scholarly journals A scheme for calculating soil moisture content by using routine weather data

2007 ◽  
Vol 7 (19) ◽  
pp. 5197-5206 ◽  
Author(s):  
K. Z. Shang ◽  
S. G. Wang ◽  
Y. X. Ma ◽  
Z. J. Zhou ◽  
J. Y. Wang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Soil Moisture ◽  
Moisture Content ◽  
Cloud Cover ◽  
Soil Moisture Content ◽  
Global Radiation ◽  
Model Systems ◽  
Weather Data ◽  
Daily Routine ◽  
Meteorological Elements

Abstract. Soil moisture content is one of the most important parameters as input conditions in forecasting model systems of dust storm, but it can not be directly obtained from daily routine weather report. In this paper, a scheme is developed to calculate the surface soil moisture content in China by using both precipitation and evaporation. Precipitation is directly from routine weather report, while evaporation is indirectly calculated by using meteorological elements which are also from routine weather report. According to the formula by Penman, evaporation can be considered as a linear composition of dynamic evaporation and thermodynamic evaporation caused by radiation. First, an equation for calculating daily global radiation within China is given by using regression analysis and the data of global radiation and cloud cover from 116 meteorological stations in China. Then, an equation for calculating evaporation within China is given by using regression analysis and the data of cloud cover, air temperature, precipitation, relative humidity, and wind velocity from 701 meteorological stations. Finally, a scheme for calculating soil moisture content within China is established by using regression analysis and the soil moisture content, precipitation, and evaporation at 79 agro-meteorological stations. Validation results show that the forecasting accuracy of the Chinese dust numerical model can be improved by using this scheme.

scholarly journals A scheme for calculating soil moisture content by using routine weather data

2007 ◽  
Vol 7 (3) ◽  
pp. 7451-7472 ◽  
Author(s):  
K. Z. Shang ◽  
S. G. Wang ◽  
Y. X. Ma ◽  
Z. J. Zhou ◽  
J. Y. Wang ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Soil Moisture ◽  
Moisture Content ◽  
Cloud Cover ◽  
Soil Moisture Content ◽  
Global Radiation ◽  
Model Systems ◽  
Weather Data ◽  
Daily Routine ◽  
Meteorological Elements

Abstract. Soil moisture content is one of the most important parameters as input conditions in forecasting model systems of dust storm, but it can not be directly obtained from daily routine weather report. In this paper, a scheme is developed to calculate the surface soil moisture content in China by using both precipitation and evaporation. Precipitation is directly from routine weather report, while evaporation is indirectly calculated by using meteorological elements which are also from routine weather report. According to the formula by Penman, evaporation can be considered as a linear composition of dynamic evaporation and thermodynamic evaporation caused by radiation. First, an equation for calculating daily global radiation within China is given by using regression analysis and the data of global radiation and cloud cover from 116 meteorological stations in China. Then, an equation for calculating evaporation within China is given by using regression analysis and the data of cloud cover, air temperature, precipitation, relative humidity, and wind velocity from 701 meteorological stations. Finally, a scheme for calculating soil moisture content within China is established by using regression analysis and the soil moisture content, precipitation, and evaporation at 79 agro-meteorological stations. Validation results show that the forecasting accuracy of the Chinese dust numerical model can be clearly increased by using this scheme.

Effect of weather on maize yields and the efficiency of fertilization

2005 ◽  
Vol 53 (1) ◽  
pp. 31-39 ◽  
Author(s):  
L. Huzsvay ◽  
J. Nagy
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Regression Line ◽  
Soil Layer ◽  
Nutrient Utilization ◽  
Weather Data ◽  
Water Supplies ◽  
Daily Weather Data ◽  
Maize Yields

The yield of maize is primarily influenced by sunlight, temperature, available plant nutrients and water supply. Since plants take up water through their roots, the most decisive factor is not precipitation but the quantity of water available in the soil. In this study, a simple, easy-to-reproduce, capacitive model was elaborated to determine the available moisture content for maize. During the calculations, based on the balance method, the available moisture content in the top 110 cm soil layer was determined, taking daily weather data into account. The examinations were carried out on a medium heavy chernozem soil with lime deposits, in a multifactorial experiment at the Látókép Experimental Station of the Center of Agricultural Sciences, Debrecen University, between 1990 and 2004. Annual yield fluctuation is primarily determined by the soil moisture content in the month of July and the water supplies in May, according to regression analysis. The maize yields in the past 15 years could be calculated with an accuracy of 570 kg/ha, an error limit of below 10% and an r value of 0.805, using a regression line and the data of monthly moisture supplies. However, the yields of fertilized plots can only be estimated with an accuracy of 1 t/ha on average. Fertilizer utilization is influenced by the moisture content of the soil, so it makes sense to include this in the analysis instead of the other environmental factors. Water is required for nutrient utilization. In years with poor or medium water supplies, moderate fertilizer rates are more effective, compared to higher rates in years with better water supplies. Efficient fertilization in maize production can only be achieved by harmonizing soil moisture content and the applied fertilizer rate.

