EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Estimation of soil moisture within drip irrigation context in pepper fields using ALOS-2 and Sentinel-1 data. 

Emna Ayari1,2, Zeineb Kassouk2, Zohra Lili-Chabaane2, Nicolas Baghdadi3, and Mehrez Zribi1
Emna Ayari et al.
  • 1CESBIO (CNES/CNRS/INRAE/IRD/UPS), 18 av. Edouard Belin, bpi 2801, 31401 Toulouse cedex9, France;
  • 2National Agronomic Institute of Tunisia, Carthage University, LR17AGR01 InteGRatEd Management of Natural Resources: remoTE Sensing, Spatial Analysis and Modeling (GREEN-TEAM), Tunis 1082, Tunisia;
  • 3CIRAD, CNRS, INRAE, TETIS, University of Montpellier, AgroParisTech, CEDEX 5, 34093 Montpellier, France;

To ensure food security, the irrigation water demand is increasing with the growth of the population. Therefore, the optimization of irrigation scheduling is compulsory to improve water resources management where soil moisture estimation is an essential component. Over the last decades, remote sensing demonstrated its potential to retrieve soil water content. In this work, we investigate the potential of the Synthetic Aperture Radar (SAR) data in L-band acquired by Advanced Land Observing Satellite-2 (ALOS-2) and C-band data acquired by Sentinel-1 sensor, to estimate soil moisture in heterogenous row crop fields locally irrigated with drips in a semi-arid area in the center of Tunisia.

During SAR data acquisitions, ground data gathering campaigns were carried out over irrigated pepper fields. The in-situ measurements included soil surface parameters such as soil roughness and soil moisture, and pepper biophysical parameters such as vegetation height (H), Leaf Area Index (LAI), and cover fraction (Fc) measurements. Based on the pepper field’s organization and ground observations, we calculated an average soil moisture value per field as the sum of 15% of vegetation row soil moisture and 85% bare soil moisture.

In this context, we suggested the modification of the Water Cloud Model (WCM) to simulate the L-band signal in Horizontal-Horizontal polarization (L-HH) and C-band signal in Vertical-Vertical polarization (C-VV). The total backscattering is simulated as the sum of vegetation row cover contribution weighted by Fc and bare soil contribution weighted by (1-Fc). The vegetation row contribution is calculated as the sum of the scattered signal from pepper seedlings described by vegetation height and bare soil part contribution attenuated by vegetation. The bare soil part is considered as the contribution of two parts where the first is irrigated directly by drips and the second separates two successive pepper seedlings relatively far from water emitters namely the non-irrigated part. The bare soil signal simulations are performed using the Integral Equation model modified by Baghdadi (IEM-B).  

After calibration and validation of the modified WCM using three-folds cross-validation, we investigate the potential of the proposed model by various simulations under constant roughness parameters and different conditions of pepper biophysical parameters and bare soil moisture values. The examination of linear slopes between modeled backscattering and soil moisture measurements highlights that model sensitivity decreases as a function of the increase of pepper vegetation parameters (Fc and H). The sensitivity of the modified WCM is limited where Fc and pepper height are less than 0.4 and 0.5 m, respectively, using L-HH data and lower than 0.3 and 0.3 m using C-VV data. The aforementioned findings revealed the potential of the proposed WCM to simulate SAR signal in heterogeneous context of soil moisture.

How to cite: Ayari, E., Kassouk, Z., Lili-Chabaane, Z., Baghdadi, N., and Zribi, M.: Estimation of soil moisture within drip irrigation context in pepper fields using ALOS-2 and Sentinel-1 data. , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-3937,, 2022.


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