Mapping wheat nitrogen uptake from RapidEye vegetation indices

Magney, Troy S.; Eitel, Jan U. H.; Vierling, L. A.

doi:10.1007/s11119-016-9463-8

Cited by 66 publications

(42 citation statements)

References 90 publications

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“…The sensitivity of the green and infrared bands for the estimation of LCC has previously been demonstrated [9,27]. Specifically, the Green Chlorophyll Vegetation Index (near infra-red/green; [31]) has successfully been applied in several studies for deriving crop chlorophyll content [24,[53][54][55]. Research by Wang et al [56] involved the analysis of winter wheat spectral reflectance under different N applications and demonstrated that bands centred around the green and near-infrared spectral regions were sensitive to the treatments, whereas the blue band was comparatively less sensitive.…”

Section: Sentinel-2 Bands and Gpr Modelling For Parameter Retrievalsmentioning

confidence: 99%

The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development

et al. 2019

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Leaf Area Index (LAI) and chlorophyll content are strongly related to plant development and productivity. Spatial and temporal estimates of these variables are essential for efficient and precise crop management. The availability of open-access data from the European Space Agency's (ESA) Sentinel-2 satellite-delivering global coverage with an average 5-day revisit frequency at a spatial resolution of up to 10 metres-could provide estimates of these variables at unprecedented (i.e., sub-field) resolution. Using synthetic data, past research has demonstrated the potential of Sentinel-2 for estimating crop variables. Nonetheless, research involving a robust analysis of the Sentinel-2 bands for supporting agricultural applications is limited. We evaluated the potential of Sentinel-2 data for retrieving winter wheat LAI, leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC). In coordination with destructive and non-destructive ground measurements, we acquired multispectral data from an Unmanned Aerial Vehicle (UAV)-mounted sensor measuring key Sentinel-2 spectral bands (443 to 865 nm). We applied Gaussian processes regression (GPR) machine learning to determine the most informative Sentinel-2 bands for retrieving each of the variables. We further evaluated the GPR model performance when propagating observation uncertainty. When applying the best-performing GPR models without propagating uncertainty, the retrievals had a high agreement with ground measurements-the mean R 2 and normalised root-mean-square error (NRMSE) were 0.89 and 8.8%, respectively. When propagating uncertainty, the mean R 2 and NRMSE were 0.82 and 11.9%, respectively. When accounting for measurement uncertainty in the estimation of LAI and CCC, the number of most informative Sentinel-2 bands was reduced from four to only two-the red-edge (705 nm) and near-infrared (865 nm) bands. This research demonstrates the value of the Sentinel-2 spectral characteristics for retrieving critical variables that can support more sustainable crop management practices. the value of LAI data for updating state variables in process-based agroecosystem models in order to improve estimates of crop yield [3][4][5][6] and land-atmosphere carbon dioxide exchanges [7,8]. On the other hand, chlorophyll is a key driver of plant light absorption and conversion to chemical energy and is, therefore, an indicator of plant health and potential gross primary productivity [9,10]. In particular, leaf chlorophyll content is linked to leaf photosynthetic capacity via the maximum rate of carboxylation (V max ). The Ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) enzyme, which relates to V max and leaf-level carbon fixation, correlates to leaf nitrogen (N) content [11]. Since leaf N also consists of chlorophyll, plant chlorophyll is strongly correlated to leaf N [12][13][14] including that for winter wheat [15].LAI and chlorophyll content are important factors determining crop reflectance [11] and can, therefore, be estimated from optical Earth observation satellit...

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Section: Sentinel-2 Bands and Gpr Modelling For Parameter Retrievalsmentioning

confidence: 99%

The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development

et al. 2019

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“…Magney et al (2016a) demonstrated that in-season canopy monitoring using daily Normalized Difference Vegetation Index (NDVI) data could be used to explain 83% of yield variance and 80% of grain N accumulation. Total N in the biomass and grain has also been shown to be highly correlated to vegetation indices, particularly using red-edge bands, acquired from high resolution (5 × 5 m) satellite imagery (Magney et al, 2016b). Therefore, precision agriculture based on plant-based sensing opportunities may be used in combination with tactical nutrient management to optimize wheat performance.…”

Section: Opportunities For Site-specific N Managementmentioning

confidence: 99%

Impact of Climate Change Adaptation Strategies on Winter Wheat and Cropping System Performance across Precipitation Gradients in the Inland Pacific Northwest, USA

Maaz

Schillinger

Machado

et al. 2017

Front. Environ. Sci.

