Modeling Gross Primary Production of Midwest Maize and Soybean Croplands with Satellite and Gridded Weather Data

The gross primary production (GPP) metric is useful in determining trends in the terrestrial carbon cycle. Models that determine GPP utilizing the light use efficiency (LUE) approach in conjunction with biophysical parameters that account for local weather conditions and crop specific factors are beneficial in that they combine the accuracy of the biophysical model with the versatility of the LUE model. One such model developed using in situ data was adapted to operate with remote sensing derived leaf area index (LAI) data and gridded weather datasets. The model, known as the Light Use Efficiency GPP Model (EGM), uses a four scalar approach to account for biophysical parameters including temperature, water stress, light quality, and phenology. The model was calibrated for four locations (seven fields) in the northern Midwest and was driven using remotely sensed LAI data and gridded weather data for these locations. Results showed reasonable error estimates (RMSE = 3.5 g C m-2 d-1). However, poor gridded weather atmospheric pressure and incoming solar radiation inputs, increased climatic variation in the study sites and contributed to higher RMSE that observed when the model was applied exclusively to in situ data from the Nebraska sites (2.6 g C m- 2 d- 1). Additionally, the application of LAI algorithms calibrated using solely Nebraska sites to sites in Iowa, Minnesota, and Illinois without verification of their accuracy potentially lead to increased error. Despite this, the study showed there is good correlation between measured and modeled GPP using this model for the field years under study. As the ultimate objective of research is to develop regional estimates of GPP, the decrease in model accuracy is somewhat offset by the model’s ability to function with gridded weather datasets and remotely sensed biophysical data. Advisor: Elizabeth A. Walter-She

Ecohydrology in water-limited environment using quantitative remote sensing - the Heihe River basin (China) case

