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JECAM | Joint Experiment for Crop Assessment and Monitoring

Tunisia

Project Overview

  1. Crop identification and Crop Area Estimation
    Crops types are discriminated using multitemporal NDVI data. A decision tree algorithm have been implemented for each year, we intend to develop a more general and robust method. Information about land cover type is required to parameterize the models used (ET, Biomass, etc.).
     
  2. Crop Condition/Stress

    Our main goal is to monitor crop consumption and irrigation requirements using the coupling of FAO-56 method and NDVI time series (see results section). Crop water budget is useful operational information at plot scale (farmers) and at perimeter scale (irrigation managers). This type of products is also a valuable input for watershed integrated modelling, aimed at basins scale management, including groundwater.

    Crop water stress is monitored using thermal image processing, and the results are aimed at being assimilated in the crop water budget model (PhD student).

  3. Soil Moisture
    Soil moisture is the primary objective tackled using microwave data, relying on ground measurements for cal/val purpose. This type of information may also be input in the crop water budget model (A new PhD is currently on this topic by using Sentinel-1 and Sentinel-2 satellites data).
     
  4. Yield Prediction and Forecasting
    Based on NDVI relationships and vegetation model.
     
  5. Crop Residue, Tillage and Crop Cover Mapping
    We don’t study yet residues nor tillage (this will be done in 2017). Crop cover mapping is related to the first point.

Project Reports

2017 Site Progress Report

2016 Site Progress Report

2015 Site Progress Report

2014 Site Progress Report

 

No publications are specifically linked with JECAM (Landsat images were not yet processed), but here are the publications related to the Tunisian experiment in 2013:

Articles:

  1. Boulet, G., Mougenot, B., Lhomme, J. P., Fanise, P., Lili-Chabaane, Z., Olioso, A., Bahir, M., Rivalland, V., Jarlan, L., Merlin, O., Coudert, B., Er-Raki, S., and Lagouarde, J. P., 2015, The SPARSE model for the prediction of water stress and evapotranspiration components from thermal infra-red data and its evaluation over irrigated and rainfed wheat, Hydrol. Earth Syst. Sci., pp.4653-4672.
     
  2. Gorrab, A., M. Zribi, Nicolas Baghdadi, Bernard Mougenot and Zohra Lili Chabaane, 2015a, Potential of X-band TerraSAR-X and COSMO-SkyMed SAR data for the assessment of physical soil parameters, Remote Sensing, 7(1), 747-766; doi:10.3390/rs70100747
     
  3. Gorrab, A., Mehrez Zribi, Nicolas Baghdadi, Bernard Mougenot, Zohra Lili-Chaabane, 2015b, Retrieval of both soil moisture and texture using TerraSAR-X images, Remote Sensing, 7, 10098-10116; doi:10.3390/rs70810098.
     
  4. Ouesleti, I., Lili-Chabaane, Z., Shabou, M., Zribi, M., D. Glafassi, Hoff, H., Pizzigalli, C., chapitre : WEAP model as a tool for integrated water resources management in Merguellil watershed (central Tunisia), Sustainable Water Management, CRC Press/Balkema, ISBN: 978-138-000018-6, 2015.
     
  5. Shabou, M., Bernard Mougenot, Zohra Lili Chabaane, Christian Walter, Gilles Boulet, Nadhira Ben Aissa and Mehrez Zribi , 2015, Clay content mapping using a time series of Landsat TM data in semi-arid lands, Remote Sensing, 7, 6059-6078; doi:10.3390/rs70506059.
     
  6. Saadi S., Simonneaux V. Boulet G., Raimbault  B., Mougenot B., Fanise P., Ayari H., Lili-Chabaane Z., 2015, Monitoring irrigation consumption using high resolution NDVI image time series. Calibration and validation in the Kairouan plain (Tunisia), remote sensing, 10/2015; 7(10): 13005-13028.  DOI: 10.3390/rs71013005.
     
  7. Amri, R., Zribi, M., Lili-Chabaane, Z., C. Szczypta, J. C. Calvet, G. Boulet, 2014,  FAO-56 Dual approach combined with multi-sensor remote sensing for regional evapotranspiration estimations, Remote Sens. 6(6), 5387-5406; doi:10.3390/rs6065387
     
  8. Chahbi, A., Zribi, M., Lili-Chabaane, Z., Duchemin, B., Shabou, M., Mougenot, B., Boulet, G., 2014, Estimation of the dynamics and yields of cereals in a semi-arid area using remote sensing and the SAFY growth model, International journal of Remote Sensing, 35:3, 1004-1028.
     
