# Drought Monitoring and Prediction in Agriculture: Employing Earth Observation Data, Climate Scenarios and Data Driven Methods; a Case Study: Mango Orchard in Tamale, Ghana

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## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Study Area and Precipitation Data

#### 2.2. Extracting Precipitation Data for Time Interval of 2023–2050 Using CMIP6 Data

#### 2.3. Landsat 8 and 9 Satellite Image Acquisition and Data Preprocessing

^{2}$\times $ sr $\times $ µm)), ${M}_{L}$ is the radiance multiplicative scaling factor, ${Q}_{cal}$ is the DN value of each pixel and ${A}_{L}$ is the radiance additive scaling factor. TIRS data were converted from spectral radiance to brightness temperature [35,36].

#### 2.4. NDVI and TVDI Calculation Using Landsat Satellite Imagery

_{1}and b

_{1}are the intercept and slope of dry edge line (Figure 6), i is the pixel number.

#### 2.5. Ortho Image Creation from UAV Imagery

#### 2.6. Standard Precipitation Index (SPI)

_{3}and SPI

_{6}). It is important to note that the calculated SPI is based on n-month cumulative precipitation data, which means that the precipitation accumulation $\left(x\right)$ in Equation (19) is calculated by adding up the precipitation from the current month, as well as the 2 (SPI

_{3}) and 5 (SPI

_{6}) preceding months.

#### 2.7. Prediction of Agricultural Drought by a Hybrid Wavelet-ANFIS/Fuzzy C-Means (FCM) Clustering Model

_{t}as a function of SPI

_{3,t-i}and SPI

_{6,t-i}is used:

_{TVDI, SPI}between two time series is defined by following Equation (21), i.e., the discrete cross correlation between TVDI and SPI

_{m}(m = 3, m = 6) with N data at the lag time l, which reads as [48]:

#### 2.7.1. Multiresolution Analysis of Input Data Using the Discrete Wavelet Transform

_{j}= 2

^{j}is the scale (i.e., inverse of frequency) (dilatated/compressed), b

_{k}= k 2

^{j}is the shift (translate) parameter, and $\psi \left(t\right)$ is the wavelet, i.e., ${\psi}_{j,k}\left(t\right)$ are the scaled and shifted wavelets. If the wavelet function ${\psi}_{j,k}\left(t\right)$ belongs to a so-called tight frame of wavelet classes, ${\psi}_{j,k}\left(t\right)$ forms a complete and orthonormal basis, so that the signal x(t) can be reconstructed by the classical formula

_{j max}-approximation of the signal at the highest decomposition level. The low-scale, high frequency details D

_{j}of the signal, generated by the high-pass wavelet functions ${\psi}_{j,k}\left(t\right)$ (j = 1,.., j

_{max}). Thus, Equation (24) can be simplified as [23,25,51]:

