scholarly journals Temporal and Spectral Optimization of Vegetation Indices for Estimating Grain Nitrogen Uptake and Late-Seasonal Nitrogen Traits in Wheat

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4640 ◽  
Author(s):  
Lukas Prey ◽  
Urs Schmidhalter
Keyword(s):  
Near Infrared ◽  
N Uptake ◽  
Vegetation Indices ◽  
Triticum Aestivum L ◽  
N Fertilization ◽  
Cultivar Differences ◽  
Red Edge ◽  
N Concentration ◽  
Organ Level ◽  
Grain Nitrogen

Grain nitrogen (N) uptake (GNup) in winter wheat (Triticum aestivum L.) is influenced by multiple components at the plant organ level and by pre- and post-flowering N uptake (Nup). Although spectral proximal high-throughput sensing is promising for field phenotyping, it was rarely evaluated for such N traits. Hence, 48 spectral vegetation indices (SVIs) were evaluated on 10 measurement days for the estimation of 34 N traits in four data subsets, representing the variation generated by six high-yielding cultivars, two N fertilization levels (N), two sowing dates (SD), and two fungicide (F) intensities. Close linear relationships (p < 0.001) were found for GNup both in response to cultivar differences (Cv; R2 = 0.52) and other agronomic treatments (R2 = 0.67 for Cv*F*N, R2 = 0.53 for Cv*SD*N and R2 = 0.57 for the combined treatments), notably during milk ripeness. Especially near-infrared (NIR)/red edge SVIs, such as the NDRE_770_750, outperformed NIR/visible light (VIS) indices. Index rankings and seasonal R2 values were similar for total Nup, while the N harvest index, which expresses the partitioning to the grain, was moderately estimated only during dough ripeness, primarily from indices detecting contrasting senescence between different fungicide intensities. Senescence-sensitive indices, including R787_R765 and TRCARI_OSAVI, performed best for N translocation efficiency and some organ-level N traits at maturity. Even though grain N concentration was best assessed by the red edge inflection point (REIP), the blue/green index (BGI) was more suited for leaf-level N traits at anthesis. When SVIs were quantitatively ranked by data subsets, a better agreement was found for GNup, total Nup, and grain N concentration than for several contributing N traits. The results suggest (i) a good general potential for estimating GNup and total Nup by (ii) red edge indices best used (iii) during milk and early dough ripeness. The estimation of contributing N traits differs according to the agronomic treatment.

scholarly journals Deep Phenotyping of Yield-Related Traits in Wheat

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 603
Author(s):  
Lukas Prey ◽  
Urs Schmidhalter
Keyword(s):  
Yield Components ◽  
Vegetation Index ◽  
Near Infrared ◽  
Genotypic Variation ◽  
Vegetation Indices ◽  
Growth Stages ◽  
Sowing Time ◽  
Red Edge ◽  
Grain Number Per Spike ◽  
Organ Level

The complex formation of grain yield (GY) is related to multiple dry matter (DM) traits; however, due to their time-consuming determination, they are not readily accessible. In winter wheat (Triticum aestivum L.), both agronomic treatments and genotypic variation influence GY in interaction with the environment. Spectral proximal sensing is promising for high-throughput non-destructive phenotyping but was rarely evaluated systematically for dissecting yield-related variation in DM traits. Aiming at a temporal, spectral and organ-level optimization, 48 vegetation indices were evaluated in a high-yielding environment in 10 growth stages for the estimation of 31 previously compared traits related to GY formation—influenced by sowing time, fungicide, N fertilization, and cultivar. A quantitative index ranking was evaluated to assess the stage-independent index suitability. GY showed close linear relationships with spectral vegetation indices across and within agronomic treatments (R2 = 0.47–0.67 ***). Water band indices, followed by red edge-based indices, best used at milk or early dough ripeness, were better suited than the widely used normalized difference vegetation index (NDVI). Index rankings for many organ-level DM traits were comparable, but the relationships were often less close. Among yield components, grain number per spike (R2 = 0.24–0.34 ***) and spike density (R2 = 0.23–0.46 ***) were moderately estimated. GY was mainly estimated by detecting total DM rather than the harvest index. Across agronomic treatments and cultivars, seasonal index rankings were the most stable for GY and total DM, whereas traits related to DM allocation and translocation demanded specific index selection. The results suggest using indices with water bands, near infrared/red edge and visible light bands to increase the accuracy of in-season spectral phenotyping for GY, contributing organ-level traits, and yield components, respectively.

scholarly journals Sensitivity of Vegetation Indices for Estimating Vegetative N Status in Winter Wheat

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3712 ◽  
Author(s):  
Lukas Prey ◽  
Urs Schmidhalter
Keyword(s):  
Winter Wheat ◽  
N Uptake ◽  
Vegetation Indices ◽  
N Fertilization ◽  
Yield Potential ◽  
Growth Stages ◽  
Red Edge ◽  
Wide Range ◽  
N Use ◽  
N Status

