A new vegetation index for DISPATCH algorithm mainly in vegetated areas .

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Dynamics of water resources in the Mediterranean region

It is essential to understand the distribution of water resources and the effect of climate change on water supply in the future, especially in the water-scarce regions. Understanding the impact of climate change on society is becoming more critical, particularly in water resource management. It is essential to understand the different environmental and human modifications responsible for the change in water resource management and hydrology, such as the impact of climate change on water balance, water runoff, change in land use and land cover, urbanization, and irrigation. Water is limited in the Mediterranean region, and it mainly depends on the water runoff from the mountainous regions (Viviroli and Weingartner, 2004; de Jong et al., 2009). Mountains pro-vide a significant portion of the overall runoff in Mediterranean areas, accounting for between 20% and 90% of total runoff (Viviroli et al., 2007). The increase in water demand also creates pressure on the mountainous region. Long-term climatic conditions are becoming more apparent and impacting the ecosystem of the Mediterranean mountains (Giorgi and Lionello, 2008), and hence the stability of the water supply is not maintained.
Land use and land cover (LULC) changes are the main characteristics of the Mediterranean landscape and environmental alteration. Land use affects the hydrological process through inter-ception, evapotranspiration, infiltration, and runoff (Cosandey et al., 2005; Foley et al., 2005). Land cover changes have significantly affected hydrological response at the basin scale (Andr´eas-sian, 2004). Land-use change has been rapid during recent decades due to the rapid extension of urbanization and irrigation. The development of new irrigation and crops in Mediterranean lowland areas requires more water for consumption. The newly irrigated areas cover almost 10 million ha and consume a large volume of water. These areas are far from the river valleys, so water transport in these areas puts pressure on the complex infrastructure (reservoir and canals) and over-exploits groundwater.
The proper water resources management can fulfill the increase in demand of water supply at the regional scale. The water management strategies will benefit from continuous monitoring the surface water status as a result of climate and LULC dynamics and change. Strong knowledge in different fields such as climate change, river regimes, hydrology, etc., helps develop a framework for water resource management.

Spatio-temporal variability of soil moisture

In this section, we define soil moisture, how it is linked with the water and energy budget of the surface, and how its measurement at various spatial and temporal scales will be helpful in water resource management and other applications. Soil moisture (SM) is the water content available in the unsaturated zone. SM is a key variable as it controls water and energy exchange from the land surface to the atmosphere and links the water and energy balance models (Figure 1.2 shows the water and energy balance models). The water balance model is expressed as: dW =P−ET−R (1.1) dt.
where dWdt is the change in water content in a given soil layer, P is precipitation, ET is evap-otranspiration, and R is surface runoff. Water is exchanged from the surface to the atmosphere via ET, and rainfall is partitioned into infiltration, and runoff in the water balance model. Both ET and runoff, as well as infiltration, are affected by the SM content. The energy balance model is expressed as: dQ = Rnet − H − λ ∗ LE − G (1.2).
where dQdt is energy change in a given soil layer, H is sensible heat flux, λ ∗ LE is ET, G is surface heat flux, and Rnet is net radiation. The land surface available energy is partitioned into sensible heat flux and latent heat flux. This partitioning is dependent on the SM in a transitional zone (Seneviratne et al., 2010). It can be seen from equations 1.1 and 1.2 that both the energy and water balance model is coupled through ET.
ET is the result of a combination of evaporation and transpiration. Evaporation occurs when water evaporates from soil surfaces, while transpiration occurs when water evaporates from veg-etation leaves. ET is an essential variable that controls the energy and mass exchange between the surface and the atmosphere. ET plays a vital role in flood (Bouilloud et al., 2010), rainfall forecast (Findell et al., 2011), drought forecast (Gao et al., 2011), and agriculture (Allen et al., 2005). As a result, knowledge of daily measurements of ET is essential.
A conceptual/theoretical framework is used to define the ET system as a function of SM (shown in Figure 1.3). SM limited condition and energy limited condition are the two ET systems char-acterized by the evaporative fraction (EF, which is the ratio of ET to the energy available at the surface) (Koster et al., 2004; Seneviratne et al., 2010). In SM limited conditions, SM val-ues are below the critical SM value, and SM mainly controls ET. In energy-limited conditions, SM is above critical SM value, and ET is independent of SM and primarily influenced by the atmospheric demand. The critical SM (SMcritical) is defined as the SM between the SM at field capacity (SMfc, above which water is not held against gravitational drainage) and SM at wilting point (SMwilting above which the water is retained too firmly by the soil matrix that it is not accessible to plants). The conceptual framework describes three SM modes based on the im-pact of SM on ET variability: wet (SM>SMcritical), dry (SM<SMwilting), and transitional zone (SMwilting < SM > SMcritical).

