Soil moisture estimation over cereals fields using l-band alos2 data (merguellil case – KAIROUAN)

2021 ◽  
Author(s):  
Emna Ayari ◽  
Zeineb Kassouk ◽  
Zohra Lili Chabaane ◽  
Safa Bousbih ◽  
Mehrez Zribi
Keyword(s):  
Soil Moisture ◽  
Cloud Model ◽  
Radar Data ◽  
Radar Signal ◽  
Biophysical Parameters ◽  
Vegetation Development ◽  
L Band ◽  
Cereal Fields ◽  
Bare Soils ◽  
Band Signal

<p>Soil moisture is a key component for water resources management especially for irrigation needs estimation. We analyze in the present study, the potential of L-band data, acquired by (Advanced Land Observing Satellite-2) ALOS-2, to retrieve soil moisture over bare soils and cereal fields located in semi-arid area in the Kairouan plain.</p><p>In this context, we evaluate radar signal sensitivity to roughness, soil moisture and vegetation biophysical parameters. Based on multi-incidence radar data (28°, 32.5° and 36°), high correlations characterize relationships between backscattering coefficients in dual-polarization (HH and HV) and root mean square of heights (Hrms) and Zs, parameters, Sensitivity of radar data to soil moisture was discussed for three classes of NDVI (less than 0.25 for bare soils and dispersed vegetation, between 0.25 and 0.5 for medium vegetation and greater than 0.5 for dense cereals). With vegetation development, where NDVI values are higher than 0.25, SAR signal remains sensitive to soil moisture in HH pol. This sensitivity to moisture disappears, in HV pol for dense vegetation. For covered fields, L-band signal is very sensitive to Vegetation Water Content (VWC), with R² values ranging between 0.76 and 0.61 in HH and HV polarization respectively.</p><p>Simulating signal behavior is carried out through various models over bare soils and covered cereal fields. Over bare soils, proposed empirical expressions, modified versions of Integral Equation Model (IEM-B) and Dubois models (Dubois-B) are evaluated, generally for HH and HV polarizations. Best consistency is observed between real data and IEM-B backscattering simulations in HH polarization. More discrepancies between real and modelled data are observed in HV polarization.</p><p>Furthermore, to simulate L-band signal behavior over covered fields, the inversion of Water Cloud Model (WCM) coupled to different bare soil models is realized through direct equations and Look-up tables. Two options of WCM, are tested (with and without soil-vegetation interaction scattering term). For the first option, results highlight the good performance of IEM-B coupled to WCM in HH polarization with RMSE value between estimated and in situ moisture measurements equal to 4.87 vol.%. By adding soil – cereal interaction term in the second option of WCM, results reveal a stable accuracy in HH polarization and an important improvement of soil moisture estimations in HV polarization, with RMSE values are ranging between 6 and 7 vol.%.</p>

scholarly journals Analysis of L-Band SAR Data for Soil Moisture Estimations over Agricultural Areas in the Tropics

2019 ◽  
Vol 11 (9) ◽  
pp. 1122 ◽  
Author(s):  
Mehrez Zribi ◽  
Sekhar Muddu ◽  
Safa Bousbih ◽  
Ahmad Al Bitar ◽  
Sat Kumar Tomer ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Cloud Model ◽  
Bare Soil ◽  
Radar Data ◽  
Radar Signal ◽  
Area Index ◽  
Radar Images ◽  
L Band ◽  
Agricultural Areas ◽  
Semi Empirical

The main objective of this study is to analyze the potential use of L-band radar data for the estimation of soil moisture over tropical agricultural areas under dense vegetation cover conditions. Ten radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the Berambadi watershed (south India), between June and October of 2018. Simultaneous ground measurements of soil moisture, soil roughness, and leaf area index (LAI) were also recorded. The sensitivity of PALSAR observations to variations in soil moisture has been reported by several authors, and is confirmed in the present study, even for the case of very dense crops. The radar signals are simulated using five different radar backscattering models (physical and semi-empirical), over bare soil, and over areas with various types of crop cover (turmeric, marigold, and sorghum). When the semi-empirical water cloud model (WCM) is parameterized as a function of the LAI, to account for the vegetation’s contribution to the backscattered signal, it can provide relatively accurate estimations of soil moisture in turmeric and marigold fields, but has certain limitations when applied to sorghum fields. Observed limitations highlight the need to expand the analysis beyond the LAI by including additional vegetation parameters in order to take into account volume scattering in the L-band backscattered radar signal for accurate soil moisture estimation.

