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

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.

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

10.21079/11681/42128 ◽

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.

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Combined effects of site preparation, soil properties, and sowing date on the establishment of Pinus sylvestris and Picea abies from seeds

Canadian Journal of Forest Research ◽

10.1139/x03-011 ◽

2003 ◽

Vol 33 (5) ◽

pp. 931-945 ◽

Cited By ~ 19

Author(s):

Michelle de Chantal ◽

Kari Leinonen ◽

Hannu Ilvesniemi ◽

Carl Johan Westman

Keyword(s):

Soil Moisture ◽

Picea Abies ◽

Pinus Sylvestris ◽

Soil Properties ◽

Seedling Emergence ◽

Growing Season ◽

Sowing Date ◽

Site Preparation ◽

Winter Mortality ◽

Frost Heaving

The aim of this study is to determine the effect of site preparation on soil properties and, in turn, the emergence, mortality, and establishment of Pinus sylvestris L. (Scots pine) and Picea abies (L.) Karst. (Norway spruce) seedlings sown in spring and summer along a slope with variation in soil texture and moisture. Three site preparation treatments of varying intensities were studied: exposed C horizon, mound (broken LFHAeB horizons piled over undisturbed ground), and exposed AeB horizons. Seedling emergence was higher in the moist growing season than in the dry one. During a dry growing season, mounds and exposed C horizon had negative effects on soil moisture that increased mortality. Moreover, frost heaving was an important cause of winter mortality on mounds and exposed C horizon, whereas frost heaving was low on exposed AeB horizons, even though soil moisture and the content of fine soil particles (<0.06 mm) were high. Frost heaving mortality was higher for summer-sown than for spring-sown seedlings and for P. abies than for P. sylvestris. Growing season mortality was high following a winter with frost heaving, suggesting that roots were damaged, thereby making seedlings more susceptible to desiccation.

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Changing sensitivity of global vegetation productivity to hydro-climate drivers

10.5194/egusphere-egu21-1783 ◽

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

Terrestrial vegetation couples&#160;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&#160;during the last decades is unclear, as well as potential changes in response to trends in hydro-climatic conditions.&#160;In this study, we analyze the changing sensitivity of global vegetation productivity to hydro-climate&#160;conditions by using satellite-observed vegetation indices&#160;(i.e. NDVI)&#160;at the monthly timescale from 1982&#8211;2015. Further, we&#160;repeat the analysis&#160;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&#160;random forest model to predict anomalies of productivity from&#160;a comprehensive set of hydro-meteorological variables&#160;(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&#160;training models from&#160;temporal independent subsets of the data we detect the evolution of sensitivity&#160;across time. Results based on observations&#160;show that vegetation sensitivity to energy- and water-related variables has significantly changed&#160;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&#160;to root-zone soil moisture with increases predominantly in the early growing season and decreases in the late growing season.&#160;For better understanding the mechanisms behind the sensitivity changes, we analyse land-cover changes, hydro-climatic trends, and abrupt disturbances&#160;(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&#160;its response to&#160;the drivers under&#160;climate change, which can help to understand possibly resulting&#160;changes in spatial and temporal patterns of land carbon uptake.

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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.

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Changes in Some Soil Properties and Available P Under Phosphorus Integrated management of Sunflower Plant (Helianthus annuus L.)

Basrah Journal of Agricultural Sciences ◽

10.37077/25200860.2019.281 ◽

2019 ◽

Vol 32 ◽

pp. 337-351

Author(s):

Mohammed J. Mustafa ◽

Mohammed A. Abdulkareem

Keyword(s):

