Ground-based Reflectance Research Articles

Estimating Fraction of Absorbed Photosynthetically Active Radiation of Winter Wheat Based on Simulated Sentinel-2 Data under Different Varieties and Water Stress

The fraction of absorbed photosynthetically active radiation (fPAR) is an important parameter reflecting the level of photosynthesis and growth status of vegetation, and is widely used in energy cycling, carbon cycling, and vegetation productivity estimation. In agricultural production, fPAR is often combined with the light use efficiency model to estimate crop yield. Therefore, accurate estimation of PAR is of great importance for improving the accuracy of crop yield estimation and ensuring national food security. Existing studies based on vegetation indices have not considered the effects of genetic variety, light, and water stress on fPAR estimation. This study uses ground-based reflectance data to simulate 21 common Sentinel-2 vegetation indices and compare their estimation ability for winter wheat fPAR. The stability of the vegetation index with the highest correlation in inverting fPAR under different cultivars, light, and water stress was tested, and then the model was validated at the satellite scale. Finally, a sensitivity analysis was performed. The results showed that the index model based on modified NDVI (MNDVI) had the highest correlation not only throughout the critical phenological period of winter wheat (R2 of 0.6649) but also under different varieties, observation dates, and water stress (R2 of 0.918, 0.881, and 0.830, respectively). It even performed the highest R2 of 0.8312 at the satellite scale. Moreover, through comparison, we found that considering water stress and variety differences can improve the estimation accuracy of fPAR. The study showed that using MNDVI for fPAR estimation is not only feasible but also has high accuracy and stability, providing a reference for rapid and accurate estimation of fPAR by Sentinel-2 and further exploring the potential of Sentinel-2 data for high-resolution fPAR mapping.

Vicarious calibration of the Tropospheric Monitoring Instrument (TROPOMI) short-wave infrared (SWIR) module over the Railroad Valley Playa

Abstract. The short-wave infrared (SWIR) module of the Tropospheric Monitoring Instrument (TROPOMI) on board the ESA's Sentinel-5 precursor (S5p) satellite has been very stable during its 5 years in orbit. Calibration was performed on the ground, complemented by measurements during in-flight instrument commissioning. The radiometric response and general performance of the SWIR module are monitored by on-board calibration sources. We show that after 5 years in orbit, TROPOMI-SWIR has continued to show excellent performance with degradation of at most 0.1 % in transmission and having lost less than 0.3 % of the detector pixels. Independent validation of the instrument calibration, via vicarious calibration, can be done through comparisons with ground-based reflectance data. In this work, ground measurements at the Railroad Valley Playa, a valley in central Nevada that is often used as a reference for satellite measurements, are used to perform vicarious calibration of the TROPOMI-SWIR measurements. This is done using dedicated measurement campaigns as well as automated reflectance measurements within the RADCALNET programme. As such, TROPOMI-SWIR is an excellent test case to explore the methodology of vicarious calibration applied to infrared spectroscopy. Using methodology developed for the vicarious calibration of the OCO-2 and GOSAT missions, the absolute radiometry of TROPOMI-SWIR performance is independently verified to be stable down to ∼ 6 %–10 % using the Railroad Valley when both the absolute and relative radiometric calibrations are applied. Differences with the on-board calibration originate from the bidirectional reflection distribution function (BRDF) effects of the desert surface, the large variety in viewing angles, and the different sizes of footprints of the TROPOMI pixels. Vicarious calibration is shown to be an additional valuable tool in validating radiance-level performances of infrared instruments such as TROPOMI-SWIR in the field of atmospheric composition. It remains clear that for instruments of similar design and resolution to TROPOMI-SWIR, on-board calibration sources will continue to provide superior results due to the limitations of the vicarious calibration method.