scholarly journals Simultaneously estimating surface soil moisture and roughness of bare soils by combining optical and radar data

2021 ◽  
Vol 100 ◽  
pp. 102345
Author(s):  
Xingming Zheng ◽  
Zhuangzhuang Feng ◽  
Lei Li ◽  
Bingzhe Li ◽  
Tao Jiang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Surface Soil ◽  
Radar Data ◽  
Surface Soil Moisture ◽  
Bare Soils

Mapping surface soil moisture over wheat crops in southern Mediterranean regions using the backscattering coefficient and the interferometric coherence derived from Sentinel-1

2020 ◽  
Author(s):  
Nadia Ouaadi ◽  
Lionel Jarlan ◽  
Jamal Ezzahar ◽  
Saïd Khabba ◽  
Mehrez Zribi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Resolution ◽  
Surface Soil ◽  
Cloud Model ◽  
Microwave Remote Sensing ◽  
Radar Data ◽  
Optical Data ◽  
Surface Soil Moisture ◽  
Backscattering Coefficient ◽  
New Approach

<p>High spatial and temporal resolution products of Sentinel-1 are used for surface soil moisture (SSM) mapping over wheat fields in semi-arid areas. Within these regions, monitoring the water-use is a critical aspect for optimizing the management of the limited water resources via irrigation monitoring. SSM is one of the principal quantities affecting microwave remote sensing. This sensitivity has been exploited to estimate SSM from radar data, which has the advantages of providing data independent of illumination and weather conditions. In addition, with the use of Sentinel-1 products, the spatial and temporal resolution is greatly improved. Within this context, the main objective of this work is estimate SSM over wheat fields using an approach based on the use of C-band Sentinel-1 radar data only. Over the study site, field measurement are collected during 2016-2017 and 2017-2018 growing seasons over two fields of winter wheat with drip irrigation located in the Haouz plain in the center of Morocco. Data of other sites in Morocco and Tunisia are taken for validation purposes. The validation database contains a total number of 20 plots divided between irrigated and rainfed wheat plots. Two different information extracted from Sentinel-1 products are used: the backscattering coefficient and the interferometric coherence. A total number of 408 GRD and 419 SLC images were processed for computing the backscattering coefficient and the interferometric coherence, respectively. The analysis of Sentinel-1 time series over the study site show that coherence is sensitive to the development of wheat, while the backscatter coefficient is widely linked to changes in surface soil moisture. Later on, the Water Cloud Model coupled with the Oh et al, 1992 model were used for better understand the backscattering mechanism of wheat canopies. The coupled model is calibrated and validated over the study site and it proved to goodly enough reproduce the Sentinel-1 backscatter with RMSE ranging from 1.5 to 2.52 dB for VV and VH using biomass as a descriptor of wheat. On the other side, the analysis show that coherence is well correlated to biomass. Thus, the calibrated model is used in an inversion algorithm to retrieve SSM using the Sentinel-1 backscatter and coherence as inputs. The results of inversion show that the proposed new approach is able to retrieve the surface soil moisture at 35.2° for VV, with R=0.82, RMSE=0.05m<sup>3/</sup>m<sup>3 </sup>and no bias. Using the validation database of Morocco and Tunisia, R is always greater than 0.7 and RMSE and bias are less than 0.008 m<sup>3/</sup>m<sup>3</sup> and 0.03 m<sup>3/</sup>m<sup>3</sup>, respectively even that the incidence angle is higher (40°). In order to assess its quality, the approach is compared to four SSM retrieval methods that use radar and optical data in empirical and semi-empirical approaches. Results indicate that the proposed approach shows an improvement of SSM retrieval between 17% and 42% compared to other methods. Finally, the validated new approach is used for SSM mapping, with a spatial resolution of 10*10 m, over irrigated perimeters of wheat in Morocco.</p>

