Investigation into zaria city moisture content estimation

Slides presents techniques of soil moisture estimation

1.         INVESTIGATION AND DEVELOPMENT OF SOIL MOISTURE ESTIMATION MODEL OVER ZARIA FROM MULTI-SOURCE DATASETS   BY    Aliyu Zailani ABUBAKAR (P17EVGM9003)   SECOND RESEARCH SEMINAR PRESENTED TO THE, FACULTY OF ENVIROMENTAL DESIGN, AHMADU BELLO UNIVERSITY, ZARIA   SUPERVISORY COMMITTEE: Prof. L. M. Ojigi (Chairman) Dr. S. S. Garba (Member) Prof. ( Mrs ) A. Abdulkadir ( Member) Dr. S. Azua (Member) December, 2022    

2. PRESENTATION OUTLINE Background to the Study Statement of Research Problem Aim and Objectives Justification and Significance of the Study Scope and De-limitation of the Study Materials and Methods Presentation of Results Preliminary Findings

3. BACKGROUND TO THE STUDY Meteorology Agriculture : Soil moisture ( Seneviratne et al ., 2010 ). Surface soil moisture (SSM) (Pan et al., 2003).

4. Techniques of Soil moisture estimation Gravimetric (Laboratory based): In-situ (Field measurement) : Provide point data, limited coverage, labor intensive, slow and time consuming Remote Sensing Method: (Landsat, SMOS, MODIS, ASCAT, Sentinel …..) Reanalysis Method: (NCEP, GLDAS, ECMWF ,…..) Provide large area with repetitive coverage of an area of interest at different spatial and temporal resolutions.. Relatively lower cost per unit area of coverage BACKGROUND TO THE STUDY-Continued

5. BACKGROUND TO THE STUDY-Continued Zaria ( Yakubu , 2017), ( Aminu and Jaiyeoba 2016), ( Dogara et al ., 2017) that has led to the increase in climate and decrease in soil water availability. Several theoretical-based estimation models such as Dubios et al ., (1995), Integral Equation Model (IEM) Baghdadi et al ., (2015), Water Cloud Model (WCM) (Kumar et al ., 2015), Universal Triangle Method (UTM) (Zhao et al ., 2017 ), Apparent Thermal Inertia (ATI) (Peters et al ., 2011) among others. These models are based upon the relationship between soil moisture and soil properties i.e. either backscatter coefficient or thermal properties of the soil to estimate and model the soil moisture variability. (Optical/Thermal/Microwave portion of EMS).

6. Despite the penetrating capability of using microwave remote sensing data for soil moisture estimation, vegetation cover and surface roughness affect soil moisture estimation in addition to so many approximation and complex derivation processes. empirical-based (data-based) model which relate among measured controlling variables to estimate hydrological characteristics of soil (Ahmad et al ., 2011 ). BACKGROUND TO THE STUDY-Continued

7. Statement of Research Problem Soil moisture is observed as a limiting factor for the sustainability of crop production in Zaria (Baba- kutigi , 2007). Abaje (2013 ) also observed that insufficient soil moisture is one of the two limiting factors after soil fertility affecting agricultural production. Zaria is also witnessing vegetation changes resulting from deforestation, construction, overgrazing, ( Aminu and Jaiyeoba , 2016), increase in average temperature and solar radiation ( Dogara et al., 2017), and high evapotranspiration activities and water stress ( Emeribe et al., 2021).

8. Statement of Research Problem- Continued Spatial distribution of soil moisture is affected by LULC alteration, different periods of the season and soil type ( Donglian and Rachel 2004), topography (Burt and Butcher, 1985) and vegetation (Le Roux et al ., 1995 ). Previous researches could not to come up with long-time records/inventory on soil moisture in their works and more so to investigate the factors responsible for temporal soil moisture changes in Zaria .

