of productivity from a comprehensive set of hydro-meteorological variables (temperature, solar radiation, vapor pressure deficit, surface and root-zone soil moisture and precipitation), and to infer the sensitivity to each of these variables. By training models from temporal independent subsets of the data we detect the evolution of sensitivity across time. Results based on observations show that vegetation sensitivity to energy- and water-related variables has significantly changed in many regions across the globe. In particular we find decreased (increased) sensitivity to temperature in very warm (cold) regions. Thereby, the magnitude of the sensitivity tends to differ between the early and late growing seasons. Likewise, we find changing sensitivity to root-zone soil moisture with increases predominantly in the early growing season and decreases in the late growing season. For better understanding the mechanisms behind the sensitivity changes, we analyse land-cover changes, hydro-climatic trends, and abrupt disturbances (e.g. drought, heatwave events or fires could result in breaking points of sensitivity evolution in the local interpretation). In summary, this study sheds light on how and where vegetation productivity changes its response to the drivers under climate change, which can help to understand possibly resulting changes in spatial and temporal patterns of land carbon uptake.</p>

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