Multitemporal retrieval of soil moisture from SMAP radar data at L-band

2015 ◽  
Author(s):  
Fabio Fascetti ◽  
Nazzareno Pierdicca ◽  
Luca Pulvirenti
Keyword(s):  
Soil Moisture ◽  
Radar Data ◽  
L Band

scholarly journals Large Area Aboveground Biomass and Carbon Stock Mapping in Woodlands in Mozambique with L-band Radar: Improving Accuracy by Accounting for Soil Moisture Effects Using the Water Cloud Model

2022 ◽  
Vol 14 (2) ◽  
pp. 404
Author(s):  
Yaqing Gou ◽  
Casey M. Ryan ◽  
Johannes Reiche
Keyword(s):  
Soil Moisture ◽  
Aboveground Biomass ◽  
Cloud Model ◽  
Large Area ◽  
Moisture Effects ◽  
Radar Imagery ◽  
Water Cloud ◽  
L Band ◽  
The Difference ◽  
Moisture Conditions

Soil moisture effects limit radar-based aboveground biomass carbon (AGBC) prediction accuracy as well as lead to stripes between adjacent paths in regional mosaics due to varying soil moisture conditions on different acquisition dates. In this study, we utilised the semi-empirical water cloud model (WCM) to account for backscattering from soil moisture in AGBC retrieval from L-band radar imagery in central Mozambique, where woodland ecosystems dominate. Cross-validation results suggest that (1) the standard WCM effectively accounts for soil moisture effects, especially for areas with AGBC ≤ 20 tC/ha, and (2) the standard WCM significantly improved the quality of regional AGBC mosaics by reducing the stripes between adjacent paths caused by the difference in soil moisture conditions between different acquisition dates. By applying the standard WCM, the difference in mean predicted AGBC for the tested path with the largest soil moisture difference was reduced by 18.6%. The WCM is a valuable tool for AGBC mapping by reducing prediction uncertainties and striping effects in regional mosaics, especially in low-biomass areas including African woodlands and other woodland and savanna regions. It is repeatable for recent L-band data including ALOS-2 PALSAR-2, and upcoming SAOCOM and NISAR data.

scholarly journals SURFACE MOISTURE AND IRRIGATION MAPPING AT AGRICULTURAL FIELD SCALE USING THE SYNERGY SENTINEL-1/SENTINEL-2 DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 357-361
Author(s):  
M. Zribi ◽  
N. Baghdadi ◽  
S. Bousbih ◽  
M. El-Hajj ◽  
Q. Gao
Keyword(s):  
Soil Moisture ◽  
Vegetation Cover ◽  
Cloud Model ◽  
Global Analysis ◽  
Radar Signal ◽  
Support Vector ◽  
Agricultural Field ◽  
Neural Network Approach ◽  
Area Index ◽  
Sentinel 2

<p><strong>Abstract.</strong> Soil moisture plays a key role in various processes at the soil-vegetation-atmosphere interface, such as evapotranspiration, infiltration and runoff. In this study, we firstly propose a global analysis of Sentinel-1 (S1) &amp; Sentinel-2 (S2) data potential to retrieve soil moisture. Two approaches are tested. The first one is based on neural network approach; it uses Integral Equation Model (IEM) coupled to Water Cloud Model for vegetation cover backscattering simulation (El Hajj et al., 2017). The second approach considers change detection methodology. It estimates change of soil moisture with the driest and highest moisture levels, and also change of moisture between successive radar acquisitions (Gao et al., 2017). The proposed approaches are validated over three agricultural regions, south of France, Urgell (Spain) and Merguellil (Tunisia). In these different sites, important ground campaigns have been realized over reference fields with different types of measurements (soil moisture and roughness, Leaf area Index of vegetation cover). The retrieved accuracy of estimated volumetric soil moisture is about 5 vol.%. Based on estimated moisture products, two methodologies are considered to map irrigated areas (Gao et al., 2018, Bousbih et al., 2018). An analysis of different metrics (mean, variance, correlation length, etc.) of radar signal time series and surface parameters (moisture and NDVI) are tested. The proposed classification of irrigated areas is based on a combination of Support Vector Machine (SVM) and decision tree methodologies. For Urgell and Merguellil sites, a mapping of irrigated fields is proposed. The accuracy of mapping is higher than 75% for the two studied sites.</p>

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