From: Monitoring sustainable development by means of earth observation data and machine learning: a review
SDGs | Field | Main finding | References |
---|---|---|---|
SDG 2 (Zero Hunger) | Agriculture | Results increased the potential of using Sentinel-2 to obtain cotton Leaf Area Index and comparison of methods showed that the Gradient Boosting RT was the best | [100] |
Estimate the crop yield, at a pixel level, using ML proved to be an accurate approach | [73] | ||
The results obtained from the comparison of methods showed that Boosted RT was the best to predict maize yield | [101] | ||
SDG 3 (Good Health and Well-Being) | Spread of diseases | By mapping the relationship between EO variables and vector population, the proposed RF Regression methodology was able to predict the temporal distribution of yellow fever mosquito populations | [102] |
SDG 6 (Clean Water and Sanitation) | Water quality | Landsat 7 images are a solid option for assessing water quality characteristics | [55] |
SDG 7 (Affordable and Clean Energy) | Renewable energy sources | During Spring and Autumn is harder to predict the hourly solar irradiation compared to Winter and Summer | [103] |
SDG 9 (Industry, Innovation and Infrastructure) | Pollution | RT effectively estimates carbon dynamics and allowed the analysis of its impacts on meteorology and vegetation | [49] |
The improved GPR had a high accuracy compared to the original GPR and other methods predicting the CO2 emissions | [104] | ||
SDG 11 (Sustainable Cities and Communities) | Land cover | RF Regression was very accurate (96%) in delineating house-attached, semi-public and public green spaces | [105] |
SDG 13 (Climate Action) | Drought | The use of ML to acquire the Normalised Microwave Reflection Index is an effective way to monitor the variation of vegetation water content to predict droughts | [106] |
SDG 14 (Life Below Water) | Freshwater habitat | Geographically Weighted Regression technique was accurate in the estimation of stream bathymetry of water with a depth less than 1Â m | [107] |
SGD 15 (Life on Land) | Terrestrial ecosystem | The best performance, to obtain the latent heat evaporation using a small dataset, was achieved by Kernel Ridge Regression, and using a large dataset, was achieved by Bagging RT | [108] |
Grassland | Vegetation indices acquired from Sentinel 2 have high potential concerning grasslands productivity, management, monitoring and conservation | [109] | |
Landslide | Catchment map units and model selection are crucial for the performance of landslide susceptibility maps | [110] |