  9. Zribi, M., Gorrab, A., Baghdadi, N., Lili-Chabaane, Z., Mougenot, B., 2014a, Influence of radar frequency on the relationship between bare surface soil moisture vertical profile and radar backscatter, IEEE Geoscience and Remote Sensing Letters, 11(4) 848 - 852 10.1109/LGRS.2013.2279893.
     
  10. Zribi, M., Gorrab, A., and Baghdadi, N., 2014b, A new soil roughness parameter for the modelling of radar backscattering over bare soil, Remote Sensing of Environment, 152, 62-73.
     
  11. Gorrab, A., M. Zribi, Nicolas Baghdadi, Bernard Mougenot and Zohra Lili Chabaane, 2015, Potential of X-band TerraSAR-X and COSMO-SkyMed SAR data for the assessment of physical soil parameters, Remote Sensing, 7(1), 747-766.
     
  12. Saadi, S., Simonneaux, V., Boulet, G., Lili Chabaane, Z., et al. Monitoring irrigation consumption using high resolution high repetitivity image times series. Aplication to the Kairouan area (Tunisia). Submitted to Remote Sensing special issue “SPOT4-Take5 experiment”.

 

Papers:

  1. Mehrez Z.; Azza Gorrab; Nicolas Baghdadi; Zohra Lili-Chabaane; Bernard Mougenot, 2013, Influence of Radar Frequency on the Relationship Between Bare Surface Soil Moisture Vertical Profile and Radar Backscatter, IEEE Geoscience and Remote Sensing Letters, 2013, PP99, pp. 1-5 
     
  2. Chahbi A., Zribi M., Lilli-Chabaane Z., Duchemin B., Shabou M., Mougenot B., Boulet B. Estimation of the dynamics and yields of cereals in a semi-arid area using remote sensing and the SAFY growth model, 2014. IJRS, accepted.
     
  3. Student Thesis:
     
  4. Amri, R., 2013, Estimation régionale de l'évapotranspiration sur la plaine de Kairouan (Tunisie) à partir de données satellites multi-capteurs. PhD de l’Université Paul Sabatier - Toulouse III, Avril 2013.
     
  5. Zagouani Refka, 2013, Spatialisation de l’évapotranspiration et estimation des volumes d’irrigation dans la plaine de Kairouan (Tunisie). Mémoire de Master 2, Institut National Agronomique de Tunisie, Tunis.
     
  6. Gorrab A., Simonneaux V., Zribi M., Saadi Sameh, Baghdadi N., Lili Chabaane Z., Fanise P. 2017. Bare soil hydrological balance model “MHYSAN”: Calibration and validation using SAR moisture products and continuous thetaprobe network measurements over bare agricultural soils (Tunisia). J. of Arid Environments, 139, 11-25.
     
  7. Mehrez Zribi, Ghofrane Dridi, Rym Amri and Zohra Lili-Chabaane, 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, Remote Sens. 2016, 8, 992; doi:10.3390/rs8120992.

 

Conferences:

  1. Boulet, G., Bernard Mougenot, Malik Bahir, Pascal Fanise, Sameh Saadi, Vincent Simonneaux, Wafa Chebbi, Zeineb Kassouk, Toufik Oualid, Albert Olioso, Jean-Pierre Lagouarde, Valérie Le Dantec, Vincent Rivalland, Mehrez Zribi, Zohra Lili-Chabaane, Analysis of time-series of total and plant water stress levels using a dual-source energy balance model over agricultural crops and medium to low resolution thermal infra red remote sensing data, EGU’2015, Vienne, Autriche, 13-17 Avril 2015.
     
  2. Gorrab A., Zribi M., Nicolas Baghdadi, Zohra Lili-Chabaane, Bernard Mougenot, Analyse des sensibilités des mesures radar bande-X aux paramètres des surfaces de sols nus, AUF, Dakar, Sénégal, 17-22 Février 2015.
     
