Time management

1. Mapping and analysis of evapotranspiration, biomass production
Group Members:
Amir Owlia
Mahmoud Hatim
Abdelmaged Hady
Vanessa Al Assad
M053T07: Remote Sensing for Agricultural Monitoring
Assignment 2

2. Introduction
• The Urmia Lake Basin, located in north-western Iran, has been the
subject of intense research due to its ecological and economic
importance in the region. Urmia Lake, the largest lake in the Middle East,
is a terminal lake with no outlet and relies heavily on inflows from rivers
and groundwater sources for its water supply
• over the last few decades, the lake’s water level has dropped significantly,
owing mostly to a combination of climate change, upstream water use,
and land-use changes in the basin. This has led to a number of
environmental and social concerns, such as increasing salinity, the loss of
wetlands and ecosystems, and decreased agricultural output.

3. Introduction
• Therefore, it is crucial to spatially and temporally assess the water
demand within the Urmia lake specifically around the Miandoab
irrigation scheme (MIS) as it contains agricultural areas in order to have
sustainable water use (Parsinejad et al., 2022).

4. The Surface Energy Balance Algorithm for Land
(SEBAL
The Surface Energy Balance Algorithm for Land (SEBAL) is a remote sensing
technique that uses satellite pictures to estimate surface energy flows such as real
evapotranspiration (ETa) and net radiation (Rn). To calculate these fluxes, the
program takes several essential inputs into account, including surface
temperature, vegetation index, and atmospheric conditions.

5. PySEBAL
 The pySEBAL model will be used in this work to estimate ETa, biomass, and
NDVI using satellite and meteorological data.
 The data will automatically be pre-processed by this model, which includes
radiometric calibration, atmospheric correction, and cloud/water pixel masking.
 Output maps are post-processed using reference images and statistical-based
techniques to fill in any data gaps left by cloud cover or noise using locally
weighted regression (LOESS method) in GRASS GIS v7.8.7.
 Finally, data for comparing seasonal and annual ETa, biomass, and NDVI maps
are extracted using QGIS software.

6. Methodology
Input data and software:
 The Surface Energy Balance Algorithm for Land (SEBAL) and GRASS GIS
7.8.7, were utilized.
 The first set of procedures involved two main parts, Initially, to obtain the
biomass production, evapotranspiration and vegetation maps of MIS via using
the SEBAL Model.
 Then perform spatial and temporal gap filling and finally aggregate these data
through using GRASS GIS 7.8.7 to obtain the MIS Maps of Annual Eta and
Biomass, as well as the Statistics of Seasonal/Annual ETa and Biomass per
crop/land use type over MIS.

7. The satellite images (19) as shown in Table 1 of Landsat 8 OLI/TIRS C2 L1
of the MIS study area which is located in path 168 and row 034 were
obtained from USSG between October 1, 2017, and September 30, 2018.
All images have clouds covering less than 50 percent. However, some images
with high cloud cover (for example 2017/11/10) were considered because
almost the entire irrigation scheme’s area was not covered by clouds.
Satellite Data

8. Landsat Image Collection and Dates
Names Dates of images
Amir Owlia 2018/09/26, 2018/09/10, 2018/08/25, 2018/08/09, 2018/07/24
Abdelmaged Hady 2018/03/18, 2018/02/14, 2018/01/29, 2018/01/13, 2017/12/28
Mahmoud Ahmed 2017/12/12, 2017/11/10, 2017/10/25, 2017/10/09
Vanessa Alassad 2018/07/08, 2018/06/22, 2018/06/06, 2018/05/21, 2018/04/19

9. SEBAL Model inputs
The Soil/hydraulic properties derived from HiHydroSoil, the SRTM 1 Arc-Second Global used for DEM and for the
meteorological data such as (air temperature, relative humidity, shortwave radiation, wind speed, and air pressure) data
were collected, the source of these data is the Global Land Data Assimilation System (GLDAS).

10. – SEBAL data processing and the output:
• Upon collecting the primary data, the inputs were inputted into a SEBAL
EXCEL sheet file where both satellite and corresponding meteorological
data for each satellite image date were properly utilized.
• To process this Excel sheet file as input data in SEBAL, the OSGeo4W
Shell was employed through a series of commands. The resulting output
data of SEBAL included biomass production, evapotranspiration, and
vegetation.
• This output data subsequently served as input for further temporal and
spatial gap-filling.

11. – Filling Temporal and Spatial Gaps
• Given Landsat 8’s temporal resolution of 16 days, monthly statistical data
were derived by taking the mean of each monthly dataset, weighted by
the corresponding number of days in each month, from October 1,
2017, to September 30, 2018, with each month’s dataset calibrated. To
address any gaps in the dataset, temporal gap-filling was used before
spatial gap-filling in this study.

12. Univariate Statistics and Data Aggregation
After filling any missing temporal and spatial data gaps, the
monthly dataset was aggregated using Grass 7.8.7 in QGIS to
derive seasonal and annual data.