Root Zone Soil Moisture Assessment Using Remote Sensing and Vadose Zone Modeling

2006 ◽  
Vol 5 (1) ◽  
pp. 296-307 ◽  
Author(s):  
Narendra N. Das ◽  
Binayak P. Mohanty
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Vadose Zone ◽  
Root Zone ◽  
Zone Modeling

scholarly journals Field Observations During the Fifth Microwave Water and Energy Balance Experiment (MicroWEX-5): from March 9 through May 26, 2006

EDIS ◽  
2007 ◽  
Vol 2007 (17) ◽  
Author(s):  
Joaquin Casanova ◽  
Fei Yan ◽  
Mi-young Jang ◽  
Juan Fernandez ◽  
Jasmeet Judge ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Energy Balance ◽  
Root Zone ◽  
Microwave Remote Sensing ◽  
Field Observations ◽  
Biological Engineering

Circular 1514, a 47-page illustrated report by Joaquin Casanova, Fei Yan, Mi-young Jang, Juan Fernandez, Jasmeet Judge, Clint Slatton, Kai-Jen Calvin Tien, Tzu-yun Lin, Orlando Lanni, and Larry Miller, presents the results of experiments using microwave remote sensing to determine root-zone soil moisture at UF/IFAS PSREU. Published by the UF Department of Agricultural and Biological Engineering, May 2007. CIR1514/AE407: Field Observations During the Fifth Microwave Water and Energy Balance Experiment: from March 9 through May 26, 2006 (ufl.edu)

High-resolution root zone soil moisture-based indices for drought monitoring in the Ebro basin

2021 ◽  
Author(s):  
Vivien-Georgiana Stefan ◽  
Maria-José Escorihuela ◽  
Pere Quintana-Seguí
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
High Resolution ◽  
Root Zone ◽  
Cost Effective ◽  
Strong Relationship ◽  
Drought Indices ◽  
Ebro Basin ◽  
Small Areas ◽  
Cost Effective Approach

<h3>Agriculture is an important factor on water resources, given the constant population growth and the strong relationship between water availability and food production. In this context, root zone soil moisture (RZSM) measurements are used by modern irrigators in order to detect the onset of crop water stress and to trigger irrigations. Unfortunately, in situ RZSM measurements are costly; combined with the fact they are available only over small areas and that they might not be representative at the field scale, remote sensing is a cost-effective approach for mapping and monitoring extended areas. A recursive formulation of an exponential filter was used in order to derive 1 km resolution RZSM estimates from SMAP (Soil Moisture Active Passive) surface soil moisture (SSM) over the Ebro basin. The SMAP SSM was disaggregated to a 1 km resolution by using the DISPATCH (DISaggregation based on a Physical And Theoretical scale CHange) algorithm. The pseudodiffusivity parameter of the exponential filter was calibrated per land cover type, by using ISBA-DIF (Interaction Soil Biosphere Atmosphere) surface and root zone soil moisture data as an intermediary step. The daily 1 km RZSM estimates were then used to derive 1 km drought indices such as soil moisture anomalies and soil moisture deficit indices (SMDI), on a weekly time-scale, covering the entire 2020 year. Results show that both drought indices are able to capture rainfall and drying events, with the weekly anomaly being more responsive to sudden events such as heavy rainfalls, while the SMDI is slower to react do the inherent inertia it has. Moreover, a quantitative comparison with drought indices derived from a model-based RZSM estimates has also been performed, with results showing a strong correspondence between the different indices. For comparison purposes, the weekly soil moisture anomalies and SMDI derived using 1 km SMAP-derived SSM were also estimated. The analysis shows that the anomalies and SMDI based on the RZSM are more representative of the hydric stress level of the plants, given that the RZSM is better suited than the SSM to describe the moisture conditions at the deeper layers, which are the ones used by plants during growth and development.</h3><h3>The study provides an insight into obtaining robust, high-resolution remote-sensing derived drought indices based on remote-sensing derived RZSM estimates. The 1 km resolution proves an improvement from other currently available drought indices, such as the European Drought Observatory’s 5 km resolution drought index, which is not able to capture as well the spatial variability present within heterogeneous areas. Moreover, the SSM-derived drought indices are currently used in a drought observatory project, covering a region in the Tarragona province of Catalonia, Spain. The project aims at offering irrigation recommendations to water agencies, and the introduction of RZSM-derived drought indices will further improve such advice.</h3>

Characterising recent drought events using current reanalysis and remote-sensing soil moisture products within a common framework

2021 ◽  
Author(s):  
Martin Hirschi ◽  
Bas Crezee ◽  
Sonia I. Seneviratne
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Root Zone ◽  
Microwave Remote Sensing ◽  
Agricultural Drought ◽  
Climate Data ◽  
Data Store ◽  
Common Framework ◽  
Long Term Trends