Water-limited environments exist on all continents of the globe and they cover more than 30% of the Earth’s land surface. The eco-environments of these regions tend to be fragile and they are changing in a dramatic way through processes like land desertification, shrinking of oases, groundwater depletion, and soil erosion. These are either human induced or results of a changing climate. Implications of these changes for both the regional hydrologic cycle and the vegetation have been documented. Since these changes occur over a wide range of scales in space and time, remote sensing methods are needed to monitor the land surface characteristics, to observe changes in vegetation and hydrological states, and to compare these with predictions from hydrological models. It is widely accepted that remote sensing methods offer the ability to acquire spatially continuous measurements over large areas. Remote sensing can also help to visualize complex processes because the spatial data can be captured regularly over time. China is one of several countries with large arid and semi-arid areas. The Heihe River basin, situated in the arid inland of northwestern China, is one of the areas severely affected by ecoenvironmental degradation and recovery. The problem of the degraded environment is due to overexploitation of surface and ground water leading to shrinking of oases, including the decline and death of natural vegetation, and the lowering of the groundwater table. Exhaustive (over-)use of water resources is the main cause of land degradation in the lower reaches of the basin, called the Ejina oasis. The whole Heihe River basin is therefore selected as study area in this thesis to analyze the long-term eco-environmental changes. What happens in this river basin is likely to have a growing influence on regional hydrological cycles, even affecting human life. Effective management of eco-environmental problems in this critical zone of water-limited conditions will provide scientific evidence for protecting and improving the eco-environment in these Chinese northwestern arid regions, eventually resulting in land improvement. Studies on quantifying the relationship between the vegetation and the water resources are a critical step in developing an ecohydrological approach to resources management in order to minimize environmental degradation. Remote sensing measurements can help us to better understand the effects of changes in water management on hydrological processes and their subsequent feedback to the eco-environment at the regional scale. Remote sensing methods can also provide information to quantify heterogeneity and change at a large scale. Therefore, the main objective of this thesis is to develop a methodology for the quantitative assessment of eco-environmental changes at a large scale in arid regions by integrating remote sensing methods in ecohydrological approaches. Chapter 1 outlines the significance of quantitative assessment of eco-environmental changes using remote sensing methods and applying them for ecohydrology in northwestern China, resulting in the specific research objectives of this thesis. Chapter 2 quantifies both the vertical and horizontal distribution of vegetation in the Qilian Mountains area, representing the upper reaches of the Heihe River basin, based on MODIS NDVI images from the year 2000 - 2006. Our analysis reveals that elevation and aspect are two important impact factors for the vertical distribution of vegetation in a mountainous area. The NDVI increases with the elevation and reaches a maximum value at a certain elevation threshold, and then decreases as the elevation increases beyond this threshold. The optimal vegetation growth is on the shady side of the mountains because of less evapotranspiration. The best combination of temperature and precipitation is assessed providing good conditions for vegetation growth. Chapter 3 presents an efficient method to estimate the regional annual evapotranspiration (ET) based on the SEBS algorithm (Surface Energy Balance System) in the Zhangye basin, representing the middle reaches of the Heihe River basin. The method proposed is a combination of the daily SEBS results and data collected by meteorological stations. The result shows that the annual ET increased gradually during the period 1990-2004 and the main impact factor on the long-term increase of annual ET was the vegetation change. The accuracy of the ET result is validated using a water balance for the whole watershed and the validation reveals that the SEBS algorithm can be used to effectively estimate annual ET in the Zhangye basin. Chapter 4 establishes the quantitative relationship between the runoff of the Heihe River and the long-term vegetation change of the Ejina oasis, located in the lower reaches of the Heihe River. In this part, two time periods are distinguished corresponding to before and after the implementation of a new water allocation scheme in the Heihe River basin. The GIMMS NDVI and MODIS NDVI data sets are used to quantify the long-term change of the oasis vegetation in the first period 1989-2002 and the second period 2000-2006, respectively. The vegetation change shows a decreasing trend from 1989 to 2002 and an increasing trend between 2000 and 2006. Good relation between the runoff of the river and the vegetation growth are found at both stages and the time lag of the observed hysteresis effect of the runoff of the river on the oasis vegetation is one year. In addition, the yearly smallest water amount which sustains the demand of the eco-environment of the Ejina area is estimated to be 4×108 m3 based on MODIS images. Chapter 5 explores a method to quantify the effect of the groundwater depth on the vegetation growth in the year 2000 in the oasis area by combining MODIS NDVI with groundwater observation data. The result demonstrates that the groundwater depth suitable for vegetation growth in this region ranges from 2.8 to 5 m, depending on species composition. Hardly any vegetation growth occurs when the groundwater depth is below 5 m because the rooting depth of the occurring species is limited and cannot maintain adequate water supplies to their canopies when the water depth is below 5 m. The situation changes after implementation of the new water allocation scheme since 2000. The mean NDVI increased and the annual conversion of bare land into vegetated land is about 38 km2 per year during the period 2000 – 2008. It reflects a potential recovery of the eco-environment of the Ejina area. Chapter 6 comprises the main conclusions and the outlook for possible improvements in future research. The main contribution of this study is the successful integration of remote sensing with ecohydrology in quantifying the relationship between water resources and vegetation occurrence at large scale. It provides a methodology to evaluate the long-term vegetation change and the water resources impact using remote sensing data in water-limited areas. The approach of vegetation dynamics, runoff and groundwater impacts presented in this thesis serves as a sound foundation for predicting the effects of future environmental changes. <br/