Soil Moisture ◽

Moisture Content ◽

Soil Properties ◽

Soil Ph ◽

Vegetative Growth ◽

Integrated Management ◽

Growing Season ◽

Chemical Fertilizer ◽

Seedling Stage ◽

Available P

Field experiment was conducted in Mohajaran region, Abu-Al-Khaseeb district, Basrah province during the growing season 2018. The study was aimed to evaluate the effect of integration of chemical fertilizer (triple superphosphate) with manure (cattle residue) and/ or biofertilizer (Aspergillus niger) on some soil properties and phosphorus availability to sunflower during growing season. Samples were collected at seedling, vegetative growth, flowering and post-harvest stage. pH, EC, moisture content and available P were determined. Results showed that application of chemical fertilizer significantly affected soil pH, EC, and available P, but showed no effect on soil moisture content. Soil pH decreased and EC increased at seedling stage, while EC was decreased at harvest. Available P values were increased at all growing stages. Incorporation of manure at rate of 30 Mg ha-1 considerably decreased the soil pH and increased EC at seedling stage, soil moisture, and available P at all growing stages.. Inoculation the seeds with A. niger showed no significant effect on soil pH, EC, and soil moisture but significantly increased available P, at vegetative growth and flowing stages . Results showed that the effect of biofertilizer on available P was in bar with the application of manure at rate of 15 Mg ha-1 .Highest value of available P was associated with combination of 120 Kg P ha-1 + 30 Mg ha-1 + inoculation with fungus.

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Vegetation greenness response to water availability in northern China from 1982 to 2006

10.5194/se-2016-46 ◽

2016 ◽

Author(s):

Zhang Fengtai ◽

An Youzhi

Keyword(s):

Soil Moisture ◽

Water Availability ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Growing Season ◽

Northern China ◽

Entire Study ◽

Vegetation Greenness ◽

Spatio Temporal ◽

Using Data

Abstract. An evaluation of the relationship between satellite-observed Normalized Difference Vegetation Index (NDVI) data as a proxy for vegetation greenness and water availability (rainfall and soil moisture) can greatly improve our understanding of how vegetation greenness responds to water availability fluctuations. Using Sen and Pearson’s correlation methods, we analyzed the spatio-temporal variation of vegetation greenness for both the entire year and the growing season (GS,4-10) in northern China from 1982 to 2006. Although, vegetation greenness and soil moisture during the study period changed significantly for the entire study area, there was no change in rainfall. Linear correlation analysis between NDVI and rainfall revealed higher correlations using data for all seasons. Higher correlations for NDVI and soil moisture were obtained using growing season data. This study highlights how strongly vegetation greenness responds to water availability dynamics, especially in the growing season period.

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Assessment of Soil Moisture Anomaly Sensitivity to Detect Drought Spatio-Temporal Variability in Romania

Sensors ◽

10.3390/s21248371 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8371

Author(s):

Irina Ontel ◽

Anisoara Irimescu ◽

George Boldeanu ◽

Denis Mihailescu ◽

Claudiu-Valeriu Angearu ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Root Zone ◽

Standardized Precipitation Index ◽

In Situ Measurements ◽

Near Surface ◽

Soil Moisture Anomaly

This paper will assess the sensitivity of soil moisture anomaly (SMA) obtained from the Soil water index (SWI) product Metop ASCAT, to identify drought in Romania. The SWI data were converted from relative values (%) to absolute values (m3 m−3) using the soil porosity method. The conversion results (SM) were validated using soil moisture in situ measurements from ISMN at 5 cm depths (2015–2020). The SMA was computed based on a 10 day SWI product, between 2007 and 2020. The analysis was performed for the depths of 5 cm (near surface), 40 cm (sub surface), and 100 cm (root zone). The standardized precipitation index (SPI), land surface temperature anomaly (LST anomaly), and normalized difference vegetation index anomaly (NDVI anomaly) were computed in order to compare the extent and intensity of drought events. The best correlations between SM and in situ measurements are for the stations located in the Getic Plateau (Bacles (r = 0.797) and Slatina (r = 0.672)), in the Western Plain (Oradea (r = 0.693)), and in the Moldavian Plateau (Iasi (r = 0.608)). The RMSE were between 0.05 and 0.184. Furthermore, the correlations between the SMA and SPI, the LST anomaly, and the NDVI anomaly were significantly registered in the second half of the warm season (July–September). Due to the predominantly agricultural use of the land, the results can be useful for the management of water resources and irrigation in regions frequently affected by drought.

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A pan-African high-resolution drought index dataset

10.5194/essd-2019-138 ◽

2019 ◽

Author(s):

Jian Peng ◽

Simon Dadson ◽

Feyera Hirpa ◽

Ellen Dyer ◽

Thomas Lees ◽

...

