An ISRO-National Remote Sensing Centre (NRSC) study combined satellite imagery, field-tested soil samples, and machine learning to map soil health across farmland in Indukurpet mandal of SPSR Nellore district in Andhra Pradesh. The researchers used Landsat-8 satellite data to predict soil pH and electrical conductivity, an indicator of salinity, producing digital maps that showed local variations, including smaller patches missed by widely spaced field sampling. The study, titled “Machine learning-based prediction of soil pH and EC using Landsat-8 images,” was published online in Environmental Earth Sciences on June 15, 2026. It covered about 144 sq km in Indukurpet mandal, which has an average elevation of four metres and a hot, humid climate, with paddy and legumes as major crops. The researchers collected 175 surface-soil samples from 15 village locations between Feb 12 and 27, 2025, using stratified random sampling, and used Landsat-8 imagery from the same month. The laboratory-tested samples were combined with six Landsat-8 spectral bands and 12 spectral indices, processed through Google Earth Engine. Short-wave infrared bands and salinity indices contributed more to predictions than vegetation indices. The researchers also generated spatial prediction and uncertainty maps, which could help agricultural departments concentrate soil testing in high-risk or uncertain zones and support decisions on crop suitability and fertiliser application. The findings are relevant to paddy, which requires a soil pH of about