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Ecological instability and low resource use efficiencies are concerns for the long-term productivity of conventional cereal monoculture systems, particularly those threatened by projected climate change. Crop intensification, diversification, reduced tillage, and variable N management are among strategies proposed to mitigate and adapt to climate shifts in the inland Pacific Northwest (iPNW). Our objectives were to assess these strategies across iPNW agroecological zones and time for their impacts on (1) winter wheat (WW) (Triticum aestivum L.) productivity, (2) crop sequence productivity, and (3) N fertilizer use efficiency. Region-wide analysis indicated that WW yields increased with increasing annual precipitation, prior to maximizing at 520 mm yr −1 and subsequently declining when annual precipitation was not adjusted for available soil water holding capacity. While fallow periods were effective at mitigating low nitrogen (N) fertilization efficiencies under low precipitation, efficiencies declined as annual precipitation exceeded 500 mm yr −1 . Variability in the response of WW yields to annual precipitation and N fertilization among locations and within sites supports precision N management implementation across the region. In years receiving <350 mm precipitation yr −1 , WW yields declined when preceded by crops rather than summer fallow. Nevertheless, WW yields were greater when preceded by pulses and oilseeds rather than wheat across a range of yield potentials, and when under conservation tillage practices at low yield potentials. Despite the yield penalty associated with eliminating fallow prior to WW, cropping system level productivity was not affected by intensification, diversification, or conservation tillage. However, increased fertilizer N inputs, lower fertilizer N use efficiencies, and more yield variance may offset and limit the economic feasibility of intensified and diversified cropping systems.

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“…Ten measurements were made per microplot. The NDVI is commonly used as an indicator of aerial biomass production and nutrient status of crops [32][33][34]. In canola, an optimal time window to measure NDVI for grain yield prediction is between 50 and 70 days after sowing (DAS), while later readings are poorly predictive because they are influenced by stem elongation and flowering [35,36].…”

Section: Canopy Optical Propertiesmentioning

confidence: 99%

In-Field Observation of Root Growth and Nitrogen Uptake Efficiency of Winter Oilseed Rape

et al. 2020

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Field trials were conducted with two nitrogen applications (0 or 240 kg N ha−1) and three modern cultivars of winter oilseed rape (Brassica napus L.) previously selected from a root morphology screen at a young developmental stage. The purpose is to examine the relationship between root morphology and Nitrogen Uptake Efficiency (NUpE) and to test the predictiveness of some canopy optical indices for seed quality and yield. A tube-rhizotron system was used to incorporate below-ground root growth information. Practically, clear tubes of one meter in length were installed in soil at an angle of 45°. The root development was followed with a camera at key growth stages in autumn (leaf development) and spring (stem elongation and flowering). Autumn was a critical time window to observe the root development, and exploration in deeper horizons (36–48 cm) was faster without any fertilization treatment. Analysis of the rhizotron images was challenging and it was not possible to clearly discriminate between cultivars. Canopy reflectance and leaf optical indices were measured with proximal sensors. The Normalized Difference Vegetation Index (NDVI) was a positive indicator of biomass and seed yield while the Nitrogen Balance Index (NBI) was a positive indicator of above-ground biomass N concentration at flowering and seed N concentration at harvest.

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Mapping wheat nitrogen uptake from RapidEye vegetation indices

Cited by 66 publications

References 90 publications

The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development

The Value of Sentinel-2 Spectral Bands for the Assessment of Winter Wheat Growth and Development

Impact of Climate Change Adaptation Strategies on Winter Wheat and Cropping System Performance across Precipitation Gradients in the Inland Pacific Northwest, USA

In-Field Observation of Root Growth and Nitrogen Uptake Efficiency of Winter Oilseed Rape

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