#### 2.7.2. Fuzzy C-Means (FCM) Clustering

#### 2.7.3. Hybrid Wavelet-ANFIS/FCM Model

## 3. Results and Discussions

#### 3.1. Meteorological and Agricultural Drought Index

#### 3.2. Climate Change Scenarios

#### 3.3. Wavelet ANFIS Model Results

## 4. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- IPCC. Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Pörtner, H.-O., Roberts, D.C., Tignor, M., Poloczanska, E.S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2022; 3056p. [Google Scholar] [CrossRef]
- Zare, M.; Schumann, G.J.-P.; Teferle, F.N.; Mansorian, R. Generating Flood Hazard Maps Based on an Innovative Spatial Interpolation Methodology for Precipitation. Atmosphere
**2021**, 12, 1336. [Google Scholar] [CrossRef] - Pascoe, C.; Lawrence, B.N.; Guilyardi, E.; Juckes, M.; Taylor, K.E. Documenting Numerical Experiments in Support of the Coupled Model Intercomparison Project Phase 6 (CMIP6). Geosci. Model Dev.
**2020**, 13, 2149–2167. [Google Scholar] [CrossRef] - Riahi, K.; van Vuuren, D.P.; Kriegler, E.; Edmonds, J.; O’Neill, B.C.; Fujimori, S.; Bauer, N.; Calvin, K.; Dellink, R.; Fricko, O.; et al. The Shared Socioeconomic Pathways and Their Energy, Land Use, and Greenhouse Gas Emissions Implications: An Overview. Glob. Environ. Change
**2017**, 42, 153–168. [Google Scholar] [CrossRef] - Ding, Y.; Gong, X.; Xing, Z.; Cai, H.; Zhou, Z.; Zhang, D.; Sun, P.; Shi, H. Attribution of Meteorological, Hydrological and Agricultural Drought Propagation in Different Climatic Regions of China. Agric. Water Manag.
**2021**, 255, 106996. [Google Scholar] [CrossRef] - Van Loon, A.F.; Laaha, G. Hydrological Drought Severity Explained by Climate and Catchment Characteristics. J. Hydrol.
**2015**, 526, 3–14. [Google Scholar] [CrossRef] - Paxian, A.; Ziese, M.; Kreienkamp, F.; Pankatz, K.; Brand, S.; Pasternack, A.; Früh, B. User-oriented global predictions of the GPCC drought index for the next decade. Meteorol. Z.
**2019**, 28, 3–21. [Google Scholar] [CrossRef] - Wilhite, D.A.; Sivakumar, M.V.K.; Pulwarty, R. Managing Drought Risk in a Changing Climate: The Role of National Drought Policy. Weather Clim. Extrem.
**2014**, 3, 4–13. [Google Scholar] [CrossRef] - Zare, M.; Drastig, K.; Zude-Sasse, M. Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images. Sustainability
**2020**, 12, 70. [Google Scholar] [CrossRef] - Sun, D.; Pinker, R.T. Estimation of Land Surface Temperature from a Geostationary Operational Environmental Satellite (GOES-8). J. Geophys. Res. Atmos.
**2003**, 108. [Google Scholar] [CrossRef] - Sheng, J.; Wilson, J.P.; Lee, S. Comparison of Land Surface Temperature (LST) Modeled with a Spatially-Distributed Solar Radiation Model (SRAD) and Remote Sensing Data. Environ. Model. Softw.
**2009**, 24, 436–443. [Google Scholar] [CrossRef] - Yang, J.; Wang, Y. Estimating Evapotranspiration Fraction by Modeling Two-Dimensional Space of NDVI/Albedo and Day–Night Land Surface Temperature Difference: A Comparative Study. Adv. Water Resour.
**2011**, 34, 512–518. [Google Scholar] [CrossRef] - Zare, M.; Drastig, K.; Zude-Sasse, M. Estimating Tree Water Status in Apple Orchard Using Reflectance in the Thermal Domain of Landsat 8 Satellite. In Proceedings of the 2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Portici, Italy, 24–26 October 2019; pp. 255–259. [Google Scholar] [CrossRef]
- Bedair, H.; Alghariani, M.S.; Omar, E.; Anibaba, Q.A.; Remon, M.; Bornman, C.; Alzain, H.M. Global Warming Status in the African Continent: Sources, Challenges, Policies, and Future Direction. Int. J. Environ. Res.
