scholarly journals Spatio-temporal prediction of soil moisture and soil strength by depth-to-water maps

2021 ◽  
Vol 105 ◽  
pp. 102614
Author(s):  
Marian Schönauer ◽  
Kari Väätäinen ◽  
Robert Prinz ◽  
Harri Lindeman ◽  
Dariusz Pszenny ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Soil Strength ◽  
Temporal Prediction ◽  
Spatio Temporal

scholarly journals Investigating the Spatio-Temporal Variation of Soil Moisture and Agricultural Drought towards Supporting Water Resources Management in the Red River Basin of Vietnam

2021 ◽  
Vol 13 (9) ◽  
pp. 4926
Author(s):  
Nguyen Duc Luong ◽  
Nguyen Hoang Hiep ◽  
Thi Hieu Bui
Keyword(s):  
Soil Moisture ◽  
River Basin ◽  
Temporal Dynamics ◽  
Regional Scale ◽  
Dry Season ◽  
Red River ◽  
Agricultural Drought ◽  
Severe Drought ◽  
Red River Basin ◽  
Spatio Temporal

The increasing serious droughts recently might have significant impacts on socioeconomic development in the Red River basin (RRB). This study applied the variable infiltration capacity (VIC) model to investigate spatio-temporal dynamics of soil moisture in the northeast, northwest, and Red River Delta (RRD) regions of the RRB part belongs to territory of Vietnam. The soil moisture dataset simulated for 10 years (2005–2014) was utilized to establish the soil moisture anomaly percentage index (SMAPI) for assessing intensity of agricultural drought. Soil moisture appeared to co-vary with precipitation, air temperature, evapotranspiration, and various features of land cover, topography, and soil type in three regions of the RRB. SMAPI analysis revealed that more areas in the northeast experienced severe droughts compared to those in other regions, especially in the dry season and transitional months. Meanwhile, the northwest mainly suffered from mild drought and a slightly wet condition during the dry season. Different from that, the RRD mainly had moderately to very wet conditions throughout the year. The areas of both agricultural and forested lands associated with severe drought in the dry season were larger than those in the wet season. Generally, VIC-based soil moisture approach offered a feasible solution for improving soil moisture and agricultural drought monitoring capabilities at the regional scale.

scholarly journals Enhanced Estimation of Root Zone Soil Moisture at 1 km Resolution Using SMAR Model and MODIS-Based Downscaled AMSR2 Soil Moisture Data

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5211
Author(s):  
Maedeh Farokhi ◽  
Farid Faridani ◽  
Rosa Lasaponara ◽  
Hossein Ansari ◽  
Alireza Faridhosseini
Keyword(s):  
Soil Moisture ◽  
Prediction Models ◽  
Surface Soil ◽  
Root Zone ◽  
Analytical Relationship ◽  
Moisture Profile ◽  
Essential Variable ◽  
Potential Benefits ◽  
Spatio Temporal ◽  
Hydrological Prediction

Root zone soil moisture (RZSM) is an essential variable for weather and hydrological prediction models. Satellite-based microwave observations have been frequently utilized for the estimation of surface soil moisture (SSM) at various spatio-temporal resolutions. Moreover, previous studies have shown that satellite-based SSM products, coupled with the soil moisture analytical relationship (SMAR) can estimate RZSM variations. However, satellite-based SSM products are of low-resolution, rendering the application of the above-mentioned approach for local and pointwise applications problematic. This study initially attempted to estimate SSM at a finer resolution (1 km) using a downscaling technique based on a linear equation between AMSR2 SM data (25 km) with three MODIS parameters (NDVI, LST, and Albedo); then used the downscaled SSM in the SMAR model to monitor the RZSM for Rafsanjan Plain (RP), Iran. The performance of the proposed method was evaluated by measuring the soil moisture profile at ten stations in RP. The results of this study revealed that the downscaled AMSR2 SM data had a higher accuracy in relation to the ground-based SSM data in terms of MAE (↓0.021), RMSE (↓0.02), and R (↑0.199) metrics. Moreover, the SMAR model was run using three different SSM input data with different spatial resolution: (a) ground-based SSM, (b) conventional AMSR2, and (c) downscaled AMSR2 products. The results showed that while the SMAR model itself was capable of estimating RZSM from the variation of ground-based SSM data, its performance increased when using downscaled SSM data suggesting the potential benefits of proposed method in different hydrological applications.

Spatio-Temporal Prediction of Roadside PM2.5 based on Sparse Mobile Sensing and Traffic Information

2021 ◽  
Author(s):  
A. Kakarla ◽  
V. S. K. R. Munagala ◽  
T. Ishizaka ◽  
A. Fukuda ◽  
S. Jana
Keyword(s):  
Mobile Sensing ◽  
Traffic Information ◽  
Temporal Prediction ◽  
Spatio Temporal

scholarly journals Shift in the Spatio-Temporal Natural Fruiting of Boletes in West African Woodlands: the Key Role of the Soil Moisture

2020 ◽  
Author(s):  
Akotchayé Sylvestre Badou ◽  
Roel D. Houdanon ◽  
Kassim I. Tchan ◽  
D.M.T. Apollon Hègbè ◽  
Nourou Soulemane Yorou
Keyword(s):  
Species Richness ◽  
Soil Moisture ◽  
Ectomycorrhizal Fungi ◽  
Linear Models ◽  
Positive Influence ◽  
Permanent Plots ◽  
Data Logger ◽  
Fresh Biomass ◽  
Natural Production ◽  
Spatio Temporal

Abstract Background: The ectomycorrhizal fungi display strong fluctuations during the mycological season. However, how abiotic parameters affect the fruiting sequences of ectomycorrhizal fungi and also the direction and extent of this effect are not yet tapped adequately. The present study seeks to assess the microclimate effect on the natural production of boletes. Nine permanent plots of 2500 m2 (50m x 50m) split into 25 subplots of 100 m2 (10m x 10m) were installed in three different vegetation dominated respectively by Isoberlinia doka, Isoberlinia tomentosa and Uapaca togoensis. Microclimatic parameters were recorded each 30 minutes throughout by mean of a Micro Station Data Logger - H21-002 the mycological seasons. Each plot was surveyed twice a week (from May to October) over three years (2015, 2016 and 2017) to record the presence/absence of fruit bodies and fresh biomass of boletes. To evaluate the effect of time and microclimate variables on natural production, we used mixed effects and generalized linear models using R version 3.5.3. Results: In total, during the three years (2015, 2016 and 2017), we recorded 14 species of boletes. Species richness does not change over time (P > 0.05). In addition, fresh biomass varies within years and vegetation (P < 0.05). The combination of year and month of collection has a significant effect on the number of fruit bodies (P < 0.05). Only the soil moisture has a significant positive influence on the species richness of boletes (P > 0.05). Conclusions: When the soil moisture decreases by four units, the number of fruit bodies of ectomycorrhizal fungi is significantly reduced by one unit. Therefore, above 0.25 m3 / m3 and below 0.05 m3 / m3 there is a decrease in the number of fruit bodies.

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