Estimation of Leaf Area Index Using DEIMOS-1 Data: Application and Transferability of a Semi-Empirical Relationship between two Agricultural Areas

Vuolo, Francesco; Neugebauer, Nikolaus; Bolognesi, Salvatore Falanga; Atzberger, Clement; D’Urso, Guido

doi:10.3390/rs5031274

Cited by 66 publications

(48 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The effect of weighting the red band with the slope of the soil line is the maximization of the vegetation signal in the near-infrared band and the minimization of the effect of soil brightness. The LAI is related with WDVI of the observed vegetation, through the following expression, deduced from a simplified analysis of the radiative behavior of different types of crops [50,55,56]:…”

Section: Data Processing For Deriving Eo-based Crop Development Mapsmentioning

confidence: 99%

Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment

et al. 2015

Self Cite

View full text Add to dashboard Cite

Abstract:The sustainable management of water resources plays a key role in Mediterranean viticulture, characterized by scarcity and competition of available water. This study focuses on estimating the evapotranspiration and crop coefficients of table grapes vineyards trained on overhead "tendone" systems in the Apulia region (Italy). evapotranspiration derived from remotely-sensed data could be helpful for downscaling to the field the local weather conditions and agronomic practices and thus may be the basis for supporting grape growers and irrigation managers.

show abstract

Section: Data Processing For Deriving Eo-based Crop Development Mapsmentioning

confidence: 99%

Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…The differences are probably a direct result of the different spatial resolutions of the two satellite sensors. A field campaign was organized on 24 June 2016 in an agricultural area in Austria [28] to measure surface reflectance with a field spectro-radiometer in coincidence (±2 days) of two Sentinel-2 overpasses (tile 33UXP on 22 and 25 June, respectively). A second campaign was organized on 31 August in the same area and a Sentinel-2 image was acquired on the same day.…”

Section: Surface Reflectancementioning

confidence: 99%

“…The spectral reflectance was measured at ground over homogeneous targets using a Spectral Evolution PSR-2500 radiometer operating in the range 350-2500 nm with a spectral resolution of 3.5 nm (in visible, VIS, and near-infrared, NIR) and 22 nm (in the short-wave infrared, SWIR) [29]. The A field campaign was organized on 24 June 2016 in an agricultural area in Austria [28] to measure surface reflectance with a field spectro-radiometer in coincidence (±2 days) of two Sentinel-2 overpasses (tile 33UXP on 22 and 25 June, respectively). A second campaign was organized on 31 August in the same area and a Sentinel-2 image was acquired on the same day.…”

Section: Surface Reflectancementioning

confidence: 99%

Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples

et al. 2016

Self Cite

View full text Add to dashboard Cite

This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency's (ESA) Sen2Cor algorithm, the platform processes ESA's Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data . Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R 2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R 2 = 0.83) and a RMSE of 0.32 m 2 /m 2 (12% of mean value).

show abstract

“…For example, several of the cited statistical models are easy to apply. In addition, suitable software is often readily available [62][63][64]. This study was conducted on field experimental data acquired over nine consecutive years that included seven varieties of wheat, four eco-sites, and 455 samples in the calibration set and 366 samples in the validation set, corresponding to different N levels and growth stages.…”

Section: The Applicability Of the Six Algorithms To Different Groups mentioning

confidence: 99%

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

et al. 2015

View full text Add to dashboard Cite

Abstract:The rapid and non-destructive monitoring of the canopy leaf nitrogen concentration (LNC) in crops is important for precise nitrogen (N) management. Nowadays, there is an urgent need to identify next-generation bio-physical variable retrieval algorithms that can be incorporated into an operational processing chain for hyperspectral satellite missions. We assessed six retrieval algorithms for estimating LNC from canopy reflectance of winter wheat in eight field experiments. These experiments represented variations in the N application rates, planting densities, ecological sites and cultivars and yielded a total of 821 samples from various places in Jiangsu, China over nine consecutive years. Based on the reflectance spectra and their first derivatives, six methods using different numbers of wavelengths were applied to construct predictive models for estimating wheat LNC, including continuum removal (CR), vegetation indices (VIs), stepwise multiple linear regression (SMLR), partial least squares regression (PLSR), artificial neural networks (ANNs), and support vector machines (SVMs). To assess the performance of these six methods, we provided a systematic evaluation of the estimation OPEN ACCESS Remote Sens. 2015, 7 14940 accuracies using the six metrics that were the coefficients of determination for the calibration (R 2 C) and validation (R 2 V) sets, the root mean square errors of prediction (RMSEP) for the calibration and validation sets, the ratio of prediction to deviation (RPD), the computational efficiency (CE) and the complexity level (CL). The following results were obtained: (1) For the VIs method, SAVI(R1200, R705) produced a more accurate estimation of the LNC than other indices, with R² C, R² V, RMSEP, RPD and CE values of 0.844, 0.795, 0.384, 2.005 and 0.10 min, respectively; (2) For the SMLR, PLSR, ANNs and SVMs methods, the SVMs using the first derivative canopy spectra (SVM-FDS) offered the best accuracy in terms of R² C, R² V, RMSEP, RPD, and CE, at 0.96, 0.78, 0.37, 2.02, and 21.17, respectively; (3) The PLSR-FDS, ANN-OS and SVM-FDS methods yield similar accuracies if the CE and CL are not considered, however, ANNs and SVMs performed better on calibration set than the validation set which indicate that we should take more caution with the two methods for over-fitting. Except PLS method, the performance for most methods did not enhance when the spectrum were operated by the first derivative. Moreover, the evaluation of the robustness demonstrates that SVM method may be better suited than the other methods to cope with potential confounding factors for most varieties, ecological site and growth stage; (4) The prediction accuracy was found to be higher when more wavelengths were used, though at the cost of a lower CE. The findings are of interest to the remote sensing community for the development of improved inversion schemes for hyperspectral applications concerning other types of vegetation. The examples provided in this paper may also serve to illustrate the advantages and shortcomings...

show abstract

Estimation of Leaf Area Index Using DEIMOS-1 Data: Application and Transferability of a Semi-Empirical Relationship between two Agricultural Areas

Cited by 66 publications

References 47 publications

Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment

Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment

Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples

Evaluation of Six Algorithms to Monitor Wheat Leaf Nitrogen Concentration

Contact Info

Product

Resources

About