## 3 Prediction of Short-Term Land Surface Phenology Using Machine Learning
### Prediction of Cropland Suitability Based on Machine Learning  
Cropland suitability research according to abiotic factors, especially considering climate, soil, and topography, has been growing steadily since year 2000 (Figure 3). Machine learning models are also increasingly employed for predicting cropland suitability by according to a variety of environmental and soil data to assess the optimal conditions for crop cultivation [13]. These models utilize algorithms such as decision trees, random forests, and neural networks to analyze complex relationships between crop yields and various abiotic factors, including soil composition, climate variables, and topography. Furthermore, machine learning models can handle diverse datasets with varying spatial resolutions, enabling them to downscale district-level yield data to farm-level predictions effectively [108]. Machine learning offers several advantages over GIS-based multicriteria analysis using the analytical hierarchy process (AHP) for predicting cropland suitability [109]. One significant advantage is the ability of machine learning algorithms to process large and complex datasets, integrating diverse environmental variables such as soil properties, climate data, and remote sensing information, which can enhance prediction accuracy. Unlike AHP, which often relies on subjective weighting of criteria that can introduce bias, machine learning models can automatically learn the importance of different features from the data itself, reducing human error and enhancing objectivity in suitability assessments [110]. Furthermore, machine learning techniques, such as random forests and support vector machines, can effectively handle nonlinear relationships and interactions among variables that traditional AHP may overlook [13]. Additionally, machine learning models can be continuously updated with new data, allowing for real-time adjustments and improved predictions as environmental conditions change [111]. This adaptability makes machine learning particularly valuable in dynamic agricultural settings where conditions can vary significantly over time and space.  
Machine learning models for cropland suitability assessments incorporate a variety of specific abiotic criteria that enhance their predictive capabilities. Key abiotic factors include soil properties, such as soil texture (clay, silt, and sand content), pH, organic matter content, and bulk density, which are often analyzed at various depth intervals to capture the variability within the soil profile [109]. Additionally, climatic variables have a crucial role, with models utilizing detailed metrics such as mean monthly temperatures, precipitation patterns, and solar radiation levels to assess the suitability of land for specific crops [12]. Machine learning approaches also integrate topographical features, including slope and elevation derived from digital elevation models (DEMs), which can influence water drainage and microclimate conditions. Furthermore, some models consider bioclimatic variables like growing degree days and frost-free periods to better align crop requirements with local climatic conditions [112].