Tethys’s Heat Fluxes Varied with Time in the Ithaca Chasma and Telemus Basin Region

We investigated how lithospheric heat fluxes varied temporally and spatially on the Saturnian moon Tethys, focusing on the region of Ithaca Chasma that overprints Telemus Impact Basin. Our results, derived from flexure associated with Ithaca, indicate elastic thicknesses of 4.1 ± 0.3 km to 6.4 ± 0.4 km and heat fluxes ranging from 12 to 39 mW m−2 assuming a nonporous pure H2O ice lithosphere. Our results for Ithaca’s south limb are similar to previous estimates within the north limb, indicating consistent heat fluxes across a large spatial extent in this area. However, our estimates are lower than those for the older Telemus Basin (>60 mW m−2), revealing evidence that Tethys experienced a substantial temporal variation in heat fluxes in this region. Heat fluxes reflected by Ithaca are similar to previous estimates for Tethys’s two youngest impact basins, Melanthius and Odysseus, suggesting that Ithaca may also be relatively young. If Tethys’s lithosphere is porous, then our heat flux estimates for Ithaca Chasma drop to 12–38 mW m−2, 11–35 mW m−2, and 10–33 mW m−2 for 5%, 15%, and 25% porosities, respectively. If Tethys’s lithosphere includes ∼10% NH3-hydrates, then the estimates are 5–16 mW m−2, 5–15 mW m−2, 4–14 mW m−2, and 4–13 mW m−2 for 0%, 5%, 15%, and 25% porosities, respectively. Although we find that some ground-based reflectance spectra hint at 2.2 μm bands that may result from NH3-bearing species, the detected features are weak and may not result from surface constituents. Consequently, our heat flux estimates that assume a pure H2O ice lithosphere are likely more accurate.

DLR HySU—A Benchmark Dataset for Spectral Unmixing

Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers. However, there is still a lack of publicly available reference data sets suitable for the validation and comparison of different spectral unmixing methods. In this paper, we introduce the DLR HyperSpectral Unmixing (DLR HySU) benchmark dataset, acquired over German Aerospace Center (DLR) premises in Oberpfaffenhofen. The dataset includes airborne hyperspectral and RGB imagery of targets of different materials and sizes, complemented by simultaneous ground-based reflectance measurements. The DLR HySU benchmark allows a separate assessment of all spectral unmixing main steps: dimensionality estimation, endmember extraction (with and without pure pixel assumption), and abundance estimation. Results obtained with traditional algorithms for each of these steps are reported. To the best of our knowledge, this is the first time that real imaging spectrometer data with accurately measured targets are made available for hyperspectral unmixing experiments. The DLR HySU benchmark dataset is openly available online and the community is welcome to use it for spectral unmixing and other applications.

Thunderstorm identification algorithm research based on simulated airborne weather radar reflectivity data

In the past few decades, radar reflectivity data have been widely used in thunderstorm identification research. Many thunderstorm identification algorithms for ground-based weather radar have been developed. But for airborne weather radar, due to the relative scarcity of data, the thunderstorm identification research is insufficient and there are still few effective identification methods. Airborne weather radar has the realization capability of close-range detection, but most existing airborne weather radars do not have scanning capability. This paper proposes an airborne weather radar volume scan mode, under which there is a total of 31 sector scans at 31 elevations in a volume scan. And a reflectivity data simulation model of the airborne weather radar is established based on this scan strategy, then the ground-based X-band radar reflectivity data are used as input to obtain the simulated X-band airborne radar reflectivity data. Moreover, this paper studies a thunderstorm identification algorithm for the X-band airborne radar with the proposed scan mode. An improved SCI (storm cell identification) algorithm is proposed on the basis of the traditional SCI algorithm which is applicable to S-band ground-based weather radar. The results of thunderstorm identification carried out on the simulated airborne radar data show that the algorithm can effectively identify the thunderstorm cells in the mature stage and the developing stage.

The Convective Features within and Surrounding Severe Tropical Cyclone Larry (2006)

A sequence of ground-based radar reflectivity images sampled in the 17 hours prior to, and during the landfall of Severe Tropical Cyclone Larry (2006) are presented and analyzed using Fourier and wavelet analysis techniques. A range of mesoscale convective anomalies were detected, with characteristics and behavior consistent with vortex Rossby wave initiation. Cyclonically propagating eye-wall kinks, elongations and mesoscale reflectivity maxima were all observed throughout the sampling period, along with intense inner spiral bands. Various deep convective maxima propagated within the eye-wall at speeds consistent with predictions derived by linear barotropic wave theory. Three eye-wall breakdown episodes were observed during the study period, along with corresponding increases in storm-core asymmetric wave power and reductions in estimated storm intensity. Vortex Rossby wave initiated radial flows are also suggested by the presence of a possible mesovortex within a broken section of the eye-wall during landfall, and the outward ejection of filaments of deep convection from an adjacent inner spiral band. The possible influence of this wave activity upon the storm intensity and integrity is discussed.

