Improve the spatio-temporal resolution of MODIS LST data: radar-based SMP

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Sites and in situ data description

Study areas

The Tensift Haouz region is situated in the Midwest Morocco-North Africa (Fig. II.1), covers 4.4% of the Moroccan territory (24 000 km2). This area is known by a very contrasting relief with a very varying altitudes. The Tensift watershed consists of a vast semi-arid plain receiving about 250 mm/year of rainfall, while the reference evapotranspiration (ET0) is about 1600 mm/year, according to the FAO-56 model (Allen et al., 1998), which leads to irrigation and increasing concerns related to optimizing the management of water resources.
The plain (Fig. II.2) is alimented by water from the south, a mountains area (Haut Atlas) which contains the highest reliefs of the Kingdom (4167 m, Jbel Toubkal). The massive amount of water coming from south is stored and formed “chateau d’eau” composed of several mountainous sub-basins. This water feeds permanently watercourses which called L’Oued by the North Africans “rivers” flowing to the plain. The North side is formed of low altitude mountains called Jbilet where the highest mountains do not exceed 947 m. This north area, characterized by rivers that are fed with water just in case of heavy rain, otherwise they remains dry most of the time. The plain is crossed from South to North by several Oueds and join the main collector river “Oued Tensift” which flows from East to West before to reach the Atlantic Ocean. Agriculture is the biggest consumer of water in this region, where 85 % of the mobilized water resources is consumed by the agricultural fields dominated in the plain (Duchemin et al., 2006; Er-Raki et al., 2007; Hadria et al., 2006b; M. H. Kharrou et al., 2013).
Since the Tensift Al Haouz is characterized by a semi-arid climate and under the effect of climate change, the water resources are frequently used which put a strong tension on water use. This leads to an overexploitation of water resources via irrigation. A complex network called by “seguias” formed the main carrying and distribution system for irrigation water. The reached water to the agricultural area coming from the mountains, Sidi Driss and Moulay Youssef dams is controlled and managed by the Tensift basin agency (ABHT). Even the effort done by the ABHT agency, the farmer used the ground water in a non-controlled way and loss water by evaporation using the traditional flooding systems which is the dominant irrigation system in this area. Therefore, a rational management of irrigation water is important.
In this context the International Joint Laboratory (LMI-TREMA, lmi-trema.ma) installed in Marrakech (center of Morocco), aims to improve the management of irrigation water by developing tools that can help to use water in a rational way. The LMI-TREMA’s selected study areas where the experiments have been conducted since 2002, R3 perimeter and Sidi Rahal study rainfed sites have been used to test our approaches. These selected sites are considered as a typical study area and they have been widely used until nowadays (Amazirh et al., 2017; Chehbouni et al., 2008; Duchemin et al., 2006; Er-raki et al., 2007; Hadria et al., 2006b; Jarlan et al., 2015; Khabba et al., 2013; Kharrou et al., 2013) due to the rich and a very large data base provided.
The first area is an irrigated agricultural zone (called R3) known by its heterogeneity and occupied by different culture types (alfalfa, wheat, olive, orange and horticulture), where wheat crops is the dominating culture (50 %) ( Fig. II.3). Flood irrigation is the main irrigation mode used in this area. Four experimental fields were selected over the R3 perimeter during 2015/2016 agricultural season: two sites are slected from 22 monitored parcelles which maintained as a bare soil throughout the season (3 ha of size for each one) and two other sites permanently monitored wheat fields over this season (named drip and flood sites) (Fig. II.3).
The second area is a rainfed agricultural area (called Sidi Rahal) mainly dominated by trees (olive, about 80%) while the remaining surface is comprised of bare soil, small forest and impervious surfaces (e.g., buildings and roads). One experimental field is selected (named bare soil site): a 1 ha rainfed wheat field, this field had remained under bare soil conditions during the 2015–2016 agricultural season.
Based on soil analysis (Er-Raki et al., 2007), soil texture is clayey and sandy in the majority of fields within the R3 and Sidi Rahal areas, respectively.

Meteorological data

Over the studied sites two meteorological station have been installed over an alfalfa cover near of the monitored plots for R3 perimeter and within the monitored site for the Sidi Rahal area during the 2015/2016 agricultural season as shown in Fig. II.3.
Figure II.3: Location of weather and flux stations within the study areas during the 2015/2016 agricultural season in R3 (b) and Sidi Rahal (a) sites, (c): The land use over R3 area. (d): Weather station installed over R3 site. The images are derived from Landsat data, the 07 February and the 24 July for Sidi Rahal and R3 sites, respectively.
Those stations are equipped with instruments for monitoring solar radiation, speed and direction of wind, air temperature and humidity, and rain. These meteorological forcing data was continuously monitored at the 30 min step, are stored on a central data logger, before being collected and returned to the laboratory for processing and analysis. The different parameters measured as well as the different sensors used for the measurement of meteorological variables are:
Wind speed ua measured by CSAT3D sonic Anemometer Incident solar radiation Rg measured by pyranometer Air temperature Tair and relative humidity rha measured by a HMP155 probe Precipitation measured by a rain gauge All this variables are measured at a reference height of 2 m.