Synergy between active and passive microwave data :

It is found that the L-band radiometer is very efficient in providing SM information more accurately as compared to other satellite information. Based on this concept, the low resolution L-band derived SM is disaggregated at high resolution using fine scale remotely sensed ancillary data. Especially the SMAP mission combines the L-band radiometer at low resolution with the Sentinel-1 radar to downscale SM at 3 km and 1 km. But the main limitation is that it depends on the quasi-simultaneous overpass time of the Sentinel-1 and SMAP data.

Synergy between optical/thermal and passive microwave data :

Merlin et al. (2013) developed the DISaggregation based on the Physical and Theoretical scale CHange (DISPATCH) algorithm that uses optical/thermal data as a SM proxy to down-scale low resolution microwave SM data. The DISPATCH algorithm uses the evaporation-based method. DISPATCH is a physical and theoretical approach to disaggregate coarser microwave low resolution to provide high resolution SM. Peng et al. (2015) use the VTCI as the thermal-based SM proxy to downscale low resolution SM. VTCI is calculated from triangular/trapezoidal feature space from optical/thermal data at high resolution. Fang et al. (2013) use the thermal inertia relationship between daily temperature change and daily average SM by using SM at low resolution and optical/thermal data at high resolution. Song et al. (2013) downscale microwave brightness temperature using high-resolution optical/thermal data. Then, high-resolution bright-ness temperature is used to retrieve SM using a single-chain algorithm (Jackson, 1993).

Research objectives and outline of the thesis

For SM monitoring, in situ measurements and land surface modeling are useful as a localized reference for validation purposes and as a physical tool to extrapolate results in both space and time, respectively. However, both have substantial limitations related to the spatial represen-tativeness of SM estimates. In this context, remote sensing techniques have a strong potential to provide SM estimates at various spatial scales, which are required in many applications, in-cluding meteorology and climatology, hydrology, and agriculture (e.g., irrigation scheduling, for instance). Spaceborne sensors based on passive microwave, active microwave, and optical/ ther-mal data can provide SM information at different spatial and temporal scales.
The remote sensing community generally acknowledges passive microwave at L-band as one of the most accurate techniques. However, on the order of several tens of kilometers, its spa-tial resolution is not adapted to most fine-scale hydrological and agricultural uses. Therefore, other non-optimal but complementary methods are investigated based on radar and/or opti-cal/thermal data available at higher spatial resolution. Still, no approach combines the available multi-sensor (passive/active microwave/optical/thermal) data to exploit each technique’s advan-tages efficiently. This thesis aims to develop an algorithm that combines multi-sensor/multi-resolution/multi-wavelength data to provide SM data with improved robustness and accuracy at high spatio-temporal resolution.
Based on this idea, the research proposed herein develops and evaluates a new algorithm and methodology for SM monitoring. In particular, a synergy is investigated between the SMAP/SMOS passive microwave-derived SM data disaggregated using optical/thermal data (with DISPATCH downscaling algorithm) and the SM retrieved from radar data (with an active microwave radia-tive transfer model). To do this, three successive steps are identified to disaggregated the 40 km resolution SMOS/SMAP data at the 1 km resolution (step 1), to disaggregate the SMOS/SMAP data at the 100 m resolution (step 2), and to build a synergy at the 100 m resolution with active microwave data (step 3). As a final step, the disaggregated SM data are assimilated into a land surface model to improve the accuracy and the frequency of SM estimates. The main objectives of the research are illustrated in Figure 1.7.