**2023**, 17, 45. [Google Scholar] [CrossRef] - Yi, Q.; Bao, A.; Wang, Q.; Zhao, J. Estimation of Leaf Water Content in Cotton by Means of Hyperspectral Indices. Comput. Electron. Agric.
**2013**, 90, 144–151. [Google Scholar] [CrossRef] - Guermazi, E.; Bouaziz, M.; Zairi, M. Water Irrigation Management Using Remote Sensing Techniques: A Case Study in Central Tunisia. Environ. Earth Sci.
**2016**, 75, 202. [Google Scholar] [CrossRef] - Ozelkan, E.; Chen, G.; Ustundag, B.B. Multiscale Object-Based Drought Monitoring and Comparison in Rainfed and Irrigated Agriculture from Landsat 8 OLI Imagery. Int. J. Appl. Earth Obs. Geoinf.
**2016**, 44, 159–170. [Google Scholar] [CrossRef] - Veysi, S.; Naseri, A.A.; Hamzeh, S.; Bartholomeus, H. A Satellite Based Crop Water Stress Index for Irrigation Scheduling in Sugarcane Fields. Agric. Water Manag.
**2017**, 189, 70–86. [Google Scholar] [CrossRef] - Nugraha, A.S.A.; Gunawan, T.; Kamal, M. Modification of Temperature Vegetation Dryness Index (TVDI) Method for Detecting Drought with Multi-Scale Image. IOP Conf. Ser. Earth Environ. Sci.
**2022**, 1039, 012048. [Google Scholar] [CrossRef] - Brion, G.M.; Neelakantan, T.R.; Lingireddy, S. A Neural-Network-Based Classification Scheme for Sorting Sources and Ages of Fecal Contamination in Water. Water Res.
**2002**, 36, 3765–3774. [Google Scholar] [CrossRef] [PubMed] - Goel, A.; Goel, A.K.; Kumar, A. The Role of Artificial Neural Network and Machine Learning in Utilizing Spatial Information. Spat. Inf. Res.
**2023**, 31, 275–285. [Google Scholar] [CrossRef] - Nayak, P.C.; Sudheer, K.P.; Rangan, D.M.; Ramasastri, K.S. A Neuro-Fuzzy Computing Technique for Modeling Hydrological Time Series. J. Hydrol.
**2004**, 291, 52–66. [Google Scholar] [CrossRef] - Zare, M.; Koch, M. Hybrid Signal Processing/Machine Learning and PSO Optimization Model for Conjunctive Management of Surface–Groundwater Resources. Neural Comput. Appl.
**2021**, 33, 8067–8088. [Google Scholar] [CrossRef] - Jang, J.-S. ANFIS: Adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern.
**1993**, 23, 665–685. [Google Scholar] [CrossRef] - Jang, J.-S.; Sun, C.-T. Neuro-Fuzzy Modeling and Control. Proc. IEEE
**1995**, 83, 378–406. [Google Scholar] [CrossRef] - Zare, M. Application and Analysis of Physical and Data-Driven Stochastic Hydrological Simulation-Optimization Methods for the Optimal Management of Surface-Groundwater Resources Systems; University of Kassel: Kassel, Germany, 2017. [Google Scholar]
- Dikshit, A.; Pradhan, B.; Santosh, M. Artificial Neural Networks in Drought Prediction in the 21st Century—A Scientometric Analysis. Appl. Soft Comput.
**2022**, 114, 108080. [Google Scholar] [CrossRef] - Mohammed, S.; Elbeltagi, A.; Bashir, B.; Alsafadi, K.; Alsilibe, F.; Alsalman, A.; Zeraatpisheh, M.; Széles, A.; Harsányi, E. A Comparative Analysis of Data Mining Techniques for Agricultural and Hydrological Drought Prediction in the Eastern Mediterranean. Comput. Electron. Agric.
**2022**, 197, 106925. [Google Scholar] [CrossRef] - Prodhan, F.A.; Zhang, J.; Hasan, S.S.; Pangali Sharma, T.P.; Mohana, H.P. A Review of Machine Learning Methods for Drought Hazard Monitoring and Forecasting: Current Research Trends, Challenges, and Future Research Directions. Environ. Model. Softw.
**2022**, 149, 105327. [Google Scholar] [CrossRef] - Adnan, R.M.; Dai, H.-L.; Kuriqi, A.; Kisi, O.; Zounemat-Kermani, M. Improving Drought Modeling Based on New Heuristic Machine Learning Methods. Ain Shams Eng. J.
**2023**, 14, 102168. [Google Scholar] [CrossRef] - Zare, M.; Koch, M. Groundwater Level Fluctuations Simulation and Prediction by ANFIS- and Hybrid Wavelet-ANFIS/Fuzzy C-Means (FCM) Clustering Models: Application to the Miandarband Plain. J. Hydro-Environ. Res.
**2018**, 18, 63–76. [Google Scholar] [CrossRef] - Petrie, R.; Denvil, S.; Ames, S.; Levavasseur, G.; Fiore, S.; Allen, C.; Antonio, F.; Berger, K.; Bretonnière, P.-A.; Cinquini, L.; et al. Coordinating an Operational Data Distribution Network for CMIP6 Data. Geosci. Model Dev.
**2021**, 14, 629–644. [Google Scholar] [CrossRef] - Zare, M. Download CMIP6 Data. Available online: https://Github.Com/Hyddata/CMIP6_data (accessed on 21 March 2024).
- Giustarini, L.; Schumann, G.J.-P.; Kettner, A.J.; Smith, A.; Nawrotzki, R. Simulating Changes in Hydrological Extremes—Future Scenarios for Morocco. Water
