Integrating Multiscale Geospatial Analysis for Monitoring Crop Growth, Nutrient Distribution, and Hydrological Dynamics in Large-Scale Agricultural Systems

Olatunde D. Akanbi1,2,3, Deepa C. Bhuvanagiri2,3,4, Erika I. Barcelos1,2,3, Arafath Nihar2,4, Brian Gonzalez Hernandez2,4, Jeffrey M. Yarus1,2,3, Roger H. French1,2,3,4
1Department of Material Science and Engineering, Case Western Reserve University, Cleveland, USA
2SDLE Research Center, Case Western Reserve University, Cleveland, USA
3Center for Advancing Sustainable and Distributed Fertilizer Production, Case Western Reserve University, Cleveland, USA
4Department of Computer and Data Sciences, Case Western Reserve University, Cleveland, USA

Tóm tắt

Monitoring crop growth, soil conditions, and hydrological dynamics are imperative for sustainable agriculture and reduced environmental impacts. This interdisciplinary study integrates remote sensing, digital soil mapping, and hydrological data to elucidate intricate connections between these factors in the state of Ohio, USA. Advanced spatiotemporal analysis techniques were applied to key datasets, including the MODIS sensor satellite imagery, USDA crop data, soil datasets, Aster GDEM, and USGS stream gauge measurements. Vegetation indices derived from MODIS characterized crop-specific phenology and productivity patterns. Exploratory spatial data analysis show relationships of vegetation dynamics and soil properties, uncovering links between plant vigor, edaphic fertility, and nutrient distributions. Correlation analysis quantified these relationships and their seasonal evolution. Examination of stream gauge data revealed insights into spatiotemporal relationships of nutrient pollution and stream discharge. By synthesizing diverse geospatial data through cutting-edge data analytics, this work illuminated complex interactions between crop health, soil nutrients, and water quality in Ohio. The methodology and findings provide actionable perspectives to inform sustainable agricultural management and environmental policy. This study demonstrates the significant potential of open geospatial resources when integrated using a robust spatiotemporal framework. Integrating additional measurements and high-resolution data sources through advanced analytics and interactive visualizations could strengthen these insights.

Tài liệu tham khảo

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