Precise sensor-based non-destructive estimation of crop nitrogen (N) status is essential for low-cost, objective optimization of N fertilization, as well as for early estimation of yield potential and N use efficiency. Several studies assessed the performance of spectral vegetation indices (SVI) for winter wheat (Triticum aestivum L.), often either for conditions of low N status or across a wide range of the target traits N uptake (Nup), N concentration (NC), dry matter biomass (DM), and N nutrition index (NNI). This study aimed at a critical assessment of the estimation ability depending on the level of the target traits. It included seven years’ data with nine measurement dates from early stem elongation until flowering in eight N regimes (0–420 kg N ha−1) for selected SVIs. Tested across years, a pronounced date-specific clustering was found particularly for DM and NC. While for DM, only the R900_970 gave moderate but saturated relationships (R2 = 0.47, p < 0.001) and no index was useful for NC across dates, NNI and Nup could be better estimated (REIP: R2 = 0.59, p < 0.001 for both traits). Tested within growth stages across N levels, the order of the estimation of the traits was mostly Nup ≈ NNI > NC ≈ DM. Depending on the number (n = 1–3) and characteristic of cultivars included, the relationships improved when testing within instead of across cultivars, with the relatively lowest cultivar effect on the estimation of DM and the strongest on NC. For assessing the trait estimation under conditions of high–excessive N fertilization, the range of the target traits was divided into two intervals with NNI values < 0.8 (interval 1: low N status) and with NNI values > 0.8 (interval 2: high N status). Although better estimations were found in interval 1, useful relationships were also obtained in interval 2 from the best indices (DM: R780_740: average R2 = 0.35, RMSE = 567 kg ha−1; NC: REIP: average R2 = 0.40, RMSE = 0.25%; NNI: REIP: average R2 = 0.46, RMSE = 0.10; Nup: REIP: average R2 = 0.48, RMSE = 21 kg N ha−1). While in interval 1, all indices performed rather similarly, the three red edge-based indices were clearly better suited for the three N-related traits. The results are promising for applying SVIs also under conditions of high N status, aiming at detecting and avoiding excessive N use. While in canopies of lower N status, the use of simple NIR/VIS indices may be sufficient without losing much precision, the red edge information appears crucial for conditions of higher N status. These findings can be transferred to the configuration and use of simpler multispectral sensors under conditions of contrasting N status in precision farming.

scholarly journals Assessing Leaf Biomass of Agave sisalana Using Sentinel-2 Vegetation Indices

2021 ◽  
Vol 13 (2) ◽  
pp. 233
Author(s):  
Ilja Vuorinne ◽  
Janne Heiskanen ◽  
Petri K. E. Pellikka
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Fibre Production ◽  
Additive Models ◽  
Leaf Biomass ◽  
Ground Vegetation ◽  
Perennial Crop ◽  
Red Edge ◽  
Agave Sisalana ◽  
Sentinel 2

Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small.

scholarly journals VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

2020 ◽  
Vol 12 (14) ◽  
pp. 2254 ◽  
Author(s):  
Hafiz Ali Imran ◽  
Damiano Gianelle ◽  
Duccio Rocchini ◽  
Michele Dalponte ◽  
M. Pilar Martín ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spatial Scales ◽  
Vegetation Indices ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Sharp Change ◽  
Full Potential ◽  
Area Index ◽  
Red Edge ◽  
Temporal And Spatial

Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands.

Grain nitrogen concentration differences among three sorghum hybrids with similar grain yield

1999 ◽  
Vol 50 (2) ◽  
pp. 137 ◽  
Author(s):  
A. Kamoshita ◽  
M. Cooper ◽  
R. C. Muchow ◽  
S. Fukai
Keyword(s):  
Grain Yield ◽  
Nitrogen Concentration ◽  
N Uptake ◽  
Grain Filling ◽  
Yield Potential ◽  
Yield Reduction ◽  
N Availability ◽  
N Accumulation ◽  
N Concentration ◽  
Grain Nitrogen

The differences in grain nitrogen (N) concentration among 3 sorghum (Sorghum bicolor (L.) Moench) hybrids with similar grain yield were examined under N-limiting conditions in relation to the availability of assimilate and N to grain. Several manipulation treatments [N fertiliser application, lower leaves shading, thinning (reduced plant population), whole canopy shading, canopy opening, spikelet removal] were imposed to alter the relative N and assimilate availability to grain under full irrigation supply. Grain N concentration increased by either increased grain N availability or yield reduction while maintaining N uptake. Grain N concentration, however, did not decrease in the treatments where relative abundance of N compared with assimilate was intended to be reduced. The minimum levels of grain N concentration differed from 0.95% (ATx623/RTx430) to 1.14% (DK55plus) in these treatments. Regardless of the extent of variation in assimilate and N supply to grain, the ranking of hybrids on grain N concentration was consistent across the manipulation treatments. For the 3 hybrids examined, higher grain N concentration was associated with higher N uptake during grain filling and, to a lesser extent, with higher N mobilisation. Hybrids with larger grain N accumulation had a larger number of grains. There was no tradeoff between grain N concentration and yield, suggesting that grain protein concentration can be improved without sacrificing yield potential.