**2023**, 15, 2722. [Google Scholar] [CrossRef] - USGS. Landsat 8 Data Users Handbook; USGS: Sioux Falls, SD, USA, 2016. [Google Scholar]
- USGS. Landsat 9 Data Users Handbook; USGS: Sioux Falls, SD, USA, 2022. [Google Scholar]
- Tucker, C.J. Red and Photographic Infrared Linear Combinations for Monitoring Vegetation. Remote Sens. Environ.
**1979**, 8, 127–150. [Google Scholar] [CrossRef] - Mao, K.; Qin, Z.; Shi, J.; Gong, P. A Practical Split-window Algorithm for Retrieving Land-surface Temperature from MODIS Data. Int. J. Remote Sens.
**2005**, 26, 3181–3204. [Google Scholar] [CrossRef] - Rozenstein, O.; Qin, Z.; Derimian, Y.; Karnieli, A. Correction: Rozenstein, O.; et al. Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm. Sensors
**2014**, 14, 11277. [Google Scholar] [CrossRef] - Qin, Z.; Dall’Olmo, G.; Karnieli, A.; Berliner, P. Derivation of Split Window Algorithm and Its Sensitivity Analysis for Retrieving Land Surface Temperature from NOAA-Advanced Very High Resolution Radiometer Data. J. Geophys. Res. Atmos.
**2001**, 106, 22655–22670. [Google Scholar] [CrossRef] - Yang, L.; Cao, Y.; Zhu, X.; Zeng, S.; Yang, G.; He, J.; Yang, X. Land Surface Temperature Retrieval for Arid Regions Based on Landsat-8 TIRS Data: A Case Study in Shihezi, Northwest China. J. Arid Land
**2014**, 6, 704–716. [Google Scholar] [CrossRef] - Sobrino, J.A.; Jiménez-Muñoz, J.C.; Paolini, L. Land Surface Temperature Retrieval from LANDSAT TM 5. Remote Sens. Environ.
**2004**, 90, 434–440. [Google Scholar] [CrossRef] - Nikam, B.R.; Ibragimov, F.; Chouksey, A.; Garg, V.; Aggarwal, S.P. Retrieval of Land Surface Temperature from Landsat 8 TIRS for the Command Area of Mula Irrigation Project. Environ. Earth Sci.
**2016**, 75, 1169. [Google Scholar] [CrossRef] - Abuzar, M.; O’Leary, G.; Fitzgerald, G. Measuring Water Stress in a Wheat Crop on a Spatial Scale Using Airborne Thermal and Multispectral Imagery. Field Crops Res.
**2009**, 112, 55–65. [Google Scholar] [CrossRef] - Zhang, F.; Zhang, L.-W.; Shi, J.-J.; Huang, J.-F. Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data. Pedosphere
**2014**, 24, 450–460. [Google Scholar] [CrossRef] - Mckee, T.B.; Doesken, N.J.; Kleist, J.R. The Relationship of Drought Frequency and Duration to Time Scales. In Proceedings of the 8th Conference on Applied Climatology, Anaheim, CA, USA, 17–22 January 1993; pp. 179–184. [Google Scholar]
- Mansorian, R.; Zare, M.; Schumann, G. Study on the Correlation between Meteorological and Agricultural Drought, Based on Remotely Sensed Indices. In Proceedings of the EGU General Assembly Conference Abstracts, Vienna, Austria, 4–8 May 2020; p. 13925. [Google Scholar] [CrossRef]
- Fathian, F. Chapter 3—Introduction of Multiple/Multivariate Linear and Nonlinear Time Series Models in Forecasting Streamflow Process. In Advances in Streamflow Forecasting; Sharma, P., Machiwal, D., Eds.; Elsevier: Amsterdam, The Netherlands, 2021; pp. 87–113. ISBN 978-0-12-820673-7. [Google Scholar]
- Jukić, D.; Denić-Jukić, V.; Kadić, A. Temporal and Spatial Characterization of Sediment Transport through a Karst Aquifer by Means of Time Series Analysis. J. Hydrol.
**2022**, 609, 127753. [Google Scholar] [CrossRef] - Mallat, S.G. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation. IEEE Trans. Pattern Anal. Mach. Intell.
**1989**, 11, 674–693. [Google Scholar] [CrossRef] - Kim, J.; Chun, C.-Y. Cho Implementation of EKF Combined with Discrete Wavelet Transform-Based MRA for Improved SOC Estimation for a Li-Ion Cell. In Proceedings of the 2013 Twenty-Eighth Annual IEEE Applied Power Electronics Conference and Exposition (APEC), Long Beach, CA, USA, 17–21 March 2013; pp. 2720–2725. [Google Scholar] [CrossRef]
- Holzkämper, A. Adapting Agricultural Production Systems to Climate Change—What’s the Use of Models? Agriculture
**2017**, 7, 86. [Google Scholar] [CrossRef]