Using Landsat Surface Reflectance Data as a Reference Target for Multiswath Hyperspectral Data Collected Over Mixed Agricultural Rangeland Areas

Low-cost flight-based hyperspectral imaging systems have the potential to provide important information for ecosystem and environmental studies as well as aide in land management. To realize this potential, methods must be developed to provide large-area surface reflectance data allowing for temporal data sets at the mesoscale. This paper describes a bootstrap method of producing a large-area, radiometrically referenced hyperspectral data set using the Landsat surface reflectance (LaSRC) data product as a reference target. The bootstrap method uses standard hyperspectral processing techniques that are extended to remove uneven illumination conditions between flight passes, allowing for radiometrically self-consistent data after mosaicking. Through selective spectral and spatial resampling, LaSRC data are used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from two hyperspectral flights over the same managed agricultural and unmanaged range land covering approximately 5.8 km2 acquired on June 21, 2014 and June 24, 2015 are presented. Data from a flight over agricultural land collected on June 6, 2016 are compared with concurrently collected ground-based reflectance spectra as a means of validation.

Improved Aerosol Optical Thickness, Columnar Water Vapor, and Surface Reflectance Retrieval from Combined CASI and SASI Airborne Hyperspectral Sensors

An increasingly common requirement in remote sensing is the integration of hyperspectral data collected simultaneously from different sensors (and fore-optics) operating across different wavelength ranges. Data from one module are often relied on to correct information in the other, such as aerosol optical thickness (AOT) and columnar water vapor (CWV). This paper describes problems associated with this process and recommends an improved strategy for processing remote sensing data, collected from both visible to near-infrared and shortwave infrared modules, to retrieve accurate AOT, CWV, and surface reflectance values. This strategy includes a workflow for radiometric and spatial cross-calibration and a method to retrieve atmospheric parameters and surface reflectance based on a radiative transfer function. This method was tested using data collected with the Compact Airborne Spectrographic Imager (CASI) and SWIR Airborne Spectrographic Imager (SASI) from a site in Huailai County, Hebei Province, China. Various methods for retrieving AOT and CWV specific to this region were assessed. The results showed that retrieving AOT from the remote sensing data required establishing empirical relationships between 465.6 nm/659 nm and 2105 nm, augmented by ground-based reflectance validation data, and minimizing the merit function based on AOT@550 nm optimization. The paper also extends the second-order difference algorithm (SODA) method using Powell’s methods to optimize CWV retrieval. The resulting CWV image has fewer residual surface features compared with the standard methods. The derived remote sensing surface reflectance correlated significantly with the ground spectra of comparable vegetation, cement road and soil targets. Therefore, the method proposed in this paper is reliable enough for integrated atmospheric correction and surface reflectance retrieval from hyperspectral remote sensing data. This study provides a good reference for surface reflectance inversion that lacks synchronized atmospheric parameters.

Mapping of Scotch Broom (Cytisus scoparius) with Landsat Imagery

Methods were developed and tested for mapping the distribution of Scotch broom, an invasive shrub species expanding its range and disrupting native species and habitats in several parts of the world. During spring, the Scotch broom produces yellow flowers. Landsat imagery during the flower bloom period and during summer was acquired for several years for a study area on Vancouver Island, British Columbia, Canada. Ground-based reflectance measurements plus statistical separability tests were conducted to determine the effectiveness for identifying Scotch broom with Landsat spectral bands, band ratios, vegetation indices, and combinations of bloom and nonbloom imagery. Maximum likelihood classifications of three Scotch broom density classes (dense, ≥ 75% cover; moderate, 25 to 75%; low, 10 to 25%) and other land covers were run with various image and band sets and tested against independent reference sites. Accuracies of classifications using the better band combinations for moderate and dense Scotch broom patches combined were on the order of 80%, with unreliable results for sites of low Scotch broom density. Scotch broom patches less than 0.5 ha were often missed. Some commission error occurred (areas erroneously classified as Scotch broom). Suggested improvements are the use of time series of classifications over multiple years, incorporating knowledge of Scotch broom spread mechanisms or temperature and elevation limitations, and use of higher resolution satellites if the expense warrants it. Despite some limitations, a satellite-based remote sensing approach may be useful for aspects of Scotch broom management.