The uncertainty analysis of the MODIS GPP product in global maize croplands

Gross primary productivity (GPP) is very important in the global carbon cycle. Currently, the newly released estimates of 8-day GPP at 500 m spatial resolution (Collection 6) are provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Science Team for the global land surface via the improved light use efficiency (LUE) model. However, few studies have evaluated its performance. In this study, the MODIS GPP products (GPPMOD) were compared with the observed GPP (GPPEC) values from site-level eddy covariance measurements over seven maize flux sites in different areas around the world. The results indicate that the annual GPPMOD was underestimated by 6%‒58% across sites. Nevertheless, after incorporating the parameters of the calibrated LUE, the measurements of meteorological variables and the reconstructed Fractional Photosynthetic Active Radiation (FPAR) into the GPPMOD algorithm in steps, the accuracies of GPPMOD estimates were improved greatly, albeit to varying degrees. The differences between the GPPMOD and the GPPEC were primarily due to the magnitude of LUE and FPAR. The underestimate of maize cropland LUE was a widespread problem which exerted the largest impact on the GPPMOD algorithm. In American and European sites, the performance of the FPAR exhibited distinct differences in capturing vegetation GPP during the growing season due to the canopy heterogeneity. In addition, at the DE-Kli site, the GPPMOD abruptly produced extreme low values during the growing season because of the contaminated FPAR from a continuous rainy season. After correcting the noise of the FPAR, the accuracy of the GPPMOD was improved by approximately 14%. Therefore, it is crucial to further improve the accuracy of global GPPMOD, especially for the maize crop ecosystem, to maintain food security and better understand global carbon cycle

Vegetation Dynamics Revealed by Remote Sensing and Its Feedback to Regional and Global Climate

This book focuses on some significant progress in vegetation dynamics and their response to climate change revealed by remote sensing data. The development of satellite remote sensing and its derived products offer fantastic opportunities to investigate vegetation changes and their feedback to regional and global climate systems. Special attention is given in the book to vegetation changes and their drivers, the effects of extreme climate events on vegetation, land surface albedo associated with vegetation changes, plant fingerprints, and vegetation dynamics in climate modeling

Responses of the terrestrial ecosystem productivity to droughts inside China

The terrestrial ecosystem productivity (hereafter, TEP) is a key index of global carbon cycles and a fundamental constraint of carbon sequestration capacity, and also an important measure of ecosystem services and food security. However, the TEP has been significantly affected by the long-lasting droughts. Identifying the spatial relationship between droughts and the TEP is crucial for enhancing ecosystem services in China. Here the net primary production (hereafter, NPP) derived from the Carnegie-Ames-Stanford Approach model (CASA-NPP) and two drought indices, namely the Standard Precipitation Index (hereafter, SPI) and the Standard Precipitation Evaporation Index (hereafter, SPEI), are used to examine the spatial relationship between droughts and the NPP in China for the period of 1982-2012. Our main results have shown that: (1) China’s annual NPP has increased slowly from 3.82 to 4.35 PgC per year (hereafter, PgC/yr), while droughts have become much severer from 1982 to 2012; (2) On the 3-month timescale, the NPP in arid and semi-arid ecosystems has decreased at a rate of 1.28 TgC per month with per “unit” decrease in the drought index (indicating drier conditions). (3) Overall, the NPP in China has increased 5.71 TgC per month with per “unit” increase in the drought index (indicating wetter conditions); The contribution of this NPP increase is mainly from forests and farmlands; (4) The SPEI is a relatively more effective and sensitive index in representing China’s droughts. In southern China, the lagging period for the NPP response to droughts is about 3-month, while a 6-month lagging period is found in the arid and semi-arid ecosystems in northern China

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

Evapotranspiration partitioning, stomatal conductance, and components of the water balance: A special case of a desert ecosystem in China

© 2016 Elsevier B.V. Partitioning evapotranspiration (ET) into its components reveals details of the processes that underlie ecosystem hydrologic budgets and their feedback to the water cycle. We measured rates of actual evapotranspiration (ETa), canopy transpiration (Tc), soil evaporation (Eg), canopy-intercepted precipitation (EI), and patterns of stomatal conductance of the desert shrub Calligonum mongolicum in northern China to determine the water balance of this ecosystem. The ETa was 251 ± 8 mm during the growing period, while EI, Tc, and Eg accounted for 3.2%, 63.9%, and 31.3%, respectively, of total water use (256 ± 4 mm) during the growing period. In this unique ecosystem, groundwater was the main water source for plant transpiration and soil evaporation, Tc and exceeded 60% of the total annual water used by desert plants. ET was not sensitive to air temperature in this unique desert ecosystem. Partitioning ET into its components improves our understanding of the mechanisms that underlie adaptation of desert shrubs, especially the role of stomatal regulation of Tc as a determinant of ecosystem water balance