9. Statement of Research Problem-Continued This study thus attempts to answer the following research questions : What is the reliability of the retrieved reanalysis dataset in comparison with in-situ field campaign data in the study area? What is the spatio -temporal trend of soil moisture in the study area between 2000 – 2020 What is the relationship between climatic data variables with soil moisture variability in Zaria 2000 – 2020; What is the influence of various landuse / landcover on evapotranspiration activities in the study area? What is the combined influence of climatic data and physiographic factors on soil moisture variability in the study area?

10. Aim and Objectives of the study The study aims to develop a surface soil moisture model using multi-mission datasets through integrating climatic data and physiographic variables to assist in understanding heat, and water fluxes in climate and effective irrigation planning and management . Therefore , the objectives of this study are to: compare the reanalysis-based measurements with the In-situ measured soil moisture in the study area ; determine the temporal variability of soil moisture of the study area using ERA-I produced by (ECMWF) reanalysis dataset from 2000 - 2020; examine the Spatio -temporal relationship between climatic data (precipitation and temperature) and soil moisture variability in Zaria; determine the influence of landuse / landcover dynamics on evapotranspiration activities in the study period. develop an empirical surface soil moisture models based on the combined influence of climatic data and physiographic factors

11. Justification and Significance of the study Appraising the Spatio -temporal variability of soil moisture is not only important in agricultural activities, but also critical for environmental monitoring and forecasting such as water cycle, flood and drought monitoring. The estimation of the quantity of water to be added to a field and timing of the application which depend on the soil moisture status of an area is a major task in irrigation scheduling ( Igbadun and Mudiare , 2001). Inaccurate representation of this variable is reported to result in an incorrect representation of near-surface temperature and humidity (Pryor and Schoof , 2016) It is for these reasons that the soil moisture variable is included in the list of Essential Climate Variables (ECVs) that are deemed important by the Intergovernmental Panel on Climate Change (IPCC, 1996) for global observation and monitoring .

12. Scope and De-limitation of the Study The scope of this research is limited to Zaria and environs for a study period of 2000-2020. The soil moisture to the depth 0-10cm in different parts of the study area is determined. Only the influence of climatic data (Precipitation, LST and evapotranspiration) physiographic parameters and LULC dynamics were evaluated while physiochemical (soil ph and soil salinity) characteristics of the soil were not considered.

13. The study Area It is situated between lat 11° 02’ 00” to 11° 12’ 00”N and long 7° 36’ 00” to 7° 46’ 00” E Average height of 670m and annual rainfall of about 1000 mm ( Sawa and Buhari , 2011 ). It covers an area of 542.9 km 2 and experiences two distinct climatic seasons; the wet and dry seasons. Temperature reach 13 o c and 42 o c in January and April, respectively ( Sawa and Buhari 2011).

14. MATERIALS AND METHODS Product technique Parameters Units Spatial Resolution Temporal coverage/extent Source of data Purpose(s) Data Type Reanalysis Monthly Means SM , and LST m 3 /m 3 and C   (0.125 ×0.125 ) grid   Global January 2000- December 2020 ECMWF ERA-I and ERA5 NetCDF Soil Moisture, Skin Temperature Estimation Secondary                 Reanalysis Monthly Mean Evapotranspiration mm   (0.125 ×0.125 ) grid   Global January 2000- December 2020 GLDAS NetCDF Evapotranspiration Estimation Secondary Remotely Sensed   NDVI,LST SMI Unit less 30m (189/52 ) 2000, 2010 and 2020   USGS Landsat 7 ETM+ and 8 (OLI)   Determination of and SMI Secondary Remotely Sensed Slope/Height Unit less 30m 2000, 2010 and 2020   USGS ASTER Determination of Altitude and Slope Secondary Remotely Sensed Monthly Mean Precipitation mm (0.25 ×0.25 )grid January 2000 – December 2020 TAMSAT NetCDF   Determination of Precipitation Secondary In-situ Monthly Mean Precipitation, Temperature and Evapotranspiration mm N/A January 2000 – December 2020 NIMET Validation of Climatic Data Secondary                 In-situ Soil texture % N/A April 2020 Lab Textural Determination Primary In-situ   Soil Moisture   m 3 /m 3 N/A April 2020   Lab   Instrument recalibration   Primary In-situ   Soil Moisture   m 3 /m 3 4m×4m May-November 2020   Field Data   Soil Moisture Estimation   Primary                 Table 3.1 Datasets, Sources and Purposes

