A fast operative method for NDVI uncertainty estimation and its role in vegetation analysis

Mondino, E. Borgogno; Lessio, A.; Gomarasca, Mario A.

doi:10.5721/eujrs20164908

Cited by 44 publications

(29 citation statements)

References 39 publications

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“…Evaluation of the background and spatial leaf distribution at each measured point is needed to precisely simulate the canopy reflectance. However, the average of the obtained reflectance and vegetation indices for each growth period were similar to the simulation results, and RMSE of NDVI decreased as NDVI became larger (Table 3), which agreed with the results in Borgogno-Mondino et al [23]. These results suggest that the simulation is accurate enough to evaluate the average behavior of photons and the effect of solar radiation conditions on canopy reflectance.…”

Section: Discussionsupporting

confidence: 88%

“…Therefore, compared with satellite images, a UAV image can be obtained under various solar radiation conditions including various proportions of diffuse light for incident sunlight and various incident angles of direct sunlight (solar zenith angle). Different solar radiation conditions cause a difference in the observed reflectance for the same object [22,23]. Consequently, the difference in observed reflectance might affect the vegetation index, even though the vegetation index adjusts for such difference by using ratio or normalization of reflectance values.…”

Section: Introductionmentioning

confidence: 99%

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Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

et al. 2019

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Reflectance and vegetation indices obtained from aerial images are often used for monitoring crop fields. In recent years, unmanned aerial vehicles (UAVs) have become popular and aerial images have been collected under various solar radiation conditions. The value of observed reflectance and vegetation indices are considered to be affected by solar radiation conditions, which may lead to inaccurate estimations of crop growth. In this study, in order to evaluate the effect of solar radiation conditions on aerial images, canopy reflectance in paddy fields was simulated by a radiative transfer model, FLiES (Forest Light Environmental Simulator), for various solar radiation conditions and canopy structures. Several parameters including solar zenith angle, proportion of diffuse light for incident sunlight, plant height, coordinates of plants and leaf area density, were tested in FLiES. The simulation results showed that the solar zenith angle did not vary the canopy reflectance under the conditions of the proportion of diffuse light at 1.0, but the variation was greater at lower proportions of diffuse light. The difference in reflectance caused by solar radiation was 0.01 and 0.1 at the maximum for red and near-infrared bands, respectively. The simulation results also showed that the differences in reflectance affect vegetation indices (Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index 2 (EVI2)). The variation caused by solar radiation conditions was the least for NDVI and the greatest for SR. However, NDVI was saturated at the least leaf area index (LAI), whereas SR was only slightly saturated. EVI2 was intermediate between SR and NDVI, both in terms of variation and saturation. The simulated reflectance and vegetation indices were similar to those obtained from the aerial images collected in the farmers’ paddy fields. These results suggest that a large proportion of diffuse light (close to 1.0) or a middle range of solar zenith angle (45 to 65 degrees) may be desirable for UAV monitoring. However, to maintain flexibility of time and occasion for UAV monitoring, EVI2 should be used to evaluate crop growth, although calibration based on solar radiation conditions is recommended.

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

et al. 2019

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“…built-up, vegetation and open water, were considered here. NDVI, Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI) were computed from Landsat data to represent those three classes [Xu, 2007;Borgogno-Mondino et al, 2016]:…”

Section: Downscaling Frameworkmentioning

confidence: 99%

Downscaling Landsat Land Surface Temperature over the urban area of Florence

Bonafoni

Anniballe

Gioli

et al. 2016

European Journal of Remote Sensing

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A new downscaling algorithm for land surface temperature (LST) images retrieved from Landsat Thematic Mapper (TM) was developed over the city of Florence and the results assessed against a high-resolution aerial image. The Landsat TM thermal band has a spatial resolution of 120 m, resampled at 30 m by the US Geological Survey (USGS) agency, whilst the airborne ground spatial resolution was 1 m. Substantial differences between Landsat USGS and airborne thermal data were observed on a 30 m grid: therefore a new statistical downscaling method at 30 m was developed. The overall root mean square error with respect to aircraft data improved from 3.3 °C (USGS) to 3.0 °C with the new method, that also showed better results with respect to other regressive downscaling techniques frequently used in literature. Such improvements can be ascribed to the selection of independent variables capable of representing the heterogeneous urban landscape.

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“…From an operational point of view, this is mandatory to determine if inter-dataset spectral differences are significant, or not, in respect of the expected intra-dataset uncertainty. Some works can be found in literature that tries to define and map uncertainty of reflectances and spectral indices using a sensibility analysis concerning the adopted RTM [33,34].…”

Section: Introductionmentioning

confidence: 99%

Preliminary Tests and Results Concerning Integration of Sentinel-2 and Landsat-8 OLI for Crop Monitoring

2017

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Abstract:The Sentinel-2 data by European Space Agency were recently made available for free. Their technical features suggest synergies with Landsat-8 dataset by NASA (National Aeronautics and Space Administration), especially in the agriculture context were observations should be as dense as possible to give a rather complete description of macro-phenology of crops. In this work some preliminary results are presented concerning geometric and spectral consistency of the two compared datasets. Tests were performed specifically focusing on the agriculture-devoted part of Piemonte Region (NW Italy). Geometric consistencies of Sentinel-2 and Landsat-8 datasets were tested "absolutely" (in respect of a selected reference frame) and "relatively" (one in respect of the other) by selecting, respectively, 160 and 100 well distributed check points. Spectral differences affecting at-the-ground reflectance were tested after images calibration performed by dark object subtraction approach. A special focus was on differences affecting derivable NDVI and NDWI spectral indices, being the most widely used in the agriculture remote sensing application context. Results are encouraging and suggest that this approach can successfully enter the ordinary remote sensing-supported precision farming workflow.

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A fast operative method for NDVI uncertainty estimation and its role in vegetation analysis

Cited by 44 publications

References 39 publications

Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields

Downscaling Landsat Land Surface Temperature over the urban area of Florence

Preliminary Tests and Results Concerning Integration of Sentinel-2 and Landsat-8 OLI for Crop Monitoring

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