Estimation of the dynamics and yields of cereals in a semi-arid area using remote sensing and the SAFY growth model

International audienceIn semi-arid areas, a strongly variable climate represents a major risk for food safety. An operational grain yield forecasting system, which could help decision-makers to make early assessments and plan annual imports, is thus needed. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. In this context, the aim of the present study is to analyse the characteristics of two types of irrigated and non-irrigated cereals: barley and wheat. Through the use of a rich database, acquired over a period of two years for more than 30 test fields, and from 20 optical satellite SPOT/HRV images, two research approaches are considered. First, statistical analysis is used to characterize the vegetation's dynamics and grain yield, based on remotely sensed (satellite) normalized difference vegetation index (NDVI) measurements. A relationship is established between the NDVI and LAI (leaf area index). Different robust relationships (exponential or linear) are established between the satellite NDVI index acquired from SPOT/HRV images, just before the time of maximum growth (April), and grain and straw, for barley and wheat vegetation covers. Following validation of the proposed empirical approaches, yield maps are produced for the studied site. The second approach is based on the application of a Simple Algorithm for Yield Estimation (SAFY) growth model, developed to simulate the dynamics of the LAI and the grain yield. An inter-comparison between ground yield measurements and SAFY model simulations reveals that yields are underestimated by this model. Finally, the combination of multi-temporal satellite measurements with the SAFY model estimations is also proposed for the purposes of yield mapping. Although the results produced by the SAFY model are found to be reasonably well correlated with those determined by satellite measurements (NDVI), the grain yields are nevertheless underestimated

Characterization of cereals in a semi-arid context based on remote sensing indicators from high spatial resolution images from the Sentinel 1 and Sentinel 2 satellite in central Tunisia

International audience<p>Global food security is based on a limited number of species mainly cereals, maize and rice.                                     <br>In semi-arid region, the availability of cereals on the international market at competitive prices in relation to local production has led to a change in domestic demand in these countries and has affected the capacity of populations to cover their basic food needs. An operational early grain yield prediction system has been needed to assist policy makers in making initial assessments and planning for annual grain imports. In this context, the main objective of this study is to develop a method for the early estimation of grain and grain straw yields based on high spatial resolution optical satellite data and radar data. Thus, we used two lines of research: the first is based on analysing the relationship between vegetation index and the VH/VV ratio with cereals yields measured in situ. The second axis is based on the estimation of the cereal yields based on a combined index. This last is a combination of the radar index VH/VV and an optical index.</p><p>For the first axe, a 22 Sentinel-2 and 55 Sentinel-1 images acquired between 01/09/2017 and 31/08/2018 are used. From the optical data, three spectral indices (NDVI, EVI and EVI2) are calculated and from the Radar data, we calculated the VH/VV polarization ratio. At the same time, we realized experimental measurements made on 54 test plots of dry or irrigated cereals carried out in study area during the 2017-2018 agriculture year. The first approach based on a statistical analysis between the NDVI, EVI and EVI2 vegetation indices and the yields measured showed that NDVI is the best optical index allowing an estimate of grain yield from mid-March with a correlation coefficient R<sup>2</sup> = 69.22% for the average weight of the grains and R<sup>2</sup> = 72.38% for the average weight of the straw. Validation of estimates obtained by remote sensing shows that this approach is robust, with an error of 1.79qx/ha and 1.21 qx/ha, respectively, for seed and straw yields. The evolution of yields as a function of the VH/VV ratio was then studied for different dates. The analysis allows that an early estimate can be made the 10th of March based on this ratio with a correlation coefficient R<sup>2</sup> = 53.79% for the average weight of the seeds and R<sup>2</sup> = 56% for the average weight of the straw.</p><p>For the second axe, a combined index was developed based on the combination of the radar index VH/VV and the optical index. The results show that the most suitable combination is the one between the Radar Index and the NDVI where correlations R<sup>2</sup> = 63.64% for the average seed weight and R<sup>2</sup> = 64.03% for the average straw weight. The validation of the estimates obtained by this combined index is made with an error equal to 1.97 qx/ha and 1.31 qx/ha, respectively for the seed and straw yields.</p&gt

Analysis of the Effects of Drought on Vegetation Cover in a Mediterranean Region through the Use of SPOT-VGT and TERRA-MODIS Long Time Series