15. Table 3.2: Software, Instruments, and purpose Software Purpose Panoply 4.3.2 Data Extraction Matlab R2018b Data Extraction Microsoft Excel 2013 Data Assembling Surfer 15 Data interpolation QGIS 3.14.0 Image Classification ArcGIS 10.3.1 Map Production SPSS version 26 Theta Probe GPS Data Analysis and Representation Field Data collection Data collection and Navigation

16. Objective one: compare the reanalysis-based measurements with the In-situ measured soil moisture . The choice of this method of sampling was because of its high precision and accuracy ( Razaq and Ajayi , 2000; Azua , 2018). This is to ensure that every part of the study area was reasonably covered. The total sample plot was determined from the sum of sample plots of each stratum Strata Number of sample point per stratum Area (km 2 ) N ZR1 N ZR2 N ZR3 N ZR4 N ZR5 N ZR6 N ZR7 TOTAL 31 12 6 28 12 17 3 109 152.50 61.20 30.10 138.5 61.20 82.30 17.00 542.80     Table 3.3: Number of sample point per strata

17. Due to non availability of soil moisture monitoring station in Zaria, eleven (11) validation sites were chosen after consideration of logical issues, access permission, and above all, for security ( Sohrabinia , 2013 ). The readings were taken in-situ daily by inserting the Theta probe instrument into the soil and recording the soil moisture values Objective one: continued Researcher in one of the validation Plots Theta probe

18. Sample sites Soil Moisture   May June July Aug Sep Oct Nov   ABUZ Measured Estimated   0.1723 0.1885 0.2180 0.2084 0.23899 0.22954 0.2700 0.2812 0.2999 0.2890 0.2610 0.2407 0.1850 0.1755   Yankarfe   Measured Estimated   0.1720 0.1887 0.2250 0.2084 0.2640 0.2294 0.3010 0.2813 0.2700 0.2891 0.2570 0.2408 0.1870 0.1753   DanMagaji   Measured Estimated   0.2100 0.1885 0.1950 0.2088 0.2370 0.2298 0.2950 0.2818 0.3050 0.2898 0.2350 0.2418 0.1830 0.1753   Saye   Measured Estimated   0.2010 0.1893 0.1850 0.2099 0.2200 0.2307 0.3010 0.2832 0.2780 0.2920 0.2330 0.2448 0.2020 0.1754   NUBP   Measured Estimated   0.1790 0.1894 0.1970 0.2101 0.2520 0.2308 0.3520 0.2833 0.3120 0.2922 0.3070 0.2451 0.1950 0.1754   Kofan Kona Measured Estimated   0.1850 0.1891 0.2000 0.2096 0.2170 0.2301 0.2790 0.2825 0.3240 0.2910 0.2350 0.2434 0.1840 0.1753   Nagoyi   Measured Estimated   0.1950 0.1889 0.2700 0.2092 0.263 0.2296 0.2700 0.2819 0.2850 0.2901 0.2650 0.2422 0.1980 0.1753   Kofan Doka   Measured Estimated   0.2020 0.1888 0.2180 0.2090 0.2700 0.2298 0.2650 0.2820 0.3210 0.2901 0.2760 0.2421 0.2010 0.1753   Dogarawa   Measured Estimated   0.1910 0.1879 0.1750 0.2072 0.1990 0.2281 0.3220 0.2796 0.3150 0.2865 0.2650 0.2372 0.2020 0.1752   Basawa   Measured Estimated   0.1722 0.1881 0.2350 0.2076 0.2500 0.2288 0.2600 0.2804 0.3100 0.2876 0.2180 0.2386 0.1950 0.1753   Hayin Dogo   Measured Estimated   0.1920 0.1881   0.2420 0.2076 0.2550 0.2289 0.2710 0.2804 0.3180 0.2876 0.2540 0.2388 0.1920 0.1753   Table 4.0 Monthly Average of Soil moisture (m 3 /m 3 ) at eleven sample locations (May- Nov 2020) Objective one:_continued

