## 5 Conclusions
Available online: [[https://www.fao.org/3/x5310e/x5310e04.htm#chapter%203:%20](https://www.fao.org/3/x5310e/x5310e04.htm#chapter%203:%20) land%20 suitability%20 classifications]([https://www.fao.org/3/x5310e/x5310e04.htm#chapter%203:%20](https://www.fao.org/3/x5310e/x5310e04.htm#chapter%203:%20) land%20 suitability%20 classifications) (accessed on 13 July 2022). * (6) [PERSON]; [PERSON]; [PERSON] Agricultural Land Suitability Analysis: State-of-the-Art and Outlooks for Integration of Climate Change Analysis. _Agric. Syst._**2019**, _173_, 172-208. [CrossRef]
* (7) [PERSON]; [PERSON] Drought in Agriculture: Preservation, Adaptation, Migration. _Innov. Geosci._**2023**, \(1\), 100002. [CrossRef]
* (8) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Tea Cultivation Suitability Evaluation and Driving Force Analysis Based on AHP and Geodetector Results: A Case Study of Yingde in Guangdong, China. _Remote Sens._**2022**, _14_, 2412. [CrossRef]
* (9) [PERSON]; [PERSON]; [PERSON]; [PERSON] Ecological Vulnerability Assessment Based on AHP-PSR Method and Analysis of Its Single Parameter Sensitivity and Spatial Autocorrelation for Ecological Protection--A Case of Weifang City, China. _Ecol. Indic._**2021**, _125_, 107464. [CrossRef]
* (10) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Integrated GIS and AHP Techniques for Land Suitability Assessment of Cotton Crop in Perambalur District, South India. _Int. J. Syst. Assur. Eng. Manag._**2022**, _15_, 267-278. [CrossRef]
* (11) [PERSON]; [PERSON]; [PERSON] Best Crop Rotation Selection with GIS-AHP Technique Using Soil Nutrient Variability. _Agriculture_**2020**, _10_, 213. [CrossRef]
* (12) [PERSON]; [PERSON]; [PERSON] Agricultural Land Suitability Assessment Using Satellite Remote Sensing-Derived Soil-Vegetation Indices. _Land_**2021**, _10_, 223. [CrossRef]
* (13) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models. _Agronomy_**2020**, _10_, 573. [CrossRef]
* (14) [PERSON]; [PERSON] Fuzzy Multiple Criteria Forestry Decision Making Based on an Integrated VIKOR and AHP Approach. _Expert Syst. Appl._**2011**, _38_, 7326-7333. [CrossRef]
* (15) [PERSON]; [PERSON]; [PERSON] Urban Green Space Suitability Evaluation Based on the AHP-CV Combined Weight Method: A Case Study of Fuping County, China. _Sustainability_**2018**, _10_, 2656. [CrossRef]
* (16) [PERSON]; [PERSON]; [PERSON]; [PERSON] An Integrated Multicriteria Analysis Tool for Evaluating Water Resource Management Strategies. _Water_**2018**, _10_, 1795. [CrossRef]
* (17) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON]; [PERSON]; et al. SoilGrids250m: Global Gridded Soil Information Based on Machine Learning. _PLoS ONE_**2017**, 12, e0169748. [CrossRef]
* (18) [PERSON]; [PERSON]; [PERSON]; [PERSON] Evaluating the Capabilities of Sentinel-2 for Quantitative Estimation of Biophysical Variables in Vegetation. _ISPRS J. Photogramm. Remote Sens._**2013**, _82_, 83-92. [CrossRef]
* (19) [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON]; [PERSON] [PERSON] Towards Predictive Modeling of Sorghum Biomass Yields Using Fraction of Absorbed Photosynthetically Active Radiation Derived from Sentinel-2 Satellite Imagery and Supervised Machine Learning Techniques. _Agronomy_**2019**, \(9\), 203. [CrossRef]
* (20) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Characterizing and Mapping Cropping Patterns in a Complex Agro-Ecosystem: An Iterative Participatory Mapping Procedure Using Machine Learning Algorithms and MODIS Vegetation Indices. _Comput. Electron. Agric._**2020**, _175_, 105595. [CrossRef]
* (21) [PERSON]; [PERSON]; [PERSON]; [PERSON] [PERSON] Optimal Soybean (_Glycine max_ L.) Land Suitability Using GIS-Based Multicriteria Analysis and Sentinel-2 Multitemporal Images. _Remote Sens._**2020**, _12_, 1463.