Remote Sensing of Green Leaf Area Index in Maize and Soybean: From Close-Range to Satellite

This dissertation seeks to explore alternative methodologies for estimating green leaf area index (LAI) and crop developmental stages. Specifically this research [1] developed an approach for creating a Moderate Resolution Imaging Spectroradiometer (MODIS) high spatial resolution product for estimating green LAI on the base of data collected using two different close-range sensors. It was determined that the vegetation indices (VIs) Wide Dynamic Range Vegetation Index (WDRVI) and Enhanced Vegetation Index 2 (EVI2) were capable of accurate estimation of green LAI from MODIS 250 m data using models developed from hyperspectral (RMSE \u3c 0.69 m2 m-2; CV \u3c 33%) or multispectral sensors (RMSE \u3c 0.69 m2 m-2; CV \u3c 34%). [2] Explored a new approach for maximizing the sensitivity of VIs to green LAI. Rather than use one VI, we suggested using multiple VIs in different LAI dynamic ranges. Thus, the sensitivity of the VI to the green LAI was preserved and simpler linear models could be used instead of complex non-linear ones. Two combined vegetation indices (CVI) were presented using near infrared and either the red or red edge bands and were accurate in estimating green LAI. While the red band is more common in satellite sensors, the indices use red edge band were found to be species independent for maize and soybean. The two species-independent VIs used in the CVI were Red Edge Normalized Difference Index (Red Edge NDVI) and Red Edge Chlorophyll Index (CIred edge). [3] Algorithms were developed for estimating green LAI in four vastly different crops (maize, potato, soybean, and wheat) that do not require re-parameterization. The most promising VIs for developing a unified algorithm utilized either a green or red edge bands. [4] It was found that, in addition to traditionally used (VIs), the 2-dimensional spectral spaces (e.g. red vs. green reflectance) were capable of identifying four distinct stages of crop development (e.g. soil/residue, green-up, vegetative, and senescence). Advisor: Anatoly A. Gitelso

Assessment of Canopy Chlorophyll Content Retrieval in Maize and Soybean: Implications of Hysteresis on the Development of Generic Algorithms

Canopy chlorophyll content (Chl) closely relates to plant photosynthetic capacity, nitrogen status and productivity. The goal of this study is to develop remote sensing techniques for accurate estimation of canopy Chl during the entire growing season without re-parameterization of algorithms for two contrasting crop species, maize and soybean. These two crops represent different biochemical mechanisms of photosynthesis, leaf structure and canopy architecture. The relationships between canopy Chl and reflectance, collected at close range and resampled to bands of the Multi Spectral Instrument (MSI) aboard Sentinel-2, were analyzed in samples taken across the entirety of the growing seasons in three irrigated and rainfed sites located in eastern Nebraska between 2001 and 2005. Crop phenology was a factor strongly influencing the reflectance of both maize and soybean. Substantial hysteresis of the reflectance vs. canopy Chl relationship existed between the vegetative and reproductive stages. The effect of the hysteresis on vegetation indices (VI), applied for canopy Chl estimation, depended on the bands used and their formulation. The hysteresis greatly affected the accuracy of canopy Chl estimation by widely-used VIs with near infrared (NIR) and red reflectance (e.g., normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and simple ratio (SR)). VIs that use red edge and NIR bands (e.g., red edge chlorophyll index (CIred edge), red edge NDVI and the MERIS terrestrial chlorophyll index (MTCI)) were minimally affected by crop phenology (i.e., they exhibited little hysteresis) and were able to accurately estimate canopy Chl in two crops without algorithm re-parameterization and, thus, were found to be the best candidates for generic algorithms to estimate crop Chl using the surface reflectance products of MSI Sentinel-2

Informative spectral bands for remote green LAI estimation in C3 and C4 crops

Green leaf area index (LAI) provides insight into the productivity, physiological and phenological status of vegetation. Measurement of spectral reflectance offers a fast and nondestructive estimation of green LAI. A number of methods have been used for the estimation of green LAI; however, the specific spectral bands employed varied widely among the methods and data used. Our objectives were (i) to find informative spectral bands retained in three types of methods, neural network (NN), partial least squares (PLS) regression and vegetation indices (VI), for estimating green LAI in maize (a C4 species) and soybean (a C3 species); (ii) to assess the accuracy of the algorithms estimating green LAI using a minimal number of bands for each crop and generic algorithms for the two crops combined. Hyperspectral reflectance and green LAI of irrigated and rainfed maize and soybean were taken during eight years of observations (altogether 24 field-years) in very different weather conditions. The bands retained in the best NN, PLS and VI methods were in close agreement. The validity of these bands was further confirmed via the uninformative variable elimination PLS technique. The red edge and the NIR bands were selected in all models and were found the most informative. Identifying informative spectral bands across all four techniques provided insight into spectral features of reflectance specific for each species as well as those that are common to species with different leaf structures, canopy architectures and photosynthetic pathways. The analyses allowed development of algorithms for estimating green LAI in soybean and maize with no re-parameterization. These findings lay a strong foundation for the development of generic algorithms which is crucial for remote sensing of vegetation biophysical parameters

