scholarly journals Prediction Models for the Estimation of Soil Moisture Content

2011 ◽  
Author(s):  
Swathi Gorthi ◽  
Huifang Dou
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Prediction Models ◽  
Recursive Partitioning ◽  
Machine Learning Techniques ◽  
Linear Regression Models ◽  
Remotely Sensed Data ◽  
Modeling Tools ◽  
Wide Range

This paper provides a survey on different kinds of prediction models developed for the estimation of soil moisture content of an area, using empirical information including meteorological and remotely sensed data. The different models employed extend over a wide range of machine learning techniques starting from Basic Linear Regression models through models based on Bayesian framework, Decision tree learning and Recursive partitioning, to the modern non-linear statistical data modeling tools like Artificial Neural Networks. The fundamental mathematical backgrounds, pros and cons, prediction results and efficiencies of all the models are discussed.

scholarly journals Inversion of soil moisture content in the farmland in middle and lower reaches of Syr Darya River Basin based on multi-source remotely sensed data

2019 ◽  
Vol 34 (12) ◽  
pp. 2717
Author(s):  
WANG Hao ◽  
LUO Ge-ping ◽  
WANG Wei-sheng ◽  
PACHIKIN Konstantin ◽  
LI Yao-ming ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
River Basin ◽  
Soil Moisture Content ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Syr Darya

Experimental studies on some observed variations in Luffa aegyptiaca

1983 ◽  
Vol 61 (1) ◽  
pp. 202-210 ◽  
Author(s):  
Olubukanla T. Okusanya
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Type ◽  
Leaf Morphology ◽  
Soil Moisture Content ◽  
Humus Content ◽  
Experimental Studies ◽  
Soil Conditions ◽  
Wide Range ◽  
Luffa Aegyptiaca

The causes of some observed variations in plant size and leaf morphology of populations of Luffa aegyptiaca were investigated experimentally. The effects of soil type, soil salinity, soil moisture content, and mineral nutrients on the germination and growth of this species were examined. The results indicate that variations in size and leaf morphology between the population groups earlier described probably were caused by variations in soil type and soil nutrients. Nitrate was responsible for variation in leaf colour, potassium for leaf invagination, and phosphorus and nitrate together for leaf texture. Nitrate also played a major role in the variation in leaf size while calcium played a subsidiary role. Within a single population, variations would possibly also be caused by differences in soil conditions, principally humus content, soil moisture content, and salinity. The wide adaptability exhibited by this species also helps to explain its distribution on a wide range of soils.

The interactive effects of increased evaporative demand and soil water on photosynthesis in maize

1991 ◽  
Vol 71 (1) ◽  
pp. 31-39 ◽  
Author(s):  
R. G. Berard ◽  
G. W. Thurtell
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Water ◽  
Soil Moisture Content ◽  
Water Status ◽  
Interactive Effects ◽  
Evaporative Demand ◽  
Photosynthetic Rates ◽  
Root Medium ◽  
Wide Range

A field-portable whole-plant enclosure system was used to study the effect of increased evaporative demand on photosynthetic rates of maize (Zea mays L.) subjected to various root medium treatments. The system consisted of two transparent chambers, each capable of maintaining a fully grown maize plant at ambient conditions while enabling different evaporative demand treatments by controlling the humidity. The rooting media consisted of silt loam soil held at three levels of soil moisture content covering a wide range of available moisture, and a hydroponic medium consisting of 25-L pails containing "Turface" and supplied three times daily with a nutrient solution. Measurements were carried out during the post-silking period from 22 July to 6 Sept. 1987 and consisted of at least 4 d of continuous monitoring of photosynthesis and transpiration rates from early morning till sundown. The effect of increased evaporative demand on photo-synthetic rates was relatively small, with average photosynthetic reductions of approximately 4–6% in all root medium treatments. Soil moisture content did not have any effect on the reduction of photosynthesis which occurred at high evaporative demand. However, absolute photosynthetic rates were significantly reduced by low soil moisture. It is suggested that atmospheric conditions leading to high transpiration rates are much less important than soil moisture conditions in causing yield reductions due to reduced photosynthetic rates. The results support recent evidence by other workers that soil water status can influence stomatal conductance and photosynthesis without the intermediary influence of leaf water status. Key words: Photosynthesis, transpiration, maize, soil water content, VPD, leaf conductance

scholarly journals Comparison of soil moisture content measurement between Internet of Things (IoT) and traditional methods