In situ soil moisture data

For both R3 bare parcels (P15 and P16), the near-surface (0–5 cm) soil moisture was measured within ±2 h of the L7/8 and S1 satellites overpasses using a frequency domain sensor (Theta probe) at 5 locations (10 m from the plots extrems) on both sides of each field (Fig. II.4). For each sampling date and parcels, an average of the 10 measurements was computed to reduce uncertainties in field-scale SM estimates. Soil samples over a 0 to 5 cm depth were also taken over both sites in order to calibrate Theta probe measurements using the gravimetric technique.
For Sidi Rahal rainfed site the surface soil moisture is continuously measured using time domain reflectometer probes (CS616) installed at different depths (5, 10, 20, 30, 50, 70 cm). For this study the measured 5 cm surface soil moisture have been used as a validation data set. S1 and L7/8 never overpass the study areas on the same day. However, Table II.1 lists the dates with quasi-concurrent (one day offset) L7/8 and S1 overpasses. In the R3 area in situ SM sampling were undertaken on those particular dates, either on S1 and L7/L8 overpass date, or on both successive dates. The SM sampling dates are also reported in Table II.1.
During the investigated agricultural season 2016, the R3’s two wheat sites and the Sidi Rahal’s bare soil parcel were equipped with different sensors to collect water and heat fluxes exchanged between vegetation, soil and atmosphere. The eddy covariance systems installed on the studied sites (Fig. II.6) consists principally of a Krypton hygrometer which measures the density of water vapor in the air. Both Kh20 and Kh21 used over the study area are a UV absorption hygrometer which are suitable for applications using turbulent correlation (Eddy Correlation). The installed hygrometer provides continuous measurements of vertical sensible heat (H) and latent heat (LE) fluxes. Note that, the sensible and latent heat fluxes are not provided directly by the instrument, but they are extracted from the measured fluctuation of the vapor pressure around the mean value. If necessary, absolute readings (absolute air water vapor) can be obtained by making an independent measurement of the absolute atmospheric humidity with a humidity sensor as an example. A humidity probe (Vaissala) fixed in the tower intended for measuring the temperature and the relative humidity of the air, aims to correct the water vapor density measured by krypton. The raw data of Eddy covariance at 20 Hz are processed at the Laboratory using the EC-pack software developed by the meteorological and air quality group, Wageningen University (available for the download (http://www.met.wau.nl/)). Additional instruments are installed in the tower providing extra measurements such as the net radiation (Rn) which was measured by the net radiometer Kipp and Zonen CNR4. A Sonic 3D anemometer designed to measure the wind speed over the 3 orthogonal axes. From these measurements can be deduced the wind speed and the sonic temperature in the three orthogonal components. The provided measurements by this sensor are a key parameter to estimate the turbulent fluxes that depends on the measurement of air turbulence at a high level of accuracy.
The soil heat flux (G) is the missed component to loop the energy balance (regarding the energy storage in the canopy) which was controlled at a 5 cm depth using soil heat flux plates HPF01.
Before using the data of latent heat flux (Evapotranspiration) measured by the eddy covariance system, it is important to check the reliability and the quality of these measurements. This is undertaken through the analysis of the energy balance closure. By ignoring the term of canopy heat storage and the radiative energy used by vegetation photosynthesis (Testi et al., 2004a), the energy balance closure is defined as:
To check the budget closure during the study period, we compared the available energy at the surface (Rn – G) with the sum of turbulent fluxes measured by the Eddy covaraince (EC) sattion (HEC + LEEC) at half-hourly scale. The quality of the correlation between (Rn – G) and (HEC + LEEC) was evaluated by the regression line and the determination coefficient R2. Fig.
Results show that the closure of the energy balance is relatively well verified by comparison with other studies (Ezzahar et al., 2009; Testi et al., 2004b). The regression lines are close to the 1:1 line and R2 values are generally close to 1 (0.91 for both flood and bare soil sites and 0.88 for drip irrigated field). However, the slope of the regression forced through the origin was about 1.3 for both irrigated sites and 1.13 for the bare soil site (Sidi Rahal), indicating some underestimation of turbulent fluxes (HEC + LEEC) not exceed 30% (slope of 1.3) of the available energy (Rn – G). This due to the attenuation of turbulence at low or high frequency signals (Ezzahar et al., 2009). Also, the difference between the sensors source area has a very important impact on the energy balance closure. In fact, the surface area of the sensors measuring the available energy (net radiation and soil heat flux) is very small compared to that of EC system, which can quickly change depending on wind speed and direction and surface conditions. Moreover, the energy absorbed by the plant has not been considered in the energy balance. In this context, Scott et al. (2003) evaluated the storage in the biomass to about 5-10 % of the available energy, which could partially explain the overestimation of available energy at the surface.

