MODIS-Based Fractional Crop Mapping in the U.S. Midwest with Spatially Constrained Phenological Mixture Analysis

Since the 2000s, bioenergy land use has been rapidly expanded in U.S. agricultural lands. Monitoring this change with limited acquisition of remote sensing imagery is difficult because of the similar spectral properties of crops. While phenology-assisted crop mapping is promising, relying on frequently observed images, the accuracies are often low, with mixed pixels in coarse-resolution imagery. In this paper, we used the eight-day, 500 m MODIS products (MOD09A1) to test the feasibility of crop unmixing in the U.S. Midwest, an important bioenergy land use region. With all MODIS images acquired in 2007, the 46-point Normalized Difference Vegetation Index (NDVI) time series was extracted in the study region. Assuming the phenological pattern at a pixel is a linear mixture of all crops in this pixel, a spatially constrained phenological mixture analysis (SPMA) was performed to extract crop percent covers with endmembers selected in a dynamic local neighborhood. The SPMA results matched well with the USDA crop data layers (CDL) at pixel level and the Crop Census records at county level. This study revealed more spatial details of energy crops that could better assist bioenergy decision-making in the Midwest

Multiple cropping systems of the world and the potential for increasing cropping intensity

Multiple cropping, defined as harvesting more than once a year, is a widespread land management strategy in tropical and subtropical agriculture. It is a way of intensifying agricultural production and diversifying the crop mix for economic and environmental benefits. Here we present the first global gridded data set of multiple cropping systems and quantify the physical area of more than 200 systems, the global multiple cropping area and the potential for increasing cropping intensity. We use national and sub-national data on monthly crop-specific growing areas around the year 2000 (1998–2002) for 26 crop groups, global cropland extent and crop harvested areas to identify sequential cropping systems of two or three crops with non-overlapping growing seasons. We find multiple cropping systems on 135 million hectares (12% of global cropland) with 85 million hectares in irrigated agriculture. 34%, 13% and 10% of the rice, wheat and maize area, respectively are under multiple cropping, demonstrating the importance of such cropping systems for cereal production. Harvesting currently single cropped areas a second time could increase global harvested areas by 87–395 million hectares, which is about 45% lower than previous estimates. Some scenarios of intensification indicate that it could be enough land to avoid expanding physical cropland into other land uses but attainable intensification will depend on the local context and the crop yields attainable in the second cycle and its related environmental costs. © 2020 The Author(s

MODELING LAND SURFACE HETEROGENEITY IN LAND SURFACE AND REGIONAL CLIMATE MODELS

We all live on Earth’s land surface. The state of and changes to land surface conditions can strongly alter surface energy and water balance, eventually affecting the weather and climate. An essential component in regional climate models and Earth system models, the land surface provides lower boundary conditions, which are critical both for weather forecasting and projecting the future climate. This research advances knowledge in representing land surface heterogeneity, including the energy-water-carbon cycle and land surface feedback to the regional climate in Central North America, where land use and hydrological conditions are complex. An extensive area of fine-scale surface heterogeneity, this region includes the U.S. corn belt agricultural land and wetlands that dominate the landscape in the Prairie Pothole Region (PPR) across the Northern Great Plains and Canadian Prairies. This study highlights two distinct landscapes—wetlands and croplands—for their dominance in the region, important roles in land-atmosphere interaction, and unique characteristics impacted by human activities. In addition, advances in high-resolution convection-permitting models provide a unique opportunity to investigate these interactions, especially to explicitly resolve land surface heterogeneity. This thesis first investigates the soil moisture conditions of the land and their feedback to extreme temperatures during heatwave events in a long-term high-resolution convection-permitting simulation. Second, a joint crop-irrigation simulation is conducted, which shows the capability of land surface models (LSMs) to estimate crop phenology and biomass and irrigation, the key impacts of human decisions. Third, the thesis explores the shallow groundwater dynamics and the hydrological cycle in the PPR under current and future climate change scenarios; fourth, the soil moisture conditions from the current and future climate are used to statistically estimate the future distribution of the prairie wetlands. Finally, a surface wetland scheme is developed to represent spatial wetland extents and dynamic wetland storage in the PPR. This scheme is incorporated into an LSM (Noah-MP) and regional climate model (Weather Research & Forecasting model) to study its impacts on energy-water balance and feedback to the regional climate. This research allows potential future research on the wetland-climate feedback at a local/regional scale and on the potential on-farm benefits of wetland retention and restoration. This research has critical implications for understanding the land and climate interactions in this unique and complex terrain and has potential to help human beings to develop a sustainable lifestyle