2021 ◽  
Vol 309 ◽  
pp. 01191
Author(s):  
Swetha Guduru ◽  
K Radhika ◽  
Chandana Sukesh ◽  
P Srilakshmi
Keyword(s):  
Soil Moisture ◽  
Internet Of Things ◽  
Moisture Content ◽  
Water Content ◽  
Soil Moisture Content ◽  
Sieve Analysis ◽  
Traditional Methods ◽  
Content Measurement ◽  
Wide Range ◽  
Moisture Content Measurement

Soil is a composition of Sand, Silt and Clay. From three phase concept, it is clear that the soil consists of solids, water and air. The ratio of weight of water to weight of solids for a given soil mass is known as water content of soil. In other words, the water content (w) also known as natural water content or natural moisture content. Water content is used in a wide range of scientific and technical areas, and is expressed as a ratio, which can range from zero to the value of the soil porosity at saturation. Traditionally, the water content is measured by pycnometer or oven dry methods which would generally take 24 hours to determine the water content soil. As the time is important these days, several smart advances are occurred in determining the moisture content through Internet of Things (IoT). In this project, the water content of soil is measured through IoT sensors and traditional methods. Present work involves in which different soil samples are taken along the road construction site and classifying them with the help of sieve analysis, Atterberg limits and plasticity chart and moisture content measurement using internet of things (IoT) and traditional methods are compared. Also, find a possible correlation developed between the soil moisture content by traditional methods and through IoT.

scholarly journals Remote Soil Moisture Measurement from Drone-Borne Reflectance Spectroscopy: Applications to Hydroperiod Measurement in Desert Playas

2021 ◽  
Vol 13 (5) ◽  
pp. 1035
Author(s):  
Joseph S. Levy ◽  
Jessica T. E. Johnson
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Surface Soil ◽  
Clay Content ◽  
Test Site ◽  
Surface Soil Moisture ◽  
Remotely Sensed Data ◽  
Moisture Index ◽  
Spatial And Temporal Scales

The extent, timing, and magnitude of soil moisture in wetlands (the hydropattern) is a primary physical control on biogeochemical processes in desert environments. However, determining playa hydropatterns is challenged by the remoteness of desert basin sites and by the difficulty in determining soil moisture from remotely sensed data at fine spatial and temporal scales (hundreds of meters to kilometers, and hours to days). Therefore, we developed a new, reflectance-based soil moisture index (continuum-removed water index, or CRWI) that can be determined via hyperspectral imaging from drone-borne platforms. We compared its efficacy at remotely determining soil moisture content to existing hyperspectral and multispectral soil moisture indices. CRWI varies linearly with in situ soil moisture content (R2 = 0.89, p < 0.001) and is comparatively insensitive to soil clay content (R2 = 0.4, p = 0.01), soil salinity (R2 = 0.82, p < 0.001), and soil grain size distribution (R2 = 0.67, p < 0.001). CRWI is negatively correlated with clay content, indicating it is not sensitive to hydrated mineral absorption features. CRWI has stronger correlation with surface soil moisture than other hyperspectral and multispectral indices (R2 = 0.69, p < 0.001 for WISOIL at this site). Drone-borne reflectance measurements allow monitoring of soil moisture conditions at the Alvord Desert playa test site over hectare-scale soil plots at measurement cadences of minutes to hours. CRWI measurements can be used to determine surface soil moisture at a range of desert sites to inform management decisions and to better reveal ecosystem processes in water-limited environments.

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.

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