**Figure 3.**List of Coupled Model Intercomparison Project Phase 6 (CMIP6) models (

**left**) and monthly precipitation for different socioeconomic pathways of one model for december 2050 (

**right**).

**Figure 4.**The monthly precipitation values for of CMIP6 climate change scenarios for Tamale, Ghana (lat/lon: 9°32′44.1″N/0°55′58.2″W).

**Figure 6.**Land surface temperature (LST)—NDVI trapezoidal space for temperature vegetation dryness index (TVDI [0, 1]) estimation, from [13].

**Figure 8.**Cross correlation function between meteorological and agricultural drought indices, namely standard precipitation index (SPI) and temperature vegetation dryness index (TVDI).left panel: SPI

_{3-month}and TVDI; right panel: SPI

_{6-month}and TVDI.

**Figure 11.**Fitted Gamma distribution on observed monthly precipitation accumulation (mm) for calculating SPI

_{3}(top panel) and SPI

_{6}(bottom panel).

**Figure 13.**(

**a**) The spatial distribution of normalized difference vegetation index (NDVI), (

**b**) land surface temperature (LST) using the split window (SW) method, (

**c**) the Trapezoidal space of LST/NDVI, and (

**d**) the Temperature Vegetation Dryness Index (TVDI) based on Landsat 9 satellite images (path/row: 194/53) acquired on 6th November 2022. The wet edge is represented by the blue line (TVDI = 0), while the dry edge is marked by the red line (TVDI = 1).

**Figure 14.**(

**Upper panel**) statistical evaluation of Temperature Vegetation Dryness Index (TVDI) maps across 51 Landsat 8/9 images; (

**Lower panel**) the spatial distribution of TVDI in four satellite overpasses and one UAV (Unmanned Aerial Vehicle) image.

**Figure 15.**Analysis of Absolute Error in Temperature Vegetation Dryness Index (TVDI) maps derived from Landsat and UAV based data.

**Figure 16.**The standard precipitation index (SPI) with time scale of 3 and 6 months using 4 different Shared Socioeconomic Pathways (SSPs) within the framework of CMIP6 (Coupled Model Intercomparison Project Phase 6).

**Figure 18.**Wavelet-ANFIS/Sym4—simulated and observed TVDI for the training (

**upper panel**) and testing (

**lower panel**) phases.

**Figure 20.**TVDI predictions for the period of 2023–2050 under the Shared Socioeconomic Pathways (SSPs) climate change scenarios.