The Spatial Distribution of Terrestrial Stable Carbon Isotopes in North America, and the Impacts of Spatial and Temporal Resolution on Static Ecological Models

Due to the unique spatial and temporal characteristics of ecological phenomena, the extent and grain size of spatial data sets essentially filter the observations. This thesis examines the impacts of temporal and spatial resolution on the modeling of terrestrial stable carbon isotopic landscapes (isoscapes). I model the distribution of leaf stable carbon isotope composition (delta13C) for the continent of North America at multiple temporal and spatial resolutions. I generate each delta13C isoscape variation by first predicting the relative abundance of C3/C4 vegetation cover using monthly climate grids, crop distribution/type grids, and remote sensing data of plant life form, and then applying the respective leaf delta13C endmembers to each pixel. One application of isoscapes is predicting the geographic origin of migratory animals by relating the isotopic signature of animal tissue to environmental isotope values. I conduct multiple exercises in geographic origin assignment using known-origin feather isotope data of mountain plover (Charadrius montanus) chicks as an indirect means of testing the impact of resolution on delta13C isoscapes. Results indicate that temporal resolution does have a significant impact on predicted isoscape layers, and in turn, geographic origin assignment efficacy. Temporal periods that did not correspond to tissue growth exhibited a mismatch in the range of predicted vegetation delta13C values relative to the range of measured feather delta13C values and therefore were not useful in generating geographic origin assignments. The spatial resolution of modeled delta13C minimally impacted assignment accuracy and precision compared to temporal resolution; however, the current analysis was limited by the spatial resolution of the input data set. These results should be further explored to better characterize spatiotemporal ecological characteristics of migratory animals and to improve modeling of the isotopic landscape itself

Is there a solution to the spatial scale mismatch between ecological processes and agricultural management?

The major limit to develop robust landscape planning for biodiversity conservation is that the spatial levels of organization of landscape management by local actors rarely match with those of ecological processes. This problem, known as spatial scale mismatch, is recognized as a reason of lack of effectiveness of agri-environment schemes. We did a review to describe how authors identify the problem of spatial scale mismatch in the literature. The assumption is made that the solutions proposed in literature to conciliate agricultural management and conservation of biodiversity are based on theoretical frameworks that can be used to go towards an integration of management processes and ecological processes. Hierarchy Theory and Landscape Ecology are explicitly mobilized by authors who suggest multiscale and landscape scale approaches, respectively, to overcome the mismatch problem. Coordination in management is proposed by some authors but with no theoretical background explicitly mentioned. The theory of organization of biological systems and the theories of Social-Ecological Systems use the concept of coordination and integration as well as concepts of organization, adaptive capabilities and complexity of systems. These theories are useful to set up a new framework integrating ecological processes and agricultural management. Based on this review we made two hypotheses to explain difficulties to deal with spatial scale mismatch: (1) authors generally do not have an integrated approach since they consider separately ecological and management processes, and (2) an inaccurate use of terminology and theoretical frameworks partially explain the inadequacy of proposed solutions. We then specify some terms and highlight some ‘rules’ necessary to set up an integrative theoretical and methodological framework to deal with spatial scale mismatch.(Presentation des résumés n°186, p. 95-96, non paginé

Crop modeling for assessing and mitigating the impacts of extreme climatic events on the US agriculture system

The US agriculture system is the world’s largest producer of maize and soybean, and typically supplies more than one-third of their global trading. Nearly 90% of the US maize and soybean production is rainfed, thus is susceptible to climate change stressors such as heat waves and droughts. Process-based crop and cropping system models are important tools for climate change impact assessments and risk management. As data- science is becoming a new frontier for agriculture growth, the incoming decade calls for operational platforms that use hyper-local growth monitoring, high-resolution real-time weather and satellite data assimilation and cropping system modeling to help stakeholders predict crop yields and make decisions at various spatial scales. The fundamental question addressed by this dissertation is: How crop and cropping system models can be “useful” to the agriculture production, given the recent advent of cloud computing and earth observatory power? This dissertation consists of four main chapters. It starts with a study that reviews the algorithms of simulating heat and drought stress on maize in 16 major crop models, and evaluates algorithm performances by incorporating these algorithms into the Agricultural Production Systems sIMulator (APSIM) and running an ensemble of simulations at typical farms from the US Midwest. Results show that current parameterizations in most models favor the use of daylight temperature even though the algorithm was designed for using daily mean temperature. Different drought algorithms considerably differed in their patterns of water shortage over the growing season, but nonetheless predicted similar decreases in annual yield. In the next chapter of climate change assessment study, I quantify the current and future yield responses of US rainfed maize and soybean to climate extremes with and without considering the effect of elevated atmospheric CO2concentrations, and for the first time characterizes spatial shifts in the relative importance of temperature, heat and drought stresses. Model simulations demonstrate that drought will continue to be the largest threat to rainfed maize and soybean production, yet shifts in the spatial pattern of dominant stressors are characterized by increases in the concurrent stress, indicating future adaptation strategies will have trade-offs between multiple objectives. Following this chapter, I presented a chapter that uses billion-scale simulations to identify the optimal combination of Genotype × Environment × Management for the purpose of minimizing the negative impact of climate extremes on the rainfed maize yield. Finally, I present a prototype of crop model and satellite imagery based within-field scale N sidedress prescription tool for the US rainfed maize system. As an early attempt to integrate advances in multiple areas for precision agriculture, this tool successfully captures the subfield variability of N dynamics and gives reasonable spatially explicit sidedress N recommendations. The prescription enhances zones with high yield potentials, while prevents over-fertilization at zones with low yield potentials

