Using satellite remote sensing and hydrologic modeling to improve understanding of crop management and agricultural water use at regional to global scales.

Thesis (Ph. D.)--Boston UniversityCroplands are essential to human welfare. In the coming decades , croplands will experience substantial stress from climate change, population growth, changing diets, urban expansion, and increased demand for biofuels. Food security in many parts of the world therefore requires informed crop management and adaptation strategies. In this dissertation, I explore two key dimensions of crop management with significant potential to improve adaptation pathways: irrigation and crop calendars. Irrigation, which is widely used to boost crop yields, is a key strategy for adapting to changes in drought frequency and duration. However, irrigation competes with household, industrial, and environmental needs for freshwa t er r esources. Accurate information regarding irrigation patterns is therefore required to develop strategies that reduce unsustainable water use. To address this need, I fused information from remote sensing, climate datasets, and crop inventories to develop a new global database of rain-fed, irrigated, and paddy croplands. This database describes global agricultural water management with good realism and at higher spatial resolution than existing maps. Crop calendar management helps farmers to limit crop damage from heat and moisture stress. However, global crop calendar information currently lacks spatial and temporal detail. In the second part of my dissertation I used remote sensing to characterize global cropping patterns annually, from 2001-2010, at 0.08 degree spatial resolution. Comparison of this new dataset with existing sources of crop calendar data indicates that remote sensing is able to correct substantial deficiencies in available data sources. More importantly, the database provides previously unavailable information related to year-to-year variability in cropping patterns. Asia, home to roughly one half of the Earth's population, is expected to experience significant food insecurity in coming decades. In the final part of my dissertation, I used a water balance model in combination with the data sets described above to characterize the sensitivity of agricultural water use in Asia to crop management. Results indicate that water use in Asia depends strongly on both irrigation and crop management, and that previous studies underestimate agricultural water use in this region. These results support policy development focused on improving the resilience of agricultural systems in Asia

Remote Sensing of Land Surface Phenology

Land surface phenology (LSP) uses remote sensing to monitor seasonal dynamics in vegetated land surfaces and retrieve phenological metrics (transition dates, rate of change, annual integrals, etc.). LSP has developed rapidly in the last few decades. Both regional and global LSP products have been routinely generated and play prominent roles in modeling crop yield, ecological surveillance, identifying invasive species, modeling the terrestrial biosphere, and assessing impacts on urban and natural ecosystems. Recent advances in field and spaceborne sensor technologies, as well as data fusion techniques, have enabled novel LSP retrieval algorithms that refine retrievals at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. Meanwhile, rigorous assessment of the uncertainties in LSP retrievals is ongoing, and efforts to reduce these uncertainties represent an active research area. Open source software and hardware are in development, and have greatly facilitated the use of LSP metrics by scientists outside the remote sensing community. This reprint covers the latest developments in sensor technologies, LSP retrieval algorithms and validation strategies, and the use of LSP products in a variety of fields. It aims to summarize the ongoing diverse LSP developments and boost discussions on future research prospects

Book of short Abstracts of the 11th International Symposium on Digital Earth

The Booklet is a collection of accepted short abstracts of the ISDE11 Symposium

Drought impacts assessment in Brazil - a remote sensing approach

Climate extremes are becoming more frequent in Brazil; studies project an increase in drought occurrences in many regions of the country. In the south, drought events lead to crop yield losses affecting the value chain and, therefore, the local economy. In the northeast, extended periods of drought lead to potential land degradation, affecting the livelihood and hindering local development. In the southern Amazon, an area that experienced intense land use change (LUC) in the last, the impacts are even more complex, ranging from crop yield loss and forest resilience loss, affecting ecosystem health and putting a threat on the native population traditional way of living. In the studies here we analyzed the drought impacts in these regions during the 2000s, which vary in nature and outcomes. We addressed some of the key problems in each of the three regions: i) for the southern agriculture, we tackled the problem of predicting soybean yield based on within-season remote sensing (RS) data, ii) in the northeast we mapped areas presenting trends of land degradation in the wake of an extended drought and, iii) in southern Amazon, we characterized a complex degradation cycle encompassing LUC, fire occurrence, forest resilience loss, carbon balance, and the interconnectedness of these factors impacting the local climate. Advisor: Brian D. Wardlo

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)

Decarbonization, Irrigation, and Energy System Planning: Analyses in New York State and Ethiopia

This dissertation contains two collections of analyses, both broadly focused on energy system planning, but motivated by different research objectives in distinct geographic settings. Part I – Chapters I-III – evaluates decarbonization strategies in New York. These studies are characteristic of the primary energy-related challenge faced by the Global North: How can states cost-effectively meet time-bound emissions reduction targets? A series of linear programs are developed to answer this question, culminating in the System Electrification and Capacity TRansition (SECTR) model, a high-fidelity representation of the New York State energy system that characterizes statewide emissions and allows for comparative study of various decarbonization pathways. SECTR simulations indicate that prioritizing heating and vehicle electrification alongside an expansion of instate wind and solar generation capacity allows New York to meet recently legislated climate goals more affordably than through approaches that mandate substantial low-carbon electricity targets. Additional work also explores the optimal distribution of energy infrastructure within New York to meet specified decarbonization targets, along with the value of supply-side, demand-side, and bidirectional methods of system flexibility. Part II of this dissertation – Chapters IV-VII – is concerned with the energy system challenges faced by the lowest income countries. Set in the Ethiopian Highlands, this work first aims to locate smallholder irrigated areas, as irrigation has attendant energy requirements that are larger and more likely to generate supplementary sources of revenue compared to residential demands. Here, a novel classification methodology is developed to collect labeled data, train a machine learning-based irrigation detection model, and understand the spatial extent of model applicability. Across isolated plots of land as small as 30m by 30m, the resulting model achieves >95% prediction accuracy. Further studies then explore the system planning implications of simulated electricity demands associated with these irrigated areas