Tensift basin, central Morocco

The Tensift basin is located amid Morocco’s western region, near Marrakech. It covers 20,450 km², extending between latitudes 32°10 ’and 30°50’ North and longitudes 9°25 ’and 7°12 West. The basin is bordered on the north by mountainous terrain, south by the high Atlas mountains, west by the Atlantic Ocean, and east by the Tensift drainage basin. The basin includes the Haouz plain and the high Atlas mountains. The Haouz plain was chosen as the study area. The climate on the Haouz plain is semi-arid, with cold winters and hot summers. The average annual precipitation is 250 mm, with an evaporative demand of 1600 mm.
Agriculture in this region is the principal source of economic growth and food security. In sum-mer, due to hot climatic conditions, there is a tendency for irregular and intense rainfall. 87% of the area is rainfed, and the agricultural productivity depends on the rain. The demand for water irrigation will rise as the temperature rises in summer, putting pressure on the limited water resources. The dry period diminishes the impact of agriculture by 50 to 70% in rainfed areas. Water scarcity for irrigation reduces productivity and SM. Evapotranspiration rises due to warmer temperatures, resulting in lower agricultural yields and productivity.
The Tensift basin receives water from the high atlas range, basin transfer, and groundwater. The high atlas range receives precipitation either in the form of snow or rain, and then the main river coming from the mountains drains the water into the basin through the Tensift river and its tributaries. The water supply from the basin uses different infrastructures and is categorized into the irrigation technique: Traditional irrigation technique where water is transferred through ”seguis” consisting of small canals that divert the river’s main flow into irrigated areas. Modern irrigation technique where water is transferred from the dam through the canal de Rocade and Lalla Takerkourst dam. Private irrigation technique where water is used from groundwater and puts higher pressure on the aquifer.
The R3 irrigated zone, Sidi Rahal site, and Chichaoua site are selected for the study over the Haouz plain. The R3 irrigated zone, Chichoua and Sidi Rahal study sites are 100 kilometers from Marrakesh in Morocco’s Haouz plain. The soil texture is clayey for R3 irrigated zone, sandy for Sidhi Rahal and loamy clayey for the Chichaoua site. Wheat is the most widely grown crop for all the three sites.
In R3 irrigation district, crop fields have a typical size of 2-4 ha. Flood irrigation is used to culti-vate the crop. Wheat is typically planted in November or December, with irrigation beginning in February and ending in April. A total of six irrigations are generally employed during the wheat crop’s growth. Harvest is completed by May or June. Theta probes are used to manually collect SM at a depth of 0-5 cm. Then, the collected SM is calibrated using gravimetric measurements based on the soil samples collected for each sampling date. For each crop field, theta probes were utilized to measure the SM for ten discrete SM measurements. The in situ SM is collected at 5 cm for the five sampling days of year 14, 30, 38, 62, 78 for 2016.
In Chichaoua and Sidi Rahal a TDR sensor are used to collect the SM for every 30 min auto-matically. The gravimetric SM is used for the calibration of soil dielectric properties from the sensor. The in situ SM was collected at 5 cm from 2017 to 2018.

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Ebro basin, northeastern Spain

The Ebro basin has a triangular structure with a coverage of 85,530 km². It is one of Spain’s greatest hydrographic basins, accounting for 17.3% of the country’s total surface area. The basin is surrounded on the west by the Pyrenees and Cantabrian mountains, north and east by the Catalan coastal range, and south by the Iberian massif. The middle of the Ebro basin area is flat and lies between the Pyrenees and the Iberian peninsula, which is called the Ebro depression. The average annual rainfall ranges from 3000 mm in the Pyrenees to less than 100 mm in the plain. Rainfall in the basin varies from year to year. The area in the Ebro depression has a Mediterranean climate (hot in summer and cold in winter) and the annual evapotranspiration in the Pyrenees is 600 mm and 800 mm in the central depression. The population density of this basin is 2.8 million. The population is heterogeneously distributed, and half of the population is situated in the central basin. The middle and lower parts of the Ebro basin are irrigated, and land cover is agricultural-type crops such as vineyards, maize, and orchards.
According to The Confederaci´on Hidrogr´afica del Ebro (CHE, 2005), the annual water yield in the river basin is 18 km3, of which 12 km3 of water runoff to the sea. As a result, only 6 km3 of water is consumed. The majority of water taken from the Ebro basin is used for irrigation, with the remainder going to urban and other industrial uses and hydroelectric, thermal, and nuclear-generating facilities. Only 60% of the entire average mean runoff is stored in dams intended for storage. Low precipitation and an increase in yearly temperature have resulted in a 15% decrease in precipitation and a 4% increase in temperature in the Pyrenees (L´opez-Moreno et al., 2008). The different study areas used from the Ebro-basin are explained below in detail. The sites used in this study are Urgell, Agramunt, Fordada, in the north west of the basin and a dry land area in the south east. Urgell has a Mediterranean climate, which is hot in the summer and cold in the winter. The annual precipitation averages 376 mm. An ancient flood irrigation technology and a new sprin-kler or drip irrigation methods are used to irrigate the study area. The Segarra–Garrigues (SG) system, which aims to transform dry grounds into agricultural land, uses spray or drip irriga-tion. The area that is not included in the SG irrigation system plan stays dry. As a result, the irrigated region is bordered by unirrigated dryland. Irrigation is mostly done in the summer. The location was chosen because of its unique characteristics, e.g. fact that the area remains the same as the surrounding area during the winter, but the agricultural area becomes wet during the summer, while the surrounding area remains dry. The land is covered by agricultural crops, mainly corn, wheat, and alfalfa. The trees grown primarily in this region are olive trees, fruit trees, and vineyards.
Fordada and Agramunt sites are a part of SG system located at 41.866°N, 1.015°E and 41.782°N, 1.089°E. Fordada experimental field covers an area of 20 ha and Agramunt experimental field covers an area of 20.5 ha. The soil texture of Fordada is 41.5% sand, 42.3% silt, and 16.2% clay, and the soil texture of the Agramunt is 52.1% sand, 35.3% silt, and 12.6% clay. The area is irrigated with sprinkler irrigation, and the Agramunt area is irrigated with subsurface drippers. SM data collected for Agramunt and Fordada is 2015 and 2017, respectively.
The dryland experimental crop selected in the southern part of the basin is located in Tarragona province, Catalunya, east of Spain. The area has a semi-arid Mediterranean climate with annual average precipitation of 385 mm. The site has mainly rainfed crops, and the soil texture is clayey. TDR sensors are used to measure the SM at a depth of 5 cm. The network consists of 7 stations, and the SM is collected for six months from June to November 2019.