19. Table 4.1 Description of validation sample sites and their reliability metrics . Objective one: _ continued Site Easting (m) Northing (m) Height (m) MAE RMSE MAPE NSE R 2 Observation Period Soil Textural Class ABUZ 351224 1233729 681 0.0125 0.0131 0.06 0.9184 0.9184 May - Nov Clay loam Yankarfe 356497 1229391 630 0.0192 0.0203 0.08 0.7796 0.8239 May - Nov Clay loam DanMagaji 356745 1227150 647 0.0115 0.0148 0.05 0.9072 0.9205 May - Nov Clay loam Saye 354318 1222719 665 0.0168 0.0179 0.08 0.7986 0.8164 May - Nov Sandy loam NUBP 355837 1220911 640 0.0307 0.0379 0.11 0.9794 0.8616 May - Nov Clay loam KofanKona 360425 1220956 632 0.0122 0.0131 0.05 0.9100 0.932 May - Nov Clay loam Nagoyi 361535 1222818 622 0.0233 0.0294 0.09 0.5568 0.6818 May - Nov Sandy loam KofanDoka 358575 1224997 635 0.0243 0.0265 0.10 0.5919 0.8165 May - Nov Sandy loam Dogarawa 362471 1232692 619 0.0271 0.0293 0.12 0.7358 0.8048 May - Nov Sandy loam Basawa 355983 1234315 649 0.0211 0.0213 0.09 0.7409 0.7608 May - Nov Sandy loam HayinDogo 354175 1235434 661 0.0194 0.0220 0.08 0.7119 0.8795 May - Nov Clay loam

20. Objective one: _ continued Based on the findings, The Kofan Kona (MAE: 0.0122, RMSE: 0.0131, MAPE: 0.0500), followed by Dan Magaji sample site (MAE: 0.0115, RMSE: 0.1477, MAPE: 0.0500). While Dogarawa sample site presented the least reliable MAE, RMSE, and MAPE coefficients. T he Nagoyi sample site displays less reliable coefficients ( NSE 0.2568, R 2 0.6818 ,) followed by the Kofan Doka sample site (NSE: 0.5919 R 2: 0.8165). In addition to that Kofan Kona, NUBP, and ABUZ sample sites reveal reliable values of 1.842, 1.841, and 1.837 respectively. Figure 4.1 Metrics chart of Insi-tu /Reanalysis at eleven sample sites

21. Figure 4.2 The trend of SM (In-situ vs ERA-I) May-Nov 2020 Figure 4.3 Scatter plot of SM (ERA-I vs Insitu ) May-Nov 2020 This two data sources (ERA and In-situ) provide a matching/similar trend with a strong positive correlation of 0.982. Hence the ECMWF soil moisture data can be used in the study area Objective one: _ continued

22. Objective Two: determine the spatio -temporal variability of soil moisture of the study area The datasets from the ECMWF system are available at http://www.ecmwf.int/ from 2000 to 2020 over the study area. The datasets was processed using Panoply software version 4.3.2 so as to visualize the spatial plot and gridded arrays of the soil moisture numerical values. T he study area was clipped, extracted, and exported as grid text files to Microsoft (MS-excel), and Sorting and formulations were carried out to arrange all values in the required script format in line with the geospatial coordinates of the study area .