  3. Gorrab,A., Mehrez Zribi, Nicolas Baghdadi, Bernard Mougenot, Gilles Boulet, and Zohra Lili Chabaane, Surface soil moisture retrieval over a Mediterranean semi-arid region using X-band TerraSAR-X SAR data, EGU’2015, Vienne, Autriche, 13-17 Avril 2015.
     
  4. Gorrab A., M. Zribi, N. Baghdadi, Z. Lili Chabaane, "Mapping of bare soil surface parameters from TerraSAR-X radar images over a semi-arid region", in Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, Christopher M. U. Neale; Antonino Maltese, Editors, Proceedings of SPIE Vol. 9637 (SPIE, Bellingham, WA 2015), 96371F.
     
  5. Le Page M., et al., Comparison of meteorological forcing (WFDEI, AGRI4CAST) to in-situ observations in a semi arid catchment. The case of Merguellil in Tunisia. EGU’2015, Vienne, Autriche, 13-17 Avril 2015.
     
  6. Saadi S., Simonneaux V., Boulet G., Raimbault B., Mougenot B., Fanise P., Ayari H. and Lili Chabaane, Z., 2015, Monitoring irrigation consumption using high resolution NDVI image time series. Calibration and validation in the Kairouan plain (Tunisia), Remote Sens. 2015, 7(10), 13005-13028; doi:10.3390/rs71013005
     
  7. Saadi Sameh, V. Simonneaux, G. Boulet, B. Mougenot, Z. Lili Chabaane, Monitoring irrigation volumes using high-resolution NDVI image time series: calibration and validation in the Kairouan plain  (Tunisia), in Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, Christopher M. U. Neale; Antonino Maltese, Editors, Proceedings of SPIE Vol. 9637 (SPIE, Bellingham, WA 2015), 96371F.
     
  8. Saadi S., et al., Monitoring irrigation water consumption using high resolution NDVI image time series (Sentinel-2 like). Calibration and validation in the Kairouan plain (Tunisia), EGU’2015, Vienne, Autriche, 13-17 Avril 2015.
     
  9. Zribi, M., G. Dridi, R. Amri, Z. Lili-Chabaane Analysis of ,  events in a Mediterranean semi-arid region, Using SPOT-VGT and TERRA-MODIS satellite products, EGU’2015, Vienne, Autriche, 13-17 Avril 2015.
     
  10. Chahbi Aicha, Mehrez Zribi, Zohra Lili-Chabaane, Bernard Mougenot, "Forecasting of cereals yields in a semi-arid area using the agrometeorological model «SAFY» combined to optical SPOT/HRV images", in Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII, Christopher M. U. Neale; Antonino Maltese, Editors, Proceedings of SPIE Vol. 9637 (SPIE, Bellingham, WA 2015), 963729.
     
  11. Amri, R., M. Zribi,  Z. Lili-Chabaane, B. Duchemin, Gruhier, G., Analysis of vegetation dynamics and persistent behavior in a North Africa semi-arid region, using SPOT VEGETATION NDVI data, Global Vegetation Monitoring and Modeling GV2M, Avignon France 3-7 février 2014.
     
  12. Amri, M. Zribi, Z. Lili-Chabaane, C. Szczypta, J. C. Calvet , G. Boulet, FAO-56 Dual approach combined with multi-sensor remote sensing for regional evapotranspiration estimations, IEEE International conference on Advanced Technologies for Signal and Image Processing, ATSIP, 17-19 march 2014 Sousse, Tunisia.
     
  13. Chahbi, A., M. Zribi, Z., Lili-Chabaane, B. Duchemin, B. Mougenot, Analysis of optical remote sensing potential for yields of cereals estimation, IEEE International conference on Advanced Technologies for Signal and Image Processing, ATSIP, 17-19 march 2014 Sousse, Tunisia.
     
  14. Chahbi, A., Zribi, M., Lili-Chabaane, Z., Duchemin, B., Mougenot, B.,  Early forecasting of cereals yields in a semi arid area using spot/hrv images and the agro-meteorological model «SAFY». Global Vegetation Monitoring and Modeling GV2M, Avignon France 3-7 février 2014.
     
  15. Gorrab, A., Zribi, M., N. Baghdadi, Z. Lili-Chabaane, B. Mougenot, Influence of radar frequency on the relationship between bare surface soil moisture vertical profile and radar backscatter, IEEE International conference on Advanced Technologies for Signal and Image Processing, ATSIP, 17-19 march 2014 Sousse, Tunisia.
     