The Influence of Soil Moisture Cotent on the Numerical Simulation of Dust Storms

2014 ◽  
Vol 522-524 ◽  
pp. 235-238
Author(s):  
Jin Yan Wang ◽  
Lin Bo Wei ◽  
Zong Suo Zhao ◽  
Xu Li ◽  
Zhi Wei Zhang
Keyword(s):  
Numerical Simulation ◽  
Soil Moisture ◽  
Moisture Content ◽  
Soil Moisture Content ◽  
Surface Soil ◽  
Dust Emission ◽  
Dust Storms ◽  
Model Systems ◽  
Physical Mechanisms ◽  
Prediction Capability

In this paper, the influence of the soil moisture content on the the dust emission proceed is discussed by using an integrated numerical simulation and forecast system of dust storms based on the physical mechanisms of sand and dust emissions by wind erosion. The results show that the influence of soil moisture content on the dust emission is very distinguished, and soil moisture content is one of the most important parameters as input conditions in the forcasting model systems of dust storm. The prediction capability of numerical dust model is improved by estimated the surface soil moisture content for dust model system.

scholarly journals Spatiotemporal characteristics of smallholder milk production under changing climate: A case of Nandi County, Kenya

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Josephine Kirui ◽  
Joshua Ngaina ◽  
Nzioka John Muthama ◽  
Gachuiri Charles Karuku
Keyword(s):  
Regression Analysis ◽  
Soil Moisture ◽  
Moisture Content ◽  
Trend Analysis ◽  
Milk Production ◽  
Soil Moisture Content ◽  
Mitigation Measures ◽  
Daily Average ◽  
Changing Climate ◽  
Spatiotemporal Characteristics

Milk production in Kenya is predominantly smallholder and dependent on rainfall. The study assesses spatiotemporal characteristics of smallholder milk production in Nandi County under changing climate. Climate (Rainfall and temperature), fodder availability (Normalized Difference Vegetation Index (NDVI) and soil moisture content) and milk production data were used. Methods included trend analysis, spatial plots, correlation and multi-regression analysis. Monthly NDVI and soil moisture content were high between April and November with seasonal analysis indicating highest/lowest June-August (JJA)/December-February (DJF) values. Percentage change (%Δ) for NDVI was 6.0% (DJF), 1.96% (March-May, MAM), 2.13% (JJA), 4.16% (September-November, SON) and (2.53% (Annual). Seasonal and annual %Δ for soil moisture content ranged 7.2-17.1% at 0-10cm level and 8.1-23.7% at 10-40 level. Trend analysis of milk production showed positive change from 2007 to 2016 and highest/lowest in December/April with seasonal %Δ of up to 186% (MAM), 183% (JJA), 202% (SON), 214% (DJF) and 204% (Annual). Majority of household (HH) owned between 1 and 20 acres of land with only 0.5 to 2 acres allocated to dairy farming while those allocating less than 1 acre practiced zero grazing. On average, HH had 2 lactating cows throughout the year with majority of dairy farmers (98.6%) owning improved cow breeds. Amount of milk per HH supplied to the farmer organization varied between 2.3 litres and 3.8 litres with computed daily average milk produced per HH being 18.8 litres. Active milk suppliers were highest/lowest in December/April whereas daily average milk production per HH between 2010 and 2016 was highest/lowest in January (23.7 litres)/August (15.6 litres). Lowest/highest correlation coefficients were found in precipitation/minimum temperature. Multi-regression analysis indicated that precipitation had significant contribution to dairy productivity. Given the sensitivity of milk production to climate and fodder availability, adequate adaptation and mitigation measures are necessary in order to sustainably enhance milk production.

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.

Sign in / Sign up

Export Citation Format

Share Document