23. Matlab (R2018b) 9.5.0 was used to extract the collocating monthly means as end products of (109) sample point which were arranged inform of three-column table (latitude, longitude, and soil moisture) for the period of January to December each year from 2000-2020. This process was used to also retrieve land surface temperature, and precipitation respectively. Objective Two: determine the spatio -temporal variability of soil moisture of the study area

24. Objective Two: determine the spatio -temporal variability of soil moisture of the study area Table 4.7 Monthly Time series of Soil m oisture of Zaria (2000-2020) Fig 4.5 Annual variation of Soil moisture of Zaria (2000-2020) Fig 4.4Monthly variation of Soil moisture of Zaria (2000-2020)

25. Over the (2000 – 2020 ) based on monthly scale September 0.311m 3 /m 3 February 0.1724 m 3 /m 3 . (2000 to 2020), on annual scale 2003: 0.228 m 3 /m 3 2005: 0.194 m 3 /m 3 . overall average is 0.2134 m 3 /m 3 The 2000, 2001, 2002, 2003, 2004, 2006, 2010, 2012, 2013, 2016, and 2019 epochs were observed to be higher than the annual average between 0.0019 m 3 /m 3 and 0.0264 m 3 /m 3 across the study period . Objective Two- continued

26. Conversely, the 2005, 2007, 2011, 2014, 2015, 2017, 2018, and 2020 epochs exhibited a soil moisture deficit between -0.0191 and -0.0018 m 3 /m 3 with exception of 2008 (0.2141 m 3 /m 3 ) and 2009 (0.2130 m 3 /m 3 ) close to the total annual average of 0.2134 m 3 /m 3 respectively . This is in an agreement with findings within same study area as reported by Baba- kutigi (2007 ). Objective Two- continued

27. Wet season (May-October) May 0.194 m 3 /m 3 June 0.2227 m 3 /m 3 October 0.2423 m 3 /m 3 July 0.2602 m 3 /m 3 August 0.3006 m 3 /m 3 September 0.3061 m 3 /m 3 Dry and hot season (March-May) March 0.172 m 3 /m 3 , April 0.1801 m 3 /m 3 May 0.1942 m 3 /m 3 Objective Two -continued Figure 4.5 Monthly Variation for Wet Season Figure 4.6 Monthly Variation for Dry and hot season

28. Dry and hot season (Dec-Feb) December 0.1733 m 3 /m 3 January 0.1729 m 3 /m 3 , February 0.1724 m 3 /m 3 The findings revealed that the study area experience higher amount of soil moisture in wet season compare with the dry season from 2000 to 2020 Objective Two -continued Figure 4.7 Monthly Variation for Wet Season

29. Objective three : examine the Spatio -temporal relationship between climatic data and soil moisture Due to availability of the weather stations located within the study area (IAR, NCAT and FCE), the Precipitation and LST were assessed at these stations. Shapiro- Wilk’s test for Normality was used between the in-situ at IAR , NCAT and FCE and retrieved precipitation and LST datasets in order to assess the agreement between the In situ and retrieved datasets The normality test revealed that they are statistically significant with p values of 0.0978 ( p recipitation) and 0.0771 (LST) all above 0.05 The z- values obtained were neither below -1.96 nor above +1.96 for skewness and kurtosis for the datasets which imply that the datasets were a little skewed and kurtotic .

30. Table4.11 Monthly Mean Time S eries of Precipitation (mm) of Zaria (2000-2020) Objective three : examine the Spatio -temporal relationship between climatic data and soil moisture variability Fig 4.10 Monthly variation of Soil moisture of Zaria (2000-2020) Fig 4.11 Annual variation of Soil moisture of Zaria (2000-2020)