Toward Generic Models for Green LAI Estimation in Maize and Soybean: Satellite Observations

Informative spectral bands for green leaf area index (LAI) estimation in two crops were identified and generic models for soybean and maize were developed and validated using spectral data taken at close range. The objective of this paper was to test developed models using Aqua and Terra MODIS, Landsat TM and ETM+, ENVISAT MERIS surface reflectance products, and simulated data of the recently-launched Sentinel 2 MSI and Sentinel 3 OLCI. Special emphasis was placed on testing generic models which require no re-parameterization for these species. Four techniques were investigated: support vector machines (SVM), neural network (NN), multiple linear regression (MLR), and vegetation indices (VI). For each technique two types of models were tested based on (a) reflectance data, taken at close range and resampled to simulate spectral bands of satellite sensors; and (b) surface reflectance satellite products. Both types of models were validated using MODIS, TM/ETM+, and MERIS data. MERIS was used as a prototype of OLCI Sentinel-3 data which allowed for assessment of the anticipated accuracy of OLCI. All models tested provided a robust and consistent selection of spectral bands related to green LAI in crops representing a wide range of biochemical and structural traits. The MERIS observations had the lowest errors (around 11%) compared to the remaining satellites with observational data. Sentinel 2 MSI and OLCI Sentinel 3 estimates, based on simulated data, had errors below 8%. However the accuracy of these models with actual MSI and OLCI surface reflectance products remains to be determined

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

Gross primary production (GPP) is a useful metric for determining trends in the terrestrial carbon cycle. To estimate daily GPP, the cloud-adjusted light use efficiency model (LUEc) was developed by adapting a light use efficiency (LUE, ε) model to include in situ meteorological data and biophysical parameters. The LUEc uses four scalars to quantify the impacts of temperature, water stress, and phenology on ε. This study continues the original investigation in using the LUEc, originally limited to three AmeriFlux sites (US-Ne1, US-Ne2, and US-Ne3) by applying gridded meteorological data sets and remotely sensed green leaf area index (gLAI) to estimate daily GPP over a larger spatial extent. This was achieved by including data from four additional AmeriFlux locations in the U.S. Corn Belt for a total of seven locations. Results show an increase in error (RMSE = 3.5 g C m−2 d−1) over the original study in which in situ data were used (RMSE = 2.6 g C m−2 d−1). This is attributed to poor representation of gridded weather inputs (vapor pressure and incoming solar radiation) and application of gLAI algorithms to sites in Iowa, Minnesota, and Illinois, calibrated using data from Nebraska sites only, as well as uncertainty due to climatic variation. Despite these constraints, the study showed good correlation between measured and LUEc-modeled GPP (R2 = 0.80 and RMSE of 3.5 g C m−2 d−1). The decrease in model accuracy is somewhat offset by the ability to function with gridded weather datasets and remotely sensed biophysical data. The level of acceptable error is dependent upon the scope and objectives of the research at hand; nevertheless, the approach holds promise in developing regional daily estimates of GPP

Black Lives Matter and the Racialized Support for the January 6th Insurrection

Does support for the January 6th insurrection come mostly from concerned citizens worried over illegal voting, or from racists spurred to action by the highly visible Black Lives Matter protests and Donald Trump’s 2020 defeat? We field a survey experiment aimed at disentangling links between old and new racial grievances, anti-immigrant beliefs, Black activism, and support for the January 6th insurrection. We find that the people most likely to be supportive of the insurrection are whites who hold negative attitudes toward immigrants and subscribe to white replacement theory. Beliefs about the George Floyd protests also explain January 6th support, above and beyond demographics and other racial and political views. These results are validated by the 2020 Collaborative Multiracial Post-Election Survey. We also conduct a survey vignette experiment and find that anti-BLM rhetoric spread by Trump and right-wing news sources likely soured opinions on the movement and set the stage for widespread insurrection support

Introducing the 2-DROPS model for two-dimensional simulation of crop roots and pesticide within the soil-root zone

Mathematical models of pesticide fate and behaviour in soils have been developed over the last 30 years. Most models simulate fate of pesticides in a 1-dimensional system successfully, supporting a range of applications where the prediction target is either bulk residues in soil or receiving compartments outside of the soil zone. Nevertheless, it has been argued that the 1-dimensional approach is limiting the application of knowledge on pesticide fate under specific pesticide placement strategies, such as seed, furrow and band applications to control pests and weeds. We report a new model (2-DROPS; 2-Dimensional ROots and Pesticide Simulation) parameterised for maize and we present simulations investigating the impact of pesticide properties (thiamethoxam, chlorpyrifos, clothianidin and tefluthrin), pesticide placement strategies (seed treatment, furrow, band and broadcast applications), and soil properties (two silty clay loam and two loam top soils with either silty clay loam, silt loam, sandy loam or unconsolidated bedrock in the lower horizons) on microscale pesticide distribution in the soil profile. 2-DROPS is to our knowledge the first model that simulates temporally- and spatially-explicit water and pesticide transport in the soil profile under the influence of explicit and stochastic development of root segments. This allows the model to describe microscale movement of pesticide in relation to root segments, and constitutes an important addition relative to existing models. The example runs demonstrate that the pesticide moves locally towards root segments due to water extraction for plant transpiration, that the water holding capacity of the top soil determines pesticide transport towards the soil surface in response to soil evaporation, and that the soil type influences the pesticide distribution zone in all directions. 2-DROPS offers more detailed information on microscale root and pesticide appearance compared to existing models and provides the possibility to investigate strategies targeting control of pests at the root/soil interface