The analysis of vegetation dynamics and agricultural production is essential in semi-arid regions, in particular as a consequence of the frequent occurrence of periods of drought. In this paper, a multi-temporal series of the Normalized Difference of Vegetation Index (NDVI), derived from SPOT-VEGETATION (between September 1998 and August 2013) and TERRA-MODIS satellite data (between September 2000 and August 2013), was used to analyze the vegetation dynamics over the central region of Tunisia in North Africa, which is characterized by a semi-arid climate. Products derived from these two satellite sensors are generally found to be coherent. Our analysis of land use and NDVI anomalies, based on the Vegetation Anomaly Index (VAI), reveals a strong level of agreement between estimations made with the two satellites, but also some discrepancies related to the spatial resolution of these two products. The vegetation’s behavior is also analyzed during years affected by drought through the use of the Windowed Fourier Transform (WFT). Discussions of the dynamics of annual agricultural areas show that there is a combined effect between climate and farmers’ behavior, leading to an increase in the prevalence of bare soils during dry years

Potential of the Modified Water Cloud Model to Estimate Soil Moisture in Drip-Irrigated Pepper Fields Using ALOS-2 and Sentinel-1 Data

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

International audience<p>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.</p><p>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.</p><p>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).  </p><p>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.</p&gt

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

International audienceThe objective of this paper was to estimate soil moisture in pepper crops with drip irrigationin a semi-arid area in the center of Tunisia using synthetic aperture radar (SAR) data. Within thiscontext, the sensitivity of L-band (ALOS-2) in horizontal-horizontal (HH) and horizontal-vertical(HV) polarizations and C-band (Sentinel-1) data in vertical-vertical (VV) and vertical-horizontal (VH)polarizations is examined as a function of soil moisture and vegetation properties using statisticalcorrelations. SAR signals scattered by pepper-covered fields are simulated with a modified versionof the water cloud model using L-HH and C-VV data. In spatially heterogeneous soil moisturecases, the total backscattering is the sum of the bare soil contribution weighted by the proportion ofbare soil (one-cover fraction) and the vegetation fraction cover contribution. The vegetation fractioncontribution is calculated as the volume scattering contribution of the vegetation and underlyingsoil components attenuated by the vegetation cover. The underlying soil is divided into irrigatedand non-irrigated parts owing to the presence of drip irrigation, thus generating different levels ofmoisture underneath vegetation. Based on signal sensitivity results, the potential of L-HH data toretrieve soil moisture is demonstrated. L-HV data exhibit a higher potential to retrieve vegetationproperties regarding a lower potential for soil moisture estimation. After calibration and validationof the proposed model, various simulations are performed to assess the model behavior patternsunder different conditions of soil moisture and pepper biophysical properties. The results highlightthe potential of the proposed model to simulate a radar signal over heterogeneous soil moisture fieldsusing L-HH and C-VV data

Analysis of C-Band Scatterometer Moisture Estimations Derived Over a Semiarid Region

International audienceSpatial and temporal variations of soil moisture strongly affect flooding, erosion, solute transport, and vegetation productivity. Their characterization offers numerous possibilities for the improvement of our understanding of complex land-surface-atmosphere interactions. In this paper, soil moisture dynamics at the soil's surface (the first centimeters) and in its root zone (at depths down to 1 m) are investigated using 25 × 25 km2 scale data (Advanced Scatterometer (ASCAT)/METorological OPerational (METOP) scatterometer), for a semiarid region in North Africa. Our study highlights the quality of the surface and root-zone soil moisture products, derived from ASCAT data recorded over a two-year period. Surface soil moisture tends to be highly variable because it is strongly influenced by atmospheric conditions (rain and evaporation). On the other hand, rootzone moisture is considerably less variable. A statistical droughtmonitoring index, referred to as the "moisture anomaly index," is derived from ASCAT and European Remote Sensing (ERS) time series. This index was tested with ERS and ASCAT products during the 1991-2010 study period. A strong correlation is found between the proposed index and the standardized precipitation index

Soil Moisture Estimation Over Cereal Fields Based on Sar ALOS-2 Data

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Potential of the Normalized Polarization Ratio and the Interferometric Coherence Sentinel-1 Data to Reconstruct the NDVI Wheat Cycle at a Field Scale

International audienceThe aim of this study is to retrieve the NDVI values during the wheat cycle using the radar data over reference fields located in the Kairouan plain in the center of Tunisia. The developed approach is based on the use of C-band Sentinel-1 acquisition specifically the cross-polarization ratio and the estimated coherence as features of curve fitting equations and machine learning algorithms such as the random forest and the support vector machine regressors. The NDVI retrieve according to the wheat growth stage, at a field scale, was marked by RMSE values lower than 0.13 and bias values under -0.03