scholarly journals Seasonal Volatile Emission Patterns of the Endemic New Zealand Shrub Dracophyllum subulatum on the North Island Central Plateau

2021 ◽  
Vol 12 ◽  
Author(s):  
Evans Effah ◽  
D. Paul Barrett ◽  
Paul G. Peterson ◽  
Murray A. Potter ◽  
Jarmo K. Holopainen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
New Zealand ◽  
Environmental Factors ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Ambient Air ◽  
Sampling Occasion ◽  
Central Plateau ◽  
Volatile Emissions ◽  
The North

Volatile organic compounds (VOCs) produced by plants are essential indicators of their physiological response to environmental conditions. But evidence of natural variation in VOC emissions and their contributing factors is still limited, especially for non-cultivated species. Here we explored the natural volatile emissions of Dracophyllum subulatum Hook.f., an endemic shrub to the North Island Central Plateau of New Zealand, and determined some environmental factors driving the plant’s emissions. Volatile emissions of D. subulatum were measured on four separate occasions from December 2017 to September 2018 using the “push-pull” headspace sampling technique and analyzed using gas chromatography-mass spectrometry (GC-MS). D. subulatum was classified based on the volatiles measured on each sampling occasion using linear discriminant analysis (LDA). On each sampling occasion, we also recorded and compared ambient air temperature, herbivory damage, total soil nitrogen (N), available phosphorus (P), potassium (K), and soil moisture content. The relationship between environmental variables that differed significantly between sampling occasions and volatile emissions were estimated using generalized linear models (GLMs). Based on VOCs measured on each sampling occasion, we were able to distinguish different chemical profiles. Overall, we found that total emission and the relative proportions of all major chemical classes released by D. subulatum were significantly higher during summer. The GLMs reveal that differences in environmental factors between the four sampling occasions are highly associated with changing emissions. Higher temperatures in summer had a consistently strong positive relationship with emissions, while the impacts of soil moisture content, P and K were variable and depended on the chemical class. These results are discussed, particularly how high temperature (warming) may shape volatile emissions and plants’ ecology.

scholarly journals Numerical Simulations of Effects of Soil Moisture and Modification by Mountains over New Zealand in Summer

2011 ◽  
Vol 139 (2) ◽  
pp. 494-510 ◽  
Author(s):  
Yang Yang ◽  
Michael Uddstrom ◽  
Mike Revell ◽  
Phil Andrews ◽  
Hilary Oliver ◽  
...  
Keyword(s):  
Soil Moisture ◽  
New Zealand ◽  
Moisture Content ◽  
Air Temperature ◽  
Land Surface ◽  
Soil Moisture Content ◽  
Surface Air Temperature ◽  
Shortwave Radiation ◽  
Lower Sensitivity ◽  
The South

Abstract Historically most soil moisture–land surface impact studies have focused on continents because of the important forecasting and climate implications involved. For a relatively small isolated mountainous landmass in the ocean such as New Zealand, these impacts have received less attention. This paper addresses some of these issues for New Zealand through numerical experiments with a regional configuration of the Met Office Unified Model atmospheric model. Two pairs of idealized simulations with only contrasting dry or wet initial soil moisture over a 6-day period in January 2004 were conducted, with one pair using realistic terrain and the other pair flat terrain. For the mean of the 6 days, the differences in the simulated surface air temperature between the dry and moist cases were 3–5 K on the leeside slopes and 1–2 K on the windward slopes and the central leeside coastal region of the South Island in the afternoon. This quite nonuniform response in surface air temperature to a uniformly distributed soil moisture content and soil type is mainly attributed to modification of the effects of soil moisture by mountains through two different processes: 1) spatial variation in cloud coverage across the mountains ranges leading to more shortwave radiation at ground surface on the leeside slope than the windward slope, and 2) the presence of a dynamically and thermally induced onshore flow on the leeside coast bringing in air with a lower sensitivity to soil moisture. The response of local winds to soil moisture content is through direct or indirect effects. The direct effect is due to the thermal contrast between land and sea/land shown for the leeside solenoidal circulations, and the indirect effect is through the weakening of the upstream blocking of the South Island for dryer soils shown by the weakening and onshore shift of the upstream deceleration and forced ascent of incoming airflow.

Variability in the Environmental Factors Driving Evapotranspiration from a Grazed Rangeland during Severe Drought Conditions

2007 ◽  
Vol 8 (2) ◽  
pp. 207-220 ◽  
Author(s):  
Joseph G. Alfieri ◽  
Peter D. Blanken ◽  
David N. Yates ◽  
Konrad Steffen
Keyword(s):  
Soil Moisture ◽  
Environmental Factors ◽  
Moisture Content ◽  
Land Surface ◽  
Soil Moisture Content ◽  
Severe Drought ◽  
Vapor Transfer ◽  
Drought Conditions ◽  
Using Data

Abstract Nearly one-half of the earth’s terrestrial surface is susceptible to drought, which can have significant social, economic, and environmental impacts. Therefore, it is important to develop better descriptions and models of the processes linking the land surface and atmosphere during drought. Using data collected during the International H2O Project, the study presented here investigates the effects of variations in the environmental factors driving the latent heat flux (λE) during drought conditions at a rangeland site located in the panhandle of Oklahoma. Specifically, this study focuses on the relationships of λE with vapor pressure deficit, wind speed, net radiation, soil moisture content, and greenness fraction. While each of these environmental factors has an influence, soil moisture content is the key control on λE. The role of soil moisture in regulating λE is explained in terms of the surface resistance to water vapor transfer. The results show that λE transitioned between being water or energy limited during the course of the drought. The implications of this on the ability to understand and model drought conditions and transitions into or out of droughts are discussed.