Modeling Land Surface Roughness Effect on Soil Microwave Emission in Community Surface Emissivity Model

Soil surface roughness is a crucial factor affecting the land surface microwave emissivity. Presented in this paper is a semiempirical model that analytically accounts for both roughness attenuation and cross-polarization-mixing effects in the frequency range of 1–100 GHz. The model is based on the finite linear superposition of hyperbolic tangent (tanh) functions over the normalized surface roughness and radiative parameter space, which proves to be very flexible and efficient in handling the distinct asymptotic features of roughness effects at the low-frequency end and high-frequency end and the nonlinear structure in between. The model performance was analyzed with the ground-based reflectivity measurements collected from different sources. In comparison with the existing semiempirical models in the literature, the new tanh-based roughness model demonstrated higher accuracy and consistent performance in the frequency range of 1.4–100 GHz and $0^\circ\!\sim\! 60^\circ$ incident angles.

Employing broadband spectra and cluster analysis to assess thermal defoliation of cotton

Growers and field scouts need assistance in surveying cotton (Gossypium hirsutum L.) fields subjected to thermal defoliation to reap the benefits provided by this nonchemical defoliation method. A study was conducted to evaluate broadband spectral data subjected to unsupervised classification for surveying cotton plots subjected to thermal defoliation. Ground-based reflectance measurements of thermally treated and non-treated cotton canopies were collected at two study Sites (Site 1 and Site 2) with a handheld hyperspectral spectroradiometer. The hyperspectral data were merged into eight broad spectral bands: coastal blue (400–450nm), blue (450–510nm), green (510–580nm), yellow (585–625nm), red (630–690nm), red-edge (705–745nm), near-infrared (770–895nm), and panchromatic (450–800nm). Also, a broadband normalized difference vegetation index (NDVI) was created with the red (630–690nm) and near-infrared bands (770–895nm). For each study Site, two datasets were analyzed: (1) two-class case (thermally treated cotton observations and non-treated cotton observations) and (2) five-class case (thermally treated cotton observations and non-treated cotton observations and three additional classes created with the weighted average of the thermally treated cotton observations and non-treated cotton observations). The clustering algorithm referred to as CLUES (CLUstEring based on local Shrinking) was employed to automatically group the data into clusters without the user selecting the number of clusters. Cluster validation was determined with the average silhouette width; also accuracy was assessed with contingency matrixes. Clustering analysis worked well in dividing the data into appropriate groups, with the best cluster structure occurring for the NDVI. User’s and producer’s accuracies for the NDVI were greater than 86%, indicating an excellent classification. Findings support future endeavors to assess airborne and satellite-borne systems equipped with sensors sensitive to the wavelengths deemed useful in this study and unsupervised classification techniques that automatically determines the numbers of clusters to evaluate thermal defoliation of cotton fields.

On the generation of broad-scale hyperspectral ground reflectance mosaics from aerial and ground-based observations

The monitoring of land cover changes from remotely sensed aerial or satellite observations, used in conjunction with ground-based observations, forms the basis of many operational monitoring systems used in regional natural resource management (NRM) programmes. Consistent and comparable observations are fundamental to monitoring, which for aerial or satellite acquired imagery requires the data to be geometrically aligned and then radiometrically normalised for atmospheric, viewing geometry and other effects. Where comparison of observations from multiple-sensors may be required, correction to the common reference of ground reflectance is often preferred over a sensor-specific scale. Here we consider the problem of correcting multiple flight lines of hyperspectral airborne data, acquired within days of each other, to ground reflectance. A method which incorporates regularisation constraints on the spatial and spectral smoothness of the coefficients of bidirectional reflectance distribution function (BRDF) kernels is used in conjunction with an existing atmospheric correction method. We describe the methodology applied and demonstrate the application of the methodology to near simultaneous ground and aerial acquired hyperspectral data collected near Mullewa, Western Australia, in February 2010. A 15% improvement in agreement between the ground-based reflectance observations and the mosaicked hyperspectral reflectance estimates was achieved after application of the model. Next, we simulated a Landsat satellite image by convolving the hyperspectral data to the equivalent bands of the Landsat 5 sensor and comparing the resulting corrected and non-corrected Landsat-simulants to an actual Landsat scene acquired at a similar date. We recorded an approximately 15% improvement in agreement between the simulated and real Landsat scenes.