  16. Gorrab A., Zribi M., Baghdadi N., Lili-Chabaane Z., and Mougenot B., 2014.  X-band TERRASAR-X and COSMO-SKYMED SAR data for bare soil parameters estimation. International Geoscience and Remote Sensing Symposium 2014 (IGARSS 2014), 13 - 18 July 2014, Quebec, Canada.
     
  17. Gorrab A., Zribi M., Baghdadi N., Mougenot B., and Lili-Chabaane Z., 2014. Potential of X-band SAR data from TerraSAR-X and COSMO-SkyMed sensors to retrieve physical soil properties. 4th International Symposium on Recent Advances in Quantitative Remote Sensing (RAQRS'IV), 22-26 September 2014, Torrent, Valencia, Spain.
     
  18. Zribi, M., Gorrab, A., Baghdadi, N., A new Zg roughness parameter for improving backscattering analysis over bare soils, International Geoscience and Remote Sensing Symposium 2014 (IGARSS 2014), 13 - 18 July 2014, Quebec, Canada.
     
  19. Zribi M., Gorrab A., and Baghdadi N., 2014. Roughness and vertical moisture heterogeneities introduction to improve soil moisture estimation with radar remote sensing. 4th International Symposium on Recent Advances in Quantitative Remote Sensing (RAQRS'IV), 22-26 September 2014, Torrent, Valencia, Spain.
     
  20. Zribi M., Kotti F., Gorrab A., Amri R., Baghdadi N., Mougenot B., Lili-Chabaane Z., and Boulet G., 2014. Operational soil moisture mapping using multitemporal ASAR/Wide Swath ENVISAT data. 4th International Symposium on Recent Advances in Quantitative Remote Sensing (RAQRS'IV), 22-26 September 2014, Torrent, Valencia, Spain.
     
  21. Boulet G., Mougenot B., Lili-Chabaane Z, Fanise  P., Olioso A., Bahir, M., Rivalland V., Jarlan L; Coudert B. and Lagouarde, J.-P., 2014. Total and component evapotranspiration retrieval performances of a single-pixel energy balance model over agricultural crops RAQRS’IV, Valencia, Spain.
     
  22. Saadi S., Simonneaux V., Boulet G., Lili Chabaane Z., Mougenot B., Fanise P., Ayari H., Zribi M., 2014, Monitoring crop water budget and irrigation consumption using high and low resolution NDVI image time series. Calibration and validation in the Kairouan plain (Tunisia), 4th International Symposium, Recent Advances in Quantitative Remote Sensing, September 22-26, Torrent, Spain;
     
  23. Mougenot B., Touhami N., Lili Chabaane Z., Boulet G., Simonneaux V., Zribi M., 2014, Trees detection for water resources management in irrigated and rainfed arid and semi-arid agricultural areas, April 1-3, 2014, PLEIADES DAYS, Toulouse.
     
  24. Azza Gorrab, Vincent Simonneaux, Mehrez Zribi, Sameh Saadi, Zohra Lili-Chabaane, Major water balance variables Estimation, soil moisture and evaporation time series, using X-band SAR moisture products, European Geosciences Union General Assembly, Vienna, Austria 17-22 April 2016.
     
  25. Azza Gorrab, Mehrez Zribi, Nicolas Baghdadi, Zohra Lili Chabaane, Mapping of surface soil parameters (roughness, moisture and texture) using one radar X-band SAR configuration over bare agricultural semi-arid region, International Geoscience and Remote Sensing Symposium, IGARSS’2016, Pekin, 10-15 Juillet, 2016.
     
  26. Aicha Chahbi, Mehrez Zribi, Zohra Lili-Chabaane, Remote sensing and modelling of vegetation dynamics for early estimation and spatial analysis of grain yields in semiarid context in central Tunisia, European Geosciences Union General Assembly, Vienna, Austria 17-22 April 2016.
     
  27. Boulet G., Bahir M., Bousbih S., Chebbi W., Saadi S., Fanise P., Mougenot B., Brut A., Le Dantec V., Rivalland V., Simonneaux V., Ayari H., Kassouk Z., Lili-Chabaane Z. Estimation de l’utilisation de l’eau par les couverts agricoles du Merguellil: observations in-situ et utilisation de la télédétection thermique basse résolution. Workshop2 du projet ANR- AMETHYST, Marrakech, Maroc, 11-12 février 2016.
     