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Preprocessing satellite data

In this part, a description of the different satellite data used in this work has been made. Firstly, the characteristics of the imaging satellites used are detailed, where are distinct by types (optical, thermal and microwave sensors). Then, the preprocessing steps of satellite data are detailed.

Table of contents :

Chapter I Bibliographic synthesis
I.1 Introduction
I.2 Soil moisture
I.2.1 In situ measurements
I.2.2 Remotely sensed approaches
I.2.2.1 Soil moisture indices
I.2.2.1.1 Shortwave-based index
I.2.2.1.2 Thermal-based Index
I.2.2.1.3 Thermal/shortwave-based index
I.2.2.1.4 Microwave-based index
I.2.2.2 Soil moisture retrieval
I.2.2.3 Soil moisture missions
I.3 Land surface temperature
I.3.1 Remote sensing approaches
I.3.2 Spatio-temporal representativeness
I.4 Evapotranspiration
I.4.1 Direct measurements of ET
I.4.2 Factors conditioning ET
I.4.3 Remote sensing-based modelling approaches
I.4.4 Surface evaporative efficiency
I.5 Conclusion
Chapter II Data & study sites description
II.1 Introduction
II.2 Sites and in situ data description
II.2.1 Study areas
II.2.2 Meteorological data
II.2.3 In situ soil moisture data
II.2.4 In situ LST data
II.2.5 Flux data
II.3 Preprocessing satellite data
II.3.1 Satellite data characteristics
II.3.1.1 Landsat
II.3.1.2 Sentinel-1
II.3.1.3 MODIS
II.3.2 Data preprocessing
II.3.2.1 Thermal infrared (TIR) data
II.3.2.1.1 MODIS
II.3.2.1.2 Landsat
II.3.2.2 Radar imagery
II.3.2.2.1 Thermal noise removal
II.3.2.2.2 Radiometric calibration
II.3.2.2.3 Terrain correction
II.3.2.2.4 Filtering speckle effects
II.3.2.3 High resolution reflectances
II.4 Conclusion
Chapter III Models & methods
III.1 Introduction
III.2 Soil moisture indices (SMP)
III.3 Endmembers temperatures estimation
III.3.1 Modelling extreme temperatures: physically based energy balance model
III.3.2 Image based extreme temperature: contextual method
III.4 Integrating the SM indices to improve the water need estimates
III.4.1 Enhance Penman-Monteith method to estimate ET: thermal-based SMP
III.4.2 Calibration of the radar data to retrieve SM: radar/thermal based SMP
III.4.2.1 Benchmark approach: based only on radar data
III.4.2.2 New approach: combined radar/thermal data
III.4.3 Improve the spatio-temporal resolution of MODIS LST data: radar-based SMP
III.4.3.1 MLR technique
III.4.3.2 RTM technique
III.4.3.2.1 Model description
III.4.3.2.2 LST endmembers
III.4.3.2.3 Backscatter endmembers
III.5 Models evaluation
III.6 Conclusion
Chapter IV Results and discussions
IV.1 Introduction
IV.2 Consistency between image- and EBsoil-based extreme soil temperatures
IV.3 Wheat evapotranspiration using thermal/optical-based approach
IV.3.1 Relationship between surface resistance and stress index
IV.3.2 Evapotranspiration estimation at parcel scale
IV.3.3 Evapotranspiration mapping at perimeter scale
IV.3.3.1 Wheat stress index mapping at 100 m resolution
IV.3.3.2 Wheat evapotranspiration mapping at 100 m resolution
IV.3.3.3 Validation over flood and drip irrigation parcels
IV.4 Improving the LST spatio-temporal resolution
IV.4.1 Application to aggregated Landsat-7/8 data: R3 and Sidi Rahal sites
IV.4.2 Application to MODIS data: R3 area
IV.5 Surface soil moisture at parcel scale
IV.5.1 Sensitivity of VV- and VH-polarized data to soil moisture
IV.5.2 Relationship between thermal-derived SMPTs and radar signal
IV.5.3 SM estimation at high spatio-temporal resolution
IV.5.3.1 SM retrieval
IV.5.3.2 Sensitivity to temperature endmembers
IV.5.3.3 SM validation: Improvement of soil evaporation estimation
IV.6 Summary and conclusion
Conclusions and perspectives

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