Ground-based spectral reflectance measurements for evaluating the efficacy of aerially- applied glyphosate treatments

Aerial application of herbicides is a common tool in agricultural field management. The objective of this study was to evaluate the efficacy of glyphosate herbicide applied using aircraft fitted with both conventional and emerging aerial nozzle technologies. A weedy field was set up in a randomised complete block experimental design using three replicates. Four aerial spray technology treatments, electrostatic nozzles with charging off, electrostatic nozzles with charging on, conventional flat-fan hydraulic nozzles and rotary atomisers, were tested. To evaluate the glyphosate efficacy and performance of aerial spray technologies, spectral reflectance measurements were acquired using a ground-based sensing system for all treatment plots. Three measurements were taken at 1, 8, and 17 days after treatment (DAT). The statistical analyses indicated that glyphosate applied with different methods killed the weeds effectively compared to untreated areas at 17 DAT. Conventional flat-fan nozzles and rotary atomisers performed better than the electrostatic nozzles with charging off. There was no evidence to show that the electrostatic nozzle performed better with charging on or charging off. The results could provide applicators with guidance equipment configurations that can result in herbicide savings and optimised applications in other crops.

Vegetation water content mapping in a diverse agricultural landscape: National Airborne Field Experiment 2006

Mapping land cover and vegetation characteristics on a regional scale is critical to soil moisture retrieval using microwave remote sensing. In aircraft-based experiments such as the National Airborne Field Experiment 2006 (NAFE 06), it is challenging to provide accurate high resolution vegetation information, especially on a daily basis. A technique proposed in previous studies was adapted here to the heterogenous conditions encountered in NAFE 06, which included a hydrologically complex landscape consisting of both irrigated and dryland agriculture. Using field vegetation sampling and ground-based reflectance measurements, the knowledge base for relating the Normalized Difference Water Index (NDWI) and the vegetation water content was extended to a greater diversity of agricultural crops, which included dryland and irrigated wheat, alfalfa, and canola. Critical to the generation of vegetation water content maps, the land cover for this region was determined from satellite visible/infrared imagery and ground surveys with an accuracy of 95.5% and a kappa coefficient of 0.95. The vegetation water content was estimated with a root mean square error of 0.33 kg/sq m. The results of this investigation contribute to a more robust database of global vegetation water content observations and demonstrate that the approach can be applied with high accuracy. Keywords: Vegetation, field experimentation, thematic mapper, NDWI, agriculture.

Monitoring leaf dry weight and leaf area index in wheat with hyperspectral remote sensing.

Aims Biomass and leaf area index (LAI) are important parameters for indicating crop growth potential and photosynthetic productivity in wheat. Non-destructive, quick assessment of leaf dry weight and LAI is necessary for growth diagnosis and cultural regulation in wheat production. The objectives of this study were to determine the relationships of leaf dry weight and LAI to ground-based canopy hyperspectral reflectance and spectral parameters and to derive regression equations for monitoring leaf dry weight and LAI in winter wheat (Triticum aestivum) with hyperspectral remote sensing. Methods Three field experiments were conducted with different wheat varieties and nitrogen levels for three growing seasons, and time-course measurements were taken on canopy hyperspectral reflectance and leaf dry weight and LAI during the experiments. Experiment one was conducted in 2005?2006 to construct a monitoring model with four N rates of 0, 90, 180 and 270 kg·hm–2 using cultivars ‘Ningmai9’ and ‘Yumai34’ (low and high protein types, respectively). Experiment two was un-dertaken in 2004?2005 to construct a monitoring model with four N rates of 0, 75, 150 and 225 kg·hm–2 using cultivars ‘Ningmai9’, ‘Yangmai12’ and ‘Yumai34’ (low, medium, high protein types, respec-tively). Experiment three was conducted in 2003?2004 to test a monitoring model with four N rates of 0, 75, 150, 225 and 300 kg·hm–2 using cultivars ‘Ningmai9’, ‘Huaimai20’ and ‘Xuzhou26’ (low, medium, high protein types, respectively). Important findings Leaf dry weight and LAI in wheat increased with increasing nitrogen rates and with significant differences between stages of growth. The dynamics of leaf dry weight and LAI during growth exhibited single peak patterns. The sensitive spectral bands were located mostly within red light and near infrared regions, with correlation coefficients –0.60 in 590~710 nm and 0.69 in 745~1 130 nm. The regression analyses between existing vegetation indices and leaf dry weight and LAI revealed that some key spectral parameters could accurately estimate changes in leaf dry weight and LAI across a broad range of stages of growth, nitrogen levels and growing seasons, with unified spectral parameters for each growth parameter. Among them, regression models based on RVI (810, 560), FD755, GMI, SARVI (MSS) and TC3 produced better estimation of leaf dry weight and LAI. Testing of the monitoring models with an independent dataset indicated that the spectral indices of RVI (810, 560), GMI, SARVI (MSS), PSSRb, (R750-800/R695-740)-1, VOG2 and mSR705 gave accurate growth estimation under the ex-perimental conditions. Overall, leaf dry weight and LAI in wheat could be monitored by key vegetation indices, with more reliable estimation from RVI (810, 560), GMI and SARVI (MSS).