  28. Mehrez Zribi , Azza Gorrab, Nicolas Baghdadi, Zohra Lili Chabaane, Mapping of bare soil surface parameters (moisture, roughness, texture) from one TERRASAR-X radar configuration, ESA Living Planet Symposuim, 9-13 mai, 2016.
     
  29. Marouen Shabou, Rafael Angulo-Jaramillo, Laurent Lassabatère, Gilles Boulet, Bernard Mougenot, Zohra Lili Chabaane, Mehrez Zribi. Large scale characterization of unsaturated soil properties in a semi-arid region combining infiltration, pedotransfer functions and evaporation tests, European Geosciences Union General Assembly, Vienna, Austria 17-22 April 2016.
     
  30. Michel Le Page, Cindy Gosset, Ines Oueslati, Roger Calvez, Mehrez Zribi, Zohra Lili Chabaane, A generic open-source toolbox to help long term irrigation monitoring for integrated water management in semi-arid Mediterranean areas, European Geosciences Union General Assembly, Vienna, Austria 17-22 April 2016.
     
  31. Saadi S., Simonneaux V., Boulet G., Mougenot B., Lili-Chabaane Z. Regional estimation of evapotranspiration and crop wate consumption using high resolution NDVI image time series in the Kairouan plain. Workshop2 du projet ANR- AMETHYST, Marrakech, Maroc, 11-12 février 2016.

Implementation Plans

Date Number of Plots
18/09/2015 72 plots
21/01/2016 74 plots
24/05/2016 151 plots
21/07/2016 135 plots


Figure 2: in situ data

 

 

Plans for Next Growing Season:

We will use Sentinel-2A (2B launched in March), Sentinel-1A and B and Landsat TM images in 2017. We will test classification methods to improve the land use mapping to produce intermediate maps and introduce bare soils characteristics in relationships with pratices. We will experiment our tools as SAMIR with these operational data to validate the model on large areas. We intend also to carry on the use of medium resolution time series (MODIS, MOD13Q1 products) with Thermal data (Landsat 8) and the new Sentinel-1 data to improve the soil water compartment control.


Site Description

Locations

Location

Top left            Latitude: N35° 42' 20"

                        Longitude: E9° 41' 45"

Bottom right   Latitude: N35° 23’ 00"

                        Longitude: E10° 07’ 00"

     
Figure 1. The study area, in red the boundary of the upper watershed, in yellow the boundary of the irrigated area.

Topography:

Alluvial plain

Soils:

Various soil texture from fine sand to clay-loam

Drainage class/irrigation:

Well drained soils

Crop calendar:

Field size:

Typically 1 to 4 ha

Climate and weather:

Semi-arid mediterranean climate, rainfall around 250 mm/y, ET0 around 1500 mm/year

Agricultural methods used:

Dry cereals and olive cultivation

Irrigation for cereals, vegetables and some fruit trees (apple, peach, etc.)

 

Photograph(s):

 Field LAI measurements with hemiview canopy images

 Flux tower installed in dry cultivated olive trees

 Orchard with olives and orange trees

 Irrigated chilli

 Field with residues and plough in progress

 Flow measurements on a pipe for irrigation


Specific Project Objectives & Deliverables

Results:

1         Crop water budget monitoring

Remote sensing has long been used for computing evapotranspiration estimates, which is an input for crop water balance monitoring. Up to now, only medium and low resolution data (e.g. MODIS) are available on regular basis to monitor cultivated areas. However, the increasing availability of high resolution high repetitivity VIS-NIR remote sensing, like the Sentinel-2, offers unprecedented opportunity to improve this monitoring.

Methods for computing evapotranspiration (ET) using remote sensing belong basically to two broad families, either using thermal remote sensing used to solve the energy budget of the surface, or using SVAT modeling forced by remotely sensed information of vegetation properties (e.g. fraction cover, leaf area index, crop coefficients…). The first group i.e. Surface Energy Balance (SEB) methods use RS data to estimate heat exchange between land surface and atmosphere compute the sensible heat flux first and then obtain the latent heat flux as the residual of the energy balance equation at the time of satellite overpass.The latter group includes the coupling of the dual crop coefficient method described in FAO paper 56 (Allen, 1998) with NDVI time series providing spatialized estimates of the fraction cover (fc) and the basal crop coefficient (Kcb). We developed in previous works the SAMIR tool (Satellite of Monitoring Irrigation) implementing this method using high resolution image times series (SPOT, Landsat, FORMOSAT, Sentinel-2) and this year for validation, sensible heat flux measurements were obtained using a large aperture scintillometer (XLAS) .