31. Table 4.22 Monthly Mean Time S eries of Land Surface T emperature ( o C ) of Zaria (2000-2020) Objective three _continued Yr /Mo Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec An Ave 2000 23.332 22.181 26.129 30.979 30.894 26.585 23.963 23.456 24.305 23.529 22.502 20.671 24.877 2001 20.18 22.836 27.612 29.386 29.411 25.993 24.257 23.278 24.22 23.94 24.303 23.158 24.811 2002 20.969 24.578 28.692 31.327 32.374 28.098 25.355 23.747 24.603 23.439 23.872 22.882 25.828 2003 22.856 26.298 28.74 31.57 31.662 26.055 24.232 23.589 24.237 25.397 24.606 22.677 25.993 2004 24.226 24.478 27.844 32.027 28.376 26.286 24.344 23.483 24.897 24.692 24.6 22.691 25.662 2005 21.564 28.578 30.672 31.307 29.477 25.91 24.102 23.3 24.818 23.715 22.841 23.238 25.794 2006 24.471 27.703 28.987 30.946 29.669 28.013 26.169 24.438 24.479 25.758 24.142 20.515 26.274 2007 20.335 24.627 27.673 31.734 32.118 29.084 25.323 23.76 24.568 26.49 26.536 23.674 26.327 2008 22.072 22.753 28.422 30.041 30.583 28.829 24.087 23.227 24.709 23.671 23.356 23.215 25.414 2009 23.46 25.367 28.181 31.324 29.367 28.037 25.678 24.252 25.024 25.529 24.298 22.692 26.101 2010 22.784 26.306 28.168 31.897 31.229 27.311 24.384 23.59 23.877 24.787 22.284 20.421 25.587 2011 20.092 25.757 27.943 29.855 30.21 27.694 26.057 24.099 24.489 24.828 23.157 21.347 25.461 2012 21.536 26.252 27.726 32.252 30.173 26.628 24.25 23.135 24.011 25.526 24.561 21.81 25.655 2013 23.322 25.454 30.012 30.521 28.541 27.491 25.185 22.996 24.643 23.643 24.408 23.443 26.805 2014 22.579 24.957 29.784 30.105 28.873 29.238 25.705 23.475 24.642 24.354 24.675 22.532 25.91 2015 20.922 25.925 29.272 30.566 32.802 29.677 25.505 23.816 24.435 25.674 24.128 20.098 26.068 2016 20.815 23.854 30.221 31.224 30.495 27.743 24.982 24.127 24.752 24.503 23.882 22.807 25.784 2017 22.816 23.591 27.536 32.18 29.942 26.303 24.927 24.303 24.813 25.813 25.891 24.801 26.076 2018 21.446 26.879 29.202 31.646 30.087 28.305 25.688 23.636 24.621 26.292 25.074 22.587 26.286 2019 24.372 25.681 30.497 32.273 28.911 26.45 24.788 23.532 25.251 24.661 23.792 22.261 26.039 2020 21.346 25.262 28.716 31.242 29.163 27.1 24.152 22.145 23.521 24.926 24.491 22.851 25.41 Mn Av 22.166 25.206 28.688 31.162 30.207 27.468 24.911 23.59 24.52 24.817 24.162 22.399 Fig 4.13 Annual variation of Land surface temperature ( o C ) of Zaria Fig 4.12 Monthly variation of Land surface temperature ( o C ) of Zaria

32. Objective three : _continued Fig 4.15 Annual Mean Relationship SM vs Precipitation (2000-2020 ) Fig 4.14Monthly Mean Relationship SM vs Precipitation (2000-2020) Fig 4.16Monthly Mean Relationship SM vs LST (2000-2020) Fig 4.17 Annual Mean Relationship SM vs LST (2000-2020