Quickbird satellite and ground-based multispectral data correlations with agronomic parameters of irrigated maize grown in small plots

Satellite remote sensing has not been practical for agronomic research conducted using small plots due to spatial resolution issues. The objectives were to determine, (1) the potential use of QuickBird high resolution multispectral images for estimating available nitrogen (N) at planting, leaf N content at the tassel (VT) growth stage, and yield (biomass and grain) of irrigated maize (Zea mays L.) grown in research size plots and (2), if image digital number (DN) values were adequate for assessing these agronomic parameters. Conventionally-tilled plots 21.3 m × 21.6 m, 10.7 m × 43.3 m, and 10.7 m × 21.6 m were used. QuickBird images (2.5 m pixel resolution) were obtained at the VT growth stage for two growing seasons along with ground-based reflectance for one growing season. Multispectral data [blue (450–520), green (520–600), red (630–690), and near-infrared (760–900 nm)] was acquired by both sources. Relative green (G) waveband DN had the highest correlations with available N at planting (r2 = 0.91), relative grain yield (r2 = 0.81) and relative total biomass (r2 = 0.59) than any of the other indices investigated. Relative spectral indices containing G and near-infrared (NIR) wavebands, i.e., the green normalised difference vegetation index (GNDVI), the NIR/G ratio and the chlorophyll index ((NIR/G)−1) produced good correlations with leaf N content at VT (r2 > 0.7). Regression parameters (slopes and intercepts) of the normalised (NIR/G)−1 index calculated from satellite DN values and ground-based reflectance versus leaf N content were not significantly different (α = 0.01). Results indicate that QuickBird multispectral images show promise for estimating agronomic parameters from small plot research. Additional research is required to expand the results to more maize growth stages and other crops.

Characterisation of the environmental impact of the Rodalquilar mine, Spain by ground-based reflectance spectroscopy

This study has investigated the utility of using field-based reflectance spectroscopy to characterise the distribution and nature of the dispersion of tailings material from the Rodalquilar mine, Spain. Field spectral measurements covering the visible to shortwave infrared wavelengths (0.35 to 2.5 m) and laboratory analyses were performed on samples collected along the length of the main river within the Rodalquilar valley. The nature and degree of contamination at locations within the river channel were calculated by a range of spectrometric analyses. The resulting mineral maps identified that tailings material with significant amounts of hematite with minor jarosite, ferrihydrite and goethite, and clays, primarily alunite and kaolinite, with minor smectite and illite, had been dispersed along the length of the river. These results have been used to improve understanding of the erosion and remediation history of the Rodalquilar mine. This study has shown the potential of field-based reflectance spectroscopy, integrated with ground positioning and digital mapping systems, as a real-time mapping methodology enabling immediate, accurate characterisation of the nature and scale of tailings material dispersion.

Use of hyperspectral remote sensing to estimate the gross photosynthesis of agricultural fields

Optimization of crop growth and yield is achieved through the use of effective management practices. However, transient weather conditions will modify crop growth and yield. To assess crop development it is therefore essential to understand the current crop ecophysiological status. Such information can be monitored continuously using micrometeorological instrumented towers over agricultural surfaces. The spatial coverage of this approach is limited to the upwind area contributing to the flux. Remote sensing becomes key in deriving carbon exchanges and crop vigour over larger spatial areas. Derived from ground-based hyperspectral reflectance measurements from five growing seasons, a relationship between the eddy covariance estimates of gross photosynthesis and the product of the standardized photochemical reflectance index and the integrated modified triangular index was expanded to the field scale through the use of Compact Airborne Spectrographic Imager (CASI) data for corn and wheat over two consecutive seasons in the same field. Imagery-derived maps of gross photosynthesis successfully identified areas of potential stress that were known to be correlated with lower yield. Results were further verified using an independent flux dataset. This approach, modified from previous attempts in natural ecosystems, offers additional promise for managed systems.

