Anthony L. Nguy-Robertson

Effects of water-saving irrigation practices and drought resistant rice variety on greenhouse gas emissions from a no-till paddy in the central lowlands of China

As pressure on water resources increases, alternative practices to conserve water in paddies have been developed. Few studies have simultaneously examined the effectiveness of different water regimes on conserving water, mitigating greenhouse gases (GHG), and maintaining yields in rice production. This study, which was conducted during the drought of 2013, examined all three factors using a split-plot experiment with two rice varieties in a no-till paddy managed under three different water regimes: 1) continuous flooding (CF), 2) flooded and wet intermittent irrigation (FWI), and 3) flooded and dry intermittent irrigation (FDI). The Methane (CH₄) and nitrous oxide (N₂O) emissions were measured using static chamber-gas measurements, and the carbon dioxide (CO₂) emissions were monitored using a soil CO₂ flux system (LI-8100). Compared with CF, FWI and FDI irrigation strategies reduced CH₄ emissions by 60% and 83%, respectively. In contrast, CO₂ and N₂O fluxes increased by 65% and 9%, respectively, under FWI watering regime and by 104% and 11%, respectively, under FDI managed plots. Although CO₂ and N₂O emissions increased, the global warming potential (GWP) and greenhouse gas intensity (GHGI) of all three GHG decreased by up to 25% and 29% (p<0.01), respectively, using water-saving irrigation strategies. The rice variety also affected yields and GHG emissions in response to different water regimes. The drought-resistance rice variety (HY3) was observed to maintain yields, conserve water, and reduce GHG under the FWI irrigation management compared with the typical variety (FYY299) planted in the region. The FYY299 only had significantly lower GWP and GHGI when the yield was reduced under FDI water regime. In conclusion, FWI irrigation strategy could be an effective option for simultaneously saving water and mitigating GWP without reducing rice yields using drought-resistant rice varieties, such as HY3.

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 reparameterization 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.

Algorithms for estimating green leaf area index in C3 and C4 crops for MODIS, Landsat TM/ETM+, MERIS, Sentinel MSI/OLCI, and Venµs sensors

This study developed a set of algorithms for satellite mapping of green leaf area index (LAI) in C3 and C4 crops. In situ hyperspectral reflectance and green LAI data, collected across eight years (2001–2008) at three AmeriFlux sites in Nebraska USA over irrigated and rain-fed maize and soybean, were used for algorithm development. The hyperspectral reflectance was resampled to simulate the spectral bands of sensors aboard operational satellites (Aqua and Terra: MODIS, Landsat: TM/ETM+), a legacy satellite (Envisat: MERIS), and future satellites (Sentinel-2, Sentinel-3, and Venµs). Among 15 vegetation indices (VIs) examined, five VIs – wide dynamic range vegetation index (WDRVI), green WDRVI, red edge WDRVI, and green and red edge chlorophyll indices – had a minimal noise equivalent for estimating maize and soybean green LAI ranging from 0 to 6.5 m2 m−2. The algorithms were validated using MODIS, TM/ETM+, and MERIS satellite data. The root mean square error of green LAI prediction in both crops from all sensors examined in this study ranged from 0.73 to 0.95 m2 m−2 and coefficient of variation ranged between 17.0 and 29.3%. The algorithms using the red edge bands of MERIS and future space systems Sentinel-2, Sentinel-3, and Venµs allowed accurate green LAI estimation over areas containing maize and soybean with no re-parameterization.

Using a Simple Leaf Color Chart to Estimate Leaf and Canopy Chlorophyll a Content in Maize (Zea mays)

This study utilized a leaf color chart (LCC) to characterize the variation in leaf chlorophyll and estimate canopy chlorophyll in maize (Zea mays). The LCC consisted of four levels of greenness and was used to sort maize leaves in 2011 for three fields near Mead, Nebraska, USA. Leaf chlorophyll content for each color chart class was determined using two leaf-level sensors. The variation within each LCC class was reasonable (CV < 56%). The darkest color class predominated and indicated adequate fertilization rates using a Minolta SPAD-502 meter. Canopy chlorophyll content was estimated using destructively measured leaf area index (LAI) and the LCC. This approach was verified with a method utilizing canopy reflectance collected by both satellite imagery and a four-band radiometer. The error between the two methods was reasonable (RMSE = 0.55–0.88 g m−2; CV = 25.6–50.4%), indicating that both leaf and canopy chlorophyll can be estimated cheaply without a wet lab or field-based sensors.

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.

Determining factors that impact the calibration of consumer-grade digital cameras used for vegetation analysis

ABSTRACT Digital cameras can collect quantitative leaf data, such as chlorophyll content and leaf area index (LAI), because they act as a simple broadband radiometer. However, a cross-calibration between cameras is needed for the purpose of extracting vegetation information from various image repositories. The objective of this study was to examine the variation between multiple consumer-grade camera types – single reflex lens (SLR), point-and-shoot, and cellphone cameras – for the purpose of collecting reliable quantitative data when monitoring vegetation. The specific objectives were to: 1) identify the optimal light conditions for the calibration procedure, 2) determine the variability of exposure value (EV)-corrected calibrated digital numbers (cDNev) values among eight consumer-grade digital cameras, and 3) compare the cDNev values with the raw digital numbers (DN), exposure-adjusted digital numbers (DNev), and calibrated digital numbers (cDN) as these latter three components are easier to compute. This study demonstrated that light intensity was important for calibrating cameras to ensure sensor saturation, and that an improper white-balance setting can negatively impact data collection. In one experiment, the coefficient of variation (CV) between the eight cameras examined in the study was reduced from 29% using raw DN to 16% using cDNev values. Likewise, the root mean square error in estimating leaf chlorophyll-a using a common vegetation index for digital camera, excess green index (EGI), was reduced from 131 to 96 mg g−2. However, for both experiments, there was only a weak statistical difference between cDNev and DNev, indicating that exposure information was the most useful in minimizing the differences between cameras. Although digital cameras are not nearly as accurate as specialized remote-sensing equipment, they do offer the potential for greater collection opportunities. This study demonstrates the potential of using consumer-grade digital cameras to derive quantitative information from citizen science projects.

The mathematical identity of two vegetation indices: MCARI2 and MTVI2

Vegetation indices (VIs), which are combinations of various remote-sensing spectral bands, are widely used to study various biophysical properties. There are many articles introducing new VIs with the intention of minimizing factors such as soil background, canopy architecture, and row structure, while maximizing sensitivity to a specific biophysical characteristic such as the green leaf area index. Two VIs introduced in a previous study for the estimation of the biophysical characteristic green leaf area index are the modified chlorophyll absorption ratio index 2 (MCARI2) and modified triangular vegetation index 2 (MTVI2). This study and another study indicated that MTVI2 and MCARI2 gave identical results, but the mathematical similarity was not described in detail. Thus, future studies investigating the accuracy of VIs for estimating the biophysical characteristics need to examine either MCARI2 or MTVI2.