Duro basin, northwestern Spain

The basin is located north of the Iberian peninsula, with a total size of 97290 km2, 80% (78954 km2) within the Spanish territory. The basin is depressed in the middle, and a mountainous region with an altitude of 2500 m surrounds most of the basin. The annual average rainfall varies from mountain to central depression. The yearly average rainfall in the mountain is 1000 mm, while the average rainfall is less than 400 mm in the central depression. The Mediterranean climate with a continental feature may be seen in the main central depression area. The land in the mountainous region is covered by forest, shrublands, grasslands, and the cropland area. This basin shows a transitional climate like the Ebro basin, i.e., summer is hot, and winter is cold.
The SM monitoring network named Rhemdus is located near the center of the Duro basin. The Rhemedus network is very dense and consists of 20 stations. Only 13 stations are used in this thesis. The network is located near the center of the Duro basin, west of Spain. The site has a semi-arid Mediterranean climate with annual average precipitation of 385 mm. The land cover of this network is rainfed croplands (78%), forests and pastures (13%), irrigated crops (5%), and vineyards (5%) (S´achez et al., 2012). Soil texture is silty and clayey sand. TDR probes measure the soil dielectric properties and the volumetric SM at a 0-5 cm depth. In situ SM measurement is collected from the International Soil Moisture Network (ISMN) for the time period of 2017.

Garonne basin, southwestern France

Garonne basin is located between the depressions of the Atlantic ocean and Mediterranean sea. The basin covers an area of 56000 km², located at 42°36 ’N and 0°57’ E. The basin is surrounded in the southeast by the Mediterranean Sea, on the west and south by the Pyrenees Mountains, and on the northeast by the Massif Central. The basin has a relatively flat surface and a low elevation. The basin is subjected to two climatic influences: one from the Atlantic Ocean, which is characterized by the western wind and heavy precipitation, and the other from the Mediter-ranean Sea, which is characterized by the hot and dry southeastern winds.
The climate varies with temperatures below freezing point in the mountains and rarely below freezing in the plains. In the mountainous terrain, forest and alpine grasslands cover the soil, while agriculture is practiced in the plain. The impact of agricultural practices on streamflow has been studied from a hydrological standpoint since 1983 in this basin. Because of the diverse environment, the area is vital from a hydrological standpoint. The average annual precipitation is 664 mm, with 1020 mm of potential evapotranspiration. The majority of the water is used in agriculture. The water quality is acceptable near the Pyrenees, but as it reaches the plain, it deteriorates due to extensive agricultural usage and other anthropogenic influences. Wheat, barley, corn, and fodder cultures are the crop types.
Aurad´e (43°3259N, 1°0622E) and Lamasqu`ere (43°2947N, 1°1416E) sites are used in this thesis. Both study areas are located near Toulouse (south-west of France), separated by a distance of 12 km. The climate is temperate, with an average annual precipitation of 700 mm. Aurad´e soil texture is clay loam (20.6 percent sand, 47.1 percent loam, and 32.3 percent clay), while Lamasqu`ere soil texture is clay (12.0% sand; 33.7% loam, and 54.3% clay). Both areas are covered by agricultural land. Winter wheat and sunflower are grown in the Aurad´e area, while winter wheat and wheat are grown in Lamasqu`ere. Crop rotation techniques are used at both sites to cultivate crops. Both study areas are irrigated mainly covered by agricultural fields. For both, the study areas, the in situ measurements are collected over 2017 and 2018.
The SM is measured every 30 minutes with CS616 TDR probes (Campbell Scientific Inc., Logan, UT, USA) at depths of 0.05 m, 0.10 m, and 0.30 m. The volumetric SM is computed from the measured soil’s dielectric permittivity and a site-specific calibration equation. Tallec et al. (2013) and B´eziat et al. (2009) provide detailed information about the study area (2013).