1.1       Spatialized estimates of evapotranspiration.
1.1.1       SAMIR model / validation

The objective of the work was to assess the operationality of SAMIR and the accuracy of the modelled evapotranspiration (ET) at the scale of irrigated perimeters, in a context of high land cover complexity (i.e. trees, winter cereals, summer vegetables) and limited data available for parameterization.

Using the spatialized computation of the crop water budget presented in last year’s JECAM report (published in Remote sensing, Saadi et al. 2015), we achieved a validation of the ET using XLAS scintillometer measurements. The model was calibrated on the basis of local ET measurements from flux towers (eddy-correlation devices) installed on irrigated wheat and barley plots. For other crops for which no calibration data was available, parameters were taken from bibliography.

For validation, half hourly sensible heat flux measurements were obtained using a large aperture scintillometer (XLAS) over the study area along a path length of 4 Km. The daily sensible heat flux(H) were used to compute daily latent heat flux (LE) using the energy budget conservation (Rn + G = H + LE). The daily net radiation (Rn) was computed using MODIS daily data at the time of satellite overpass (i.e. providing half hourly Rn estimates), and scaled at daily scale using ground meteorological station. The soil flux (G) is supposed to be null at daily scale. For the Rn scaling, we used the ration of radiation measure at the station and daily value, for both global radiation (Rg) or Rn computed using the FAO method. Both methods gave similar results. The comparison between modelled daily and measured ET are shown in figure 4

1.1.2        SPARSE model

Moreover, spatially distributed estimates of  ET were computed using the layer approach of the Soil Plant Atmosphere and Remote Sensing Evapotraspiration (SPARSE) model (Boulet et al., 2015) fed by low resolution remote sensing data (Terra and Aqua MODIS). The objective of the work was to assess the SPARSE model operationality and the accuracy of the modeled i) instantaneous  H and ii) daily evapotranspiration as well as its components (soil evaporation  and transpiration) over a semi-arid land surface, in a context of high land.

The SPARSE’s layer approach was run to compute instantaneous estimates of sensible heat flux H at the time of satellite overpass. The comparison between H estimates and large aperture scintillometer (LAS)’s H measurements over the study area along a pathlength of 4 Km showed that the SPARSE model presents satisfactory accuracy (figure 3).

 


Figure 3: Modeled vs. Observed sensible heat fluxes at Terra and Aqua time overpass (Saadi et al., 2017, in preparation)

In a subsequent step, daily modeled latent heat flux LE (i.e. ET)  computed on the basis of modeled half hourly LE were compared to observed LE returned as a residual term of the surface energy budget using the daily scintillometer’s H measurements and net radiation (computed using instantaneous MODIS data and then extrapolated to daily time step). Results are shown in figure 4.

 


Fiure 4: Daily Modeled Vs. Observed latent heat fluxes (Saadi et al., 2017, in preparation)

 

1.2       Long term irrigation monitoring / WEAP

We propose a generic toolbox based on the FAO-56 method and the Crop Coefficient/NDVI approach used in Remote Sensing. A toolbox has been used in order to build a WEAP21 model of the Merguellil basin in Tunisia for the period of 2000-2014 (Le Page et al, 2016). The toolbox can be separated in three main areas: 1) preparation of different input datasets, 2) A collection of algorithms based on the analysis of NDVI time series (MODIS) is proposed: Separation of irrigated vs non-irrigated area, a simplified annual land cover classification, Crop Coefficient, Fraction Cover and Efficient Rainfall, 3) Synthesis against points or areas produces the output data at the desired spatial and temporal resolution for Integrated Water Modeling or data analysis and comparison. Finally, the comparison to monthly statistics of three irrigated commands was performed over 4 years. Punctual evapotranspiration was compared to actual measurements obtained by flux towers on wheat and barley showing good agreements on a daily basis (r2=0.77). This late comparison showed a bad agreement which led us to suppose two things: First, the simple approach of (Evapotranspiration minus Efficient Rainfall) to estimate Irrigation at the monthly time step is not pertinent because only a subset of the irrigated commands is actually irrigated. Hence, a higher spatial resolution of remote sensing imagery is needed. Second, in this particular area, farmers have a different rationale about rainfall and irrigation water needs. Those two aspects need to be further investigated.

