Simulation of satellite reflectance data using high-frequency ground based hyperspectral canopy measurements for in-season estimation of grain yield and grain nitrogen status in winter wheat

Prey, Lukas; Schmidhalter, Urs

doi:10.1016/j.isprsjprs.2019.01.023

Cited by 50 publications

(66 citation statements)

References 60 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the indices NIR:Red, NIR:Green, and NDVI could not distinguish among genotypes at BBCH 49 and BBCH 65, they significantly correlated with the grain yield at BBCH 85. Overall, these results agree with the findings presented by other authors studying wheat under well-watered and drought stress conditions [5][6][7][8]19]. This is the first report on a comparison of correlations between spectral indices and the wheat grain yield for plots with different numbers of rows.…”

Section: Heritability Of Spectral Indices In Different Row Variantssupporting

confidence: 92%

“…In order to compare ground-and aerial-based sensing, a hand-held hyperspectral sensor and an unmanned aerial vehicle (UAV) carrying a multispectral camera were used for data acquisition at different BBCH scale (Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie) growth stages [18], i.e., BBCH 49 (booting; 28.05.2018), BBCH 65 (anthesis; 10.06.2018), and BBCH 85 (grain filling; 26.06.2018). These stages were chosen due to being most indicative for the assessment of the winter wheat yield [19].…”

Section: Spectral Reflectance Measurements Obtained By Ground-based Hmentioning

confidence: 99%

“…Canopy spectral reflectance acquired from ground-and aerial-based spectral sensing was used to calculate vegetation indices, i.e., NIR-based indices of the water band index (WBI) and NIR:NIR, and visible-and NIR-based indices of the red normalized difference vegetation index (NDVI), NIR:Red, and NIR:Green (Table 1), which are reportedly highly correlated with the plant biomass and grain yield [19,[21][22][23].…”

Section: Calculation Of Spectral Indicesmentioning

confidence: 99%

“…Reliable evaluation in smaller plots for the drivers of yield, especially those novel and rare alleles commonly lost when targeting the grain yield alone in breeder's nurseries, will enable novel phenotypes to be recycled through subsequent crossing and population development [16]. The new information may strongly support genomic selection efficiency, as well as the calibration of genomic information, by providing additional information on other complex traits, such as drought tolerance [8,28], salinity tolerance [29,30], and nitrogen use efficiency [19], which can be ascertained by spectral sensing.…”

Section: Heritability Of Spectral Indices In Different Row Variantsmentioning

confidence: 99%

See 3 more Smart Citations

Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

Knapp

Schmidhalter

2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

Enhancing plant breeding to ensure global food security requires new technologies. For wheat phenotyping, only limited seeds and resources are available in early selection cycles. This forces breeders to use small plots with single or multiple row plots in order to include the maximum number of genotypes/lines for their assessment. High-throughput phenotyping through remote sensing may meet the requirements for the phenotyping of thousands of genotypes grown in small plots in early selection cycles. Therefore, the aim of this study was to compare the performance of an unmanned aerial vehicle (UAV) for assessing the grain yield of wheat genotypes in different row numbers per plot in the early selection cycles with ground-based spectral sensing. A field experiment consisting of 32 wheat genotypes with four plot designs (1, 2, 3, and 12 rows per plot) was conducted. Near infrared (NIR)-based spectral indices showed significant correlations (p < 0.01) with the grain yield at flowering to grain filling, regardless of row numbers, indicating the potential of spectral indices as indirect selection traits for the wheat grain yield. Compared with terrestrial sensing, aerial-based sensing from UAV showed consistently higher levels of association with the grain yield, indicating that an increased precision may be obtained and is expected to increase the efficiency of high-throughput phenotyping in large-scale plant breeding programs. Our results suggest that high-throughput sensing from UAV may become a convenient and efficient tool for breeders to promote a more efficient selection of improved genotypes in early selection cycles. Such new information may support the calibration of genomic information by providing additional information on other complex traits, which can be ascertained by spectral sensing.

show abstract

Section: Heritability Of Spectral Indices In Different Row Variantssupporting

confidence: 92%

Section: Spectral Reflectance Measurements Obtained By Ground-based Hmentioning

confidence: 99%

Section: Calculation Of Spectral Indicesmentioning

confidence: 99%

Section: Heritability Of Spectral Indices In Different Row Variantsmentioning

confidence: 99%

See 2 more Smart Citations

Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

Knapp

Schmidhalter

2020

Remote Sensing

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, given that R 2 -values again decreased to a minimum until ear emergence/anthesis, as also found in [26], this early discrimination confirms the spectral detection of early vigor [49,50], but should not be generalized for the prediction of grain yield. This seasonal pattern is in line with the previous GY estimation across the full trial data [51] and with the spectral estimation of the N traits [47]. Concerning the seasonal pattern in Cont_SD1, the SVI*date interaction ( Figure 5) confirms the relative weakness of water band indices during the early phase [49] for 'planar', un-saturated canopies, being in line with [47].…”

Section: In-season Estimation Of Grain Yield and Contributing Dm Traitssupporting

confidence: 87%

Deep Phenotyping of Yield-Related Traits in Wheat

Prey

Schmidhalter

2020

Agronomy

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches

et al. 2023

View full text Add to dashboard Cite

Early prediction of crop production by remote sensing (RS) may help to plan the harvest and ensure food security. This study aims to improve the quantification of yield, grain protein concentration (GPC), and nitrogen (N) output in winter wheat with RS imagery. Ground-truth wheat traits were measured at flowering and harvest in a field experiment combining four N and two water levels in central Spain over 2 years. Hyperspectral and thermal airborne images coincident with Sentinel-1 and Sentinel-2 were acquired at flowering. A parametric linear model using all hyperspectral normalized difference spectral indices (NDSI) and two non-parametric models (artificial neural network and random forest) were used to assess their estimation ability combining NDSIs and other RS indicators. The feasibility of using freely available multispectral satellite was tested by applying the same methodology but using Sentinel-1 and Sentinel-2 bands. Yield estimation obtained the highest R2 value, showing that the visible and short-wave infrared region (VSWIR) had similar accuracy to the hyperspectral and Sentinel-2 imagery (R2 ≈ 0.84). The SWIR bands were important in the GPC estimation with both sensors, whereas N output was better estimated using red-edge-based NDSIs, obtaining satisfactory results with the hyperspectral sensor (R2 = 0.74) and with the Sentinel-2 (R2 = 0.62). When including the Sentinel-2 SWIR index, the NDSI (B11, B3) improved the estimation of N output (R2 = 0.71). Ensemble models based on Sentinel were found to be as reliable as those based on hyperspectral imagery, and including SWIR information improved the quantification of N-related traits.

show abstract

Simulation of satellite reflectance data using high-frequency ground based hyperspectral canopy measurements for in-season estimation of grain yield and grain nitrogen status in winter wheat

Cited by 50 publications

References 60 publications

Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

Advancing High-Throughput Phenotyping of Wheat in Early Selection Cycles

Deep Phenotyping of Yield-Related Traits in Wheat

Airborne hyperspectral and Sentinel imagery to quantify winter wheat traits through ensemble modeling approaches

Contact Info

Product

Resources

About