scholarly journals Nitrogen Requirements and N Status Determination of Lettuce

HortScience ◽  
2012 ◽  
Vol 47 (12) ◽  
pp. 1768-1774 ◽  
Author(s):  
Thomas G. Bottoms ◽  
Richard F. Smith ◽  
Michael D. Cahn ◽  
Timothy K. Hartz
Keyword(s):  
N Uptake ◽  
N Fertilization ◽  
Fertilizer Efficiency ◽  
Residual Soil ◽  
N Application ◽  
Fresh Biomass ◽  
N Concentration ◽  
Whole Plant ◽  
Crop N Uptake ◽  
N Rates

As concern over NO3-N pollution of groundwater increases, California lettuce growers are under pressure to improve nitrogen (N) fertilizer efficiency. Crop growth, N uptake, and the value of soil and plant N diagnostic measures were evaluated in 24 iceberg and romaine lettuce (Lactuca sativa L. var. capitata L., and longifolia Lam., respectively) field trials from 2007 to 2010. The reliability of presidedressing soil nitrate testing (PSNT) to identify fields in which N application could be reduced or eliminated was evaluated in 16 non-replicated strip trials and five replicated trials on commercial farms. All commercial field sites had greater than 20 mg·kg−1 residual soil NO3-N at the time of the first in-season N application. In the strip trials, plots in which the cooperating growers’ initial sidedress N application was eliminated or reduced were compared with the growers’ standard N fertilization program. In the replicated trials, the growers’ N regime was compared with treatments in which one or more N fertigation through drip irrigation was eliminated. Additionally, seasonal N rates from 11 to 336 kg·ha−1 were compared in three replicated drip-irrigated research farm trials. Seasonal N application in the strip trials was reduced by an average of 77 kg·ha−1 (73 kg·ha−1 vs. 150 kg·ha−1 for the grower N regime) with no reduction in fresh biomass produced and only a slight reduction in crop N uptake (151 kg·ha−1 vs. 156 kg·ha−1 for the grower N regime). Similarly, an average seasonal N rate reduction of 88 kg·ha−1 (96 kg·ha−1 vs. 184 kg·ha−1) was achieved in the replicated commercial trials with no biomass reduction. Seasonal N rates between 111 and 192 kg·ha−1 maximized fresh biomass in the research farm trials, which were conducted in fields with lower residual soil NO3-N than the commercial trials. Across fields, lettuce N uptake was slow in the first 4 weeks after planting, averaging less than 0.5 kg·ha−1·d−1. N uptake then increased linearly until harvest (≈9 weeks after planting), averaging ≈4 kg·ha−1·d−1 over that period. Whole plant critical N concentration (Nc, the minimum whole plant N concentration required to maximize growth) was estimated by the equation Nc (g·kg−1) = 42 − 2.8 dry mass (DM, Mg·ha−1); on that basis, critical N uptake (crop N uptake required to maintain whole plant N above Nc) in the commercial fields averaged 116 kg·ha−1 compared with the mean uptake of 145 kg·ha−1 with the grower N regime. Soil NO3-N greater than 20 mg·kg−1 was a reliable indicator that N application could be reduced or delayed. Neither leaf N nor midrib NO3-N was correlated with concurrently measured soil NO3-N and therefore of limited value in directing in-season N fertilization.

scholarly journals Estimating Rice Agronomic Traits Using Drone-Collected Multispectral Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 545 ◽  
Author(s):  
Dimitris Stavrakoudis ◽  
Dimitrios Katsantonis ◽  
Kalliopi Kadoglidou ◽  
Argyris Kalaitzidis ◽  
Ioannis Gitas
Keyword(s):  
Near Infrared ◽  
Agronomic Traits ◽  
Oryza Sativa L ◽  
N Uptake ◽  
Vegetation Indices ◽  
Growth Stages ◽  
Linear Regression Models ◽  
Remotely Sensed Data ◽  
Cost Efficient ◽  
N Management