No | Acronym | Name |
---|---|---|

1 | UKESM1-0-LL | United Kingdom Earth System Model |

2 | TaiESM1 | Taiwan Earth System Model version 1 |

3 | NorESM2-MM | Norwegian Earth System Model-medium atmosphere-medium ocean resolution |

4 | NorESM2-LM | Norwegian Earth System Model-low atmosphere-medium ocean resolution |

5 | NESM3 | The NUIST Earth System Model version 3 |

6 | MRI-ESM2-0 | The Meteorological Research Institute Earth System Model Version 2.0 |

7 | MPI-ESM1-2-LR | Max Planck Institute Earth System Model-Lower-Resolved version |

8 | MPI-ESM1-2-HR | Max Planck Institute Earth System Model-Higher-Resolution version |

9 | MIROC6 | Model for Interdisciplinary Research on Climate |

10 | MIROC-ES2L | MIROC-Earth System version 2 for Long-term simulations |

11 | KIOST-ESM | Korea Institute of Ocean Science and Technology Earth System Model |

12 | KACE-1-0-G | Korea meteorological Administration advanced Community Earth-system model |

13 | IPSL-CM6A-LR | Institut Pierre Simon Laplace Climate Model |

14 | INM-CM5-0 | Institute for Numerical Mathematics-Climate Model version 5.0 |

15 | INM-CM4-8 | Institute for Numerical Mathematics-Climate Model version 4.8 |

16 | IITM-ESM | Indian Institute of Tropical Meteorology-Earth System Model |

17 | HadGEM3-GC31-MM | Hadley Centre Global Environment Model ver. 3-General Circulation Model 31-Model Mean |

18 | HadGEM3-GC31-LL | HadGEM3-GC31-Low Latitude |

19 | GISS-E2-1-G | NASA Goddard Institute for Space Studies-Earth sys. model ver. 2, config.1-Grand Ensemble |

20 | GFDL-ESM4 | Geophysical Fluid Dynamics Laboratory-Earth System Model version 4 |

21 | GFDL-CM4_gr2 | Geophysical Fluid Dynamics Laboratory-Climate Model version 4, grid resolution 2 |

22 | GFDL-CM4 | Geophysical Fluid Dynamics Laboratory-Climate Model version 4 |

23 | FGOALS-g3 | Institute of Atmospheric Physics Global Ocean-Atmosphere-Land System Model |

24 | EC-Earth3-Veg-LR | ECMWF Earth System Model with Vegetation |

25 | EC-Earth3 | ECMWF Earth System Model |

26 | CanESM5 | Canadian Earth System Model |

27 | CNRM-ESM2-1 | Centre National de Recherches Météorologiques Earth System Model |

28 | CNRM-CM6-1 | Centre National de Recherches Météorologiques Climate Model |

29 | CMCC-ESM2 | Euro-Mediterranean Centre on Climate Change-Earth System Model version 2 |

30 | CMCC-CM2-SR5 | CMCC-Climate Model version 2, Spectral Resolution 5 |

31 | CESM2-WACCM | Community Earth System Model with Whole Atmosphere Community Climate Model |

32 | CESM2 | Community Earth System Model version 2 |

33 | BCC-CSM2-MR | Beijing Climate Center Climate System Model |

34 | ACCESS-ESM1-5 | Australian Community Climate and Earth System Simulator |

35 | ACCESS-CM2 | Australian Community Climate and Earth System Simulator |

**Table 2.**Landsat 8 and 9 (L8/L9) overpass over the orchards; path/row: 194/53, Time: between 10:20 and 10:21 Local time (equal to Greenwich Mean Time).

Satellite | Date | Satellite | Date | Seattleite | Date | Seattleite | Date |
---|---|---|---|---|---|---|---|

L8 | 9 January 2020 | L8 | 23 October 2020 | L8 | 7 August 2021 | L9 | 5 December 2021 |

L8 | 25 January 2020 | L8 | 24 November 2020 | L8 | 26 October 2021 | L9 | 21 December 2021 |

L8 | 10 February 2020 | L8 | 10 December 2020 | L8 | 11 November 2021 | L9 | 6 January 2022 |

L8 | 26 February 2020 | L8 | 26 December 2020 | L8 | 27 November 2021 | L9 | 22 January 2022 |