The toolbox has proven to be an interesting tool to integrate different sources of data, efficiently process them and easily produce input data for the WEAP1 model on a long term range. Yet some new challenges have been raised.

 

2         Soil water content monitoring

Soil water monitoring using microwave data has been studied at various scales, from 1km to 2m. We obtain new results on the JECAM site.

We propose the MHYSAN model (Modèle de bilan HYdrique des Sols Agricoles Nus / Bare Soil HYdrological Balance Model, Gorrab et al, 2017) for simulating soil water balance of bare soils. This model was used to simulate surface evaporation fluxes and SM content at daily time scale over a semi-arid, bare agricultural site in Tunisia (North Africa). Two main approaches are considered in this study. Firstly, the MHYSAN model was successfully calibrated for seven sites using continuous thetaprobe measurements at two depths. Then the possibility to extrapolate local SM simulations on distant sites based only on soil texture similarity was tested. This extrapolation was validated using SAR estimates and manual thetaprobe measurements of SM made on these distant sites. Results show bias about 0.63 and 3.04 % and RMSE equal to 6.11 and 4.5 % for the SAR volumetric SM and manual thetaprobe measurements, respectively. In a second approach, the MHYSAN model was calibrated using seven very high-resolution SAR (TerraSAR-X) SM outputs ranging over only two months. The simulated SM were validated using continuous thetaprobe measurements during 15 months (Fig. 5).

Although only seven dates of SM were used for calibration, the satisfying results we obtained could be attributed to the large SM variation captured by these seven images, allowing a good calibration of the soil parameters These results highlight the potential of Sentinel-1 images for daily soil moisture monitoring using simple models.


Figure 5: Estimation of time series of water balance variables, using calibrated MHYSAN SAR data and validation results (Gorrab et al., 2017).

 

 

3          Yield estimates

No field data in 2016.


In Situ Observations

  1. Parameter: Radiometric measurements
    Data Collection Protocol:

    (CropScan : 16 bands VIS-MIR, including Landsat TM bands)

    Frequency:
  2. Parameter: Crop identification ground campaigns for land cover classification training
    Data Collection Protocol:

    Data collected during three campaigns with about 150 plots observed each time

    Frequency:
  3. Parameter: LAI, fraction cover, biomass
    Data Collection Protocol:

    collected on annual crops and olive trees

    Frequency:
  4. Parameter: Temperature, Humidity, Wind Speed, Net Radiation, Rainfall
    Data Collection Protocol:

    Three permanent meteorological stations

    Frequency:
  5. Parameter: Energy, Water, Carbon
    Data Collection Protocol:

    Two Flux stations on irrigated wheat and rainfed olive orchard

    Frequency:
  6. Parameter: area-averaged surface fluxes of sensible heat
    Data Collection Protocol:

    One X-LAS scintillometer transect (1 km) starting spring 2013

    Frequency:

EO Data Requirements

Approximate Start Date of Acquisition: Feburary 1
Approximate End Date of Acquisition: October 31
Spatial Resolution: 10-30m
Temporal Frequency: 15 Days
Latency of Data Delivery: Normal
Wavelengths Required: Red, NIR, MIR, Thermal
Across Swath:
Along Track:

SAR Data Requirements

Approximate Start Date of Acquisition: February 1
Approximate End Date of Acquisition: October 31
Spatial Resolution: 2.5m - 20m
Temporal Frequency: 15 Days
Latency of Data Delivery: Normal
Wavelengths Required:
Polarization
Incidence Angle Restrictions: Various
Across Track: 100km
Along Track: 150km

Locations


Optical Sensors

JECAM | Joint Experiment for Crop Assessment and Monitoring | Group on Earth Observation

©2013 Joint Experiment for Crop Assessment and Monitoring © HER MAJESTY THE QUEEN IN RIGHT OF CANADA SA MAJESTE LA REINE DU CHEF DU CANADA (2012)