THE IMPORTANCE OF SEEPAGE ZONES IN PREDICTING SOIL MOISTURE CONTENT AND SURFACE RUNOFF USING GLEAMS AND RZWQM

2004 ◽  
Vol 47 (2) ◽  
pp. 427-438 ◽  
Author(s):  
A. Chinkuyu ◽  
T. Meixner ◽  
T. Gish ◽  
C. Daughtry
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Surface Runoff ◽  
Soil Moisture Content

scholarly journals Study on Variation of Surface Runoff and Soil Moisture Content in the Subgrade of Permeable Pavement Structure

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Lijun Hou ◽  
Yuan Wang ◽  
Fengchun Shen ◽  
Ming Lei ◽  
Xiang Wang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Water ◽  
Surface Runoff ◽  
Rainfall Intensity ◽  
Soil Moisture Content ◽  
Asphalt Pavement ◽  
Infiltration Rate ◽  
Runoff Coefficient ◽  
Flood Peak

The self-designed indoor simulated rainfall device was used to rain on five types of pavement structures with 4 types of rainfall intensity (2.5 mm/min, 3.4 mm/min, 4.6 mm/min, and 5.5 mm/min). The effect of rainfall intensity on the surface runoff, the relation between the subgrade soil moisture content changes, and the influence of initial soil water content on rain infiltration rate are studied. The test results show that the surface runoff coefficient of densely asphalted pavement is greater than 90% in drainage pavements and it has little influence on the reducing and hysteresis of the flood peak. The surface runoff coefficient of large-void asphalt pavement (permeable) is less than 40%. Although the large-void asphalt pavement (permeable) can reduce a small amount of surface runoff, it has no obvious effect on the reduction and hysteresis of the flood peak. In semipermeable pavement, with the increasing of the thickness of base (graded gravel), the surface runoff coefficient decreases at different rainfall intensities, parts of the surface runoff are reduced, and the arrival of flood peaks is delayed. In permeable roads, almost no surface runoff occurred. As time continued, the soil moisture content quickly reached a saturated state and presented a stable infiltration situation under the action of gravity and the gradient of soil water suction. As the initial moisture content increases, the initial infiltration rate decreases and the time to reach a stable infiltration rate becomes shorter. The drier the soil, the greater the initial infiltration rate and the higher the soil moisture content after infiltration stabilization. Permeable roads can greatly alleviate the pressure of urban drainage and reduce the risk of storms and floods.

scholarly journals Aspect and soil textural controls on snowmelt runoff on forested Boreal Plain hillslopes

2011 ◽  
Vol 42 (4) ◽  
pp. 250-267 ◽  
Author(s):  
Todd Redding ◽  
Kevin Devito
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Surface Runoff ◽  
Soil Moisture Content ◽  
Mineral Soil ◽  
Sandy Soils ◽  
Slope Aspect ◽  
Snowmelt Runoff ◽  
Near Surface ◽  
Spring Snowmelt

Plot studies were conducted on a jack pine forest with sandy soil and aspen forests with sandy and loam soils to examine the controls of slope aspect, soil texture and fall soil moisture content on near-surface snowmelt runoff and infiltration. It was hypothesized that near-surface runoff would be greater from north-facing slopes on loam soils with increased fall soil moisture content. Fall soil moisture had no measurable effect on spring snowmelt runoff. Infiltration of snowmelt dominated (drainage coefficients 53–100%, median 87%) over near-surface runoff (runoff coefficients 1–65%, median 7%) for most plots. Runoff was related to concrete frost at the mineral soil surface. In contrast to the processes hypothesized, south-facing hillslopes with sandy soils generated greater runoff than north-facing slopes or sites with finer-textured soils. These results were due to greater concrete frost development resulting from periodic spring snowmelt and re-freezing in the upper soil. South-facing hillslopes with sandy soils featured lower canopy cover, allowing greater solar radiation to reach the snow surface which led to the formation of concrete frost and faster melt rates resulting in near-surface runoff. Where hillslopes are connected to receiving surface waters by continuous concrete frost, snowmelt runoff at the watershed scale may be enhanced.

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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