Evaluation of Sentinel-2 vegetation indices for prediction of LAI, fAPAR and fCover of winter wheat in Bulgaria

Kamenova, Ilina; Dimitrov, Petar

doi:10.1080/22797254.2020.1839359

Cited by 66 publications

(44 citation statements)

References 47 publications

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“…Yet, the analysis revealed several deficiencies of the NIR/RE-model, raising the topics of temporal and spatial resolution. Apparently, the frequent statement that the GAI-course over the season can be well mapped with Sentinel-2 data [ 3 , 11 , 12 , 21 , 29 , 37 , 40 , 41 , 42 , 43 , 44 , 45 ] is based on large differences and continuously increasing GAI-values between different sampling dates. Yet, the Sentinel-2 based single-date GAI-estimations are at some dates systematically biased and the true GAI-variation is in most cases considerably underestimated ( Figure 4 ).…”

Section: Discussionmentioning

confidence: 99%

“…Date-specific clustered patterns were also detected by Revill et al [ 27 ] and by Dimitrov et al [ 37 ]. Other studies describe date-specific varying precision in GAI-prediction, e.g., [ 43 ]. This is no general problem of multispectral measurements, as many studies already presented approaches to predict GAI across a wide range of growth stages with one single calibration model [ 3 , 15 , 16 , 20 ].…”

Section: Discussionmentioning

confidence: 99%

“…This raises the question why so far, as to say based on the literature research carried out [ 3 , 11 , 12 , 21 , 29 , 37 , 40 , 41 , 42 , 43 , 44 , 45 ], the impact of date-specific effects when working with Sentinel-2 data is not further discussed. The common practice of distributing the sampling dates over different BBCH stages (e.g., [ 11 , 29 ]) and combining data of different locations, years and cultivars [ 40 , 41 , 43 , 44 , 45 ] might have disguised this problem so far—thereby, even if a certain shift between different satellite data acquisitions is detected, it cannot be clearly distinguished from other reasons, such as a starting VI-saturation at high GAIs, different VI-GAI-correlations at differing BBCH stages, or altering soil background. Furthermore, the significant effects of measurement date to the relationship VI-GAI can be concealed by a low number of samplings per date (the lower the number of samplings per date, the more unlikely is the rejection of the null hypothesis of equality of slope and intercept).…”

Section: Discussionmentioning

confidence: 99%

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Sentinel-2 Data for Precision Agriculture?—A UAV-Based Assessment

Bukowiecki

Rose

Kage

2021

Sensors

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An approach of exploiting and assessing the potential of Sentinel-2 data in the context of precision agriculture by using data from an unmanned aerial vehicle (UAV) is presented based on a four-year dataset. An established model for the estimation of the green area index (GAI) of winter wheat from a UAV-based multispectral camera was used to calibrate the Sentinel-2 data. Large independent datasets were used for evaluation purposes. Furthermore, the potential of the satellite-based GAI-predictions for crop monitoring and yield prediction was tested. Therefore, the total absorbed photosynthetic radiation between spring and harvest was calculated with satellite and UAV data and correlated with the final grain yield. Yield maps at the same resolution were generated by combining yield data on a plot level with a UAV-based crop coverage map. The best tested model for satellite-based GAI-prediction was obtained by combining the near-, infrared- and Red Edge-waveband in a simple ratio (R2 = 0.82, mean absolute error = 0.52 m2/m2). Yet, the Sentinel-2 data seem to depict average GAI-developments through the seasons, rather than to map site-specific variations at single acquisition dates. The results show that the lower information content of the satellite-based crop monitoring might be mainly traced back to its coarser Red Edge-band. Additionally, date-specific effects within the Sentinel-2 data were detected. Due to cloud coverage, the temporal resolution was found to be unsatisfactory as well. These results emphasize the need for further research on the applicability of the Sentinel-2 data and a cautious use in the context of precision agriculture.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Sentinel-2 Data for Precision Agriculture?—A UAV-Based Assessment

