Digital mapping of DTPA extractable micronutrients using combined AVIRIS-NG hyperspectral and multispectral data of Sentinel-2 and SRTM-DEM

Real-time assessment and prediction of soil nutrients are essential for precision soil nutrient management. Digital mapping of soil nutrients, influenced by topographical attributes, offers a promising approach for site-specific nutrient management. Although, multispectral and hyperspectral remote sensing data have been utilized separately for the digital mapping of soil nutrients, the integration of both data types for soil nutrients especially for soil micronutrients remains unexplored. Therefore, this study attempted to enhance the accuracy and reliability of predicting four important Diethylene Triamine Pentaacetic Acid (DTPA) extractable micronutrient cations such as zinc (Zn), copper (Cu), iron (Fe), and manganese (Mn) in the Katol block of Nagpur district, Maharashtra, India by integrating multispectral data of Sentinel-2, and hyperspectral data of Airborne Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) along with the Shuttle Radar Topographic Mission (SRTM) digital elevation model (DEM) data, soil and climatic data with three machine learning (ML) models. These four micronutrients were selected for digital mapping owing to their crucial roles in plant growth and development, and their continuous depletion under intensive cultivation of high-yielding varieties, which may adversely affect crop productivity and soil health in the study area. 132 georeferenced surface soil samples were collected during airborne hyperspectral spectra acquisition by the AVIRIS-NG. Micronutrients were analysed in laboratory and lab-generated data combined with multispectral, hyperspectral, soil and climatic data with the help of three ML models viz. Random Forest (RF), Quantile Random Forest (QRF) and Cubist for predicting and mapping of those four key micronutrients. Based on the prediction performances especially in the validation dataset, the RF model was found more promising in comparison to other models in micronutrient prediction. Since the RF model demonstrated superior performance, it was utilized for mapping those four micronutrients. This RF model demonstrated moderate to satisfactory accuracy, with the value of the coefficient of determination (R²) 0.71, 0.65, 0.62 and 0.63 for Zn, Cu, Fe, and Mn prediction respectively. The study utilized eight different types of datasets and applied them to each model and it was revealed that the combined use of multispectral, hyperspectral, soil and climatic datasets improved the prediction accuracy of micronutrients compared to using each dataset individually. The study also revealed that soil variable i.e., soil pH was the most influential variable for soil micronutrient especially for Zn, Cu and Fe predictions. Next to this, hyperspectral variables (PC2 for Zn, Fe, PC1, and PC3 for Mn, and PC6, and PC7 for Cu) were found crucial in predicting and mapping soil-available micronutrients. Furthermore, it was observed that after soil and hyperspectral variables, DEM-derived variables (valley depth, elevation etc.) were also found important in predicting micronutrients particularly for Mn. Prediction uncertainties, assessed through confidence and prediction intervals as well as standard deviation and standard error, were lower for Zn and Cu, but slightly higher for Fe and Mn, indicating areas needing additional soil sampling for more reliable estimation. Thus, this study underscores the enhanced predictability of micronutrients, achieved by integrating multispectral and hyperspectral remote sensing data with terrain, soil and climatic data, providing valuable digital maps of micronutrients for precision micronutrient management in the study area.