Browsing by Author "Guevara, Mario"
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Item Country to global prediction of soil organic carbon and soil moisture using digital soil mapping(University of Delaware, 2020) Guevara, MarioThe largest carbon pool in terrestrial ecosystems is contained in soils and it plays a key role regulating hydrological processes, such as the spatial variability of soil moisture dynamics. Specifically, soil moisture and soil organic carbon are variables directly linked to ecosystem services such as food production and water storage. However, there are important knowledge gaps in the spatial representation (e.g., maps) of soil moisture and soil organic information from the country specific to the global scales. There is a pressing need to update the spatial detail of soil moisture estimates and the accuracy of digital soil carbon maps for improved land management, improved Earth system modeling and improved strategies (i.e., public policy) to combat land degradation. From the country specific to the global scale, the overreaching goal of this PhD research is to develop a reproducible digital soil mapping framework to increase the statistical accuracy of spatially continuous information on soil moisture and soil organic carbon across different scales of data availability (e.g., country-specific, regional, global). Chapter 1 provides a general introduction. Chapters 2 and 3 are focused on up-scaling soil organic carbon from the country-specific scale to the continental scale. Chapter 2 provides a country-specific and multi-modeling approach for soil organic carbon mapping across Latin America, where I identify key predictors and conclude that there is no best modeling method in a quantifiable basis across all the analyzed countries. In Chapter 3, I compare and test different methods and combinations of prediction factors to model the variability of soil organic carbon across Mexico and conterminous United States (CONUS). I describe soil organic carbon stocks across different land covers across the region, quantify the model uncertainty and discuss estimates derived from previous studies. Chapters 4 and 5 are devoted to improving the statistical detail and accuracy of satellite soil moisture from the country to the global scale. Chapter 4 describes how the machine learning fusion of satellite soil moisture with Geomorphometry increase the statistical accuracy and spatial detail of current soil moisture estimates across CONUS. Chapter 5 extends the previous chapter to the global scale and identifies global soil moisture trends. I provide a novel (gap-free) soil moisture global estimate that could be potentially used to predict the global feedback between primary productivity and long-term soil moisture trends. Chapters 4 and 5 reveal evidence of soil moisture decline across large areas of the world. Finally, chapter 6 summarizes the main findings of this research, the key conclusions, emergent challenges and future steps. The results of this research were useful to generate benchmarks against which to assess the impact of climate and land cover changes on soil organic carbon stocks and soil moisture trends. This research provides a framework (including high quality data and novel methodologies) to generate environmentally relevant science that can be used for the formulation of public policy around soil and water conservation efforts.Item Downscaling satellite soil moisture for landscape applications: A case study in Delaware, USA(Journal of Hydrology: Regional Studies, 2021-10-15) Warner, Daniel L.; Guevara, Mario; Callahan, John; Vargas, RodrigoStudy region: Delaware, USA and its surrounding watersheds. Study focus: An ensemble using multiple Kernel K-nearest neighbors (KKNN) models was trained to predict daily grids of SSM at 100-meter resolution based on SSM estimates from the European Space Agency’s Climate Change Initiative Soil Moisture Product, terrain data, soil maps, and local meteorological network data. Estimated SSM was evaluated against independent in situ SSM observations and were investigated for relationships with land cover class and vegetation phenology (i.e., NDVI). New hydrological insights for the region Downscaled daily mean SSM estimates had lower error in space (27%) and greater predictive performance over time compared to the raw, coarse resolution remotely sensed SSM dataset when calibrated to field observed values. Downscaled SSM identified stronger and more widespread temporal relationships with NDVI than other estimation methods. However, both coarse and fine resolution datasets greatly underestimated SSM in wetland areas. The findings highlight the need for enhanced in situ SSM monitoring across diverse settings to improve landscape-level downscaled SSM. The downscaling methodology developed in this study was able to produce daily SSM estimates, providing a framework that can support future SSM modeling efforts, hydroecological investigations, and agricultural studies in this and other regions around the world when used in conjunction with ground-based monitoring networks.Item Hyperspectral Reflectance for Measuring Canopy-Level Nutrients and Photosynthesis in a Salt Marsh(Journal of Geophysical Research: Biogeosciences, 2022-11-04) Vázquez-Lule, Alma; Seyfferth, Angelia