Developing the framework for a risk map for mite vectored viruses in wheat resulting from pre-harvest hail damage

There is a strong economic incentive to reduce mite-vectored virus outbreaks. Most outbreaks in the central High Plains of the United States occur in the presence of volunteer wheat that emerges before harvest as a result of hail storms. This study provides a conceptual framework for developing a risk map for wheat diseases caused by mite-vectored viruses based on pre-harvest hail events. Traditional methods that use NDVI were found to be unsuitable due to low chlorophyll content in wheat at harvest. Site-level hyperspectral reflectance from mechanically hailed wheat showed increased canopy albedo. Therefore, any increase in NIR combined with large increases in red reflectance near harvest can be used to assign some level of risk. The regional model presented in this study utilized Landsat TM/ETMþ data and MODIS imagery to help gap-fill missing data. NOAA hail maps that estimate hail size were used to refine the area most likely at risk. The date range for each year was shifted to account for annual variations in crop phenology based on USDA Agriculture statistics for percent harvest of wheat. Between 2003 and 2013, there was a moderate trend (R2 ¼ 0.72) between the county-level insurance claims for Cheyenne County, Nebraska and the area determined to be at risk by the model (excluding the NOAA hail size product due to limited availability) when years with low hail claims (\u3c400 ha) were excluded. These results demonstrate the potential of an operational risk map for mite-vectored viruses due to pre-season hail events

Developing the framework for a risk map for mite vectored viruses in wheat resulting from pre-harvest hail damage

There is a strong economic incentive to reduce mite-vectored virus outbreaks. Most outbreaks in the central High Plains of the United States occur in the presence of volunteer wheat that emerges before harvest as a result of hail storms. This study provides a conceptual framework for developing a risk map for wheat diseases caused by mite-vectored viruses based on pre-harvest hail events. Traditional methods that use NDVI were found to be unsuitable due to low chlorophyll content in wheat at harvest. Site-level hyperspectral reflectance from mechanically hailed wheat showed increased canopy albedo. Therefore, any increase in NIR combined with large increases in red reflectance near harvest can be used to assign some level of risk. The regional model presented in this study utilized Landsat TM/ETMþ data and MODIS imagery to help gap-fill missing data. NOAA hail maps that estimate hail size were used to refine the area most likely at risk. The date range for each year was shifted to account for annual variations in crop phenology based on USDA Agriculture statistics for percent harvest of wheat. Between 2003 and 2013, there was a moderate trend (R2 ¼ 0.72) between the county-level insurance claims for Cheyenne County, Nebraska and the area determined to be at risk by the model (excluding the NOAA hail size product due to limited availability) when years with low hail claims (\u3c400 ha) were excluded. These results demonstrate the potential of an operational risk map for mite-vectored viruses due to pre-season hail events

Incorporation of globally available datasets into the roving cosmic-ray neutron probe method for estimating field-scale soil water content

The need for accurate, real-time, reliable, and multi-scale soil water content (SWC) monitoring is critical for a multitude of scientific disciplines trying to understand and predict the Earth’s terrestrial energy, water, and nutrient cycles. One promising technique to help meet this demand is fixed and roving cosmic-ray neutron probes (CRNPs). However, the relationship between observed low-energy neutrons and SWC is affected by local soil and vegetation calibration parameters. This effect may be accounted for by a calibration equation based on local soil type and the amount of vegetation. However, determining the calibration parameters for this equation is labor- and time-intensive, thus limiting the full potential of the roving CRNP in large surveys and long transects, or its use in novel environments. In this work, our objective is to develop and test the accuracy of globally available datasets (clay weight percent, soil bulk density, and soil organic carbon) to support the operability of the roving CRNP. Here, we develop a 1 km product of soil lattice water over the continental United States (CONUS) using a database of in situ calibration samples and globally available soil taxonomy and soil texture data. We then test the accuracy of the global dataset in the CONUS using comparisons from 61 in situ samples of clay percent (RMSE=5.45 wt %, R2=0.68), soil bulk density (RMSE=0.173 g cm-3, R2=0.203), and soil organic carbon (RMSE=1.47 wt %, R2=0.175). Next, we conduct an uncertainty analysis of the global soil calibration parameters using a Monte Carlo error propagation analysis (maximum RMSE~0.035cm3 cm-3 at a SWC=0.40 cm3 cm-3). In terms of vegetation, fast-growing crops (i.e., maize and soybeans), grasslands, and forests contribute to the CRNP signal primarily through the water within their biomass and this signal must be accounted for accurate estimation of SWC. We estimated the biomass water signal by using a vegetation index derived from MODIS imagery as a proxy for standing wet biomass (RMSEm-2). Lastly, we make recommendations on the design and validation of future roving CRNP experiments