Investigation of Multi-Frequency SAR Data to Retrieve the Soil Moisture within a Drip Irrigation Context Using Modified Water Cloud Model

Ayari, Emna; Kassouk, Zeineb; Lili-Chabaane, Zohra; Baghdadi, Nicolas; Zribi, Mehrez

doi:10.3390/s22020580

Cited by 6 publications

(2 citation statements)

References 82 publications

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“…Surface irrigation can produce opposite effects when plots are flooded, with a drop in the signal which can be interpreted as a sharp drop in soil moisture. Drip irrigation generates important soil moisture heterogeneities that make scattered radar signal sensitivity to land surface more complex to model [63]. Thus, any finer analysis taking these factors into account would be very useful in refining the classification and making it more generalizable.…”

Section: Discussionmentioning

confidence: 99%

Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data

Elwan

Page

Jarlan

et al. 2022

Water

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This study aims to propose an operational approach to map irrigated areas based on the synergy of Sentinel-1 (S1) and Sentinel-2 (S2) data. An application is proposed at two study sites in Europe—in Spain and in Italy—with two climatic contexts (semiarid and humid, respectively), with the objective of proving the essential role of multi-site training for a robust application of the proposed methodologies. Several classifiers are proposed to separate irrigated and rainfed areas. They are based on statistical variables from Sentinel-1 and Sentinel-2 time series data at the agricultural field scale, as well as on the contrasted behavior between the field scale and the 5 km surroundings. The support vector machine (SVM) classification approach was tested with different options to evaluate the robustness of the proposed methodologies. The optimal number of metrics found is five. These metrics illustrate the importance of optical/radar synergy and the consideration of multi-scale spatial information. The highest accuracy of the classifications, approximately equal to 85%, is based on training dataset with mixed reference fields from the two study sites. In addition, the accuracy is consistent at the two study sites. These results confirm the potential of the proposed approaches towards the most general use on sites with different climatic and agricultural contexts.

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Section: Discussionmentioning

confidence: 99%

Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data

Elwan

Page

Jarlan

et al. 2022

Water

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“…The Kairouan governorate ranks sixth nationally and first in central Tunisia in cereal production. This region plays an important role during drought periods in ensuring cereal production, as there are more irrigated perimeters overexploiting the Kairouan aquifer [86][87][88] than in other governorates lacking groundwater resources. The variation of cereal production in Tunisia depends both on the climate conditions and on the government's decision on the price of seeds, fertilizers, and cereal sales to encourage farmers to grow cereals.…”

Section: Kairouan Tunisiamentioning

confidence: 99%

Remotely Sensed Agriculture Drought Indices for Assessing the Impact on Cereal Yield

Khlif,

Escorihuela,

Chahbi Bellakanji

et al. 2023

Remote Sensing

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This study aims to analyze the potential of different drought indices for identifying drought periods and predicting cereal yield in two semi-arid regions, Lleida in Catalonia and Kairouan in Tunisia, which have similar Mediterranean climates but different agricultural practices. Four drought indices, namely the Soil Moisture Anomaly Index (SMAI), the Vegetation Anomaly Index (VAI), the Evapotranspiration Anomaly Index (EAI), and the Inverse Temperature Anomaly Index (ITAI), were calculated from remote sensing data. Drought periods were identified from 2010/2011 to 2021/2022 based on the aforementioned indices. A correlation study between drought indices and wheat and barley yields was performed in order to select the most informative index and month for yield prediction. In the rainfed cereal area of Lleida, the strongest correlation was found between the EAI and VAI with barley yield (0.91 and 0.83, respectively) at the time of cereal maturity in June. For wheat, the strongest correlation was found between the EAI and VAI (0.75 and 0.72, respectively) at the time of cereal maturity in July. However, the VAI, EAI, and SMAI showed the best performance as an earlier indicator in March with a correlation with barley yield of 0.72, 0.67, and 0.64, respectively; the lowest standard deviation was for the SMAI. For wheat yield, the best earlier indicator was the SMAI in March, showing the highest correlation (0.6) and the lowest standard deviation. For the irrigated cereal zone of Kairouan, the strongest correlation (0.9) and the lowest standard deviation are found between the EAI and cereal yield in April. In terms of advanced prediction, the VAI shows a high correlation in March (0.79) while the SMAI shows a slightly lower correlation in February (0.67) and a lower standard deviation. The results highlight the importance of the EAI and SMAI as key indicators for the estimation and early estimation (respectively) of cereal yield.

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An Automated Wireless Irrigation System: Without Internet Connectivity

Kumar

Mrinmoy

Mondal

et al. 2022

Sustainability of Water Resources

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Investigation of Multi-Frequency SAR Data to Retrieve the Soil Moisture within a Drip Irrigation Context Using Modified Water Cloud Model

Cited by 6 publications

References 82 publications

Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data

Irrigation Mapping on Two Contrasted Climatic Contexts Using Sentinel-1 and Sentinel-2 Data

Remotely Sensed Agriculture Drought Indices for Assessing the Impact on Cereal Yield

An Automated Wireless Irrigation System: Without Internet Connectivity

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