33. Table 4.2 Monthly correlation coefficient (r): Soil moisture vs precipitation N/A means Not applicable/No answer YEAR Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2000 N/A N/A N/A 0.8731 0.8628 0.5933 0.8803 0.9327 0.5686 0.1819 0.5818 N/A 2001 N/A N/A N/A 0.6039 0.5392 0.8521 0.4851 0.6778 0.8463 0.5131 N/A N/A 2002 N/A N/A 0.6072 0.3521 0.8133 0.8594 0.9088 0.8181 0.3027 0.2005 0.0173 N/A 2003 N/A N/A N/A 0.7179 0.7669 0.4799 0.8968 0.7763 0.6136 0.0211 0.3581 N/A 2004 N/A N/A N/A 0.0374 0.4553 0.8196 0.8936 0.5471 0.6271 0.4596 0.3165 N/A 2005 N/A N/A N/A 0.7459 0.4818 0.5603 0.6312 0.6954 0.8244 0.0283 0.4637 N/A 2006 N/A N/A N/A 0.8524 0.7442 0.7858 0.9448 0.7969 0.8631 0.1111 0.0218 N/A 2007 N/A N/A 0.5639 0.6461 0.4711 0.6515 0.5441 0.8515 0.9671 0.2261 0.1822 N/A 2008 N/A N/A N/A 0.6541 0.5131 0.8491 0.7216 0.9193 0.5571 0.1643 N/A N/A 2009 N/A N/A N/A 0.2042 0.6331 0.7363 0.8654 0.8488 0.7977 0.3406 0.2962 N/A 2010 N/A N/A N/A 0.1817 0.7651 0.8947 0.7014 0.8904 0.9249 0.1145 0.2847 N/A 2011 N/A N/A N/A 0.1068 0.4965 0.6899 0.6313 0.8446 0.7141 0.0111 N/A N/A 2012 N/A N/A N/A 0.9053 0.5155 0.6916 0.8154 0.8313 0.8064 0.0837 0.1441 N/A 2013 N/A N/A 0.6033 0.6438 0.8096 0.5043 0.8382 0.5875 0.8436 0.2252 0.0412 N/A 2014 N/A N/A 0.6106 0.3625 0.4918 0.8505 0.3986 0.7709 0.8892 0.2027 0.4365 N/A 2015 N/A N/A N/A 0.3641 0.8798 0.4919 0.7676 0.5353 0.5458 0.0781 0.1871 N/A 2016 N/A N/A 0.3445 0.2665 0.5078 0.7299 0.7826 0.5648 0.7957 0.1879 N/A N/A 2017 N/A N/A N/A 0.4848 0.4913 0.5939 0.4831 0.5885 0.5554 0.0917 N/A N/A 2018 N/A N/A 0.4609 0.7721 0.8139 0.8128 0.4937 0.5567 0.7645 0.2221 0.0424 N/A 2019 N/A N/A N/A 0.1806 0.5243 0.5223 0.6494 0.7252 0.9054 0.1664 0.3557 N/A 2020 N/A N/A 0.3552 0.3935 0.5697 0.9376 0.5671 0.5675 0.1221 0.0592 N/A N/A Objective three: _continued

34. Table 4.3 Monthly correlation coefficient (r): Soil moisture vs LST Objective three : _continued YEAR Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2000 0.8952 0.2735 0.4237 0.5732 0.9617 0.9987 0.9972 0.9881 0.9811 0.9903 0.9899 0.0111 2001 0.9852 0.4725 0.6342 0.9753 0.9457 0.9984 0.9201 0.9983 0.9971 0.9981 0.9309 0.8085 2002 0.3882 0.9701 0.9667 0.9971 0.9792 0.9984 0.9884 0.9991 0.9881 0.9561 0.9981 0.6465 2003 0.3834 0.1349 0.9357 0.8873 0.9911 0.1521 0.9813 0.9071 0.9632 0.9913 0.9891 0.8135 2004 0.5818 0.5225 0.3348 0.9862 0.9766 0.9974 0.9971 0.9987 0.9931 0.9259 0.9663 0.6731 2005 0.1661 0.3811 0.6707 0.9155 0.9834 0.7883 0.8897 0.9871 0.9516 0.9751 0.9884 0.7543 2006 0.5775 0.9034 0.8497 0.5288 0.9933 0.9952 0.9976 0.9956 0.9775 0.9821 0.7611 0.4611 2007 0.2496 0.3824 0.0031 0.9371 0.8748 0.9973 0.9981 0.9969 0.9642 0.9788 0.6176 0.7431 2008 0.8356 0.4821 0.8717 0.5626 0.9981 0.9934 0.9906 0.9896 0.9971 0.9971 0.9622 0.6742 2009 0.8338 0.3904 0.7295 0.9721 0.9989 0.9991 0.9975 0.9957 0.9761 0.9951 0.9804 0.4091 2010 0.0521 0.5303 0.4046 0.8277 0.9986 0.9944 0.9987 0.9971 0.9971 0.9964 0.9946 0.9641 2011 0.4903 0.1631 0.8222 0.9524 0.9896 0.9967 0.9908 0.9874 0.9944 0.9974 0.8874 0.3124 2012 0.5614 0.4508 0.8477 0.7677 0.9915 0.9961 0.9919 0.9978 0.9972 0.9938 0.9958 0.8606 2013 0.9633 0.6984 0.8774 0.8762 0.9911 0.7277 0.9971 0.9939 0.9979 0.9958 0.9535 0.7904 2014 0.2623 0.6294 0.7448 0.9718 0.9897 0.9954 0.9986 0.9985 0.9961 0.9969 0.8716 0.4263 2015 0.4988 0.3568 0.9492 0.9355 0.9711 0.9645 0.9981 0.9893 0.9891 0.9958 0.9769 0.5459 2016 0.5275 0.3576 0.9097 0.9511 0.9942 0.9975 0.9898 0.9962 0.9856 0.9984 0.9912 0.7257 2017 0.3667 0.5831 0.2298 0.8921 0.9971 0.9667 0.9429 0.9273 0.9754 0.9744 0.8875 0.9141 2018 0.7792 0.9609 0.3491 0.7056 0.9816 0.9674 0.9311 0.8741 0.9959 0.9981 0.5024 0.6758 2019 0.6048 0.5031 0.8766 0.7996 0.9965 0.9965 0.9904 0.9903 0.9977 0.9943 0.9596 0.8612 2020 0.4954 0.1778 0.8253 0.9529 0.9902 0.9972 0.9913 0.9879 0.9949 0.9979 0.8881 0.3842                              

