Download or read book Investigating the Role of Soil and Land Surface Properties in Agricultural and Ecosystem Modeling written by SUMANTA CHATTERJEE and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Soil and land surface properties including soil texture, terrain properties, and land cover types affect the global terrestrial water and carbon cycles including extreme events such as drought and forest fire. Soil health assessment at the field scale to drought delineation and forecasting at the continental scale is essential for sustainable agricultural production and informed management decisions for policymaking. The overall aim of this dissertation is to investigate how various soil and land surface properties influence agricultural resource monitoring including predicting soil physicochemical properties at the field scale, soil moisture modeling and drought forecasting at the continental scale, and forest wildfire modeling at the global scale. The dissertation is divided into four research chapters. Chapter 2 aims to map several key soil physicochemical properties across an 80-ha crop field in Wisconsin at different soil depths using a stepwise multi-sensor fusion approach. Management zones were generated using a combination of different proximal and remote sensing data including terrain and soil laboratory data and compared for accuracy and cost-benefit analysis at two depths across the field using a clustering algorithm. Chapter 3 aims at establishing an empirical model to retrieve surface soil moisture at the U.S. Climate reference Network using remote sensing, terrain, and soil property maps. Two machine learning models and a linear model were compared and assessed at diverse land cover types within the contiguous USA (CONUS). Chapter 4 investigates the role of root-zone soil moisture in drought forecasting across climate regimes, land cover and soil type, and irrigation management in the CONUS. Agriculture and meteorological drought indices were comprehensively compared to evaluate their ability for drought delineation and forecasting. Chapter 5 aims at detecting causal interactions of various land surface parameters (e.g., albedo, snow cover), vegetation growth (e.g., leaf area index) and stress (drought indicator-Evaporative Stress Index, precipitation) with the forest fire in the North American Boreal Forest (NABF) biome using Empirical Dynamic Modeling and Convergent Cross Mapping algorithms. It is concluded that soil moisture and physiochemical properties modeling and mapping could be done empirically using different machine learning models and multiple remote sensing, terrain, soil properties, and land cover type data with reasonable accuracy; root zone soil moisture has skills for agricultural drought forecasting; both land surface and environmental conditions influence forest fire incidents in the NABF region.