Table of contents :

1 Introduction 
1.1 Context
1.1.1 Global changes
1.1.2 Dynamics of water resources in the Mediterranean region
1.1.3 Spatio-temporal variability of soil moisture
1.2 Soil moisture monitoring
1.2.1 In situ soil moisture
1.2.2 Dynamic models
1.2.3 Satellite information
1.2.4 Downscaling methods
1.3 Research objectives and outline of the thesis
1 Introduction (fran¸cais) 
1.1 Contexte
1.1.1 Changements globaux
1.1.2 Dynamique des ressources en eau dans la r´egion m´editerran´eenne
1.1.3 Variabilit´e spatio-temporelle de l’humidit´e du sol
1.2 Suivi de l’humidit´e du sol
1.2.1 Mesure in situ de l’humidit´e du sol
1.2.2 Mod`eles dynamiques
1.2.3 Informations satellitaires
1.2.4 M´ethodes de r´eduction d’´echelle ou d´esagr´egation de donn´ees
1.3 Objectifs de recherche et plan de la th`ese
2 Study area 
2.1 Introduction
2.1.1 Tensift basin, central Morocco
2.1.2 Ebro basin, northeastern Spain
2.1.3 Duro basin, northwestern Spain
2.1.4 Garonne basin, southwestern France
2.2 Conclusion
3 Disaggregation of passive microwave soil moisture using optical/thermal data 
3.1 Introduction
3.2 Downscaling algorithm
3.3 C4DIS processor
3.4 Adaptation of C4DIS to other sensors
3.5 Conclusion
4 Extending the applicability of the disaggregation algorithm 
4.1 Introduction
4.2 A new SM index for DISPATCH algorithm mainly in vegetated areas
4.3 New estimates of temperature endmembers in DISPATCH algorithm
4.4 A new vegetation index for DISPATCH algorithm mainly in vegetated areas .
4.5 Conclusion
4.6 Article : Extending the Spatio-Temporal Applicability of DISPATCH Soil Moisture Downscaling Algorithm: A Study Case Using SMAP, MODIS and Sentinel-3 Data .
5 How to further enhance the downscaling resolution 
5.1 Introduction
5.2 Implementing a new DISPATCH algorithm at 100 m resolution
5.3 Practical algorithm to bridge the gap between SMAP and Landsat resolution .
5.4 Selecting an optimal intermediate spatial resolution and evaluating DISPATCH at 100 m resolution
5.5 Conclusion
5.6 Article : Stepwise disaggregation of SMAP soil moisture at 100 m resolution using
Landsat-7/8 data and a varying intermediate resolution
6 Building a synergy of disaggregated passive microwave soil moisture approach with radarbased  retrieval approach
6.1 Introduction
6.2 A new radar-DISPATCH coupling method
6.3 Validation
6.4 Conclusion
6.5 Article : A calibration/disaggregation coupling scheme for retrieving soil moisture at high spatio-temporal resolution: synergy between SMAP passive microwave, MODIS/Landsat optical/thermal and Sentinel-1 radar data
7 High-resolution soil moisture data at the daily scale using dynamic surface model 
7.1 Introduction
7.2 Method
7.2.1 Force restore model
7.2.2 Assimilation techniques
7.3 Results
7.4 Conclusion
8 General conclusions and perspectives 
8.1 Summary of the main findings
8.2 Future researches on soil moisture monitoring and their applications
8 Conclusions g´en´erales et perspectives (fran¸cais)
8.1 R´esum´e des principales conclusions
8.2 Recherches futures sur le suivi de l’humidit´e des sols et ses applications


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