Bukowiecki

Rose

Kage

2021

Sensors

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“…where ρ red is Sentinel 2's B4; ρ red_edge is Sentinel 2's B5; ρ nir is Sentinel 2's B6 [74,75]. , is a stochastic global optimization algorithm inspired from the widespread foraging strategy of marine species such as sharks and tunas.…”

Section: Remote Sensing-based Vegetation Indicesmentioning

confidence: 99%

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Hoang

Tran

2021

Mathematical Problems in Engineering

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Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of “green space” and “nongreen space”. The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM’s hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite’s spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use.

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“…However, this method is destructive, time-consuming, labor-intensive, and prone to sampling error, which is not suitable for large-scale surveys and breeding programs comprising thousands of plots [3]. Remote/proximal sensing-based, high-throughput, nondestructive phenotyping is increasingly applied to estimate AGDW and LAI (e.g., [1,[4][5][6]). Remote sensing-based phenotyping techniques normally depend on the premise that a plant trait is related to specific wavelengths of spectral radiation and the intensity of spectral reflectance, which can be remotely recorded by imaging techniques or sensors such as visible and multi-spectral techniques [7].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Modelling Strategies to Estimate Phenotypic Values from an Unmanned Aerial Vehicle with Spectral and Temporal Vegetation Indexes

Chapman

Jin

et al. 2021

Remote Sensing

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Aboveground dry weight (AGDW) and leaf area index (LAI) are indicators of crop growth status and grain yield as affected by interactions of genotype, environment, and management. Unmanned aerial vehicle (UAV) based remote sensing provides cost-effective and non-destructive methods for the high-throughput phenotyping of crop traits (e.g., AGDW and LAI) through the integration of UAV-derived vegetation indexes (VIs) with statistical models. However, the effects of different modelling strategies that use different dataset compositions of explanatory variables (i.e., combinations of sources and temporal combinations of the VI datasets) on estimates of AGDW and LAI have rarely been evaluated. In this study, we evaluated the effects of three sources of VIs (visible, spectral, and combined) and three types of temporal combinations of the VI datasets (mono-, multi-, and full-temporal) on estimates of AGDW and LAI. The VIs were derived from visible (RGB) and multi-spectral imageries, which were acquired by a UAV-based platform over a wheat trial at five sampling dates before flowering. Partial least squares regression models were built with different modelling strategies to estimate AGDW and LAI at each prediction date. The results showed that models built with the three sources of mono-temporal VIs obtained similar performances for estimating AGDW (RRMSE = 11.86% to 15.80% for visible, 10.25% to 16.70% for spectral, and 10.25% to 16.70% for combined VIs) and LAI (RRMSE = 13.30% to 22.56% for visible, 12.04% to 22.85% for spectral, and 13.45% to 22.85% for combined VIs) across prediction dates. Mono-temporal models built with visible VIs outperformed the other two sources of VIs in general. Models built with mono-temporal VIs generally obtained better estimates than models with multi- and full-temporal VIs. The results suggested that the use of UAV-derived visible VIs can be an alternative to multi-spectral VIs for high-throughput and in-season estimates of AGDW and LAI. The combination of modelling strategies that used mono-temporal datasets and a self-calibration method demonstrated the potential for in-season estimates of AGDW and LAI (RRMSE normally less than 15%) in breeding or agronomy trials.

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Evaluation of Sentinel-2 vegetation indices for prediction of LAI, fAPAR and fCover of winter wheat in Bulgaria

Cited by 66 publications

References 47 publications

Sentinel-2 Data for Precision Agriculture?—A UAV-Based Assessment

Sentinel-2 Data for Precision Agriculture?—A UAV-Based Assessment

Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

Comparison of Modelling Strategies to Estimate Phenotypic Values from an Unmanned Aerial Vehicle with Spectral and Temporal Vegetation Indexes

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