35. Table: 4.5 Seasonal Correlation Coefficient (r) of SM vs Precipitation and SM vs LST Objective three : _ continued Seasons SM vs Precipitation SM vs LST Wet 0.8584 -0.7703 Dry 0.0648 -0.0739 Dry and cold ( Hammatan ) N/A -0.1286 Dry and hot 0.5720 -0.3586 Hence, SM VS Precipitation correlation analysis revealed that increase in precipitation could promote soil moisture Hence, SM VS LST correlation analysis showed that an increase in LST could resulted to decrease in soil moisture.

36. Figure: 4.19 Scatter plot of SM vs Precipitation (Dry season) Figure: 4.21 Scatter plot SM vs LST (Dry season) Figure: 4.18 Scatter plot of SM vs Precipitation (Wet season) Figure: 4.20 Scatter plot of SM vs LST (wet season) Objective three : _ continued

37. The findings revealed that the Theta probe measurements and ECMWF data provide a similar trend with a strong correlation of 0.982 in the study area . This shows the estimated data (ECMWF) can be reliably used for moisture monitoring in the study area. The month of September was observed to have the highest surface soil moisture values with an average of 0.311 m 3 /m 3 while February had the least soil moisture value amount with an average of 0.1724 m 3 /m 3 from 2000 to 2020 study period . The 2000, 2001, 2002, 2003, 2004, 2008, 2010, 2012, 2013, 2016, and 2019 epochs were observed to higher soil moisture values than the annual average of 0.2134 m 3 /m 3 across the study period. Conversely , the 2005, 2006, 2007, 2011, 2014, 2015, 2017, 2018, and 2020 epochs exhibited a soil moisture deficit between -0.0191 and -0.0018 m 3 /m 3 . Preliminary Findings

38. Based on the findings, it was revealed that soil moisture is highly positively correlates with precipitation on monthly time scale (2000-2020) h igh (0.700 ≤ r ≤ 0.990) 24.6%, moderate (0.500 ≤ r ≤ 0.600 ) 24.21% weak (0.100 ≤ r ≤ 0.400) 18.25% and Not Applicable (N/A ) 32.93 % The result obtained also indicates that soil moisture is negatively correlate with LST high (0.700 ≤ r ≤ 0.990 ) 76.98%, moderate ( 0.500 ≤ r ≤ 0.600) 13.49% and weak ( 0.01 ≤ r ≤ 0.300 ) 9.50 % Preliminary Findings-continued

39. Thank you for listening