L8 | 13 March 2020 | L8 | 11 January 2021 | L8 | 13 December 2021 | L9 | 7 February 2022 |

L8 | 29 March 2020 | L8 | 27 January 2021 | L8 | 29 December 2021 | L9 | 23 February 2022 |

L8 | 14 April 2020 | L8 | 12 February 2021 | L8 | 14 January 2022 | L9 | 11 March 2022 |

L8 | 30 April 2020 | L8 | 28 February 2021 | L8 | 30 January 2022 | L9 | 27 March 2022 |

L8 | 16 May 2020 | L8 | 16 March 2021 | L8 | 15 February 2022 | L9 | 18 August 2022 |

L8 | 1 June 2020 | L8 | 1 April 2021 | L8 | 3 March 2022 | L9 | 5 October 2022 |

L8 | 17 June 2020 | L8 | 17 April 2021 | L8 | 19 March 2022 | L9 | 21 October 2022 |

L8 | 4 August 2020 | L8 | 3 May 2021 | L8 | 4 April 2022 | L9 | 6 November 2022 |

L8 | 20 August 2020 | L8 | 19 May 2021 | L8 | 29 October 2022 | L9 | 22 November 2022 |

Satellite | Parameter i | K1 | K2 |
---|---|---|---|

L8 | Band 10 | 774.8853 | 1321.0789 |

L8 | Band 11 | 480.8883 | 1201.1442 |

L9 | Band 10 | 799.0284 | 1329.2405 |

L9 | Band 11 | 475.6581 | 1198.3494 |

**Table 4.**Relation between atmospheric transmittance factor (${\tau}_{i}$, i = 10, 11) and water vapor content (w) for mid-latitude atmospheric profile.

$\mathbf{Range}\mathbf{of}\mathit{w}(\mathbf{g}/{\mathbf{c}\mathbf{m}}^{2}$) | Equation [44] |
---|---|

0.2–3.0 | ${\tau}_{10}=-0.0164{w}^{2}-0.04203w+0.9715$ |

${\tau}_{11}=-0.01218{w}^{2}-0.07735w+0.9603$ | |

3.0–6.0 | ${\tau}_{10}=-0.00168{w}^{2}-0.1329w+1.127$ |

${\tau}_{11}=0.009186{w}^{2}-0.2137w+1.181$ |

**Table 5.**SPI classification [46].

SPI | Class | SPI | Class |
---|---|---|---|

2 or more | Extreme wet | −1 to −1.49 | Moderate drought |

1.5 to 1.99 | Very wet | −1.5 to −1.99 | Severe drought |

1 to 1.49 | Moderate wet | −2 or less | Extreme drought |

−0.99 to 0.99 | Near normal |

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**MDPI and ACS Style**

Hobart, M.; Schirrmann, M.; Abubakari, A.-H.; Badu-Marfo, G.; Kraatz, S.; Zare, M.
Drought Monitoring and Prediction in Agriculture: Employing Earth Observation Data, Climate Scenarios and Data Driven Methods; a Case Study: Mango Orchard in Tamale, Ghana. *Remote Sens.* **2024**, *16*, 1942.
https://doi.org/10.3390/rs16111942

**AMA Style**

Hobart M, Schirrmann M, Abubakari A-H, Badu-Marfo G, Kraatz S, Zare M.
Drought Monitoring and Prediction in Agriculture: Employing Earth Observation Data, Climate Scenarios and Data Driven Methods; a Case Study: Mango Orchard in Tamale, Ghana. *Remote Sensing*. 2024; 16(11):1942.
https://doi.org/10.3390/rs16111942

**Chicago/Turabian Style**

Hobart, Marius, Michael Schirrmann, Abdul-Halim Abubakari, Godwin Badu-Marfo, Simone Kraatz, and Mohammad Zare.
2024. "Drought Monitoring and Prediction in Agriculture: Employing Earth Observation Data, Climate Scenarios and Data Driven Methods; a Case Study: Mango Orchard in Tamale, Ghana" *Remote Sensing* 16, no. 11: 1942.
https://doi.org/10.3390/rs16111942