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Spatial Resolution ◽

Vegetation Index ◽

Root Zone ◽

Environmental Data ◽

Climatic Research Unit ◽

Drought Vulnerability ◽

Coarse Resolution ◽

Mass Migration

Abstract. Droughts in Africa cause severe problems such as crop failure, food shortages, famine, epidemics and even mass migration. To minimize the effects of drought on water and food security over Africa, a high-resolution drought dataset is essential to establish robust drought hazard probabilities and to assess drought vulnerability considering a multi- and cross-sectorial perspective that includes crops, hydrological systems, rangeland, and environmental systems. Such assessments are essential for policy makers, their advisors, and other stakeholders to respond to the pressing humanitarian issues caused by these environmental hazards. In this study, a high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset is presented to support these assessments. We compute historical SPEI data based on Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation estimates and Global Land Evaporation Amsterdam Model (GLEAM) potential evaporation estimates. The high resolution SPEI dataset (SPEI-HR) presented here spans from 1981 to 2016 (36 years) with 5 km spatial resolution over the whole Africa. To facilitate the diagnosis of droughts of different durations, accumulation periods from 1 to 48 months are provided. The quality of the resulting dataset was compared with coarse-resolution SPEI based on Climatic Research Unit (CRU) Time-Series (TS) datasets, and Normalized Difference Vegetation Index (NDVI) calculated from the Global Inventory Monitoring and Modeling System (GIMMS) project, as well as with root zone soil moisture modelled by GLEAM. Agreement found between coarse resolution SPEI from CRU TS (SPEI-CRU) and the developed SPEI-HR provides confidence in the estimation of temporal and spatial variability of droughts in Africa with SPEI-HR. In addition, agreement of SPEI-HR versus NDVI and root zone soil moisture – with average correlation coefficient (R) of 0.54 and 0.77, respectively – further implies that SPEI-HR can provide valuable information to study drought-related processes and societal impacts at sub-basin and district scales in Africa. The dataset is archived in Centre for Environmental Data Analysis (CEDA) with link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a)

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Soil Moisture Initialization Input Scale Effect on Parameter Value Identification of a Physically Based Distributed Hydrologic Modelling

10.5194/egusphere-egu2020-22194 ◽

2020 ◽

Author(s):

Nawa Raj Pradhan ◽

Steven Brown ◽

Ian Floyd

Keyword(s):

Soil Moisture ◽

Vegetation Index ◽

Hydrological Model ◽

Root Zone ◽

Soil Column ◽

Estimation Method ◽

Model Parameters ◽

Analysis Model ◽

Soil Moisture Data ◽

Moisture Estimation

Data acquisition and an efficient processing method for hydrological model initialization, such as soil moisture, and parameter value identification are critical for a physics based distributed watershed modelling of flood and flood related disasters such as sediment and debris flow. Site measurements can provide relatively accurate estimates of soil moisture, but such techniques are limited due to the need for a variety of measurement accessories, which are difficult to obtain to cover a large area sufficiently. Available satellite-based digital soil moisture data is at 9 kilometers to 50 kilometers in resolution which completely filters the soil moisture details at the hill slope scale. Moreover, available satellite-based digital soil moisture data represents only a few centimeters of the top soil column that informs nothing about the effective root-zone wetness. A recently developed soil moisture estimation method called SERVES (Soil moisture Estimation of Root zone through Vegetation index-based Evapotranspiration fraction and Soil properties) overcomes this limitation of satellite-based soil moisture data by estimating distributed root zone soil moisture at 30 meter resolution. In this study, a distributed watershed hydrological model of a sub-catchment of Reynolds Creek Experimental Watershed was developed with GSSHA (Gridded Surface Sub-surface Hydrological Analysis) Model. SERVES soil moisture estimated at 30 meter resolution was deployed in the watershed hydrological parameter value calibration and identification process. The 30 meter resolution SERVES soil moisture data was resampled to 4500 meter and 9000 meter resolutions and was separately employed in the calibrated hydrological model to determine the effect soil moisture resolution &#160;has on the simulated outputs and the model parameters. It was found that the simulated discharge significantly decreased as the initial soil moisture resolution was coarsened. To compensate for this underestimated simulated discharge, the soil hydraulic conductivity value decreased logarithmically with respect to the decreased resolutions. This study will reduce parameter value identification uncertainty especially in flood and soil erosion modelling at multi scale watershed in a changing climate.

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