scholarly journals The Applicability of the Cosmic Ray Neutron Sensor to Simultaneously Monitor Soil Water Content and Biomass in an Acacia mearnsii Forest

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 48 ◽  
Author(s):  
Thigesh Vather ◽  
Colin S. Everson ◽  
Trenton E. Franz
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Epithermal Neutron ◽  
Cosmic Ray ◽  
Field Scale ◽  
Area Index ◽  
Neutron Intensity

Soil water content is an important hydrological parameter, which is difficult to measure at a field scale due to its spatial and temporal heterogeneity. The Cosmic Ray Neutron Sensor (CRNS) is a novel and innovative approach to estimate area-averaged soil water content at an intermediate scale, which has been implemented across the globe. The CRNS is moderated by all hydrogen sources within its measurement footprint. In order to isolate the soil water content signal from the neutron intensity, the other sources of hydrogen need to be accounted for. The CRNS’s applications are not only limited to soil water content estimation, as it can potentially be used to monitor biomass. The Two-Streams clear-felling provided the unique opportunity to monitor the cosmic ray neutron intensities before, during, and after the clear-felling. The cadmium-difference method was used to obtain the pure thermal and epithermal neutron intensities from the bare and moderated detectors. The study concluded that the presence of biomass within the site reduced the epithermal neutron intensity by 12.43% and the N0 value by 13.8%. The use of the neutron ratio to monitor biomass was evaluated and changes in the neutron ratio coincided with biomass changes and resulted in a high correlation (R2 of 0.868) with the normalized difference vegetation index (NDVI) and (R2 of 0.817) leaf area index (LAI). The use of the CRNS to simultaneously monitor soil water content and biomass will be beneficial in providing more reliable soil water content estimates, provide biomass estimates at a field scale, and aid in understanding the dynamics between soil water content and vegetation.

Spatial variability of soil water content and soil chemistry affect grapevine growth in degraded slopes

2021 ◽  
Author(s):  
Agota Horel ◽  
Imre Zagyva ◽  
Márton Dencső ◽  
Eszter Tóth ◽  
Györgyi Gelybó ◽  
...  
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Water System ◽  
Sensor Data ◽  
Slope Position ◽  
Area Index ◽  
Vegetation Growth

<p>Three slopes with grapevines were investigated to see changes in the soil-plant-water system over vegetation growth. The slopes have the following parameters: 1) young grapevine plants with tilled soil (YR), 2) older grapevines with grassland between rows next to the young grapevine (OR), and 3) older grapevines with grass between rows at a different location and slope position (OF). All experimental slopes had identical plant canopy management such as pruning or shoot and bunch thinning. All slopes are prone to erosion. For continuous hydrological monitoring soil water content and temperature sensors were placed at 15 cm and 40 cm below ground both at the top and bottom of the slopes. For indications of plant growth photosynthetically active radiation (PAR) sensors were placed below the canopy, and Normalized Difference Vegetation Index (NDVI) and Photochemical Reflectance Index (PRI) sensors were used to monitor leaf reflectance. All sites included a set of hemispherical sensor sets to measure incoming radiation. Leaf Area Index (LAI) was measured on a biweekly basis using a handheld ceptometer. We found that in the OR and OF sites the soil water content (VWC) was higher at the lower portion of the slope, while for the YR the VWC was the highest at the top. Soil temperature was higher at the top of the slopes over 6% for YR and 9% for OR sites compared to the bottom measuring points. The most notable difference in the NDVI values was observed for OR, where the plants at the top of the slope showed much lower NDVI values compared to the ones at the bottom of the slope. For the younger grapevines, this tendency was showing the opposite results, the plants at the top of the slope had much higher NDVI values than the lower ones, indicating higher leaf densities. The collected PAR values further support these findings, as the OR plants at the top of the slope had the highest PAR values signifying lower leaf areas and densities. The differences in the PRI values suggest that plants at the bottom of the slope have either better nutrient usage or less stress for drought conditions. The LAI values correlated well with the spectral reflectance sensor data. The OR and OF showed higher LAI at the bottom of the slope, while the younger grapevines showed the opposite. The highest LAI values were observed for the YR (max values were around 7) and the lowest for the OF plants (max LAI value was 3.2).</p>

scholarly journals Retrieving Surface Soil Water Content Using a Soil Texture Adjusted Vegetation Index and Unmanned Aerial System Images

