Technical Note: Field-Scale Surface Soil Moisture Patterns and Their Relationship to Topographic Indices

2007 ◽  
Vol 50 (2) ◽  
pp. 557-564 ◽  
Author(s):  
A. L. Kaleita ◽  
M. C. Hirschi ◽  
L. F. Tian
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Technical Note ◽  
Surface Soil Moisture ◽  
Field Scale ◽  
Scale Surface

Field scale spatiotemporal analysis of surface soil moisture for evaluating point-scale in situ networks

Geoderma ◽  
2012 ◽  
Vol 170 ◽  
pp. 195-205 ◽  
Author(s):  
Gary C. Heathman ◽  
Michael H. Cosh ◽  
Eunjin Han ◽  
Thomas J. Jackson ◽  
Lynn McKee ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Spatiotemporal Analysis ◽  
Surface Soil Moisture ◽  
Field Scale ◽  
Point Scale

Surface soil moisture data assimilation for irrigation amounts and timing estimation in semi-arid regions

2021 ◽  
Author(s):  
Nadia Ouaadi ◽  
Lionel Jarlan ◽  
Saïd Khabba ◽  
Jamal Ezzahar ◽  
Olivier Merlin
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Land Surface Model ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Field Scale ◽  
In Situ Data ◽  
Statistical Metrics

<p>Irrigation is the largest consumer of water in the world, with more than 70% of the world's fresh water dedicated to agriculture. In this context, we developed and evaluated a new method to predict daily to seasonal irrigation timing and amounts at the field scale using surface soil moisture (SSM) data assimilated into a simple  land surface model through a particle filter technique. The method is first tested using in situ SSM before using SSM products retrieved from Sentinel-1. Data collected on different wheat fields grown  in Morocco, for both flood and drip irrigation techniques, are used to assess the performance of the proposed method. With in situ data, the results are good. Seasonal amounts are retrieved with R > 0.98, RMSE <42 mm and bias<2 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation where more than 70% of the irrigation events are detected with a time difference from actual irrigation events shorter than 4 days, when assimilating SSM observation every 6 days to mimics Sentinel-1 revisit time. Over the drip irrigated fields, the statistical metrics are R = 0.70, RMSE =28.5 mm and bias= -0.24 mm for irrigation amounts cumulated over 15 days. The approach is then evaluated using SSM products derived from Sentinel-1 data; statistical metrics are R= 0.64, RMSE= 28.78 mm and bias = 1.99 mm for irrigation amounts cumulated over 15 days. In addition to irrigated fields, the applicationof the developed methodover rainfed fieldsdid not detect any irrigation. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.</p>

scholarly journals Irrigation Amounts and Timing Retrieval through Data Assimilation of Surface Soil Moisture into the FAO-56 Approach in the South Mediterranean Region

2021 ◽  
Vol 13 (14) ◽  
pp. 2667
Author(s):  
Nadia Ouaadi ◽  
Lionel Jarlan ◽  
Saïd Khabba ◽  
Jamal Ezzahar ◽  
Michel Le Page ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Use ◽  
Land Surface ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Field Scale ◽  
Method Performance ◽  
The Impact ◽  
Statistical Metrics

Agricultural water use represents more than 70% of the world’s freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a particle filter technique based on an ensemble of irrigation scenarios. The approach is implemented in three steps. First, synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM and the a priori constraints on the irrigation scenarios for different irrigation techniques (flooding and drip). In a second step, the method is evaluated using in situ SSM measurements with different revisit times (3, 6 and 12 days) to mimic the available SSM product derived from remote sensing observation. Finally, SSM estimates from Sentinel-1 are used. Data are collected on different wheat fields grown in Morocco, for both flood and drip irrigation techniques in addition to rainfed fields used for an indirect evaluation of the method performance. Using in situ data, accurate results are obtained. With an observation every 6 days to mimic the Sentinel-1 revisit time, the seasonal amounts are retrieved with R > 0.98, RMSE < 32 mm and bias < 2.5 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation as more than 70% of the detected irrigation events have a time difference from actual irrigation events shorter than 4 days. Over the drip irrigated fields, the statistical metrics are R = 0.74, RMSE = 24.8 mm and bias = 2.3 mm for irrigation amounts cumulated over 15 days. When using SSM products derived from Sentinel-1 data, the statistical metrics on 15-day cumulated amounts slightly dropped to R = 0.64, RMSE = 28.7 mm and bias = 1.9 mm. The metrics on the seasonal amount retrievals are close to assimilating in situ observations with R = 0.99, RMSE = 33.5 mm and bias = −18.8 mm. Finally, among four rainfed seasons, only one false event was detected. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.

scholarly journals Estimation of large scale surface soil moisture using remote sensing approach

2016 ◽  
Vol 2 (2) ◽  
pp. 45
Author(s):  
S. Suthakar ◽  
U. K. Chopra ◽  
R. N. Sahoo ◽  
I. Chand
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Large Scale ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Scale Surface

scholarly journals Detection and Quantification of Irrigation Water Amounts at 500 m Using Sentinel-1 Surface Soil Moisture

2021 ◽  
Vol 13 (9) ◽  
pp. 1727
Author(s):  
Luca Zappa ◽  
Stefan Schlaffer ◽  
Bernhard Bauer-Marschallinger ◽  
Claas Nendel ◽  
Beate Zimmerman ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Water Use ◽  
Irrigation Water ◽  
Surface Soil ◽  
Temporal Dynamics ◽  
Cropping Systems ◽  
Pearson Correlation ◽  
Surface Soil Moisture ◽  
Field Scale ◽  
Novel Approach

Detailed information about irrigation timing and water use at a high spatial resolution is critical for monitoring and improving agricultural water use efficiency. However, neither statistical surveys nor remote sensing-based approaches can currently accommodate this need. To address this gap, we propose a novel approach based on the TU Wien Sentinel-1 Surface Soil Moisture product, characterized by a spatial sampling of 500 m and a revisit time of 1.5–4 days over Europe. Spatiotemporal patterns of soil moisture are used to identify individual irrigation events and estimate irrigation water amounts. To retrieve the latter, we include formulations of evapotranspiration and drainage losses to account for vertical fluxes, which may significantly influence sub-daily soil moisture variations. The proposed approach was evaluated against field-scale irrigation data reported by farmers at three sites in Germany with heterogeneous field sizes, crop patterns, irrigation systems and management. Our results show that most field-scale irrigation events can be detected using soil moisture information (mean F-score = 0.77). Irrigation estimates, in terms of temporal dynamics as well as spatial patterns, were in agreement with reference data (mean Pearson correlation = 0.64) regardless of field-specific characteristics (e.g., crop type). Hence, the proposed approach has the potential to be applied over large regions with varying cropping systems.

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