Responsive in-season nitrogen management for cereals

Current nitrogen (N) management strategies for worldwide cereal production systems are characterized by low N use efficiency (NUE), environmental contamination, and considerable ongoing debate regarding what can be done to improve N fertilizer management. Development of innovative strategies that improve NUE and minimize off-field losses is crucial to sustaining cereal-based farming. In this paper, we review the major managerial causes for low NUE, including (1) poor synchrony between fertilizer N and crop demand, (2) uniform field applications to spatially variable landscapes that commonly vary in crop N need, and (3) failure to account for temporally variable influences on crop N needs. Poor synchronization is mainly due to large pre-plant applications of fertilizer N, resulting in high levels of inorganic soil N long before rapid crop uptake occurs. Uniform applications within fields discount the fact that N supplies from the soil, crop N uptake, and crop response are spatially variable. Current N management decisions also overlook year-to-year weather variations and sometimes fail to account for soil N mineralized in warm, wet years, ignoring indigenous N supply. The key to optimizing tradeoffs amongst yield, profit, and environmental protection is to achieve synchrony between N supply and crop demand, while accounting for spatial and temporal variability in soil N. While some have advocated a soil-based management zones (MZ) approach as a means to direct variable N applications and improve NUE, this method disregards yearly variation in weather. Thus, it seems unlikely that the soil-based MZ concept alone will be adequate for variable application of crop N inputs. Alternatively, we propose utilizing emerging computer and electronic technologies that focus on the plant to assess N status and direct in-season spatially variable N applications. Several of these technologies are reviewed and discussed. One technology showing promise is ground-based active-light reflectance measurements converted to NDVI or other similar indices. Preliminary research shows this approach addresses the issue of spatial variability and is accomplished at a time within the growing season so that N inputs are synchronized to match crop N uptake. We suggest this approach may be improved by first delineating a field into MZ using soil or other field properties to modify the decision associated with ground-based reflectance sensing. While additional adaptive research is needed to refine these newer technologies and subsequent N management decisions, preliminary results are encouraging. We expect N use efficiency can be greatly enhanced using this plant-based responsive strategy for N management in cereals.

Using Ground-Based Reflectance Measurements as Selection Criteria for Droughtߚ and Aflatoxin-Resistant Peanut Genotypes

Drought stress and aflatoxin contamination continue to challenge peanut (Arachis hypogaea L.) producers across the USA. Thus, the continued development of drought- and aflatoxin-resistant peanut cultivars is essential to maintain productivity under less than ideal growing conditions. Remote sensing of canopy reflectance is a well-established method of evaluating crop condition and shows promise as a tool for rapid selection of drought- and aflatoxin-resistant peanut genotypes. The objective of this study was to evaluate ground-based reflectance measurements to more accurately quantify differences in genotype response to drought conditions. In April 2004 and 2005 several small plots (4 m × 2 m) were established at the Gibbs Farm research facilities in Tifton, GA. Treatments consisted of five peanut genotypes encompassing a range of drought tolerance and yield characteristics. Drought conditions were simulated beginning 90 d after planting and maintained through harvest. Once drought conditions were established, a handheld radiometer was used to acquire twice-weekly reflectance measurements in the visible and near-infrared. Benchmark indices were developed based on the change in remotely sensed vegetation indices as a measure of the change in crop response between nonstressed and drought-stressed conditions. Significant treatment differences in benchmark indices were observed between drought-tolerant, moderately drought-tolerant and drought-intolerant varieties. Benchmark indices were also highly correlated with yield (r = −0.41 to −0.75, α = 0.05) in all three planting environments. However, the relationship between aflatoxin contamination and benchmark indices was less consistent, having a strong correlation with aflatoxin contamination in the second and third planting environments only (r = 0.38–0.73, α = 0.05). These indices could aid plant breeders in more accurately assessing genetic differences, which would accelerate breeding progress and the development of peanut cultivars with resistance to drought and aflatoxin contamination.