L.; Limmer, Matt A.; Mey, Paul; Guevara, Mario; Vargas, RodrigoSalt marsh ecosystems are underrepresented in process-based models due to their unique location across the terrestrial–aquatic interface. Particularly, the role of leaf nutrients on canopy photosynthesis (FA) remains unclear, despite their relevance for regulating vegetation growth. We combined multiyear information of canopy-level nutrients and eddy covariance measurements with canopy surface hyperspectral remote sensing (CSHRS) to quantify the spatial and temporal variability of FA in a temperate salt marsh. We found that FA showed a positive relationship with canopy-level N at the ecosystem scale and for areas dominated by Spartina cynosuroides, but not for areas dominated by short S. alterniflora. FA showed a positive relationship with canopy-level P, K, and Na, but a negative relationship with Fe, for areas associated with S. cynosuroides, S. alterniflora, and at the ecosystem scale. We used partial least squares regression (PLSR) with CSHRS and found statistically significant data–model agreements to predict canopy-level nutrients and FA. The red-edge electromagnetic region and ∼770 nm showed the highest contribution of variance in PLSR models for canopy-level nutrients and FA, but we propose that underlying sediment biogeochemistry can complicate interpretation of reflectance measurements. Our findings highlight the relevance of spatial variability in salt marshes vegetation and the promising application of CSHRS for linking information of canopy-level nutrients with FA. We call for further development of canopy surface hyperspectral methods and analyses across salt marshes to improve our understanding of how these ecosystems will respond to global environmental change. Plain Language Summary Canopy photosynthesis in salt marshes contributes to the carbon stored in these ecosystems; however, its relationship with canopy-level nutrients has been underrepresented in models. Reflectance from near surface remote sensing could be a cost-effective nondestructive tool to monitor canopy photosynthesis and associated nutrients in salt marshes. We combined canopy-level nutrient information with hyperspectral canopy reflectance to represent the spatial and temporal variability of canopy photosynthesis in a salt marsh in the Mid-Atlantic cost of the U.S. We found that local variability such as different salt marsh species have an influence on the relationship between canopy photosynthesis and associated nutrients, in consequence the most limiting nutrients for photosynthesis were phosphorus, potassium, and sodium. We propose that underlying sediment biogeochemistry can potentially obscure the expected relationships between plant nutrients and photosynthesis in remote sensing of coastal wetlands. These results open the possibility to use similar reflectance information from airborne or spaceborne platforms to explore these relationships at broader scales. Key Points - Local environmental variability influences the relationship of canopy nutrients with canopy photosynthesis in a salt marsh ecosystem - Sediment biogeochemistry can obscure expected relationships between plant nutrients and photosynthesis in remote sensing of coastal wetlands - Canopy surface hyperspectral remote sensing is a promising technique for studying vegetation dynamics of salt marshesItem Spatial variability and uncertainty of soil nitrogen across the conterminous United States at different depths(Ecosphere, 2022-07-27) Smith, Elizabeth M.; Vargas, Rodrigo; Guevara, Mario; Tarin, Tonantzin; Pouyat, Richard V.Soil nitrogen (N) is an important driver of plant productivity and ecosystem functioning; consequently, it is critical to understand its spatial variability from local-to-global scales. Here, we provide a quantitative assessment of the three-dimensional spatial distribution of soil N across the United States (CONUS) using a digital soil mapping approach. We used a random forest-regression kriging algorithm to predict soil N concentrations and associated uncertainty across six soil depths (0–5, 5–15, 15–30, 30–60, 60–100, and 100–200 cm) at 5-km spatial grids. Across CONUS, there is a strong spatial dependence of soil N, where soil N concentrations decrease but uncertainty increases with soil depth. Soil N was higher in Pacific Northwest, Northeast, and Great Lakes National Ecological Observatory Network (NEON) ecoclimatic domains. Model uncertainty was higher in Atlantic Neotropical, Southern Rockies/Colorado Plateau, and Southeast NEON domains. We also compared our soil N predictions with satellite-derived gross primary production and forest biomass from the National Biomass and Carbon Dataset. Finally, we used uncertainty information to propose optimized locations for designing future soil surveys and found that the Atlantic Neotropical, Pacific Northwest, Pacific Southwest, and Appalachian/Cumberland Plateau NEON domains may require larger survey efforts. We highlight the need to increase knowledge of biophysical factors regulating soil processes at deeper depths to better characterize the three-dimensional space of soils. Our results provide a national benchmark regarding the spatial variability and uncertainty of soil N and reveal areas in need of a better representation.