The knowledge of rice nitrogen (N) requirements and uptake capacity are fundamental for the development of improved N management. This paper presents empirical models for predicting agronomic traits that are relevant to yield and N requirements of rice (Oryza sativa L.) through remotely sensed data. Multiple linear regression models were constructed at key growth stages (at tillering and at booting), using as input reflectance values and vegetation indices obtained from a compact multispectral sensor (green, red, red-edge, and near-infrared channels) onboard an unmanned aerial vehicle (UAV). The models were constructed using field data and images from two consecutive years in a number of experimental rice plots in Greece (Thessaloniki Regional Unit), by applying four different N treatments (C0: 0 N kg∙ha−1, C1: 80 N kg∙ha−1, C2: 160 N kg∙ha−1, and C4: 320 N kg∙ha−1). Models for estimating the current crop status (e.g., N uptake at the time of image acquisition) and predicting the future one (e.g., N uptake of grains at maturity) were developed and evaluated. At the tillering stage, high accuracies (R2 ≥ 0.8) were achieved for N uptake and biomass. At the booting stage, similarly high accuracies were achieved for yield, N concentration, N uptake, biomass, and plant height, using inputs from either two or three images. The results of the present study can be useful for providing N recommendations for the two top-dressing fertilizations in rice cultivation, through a cost-efficient workflow.

scholarly journals Crop Type Classification Using Vegetation Indices of RapidEye Imagery

2014 ◽  
Vol XL-7 ◽  
pp. 195-198 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
Y. Kurucu
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Red Edge ◽  
Crop Type ◽  
Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

scholarly journals COMPARING BROAD-BAND AND RED EDGE-BASED SPECTRAL VEGETATION INDICES TO ESTIMATE NITROGEN CONCENTRATION OF CROPS USING CASI DATA

2016 ◽  
Vol XLI-B7 ◽  
pp. 137-143
Author(s):  
Yanjie Wang ◽  
Qinhong Liao ◽  
Guijun Yang ◽  
Haikuan Feng ◽  
Xiaodong Yang ◽  
...  
Keyword(s):  
Nitrogen Concentration ◽  
Vegetation Indices ◽  
Broad Band ◽  
Image Data ◽  
N Fertilization ◽  
Physiological Status ◽  
Red Edge ◽  
Spectral Vegetation Indices ◽  
Crop Types ◽  
Edge Based

In recent decades, many spectral vegetation indices (SVIs) have been proposed to estimate the leaf nitrogen concentration (LNC) of crops. However, most of these indices were based on the field hyperspectral reflectance. To test whether they can be used in aerial remote platform effectively, in this work a comparison of the sensitivity between several broad-band and red edge-based SVIs to LNC is investigated over different crop types. By using data from experimental LNC values over 4 different crop types and image data acquired using the Compact Airborne Spectrographic Imager (CASI) sensor, the extensive dataset allowed us to evaluate broad-band and red edge-based SVIs. The result indicated that NDVI performed the best among the selected SVIs while red edge-based SVIs didn’t show the potential for estimating the LNC based on the CASI data due to the spectral resolution. In order to search for the optimal SVIs, the band combination algorithm has been used in this work. The best linear correlation against the experimental LNC dataset was obtained by combining the 626.20nm and 569.00nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and reflection position region, respectively, and are known to be sensitive to the physiological status of the plant. Then this linear relationship was applied to the CASI image for generating an LNC map, which can guide farmers in the accurate application of their N fertilization strategies.

scholarly journals Estimating biophysical properties of coffee (Coffea canephora) plants with above-canopy field measurements, using CropSpec®

2018 ◽  
Vol 32 (2) ◽  
pp. 183-191 ◽  
Author(s):  
Bayu T. Widjaja Putra ◽  
Peeyush Soni ◽  
Eiji Morimoto ◽  
Pujiyanto Pujiyanto
Keyword(s):  
Nitrogen Content ◽  
Near Infrared ◽  
Field Experiments ◽  
Vegetation Indices ◽  
Coffea Canephora ◽  
Biophysical Properties ◽  
Robusta Coffee ◽  
Red Edge ◽  
Coffee Plants ◽  
Non Destructive

Abstract Remote sensing technologies have been applied to many crops, but tree crops like Robusta coffee (Coffea canephora) under shade conditions require additional attention while making above-canopy measurements. The objective of this study was to determine how well chlorophyll and nitrogen status of Robusta coffee plants can be estimated with the laser-based (CropSpec®) active sensor. This study also identified appropriate vegetation indices for estimating Nitrogen content by above-canopy measurement, using near-infra red and red-edge bands. Varying light intensity and different background of the plants were considered in developing the indices. Field experiments were conducted involving different non-destructive tools (CropSpec® and SPAD-502 chlorophyll meter). Subsequently, Kjeldahl laboratory analyses were performed to determine the actual Nitrogen content of the plants with different ages and field conditions used in the non-destructive previous stage. Measurements were undertaken for assessing the biophysical properties of tree plant. The usefulness of near-infrared and red-edge bands from these sensors in measuring critical nitrogen levels of coffee plants by above-canopy measurement are investigated in this study.

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