<p>Drought events cause multiple impacts on the environment, the society and the economy. Here, we analyse recent major drought events with different metrics using a common framework. The analysis is based on current reanalysis (ERA5, ERA5-Land, MERRA-2) and merged remote-sensing products (ESA-CCI soil moisture, gridded satellite soil moisture from the Copernicus Climate Data Store), focusing on soil moisture (or agricultural) drought. The events are characterised by their severity, magnitude, duration and spatial extent, which are calculated from standardised daily anomalies of surface and root-zone soil moisture. We investigate the ability of the different products to represent the droughts and set the different events in context to each other. The considered products also offer opportunities for drought monitoring since they are available in near-real time.</p><p>All investigated products are able to represent the investigated drought events. Overall, ERA5 and ERA5-Land often show the strongest, and the remote-sensing products often weaker responses based on surface soil moisture. The weaker severities of the events in the remote-sensing products are both related to shorter event durations as well as less pronounced average negative standardised soil moisture anomalies, while the magnitudes (i.e., the minimum of the standardised anomalies over time) are comparable to the reanalysis products. Differing global distributions of long-term trends may explain some differences in the drought responses of the products. Also, the lower penetration depth of microwave remote sensing compared to the top layer of the involved land surface models could explain the partly weaker negative standardized soil moisture anomalies in the remote-sensing products during the investigated events. In the root zone (based on the reanalysis products), the drought events often show prolonged durations, but weaker magnitudes and smaller spatial extents.</p>

scholarly journals Behind the scenes of streamflow model performance

2021 ◽  
Vol 25 (2) ◽  
pp. 1069-1095
Author(s):  
Laurène J. E. Bouaziz ◽  
Fabrizio Fenicia ◽  
Guillaume Thirel ◽  
Tanja de Boer-Euser ◽  
Joost Buitink ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Root Zone ◽  
Remote Sensing Data ◽  
Root Water Uptake ◽  
Data Sources ◽  
Annual Number ◽  
Hydrological Models ◽  
Sensing Data ◽  
Snow Storage

Abstract. Streamflow is often the only variable used to evaluate hydrological models. In a previous international comparison study, eight research groups followed an identical protocol to calibrate 12 hydrological models using observed streamflow of catchments within the Meuse basin. In the current study, we quantify the differences in five states and fluxes of these 12 process-based models with similar streamflow performance, in a systematic and comprehensive way. Next, we assess model behavior plausibility by ranking the models for a set of criteria using streamflow and remote-sensing data of evaporation, snow cover, soil moisture and total storage anomalies. We found substantial dissimilarities between models for annual interception and seasonal evaporation rates, the annual number of days with water stored as snow, the mean annual maximum snow storage and the size of the root-zone storage capacity. These differences in internal process representation imply that these models cannot all simultaneously be close to reality. Modeled annual evaporation rates are consistent with Global Land Evaporation Amsterdam Model (GLEAM) estimates. However, there is a large uncertainty in modeled and remote-sensing annual interception. Substantial differences are also found between Moderate Resolution Imaging Spectroradiometer (MODIS) and modeled number of days with snow storage. Models with relatively small root-zone storage capacities and without root water uptake reduction under dry conditions tend to have an empty root-zone storage for several days each summer, while this is not suggested by remote-sensing data of evaporation, soil moisture and vegetation indices. On the other hand, models with relatively large root-zone storage capacities tend to overestimate very dry total storage anomalies of the Gravity Recovery and Climate Experiment (GRACE). None of the models is systematically consistent with the information available from all different (remote-sensing) data sources. Yet we did not reject models given the uncertainties in these data sources and their changing relevance for the system under investigation.

scholarly journals Mapping Root-Zone Soil Moisture Using a Temperature–Vegetation Triangle Approach with an Unmanned Aerial System: Incorporating Surface Roughness from Structure from Motion

2018 ◽  
Vol 10 (12) ◽  
pp. 1978 ◽  
Author(s):  
Sheng Wang ◽  
Monica Garcia ◽  
Andreas Ibrom ◽  
Jakob Jakobsen ◽  
Christian Josef Köppl ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Roughness ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Structure From Motion ◽  
Land Surface ◽  
Temporal Dynamics ◽  
Root Zone ◽  
Unmanned Aerial Systems ◽  
Natural Ecosystems

High resolution root-zone soil moisture (SM) maps are important for understanding the spatial variability of water availability in agriculture, ecosystems research and water resources management. Unmanned Aerial Systems (UAS) can flexibly monitor land surfaces with thermal and optical imagery at very high spatial resolution (meter level, VHR) for most weather conditions. We modified the temperature–vegetation triangle approach to transfer it from satellite to UAS remote sensing. To consider the effects of the limited coverage of UAS mapping, theoretical dry/wet edges were introduced. The new method was tested on a bioenergy willow short rotation coppice site during growing seasons of 2016 and 2017. We demonstrated that by incorporating surface roughness parameters from the structure-from-motion in the interpretation of the measured land surface-atmosphere temperature gradients, the estimates of SM significantly improved. The correlation coefficient between estimated and measured SM increased from not significant to 0.69 and the root mean square deviation decreased from 0.045 m3∙m−3 to 0.025 m3∙m−3 when considering temporal dynamics of surface roughness in the approach. The estimated SM correlated better with in-situ root-zone SM (15–30 cm) than with surface SM (0–5 cm) which is an important advantage over alternative remote sensing methods to estimate SM. The optimal spatial resolution of the triangle approach was found to be around 1.5 m, i.e. similar to the length scale of tree-crowns. This study highlights the importance of considering the 3-D fine scale canopy structure, when addressing the links between surface temperature and SM patterns via surface energy balances. Our methodology can be applied to operationally monitor VHR root-zone SM from UAS in agricultural and natural ecosystems.

Soil moisture in the root zone and its relation to plant vigor assessed by remote sensing at management scale

Geoderma ◽  
2014 ◽  
Vol 221-222 ◽  
pp. 91-95 ◽  
Author(s):  
Walbert Júnior Reis Santos ◽  
Bruno Montoani Silva ◽  
Geraldo César Oliveira ◽  
Margarete Marin Lordelo Volpato ◽  
José Maria Lima ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Root Zone ◽  
Plant Vigor
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