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

Mapping and Monitoring Forest Cover

This book is a compilation of six papers that provide some valuable information about mapping and monitoring forest cover using remotely sensed imagery. Examples include mapping large areas of forest, evaluating forest change over time, combining remotely sensed imagery with ground inventory information, and mapping forest characteristics from very high spatial resolution data. Together, these results demonstrate effective techniques for effectively learning more about our very important forest resources

DIAGNOSTIC ANALYSIS OF TERRESTRIAL GROSS PRIMARY PRODUCTIVITY USING REMOTE SENSING AND IN SITU OBSERVATIONS

Vegetation play a critical role in the interactions between atmosphere and biosphere. CO2 fixed by plants through photosynthesis process at ecosystem scale is termed as gross primary production (GPP). It is also the first step CO2 entering the biosphere from the atmosphere. It not only fuels the ecosystem functioning, but also drives the global carbon cycle. Accurate estimation of the ecosystem photosynthetic carbon uptake at a global scale can help us better understand the global carbon budget, and the ecosystem sensitivity to the global climate change. Satellite observations have the advantage of global coverage and high revisit cycle, hence, are ideal for global GPP estimation. The simple production efficiency model that utilize the remote sensing imagery and climate data can provide reasonably well estimates of GPP at a global scale. With the solar induced chlorophyll fluorescence (SIF) being retrieved from satellite observations, new opportunities emerge in directly estimating photosynthesis from the energy absorption and partitioning perspective. In this thesis, by combining observations from both in situ and remotely acquired, I tried to (1) investigate the GPP SIF relationship using data from observations and model simulations; (2) improve a production efficiency model (vegetation photosynthesis model, VPM) and apply it to the regional and global scale; (3) investigate the GPP and SIF sensitivity to drought at different ecosystems; (4) explore the global interannual variation of GPP and its contributing factors. Chapter 2 uses site level observations of both SIF and GPP to explore their linkage at both leaf and canopy/ecosystem scale throughout a growing season. Two drought events happened during this growing season also highlight the advantage of SIF in early drought warning and its close linkage to photosynthetic activity. Chapter 3 compares the GPP and SIF relationships using both instantaneous and daily integrated observations, the daily GPP and satellite retrieved SIF are latitudinal dependent and time-of-overpass dependent. Daily integrated SIF estimation shows better correlation with daily GPP observations. Chapter 4 compares different vegetation indices with SIF to get an empirical estimation of fraction of photosynthetically active radiation by chlorophyll (fPARchl). By comparing this fPARchl estimation with ecosystem light use efficiency retrieved from eddy covariance flux towers, the light use efficiency based on light absorption by chlorophyll shows narrower range of variation that can be used for improving production efficiency models. Chapter 5 investigates the drought impact on GPP through the change of vegetation canopy optical properties and physiological processes. Forest and non-forest ecosystems shows very different responses in terms of these two limitation and need to be treated differently in GPP modelling. Chapter 6 applies the improved VPM to North America and compared with SIF retrieval from GOME-2 instrument. The comparison shows good consistency between GPP and SIF in both spatial and seasonal variation. Chapter 7 uses an ensemble of GPP product to explore the cause of hot spots of GPP interannual variability. GPP in semiarid regions are strongly coupled with evapotranspiration and show high sensitivity to interannual variation of precipitation. The results demonstrate the importance of precipitation in regional carbon flux variability