2021 ◽  
Vol 13 (1) ◽  
pp. 145
Author(s):  
Haibin Gu ◽  
Zhe Lin ◽  
Wenxuan Guo ◽  
Sanjit Deb
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Soil Texture ◽  
Vegetation Index ◽  
Surface Soil ◽  
Field Scale ◽  
Thermal Images ◽  
Model Based ◽  
Dryness Index

Surface soil water content (SWC) is a major determinant of crop production, and accurately retrieving SWC plays a crucial role in effective water management. Unmanned aerial systems (UAS) can acquire images with high temporal and spatial resolutions for SWC monitoring at the field scale. The objective of this study was to develop an algorithm to retrieve SWC by integrating soil texture into a vegetation index derived from UAS multispectral and thermal images. The normalized difference vegetation index (NDVI) and surface temperature (Ts) derived from the UAS multispectral and thermal images were employed to construct the temperature vegetation dryness index (TVDI) using the trapezoid model. Soil texture was incorporated into the trapezoid model based on the relationship between soil texture and the lower and upper limits of SWC to form the texture temperature vegetation dryness index (TTVDI). For validation, 128 surface soil samples, 84 in 2019 and 44 in 2020, were collected to determine soil texture and gravimetric SWC. Based on the linear regression models, the TTVDI had better performance in estimating SWC compared to the TVDI, with an increase in R2 (coefficient of determination) by 14.5% and 14.9%, and a decrease in RMSE (root mean square error) by 46.1% and 10.8%, for the 2019 and 2020 samples, respectively. The application of the TTVDI model based on high-resolution multispectral and thermal UAS images has the potential to accurately and timely retrieve SWC at the field scale.

Supplementary material to "Incorporation of globally available datasets into the cosmic-ray neutron probe method for estimating field scale soil water content"

2016 ◽  
Author(s):  
William Alexander Avery ◽  
Catherine Finkenbiner ◽  
Trenton E. Franz ◽  
Tiejun Wang ◽  
Anthony L. Nguy-Robertson ◽  
...  
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Cosmic Ray ◽  
Probe Method ◽  
Field Scale ◽  
Neutron Probe ◽  
Supplementary Material

scholarly journals Relating surface backscatter response from TRMM Precipitation Radar to soil moisture: results over a semi-arid region

2009 ◽  
Vol 6 (5) ◽  
pp. 6425-6454
Author(s):  
H. Stephen ◽  
S. Ahmad ◽  
T. C. Piechota ◽  
C. Tang
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Arid Regions ◽  
Vegetation Index ◽  
Temporal Trends ◽  
Model Parameters ◽  
Precipitation Radar ◽  
Semi Arid

Abstract. The Tropical Rainfall Measuring Mission (TRMM) carries aboard the Precipitation Radar (TRMMPR) that measures the backscatter (σ°) of the surface. σ° is sensitive to surface soil moisture and vegetation conditions. Due to sparse vegetation in arid and semi-arid regions, TRMMPR σ° primarily depends on the soil water content. In this study we relate TRMMPR σ° measurements to soil water content (ms) in Lower Colorado River Basin (LCRB). σ° dependence on ms is studied for different vegetation greenness values determined through Normalized Difference Vegetation Index (NDVI). A new model of σ° that couples incidence angle, ms, and NDVI is used to derive parameters and retrieve soil water content. The calibration and validation of this model are performed using simulated and measured ms data. Simulated ms is estimated using Variable Infiltration Capacity (VIC) model whereas measured ms is acquired from ground measuring stations in Walnut Gulch Experimental Watershed (WGEW). σ° model is calibrated using VIC and WGEW ms data during 1998 and the calibrated model is used to derive ms during later years. The temporal trends of derived ms are consistent with VIC and WGEW ms data with correlation coefficient (R) of 0.89 and 0.74, respectively. Derived ms is also consistent with the measured precipitation data with R=0.76. The gridded VIC data is used to calibrate the model at each grid point in LCRB and spatial maps of the model parameters are prepared. The model parameters are spatially coherent with the general regional topography in LCRB. TRMMPR σ° derived soil moisture maps during May (dry) and August (wet) 1999 are spatially similar to VIC estimates with correlation 0.67 and 0.76, respectively. This research provides new insights into Ku-band σ° dependence on soil water content in the arid regions.

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