Applying fractal analysis to detect spatio-temporal variability of soil moisture content on two contrasting land use hillslopes

CATENA ◽  
2017 ◽  
Vol 157 ◽  
pp. 163-172 ◽  
Author(s):  
Kaihua Liao ◽  
Xiaoming Lai ◽  
Zhiwen Zhou ◽  
Qing Zhu
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Moisture Content ◽  
Fractal Analysis ◽  
Temporal Variability ◽  
Soil Moisture Content ◽  
Spatio Temporal

scholarly journals Spatial-Temporal Simulations of Soil Moisture Content in a Large Basin of the Loess Plateau, China

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiqi Wang ◽  
Xiaobo Feng ◽  
Zhihong Yao ◽  
Zhaolong Ma ◽  
Guodong Ji
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Seasonal Variation ◽  
Moisture Content ◽  
Loess Plateau ◽  
Soil Moisture Content ◽  
Spatial And Temporal Variation ◽  
Slope Aspect ◽  
The Loess Plateau ◽  
Basin Scale

Soil moisture is a crucial factor limiting the growth and survival of plants on the Loess Plateau. Its level has a severe impact on plants’ growth and development and the type and distribution characteristics of communities. This study area is the Jihe Basin in the Loess Plateau, China. Multiple linear regression models with different environmental variables (land use, topographic and meteorological factors, etc.) were developed to simulate soil moisture’s spatial and temporal changes by integrating field experiments, indoor analysis, and GIS spatial analysis. The model performances were evaluated in the Jihe Basin, with soil moisture content measurements. The result shows that soil moisture content is positively correlated with soil bulk density, monthly rainfall, topographic wetness index, land use coefficient, and slope aspect coefficient but negatively correlated with the monthly-averaged temperature and the relative elevation coefficient. The selected variables are all related to the soil moisture content and can account for 75% of the variations of soil moisture content, and the remaining 25% of the variations are related to other factors. Comparing the simulated and measured values at all sampling points shows that the average error of all the simulated values is 0.09, indicating that the simulation has high accuracy. The spatial distribution of soil moisture content is significantly affected by land use and topographic factors, and seasonal variation is remarkable in the year. Seasonal variation of soil moisture content is determined by the seasonal variation of rainfall and the air temperature (determining evaporation) and vegetation growth cycle. Therefore, the proposed model can simulate the spatial and temporal variation of soil moisture content and support developing the soil and water loss model on a basin scale.

scholarly journals The effect of rainfall and extensive use of grasslands on water regime

2011 ◽  
Vol 48 (No. 3) ◽  
pp. 89-95
Author(s):  
R. Duffková
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Moisture Content ◽  
Water Regime ◽  
Soil Moisture Content ◽  
Growing Season ◽  
Water Runoff ◽  
Botanical Composition ◽  
Significant Difference ◽  
Determinant Factor

 Water regimes of extensively used grasslands (one cut per year, two cuts per year, no cut, mulching) were determined and compared by drainage lysimeters in 1998–2000. Although the botanical composition and yields of experimental swards were different, there was no statistically significant difference in their water regime (only the soil moisture content of no-cut variant was significantly higher than in other variants). A determinant factor for the water regime of grasslands (GR) is the sum of rainfall over the growing season while the GR water regime is influenced by land use immediately after the cut. Water runoff from the soil profile 0.0–0.60 m (water supply to the groundwater level) was found to be negligible in the growing season, a substantial groundwater recharge occurs in an off-season period and/or at the beginning of growing season. Mulching was not proved to reduce evaporation. The best type of management providing for the economical water regime appears to be a one-cut variant. Relationships between botanical composition and GR water regime are also described.

Stock change of soil moisture under crop cultivation: A relevant and low cost indicator to assess hydrological performance of agricultural land use systems in Tigray, northern Ethiopia.

2020 ◽  
Author(s):  
Richard Kraaijvanger
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Agricultural Land ◽  
Root Zone ◽  
Low Cost ◽  
Crop Productivity ◽  
Agricultural Land Use ◽  
Stock Change

<p>In the highlands of Tigray both crop yield and soil erosion are important concerns. At the same time the impact of climate change is felt in the form of delayed and more erratic rains. Different adaptation strategies are proposed to increase resilience. The successful implementation of most of these strategies, like for example, agroforestry, conservation tillage and water harvesting, heavily relies on improved infiltration and the amount of water stored in the root zone. In this presentation the water storage in the root zone is discussed in relation to crop productivity and hydrological performance of the local (agricultural) land use system. For this purpose measurements of (gravimetric) soil moisture content, taken at different depths in the root zone and at regular time intervals during four growing seasons in the period 2010-2013, were considered. In total 43 sites were involved, which were measured for one up to three years. In addition to soil moisture content, at selected sites also bulk density, saturation, field capacity and wilting point were determined. On the basis of the data collected, site-specific changes in soil moisture budgets were analyzed and trends observed were related to crop productivity and hydrological parameters (like rainfall and evapotranspiration). First outcomes pointed to a relatively rapid increase of soil moisture stock at the start of the growing season, followed by a more or less stable level, and ending at crop maturation with a very rapid decrease. Typical figures for gravimetric moisture content at the stable level were between 25 and 30 %. Soil depth was in most cases shallow (around 40 cm) and likely limiting moisture storage capacity. Assuming that at the start of the stable phase rainfall still is exceeding evapotranspiration, this then will point to a relatively high risk for run off at this stage. Stock change of soil moisture as such appears a relevant and low cost indicator to assess hydrological performance of land use systems in terms of infiltration capacity and soil moisture availability. In line with that, analysis of stock change of soil moisture might provide relevant clues for designing and optimizing effective land management strategies that successfully deal with erosion hazard and result in a more resilient and sustainable production of food crops.</p>

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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