scholarly journals Spatiotemporal Distribution of Soil Moisture Content over Ukraine and Its Relationship to Atmospheric Conditions

2021 ◽  
Vol 4 (1) ◽  
pp. 20
Author(s):  
Inna Semenova
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Zonal Flow ◽  
Spatiotemporal Distribution ◽  
Degree Of Saturation ◽  
Atmospheric Conditions ◽  
The North ◽  
Different Seasons ◽  
The North Atlantic Oscillation

Spatiotemporal distribution of the soil moisture content of 0–10 cm underground has been assessed across the aroclimatic zones of Ukraine for the period 2000–2019. Calculated Soil Moisture Anomaly Index (SMAI) was used to characterize the degree of saturation of the soil, comparing to normal conditions. The North Atlantic Oscillation index (NAO) and the European Continental Blocking Index (ECBI) were used for the estimation of the influence of atmospheric circulation on soil moisture content in different seasons. The clear annual soil moisture content course is observed in all agroclimatic zones of Ukraine, when the maximum is observed in February, and the minimum is in August. The lowest soil moisture values are fixed in the Western Steppe and the maximum in the Carpathian region and Polesie. The analysis of time series of the SMAI showed the tendency to transition from mostly positive values to negative values in the past decade in summer and autumn. In winter and spring, no significant trends were found in the SMAI values. Analysis of the statistical relationship between the SMAI and the NAO indices, and the SMAI and the ECBI indices showed the features of the state of zonal flow and determined certain anomalies of soil moisture content.

Analysis on Soil Moisture Content in the Middle Reaches of the Yellow River

2011 ◽  
Vol 28 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Run-chun LI ◽  
Xiu-zhi ZHANG ◽  
Li-hua WANG ◽  
Xin-yan LV ◽  
Yuan GAO
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Yellow River ◽  
The Yellow River

Distribution of soil moisture content and its effect on the potential growth of the over-story species in North-East Chalkidiki

2001 ◽  
Vol 66 ◽  
Author(s):  
M. Aslanidou ◽  
P. Smiris
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Soil Depth ◽  
Moisture Distribution ◽  
Taxus Baccata ◽  
Mixed Stands ◽  
Potential Growth ◽  
North East ◽  
Soil Moisture Distribution

This  study deals with the soil moisture distribution and its effect on the  potential growth and    adaptation of the over-story species in north-east Chalkidiki. These  species are: Quercus    dalechampii Ten, Quercus  conferta Kit, Quercus  pubescens Willd, Castanea  sativa Mill, Fagus    moesiaca Maly-Domin and also Taxus baccata L. in mixed stands  with Fagus moesiaca.    Samples of soil, 1-2 kg per 20cm depth, were taken and the moisture content  of each sample    was measured in order to determine soil moisture distribution and its  contribution to the growth    of the forest species. The most important results are: i) available water  is influenced by the soil    depth. During the summer, at a soil depth of 10 cm a significant  restriction was observed. ii) the    large duration of the dry period in the deep soil layers has less adverse  effect on stands growth than in the case of the soil surface layers, due to the fact that the root system mainly spreads out    at a soil depth of 40 cm iii) in the beginning of the growing season, the  soil moisture content is    greater than 30 % at a soil depth of 60 cm, in beech and mixed beech-yew  stands, is 10-15 % in    the Q. pubescens  stands and it's more than 30 % at a soil depth of 60 cm in Q. dalechampii    stands.

scholarly journals Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Moisture Content ◽  
Layer Thickness ◽  
Soil Moisture Content ◽  
Hyperspectral Imagery ◽  
Barrier Island ◽  
Water Layer ◽  
Radiative Transfer Model ◽  
Transfer Model

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

scholarly journals Estimating Agricultural Soil Moisture Content through UAV-Based Hyperspectral Images in the Arid Region

2021 ◽  
Vol 13 (8) ◽  
pp. 1562
Author(s):  
Xiangyu Ge ◽  
Jianli Ding ◽  
Xiuliang Jin ◽  
Jingzhe Wang ◽  
Xiangyue Chen ◽  
...  
Keyword(s):  
Data Mining ◽  
Soil Moisture ◽  
Moisture Content ◽  
Hyperspectral Imaging ◽  
Fractional Order ◽  
Arid Regions ◽  
Soil Moisture Content ◽  
Order Derivative ◽  
Imaging Data ◽  
Strategy I

Unmanned aerial vehicle (UAV)-based hyperspectral remote sensing is an important monitoring technology for the soil moisture content (SMC) of agroecological systems in arid regions. This technology develops precision farming and agricultural informatization. However, hyperspectral data are generally used in data mining. In this study, UAV-based hyperspectral imaging data with a resolution o 4 cm and totaling 70 soil samples (0–10 cm) were collected from farmland (2.5 × 104 m2) near Fukang City, Xinjiang Uygur Autonomous Region, China. Four estimation strategies were tested: the original image (strategy I), first- and second-order derivative methods (strategy II), the fractional-order derivative (FOD) technique (strategy III), and the optimal fractional order combined with the optimal multiband indices (strategy IV). These strategies were based on the eXtreme Gradient Boost (XGBoost) algorithm, with the aim of building the best estimation model for agricultural SMC in arid regions. The results demonstrated that FOD technology could effectively mine information (with an absolute maximum correlation coefficient of 0.768). By comparison, strategy IV yielded the best estimates out of the methods tested (R2val = 0.921, RMSEP = 1.943, and RPD = 2.736) for the SMC. The model derived from the order of 0.4 within strategy IV worked relatively well among the different derivative methods (strategy I, II, and III). In conclusion, the combination of FOD technology and the optimal multiband indices generated a highly accurate model within the XGBoost algorithm for SMC estimation. This research provided a promising data mining approach for UAV-based hyperspectral imaging data.

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

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