Assessments of Sentinel-2 Vegetation Red-Edge Spectral Bands for Improving Land Cover Classification

Qiu, Shi; He, Bin; Yin, Changming; Liao, Ziqi

doi:10.5194/isprs-archives-xlii-2-w7-871-2017

Cited by 17 publications

(16 citation statements)

References 6 publications

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“…WV-3, launched on 14 August 2014, has been successfully used to characterise tree health, yield and fruit quality in orchards (Rahman et al 2018;Robson et al 2017a, b). Sentinel-2, launched on 23 June 2015, has mostly been used for land cover and crop and tree species classifications (Immitzer et al 2016;Qiu et al 2017). However, the accuracy of these sensors, including hyperspectral sensors, has not been assessed for yield forecasting in vegetable crops.…”

Section: Introductionmentioning

confidence: 99%

Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data

et al. 2020

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Proximal and remote sensors have proved their effectiveness for the estimation of several biophysical and biochemical variables, including yield, in many different crops. Evaluation of their accuracy in vegetable crops is limited. This study explored the accuracy of proximal hyperspectral and satellite multispectral sensors (Sentinel-2 and WorldView-3) for the prediction of carrot root yield across three growing regions featuring different cropping configurations, seasons and soil conditions. Above ground biomass (AGB), canopy reflectance measurements and corresponding yield measures were collected from 414 sample sites in 24 fields in Western Australia (WA), Queensland (Qld) and Tasmania (Tas), Australia. The optimal sensor (hyperspectral or multispectral) was identified by the highest overall coefficient of determination between yield and different vegetation indices (VIs) whilst linear and non-linear models were tested to determine the best VIs and the impact of the spatial resolution. The optimal regression fit per region was used to extrapolate the point source measurements to all pixels in each sampled crop to produce a forecasted yield map and estimate average carrot root yield (t/ha) at the crop level. The latter were compared to commercial carrot root yield (t/ha) obtained from the growers to determine the accuracy of prediction. The measured yield varied from 17 to 113 t/ha across all crops, with forecasts of average yield achieving overall accuracies (% error) of 9.2% in WA, 10.2% in Qld and 12.7% in Tas. VIs derived from hyperspectral sensors produced poorer yield correlation coefficients (R2 < 0.1) than similar measures from the multispectral sensors (R2 < 0.57, p < 0.05). Increasing the spatial resolution from 10 to 1.2 m improved the regression performance by 69%. It is impossible to non-destructively estimate the pre-harvest spatial yield variability of root vegetables such as carrots. Hence, this method of yield forecasting offers great benefit for managing harvest logistics and forward selling decisions.

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Section: Introductionmentioning

confidence: 99%

Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data

et al. 2020

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“…Our method relies on the sensors' capacity to detect the sharp change in leaf reflectance around 700 nm (red-edge) [59]. It is a key wavelength range sensitive to vegetation conditions and has proven to be useful for quantitative assessment of vegetation properties [60], such as Leaf Area Index (LAI) [61][62][63], chlorophyll or nitrogen content [61,64], chlorophyll-a absorption in turbid and productive waters [65], mapping crop types [66], burn severity [67], and LC mapping [9,[68][69][70][71]. Our work represents a novel application aimed at quantifying anthocyanin content in above treeline plant canopies.…”

Section: Methods Caveats and Perspectivesmentioning

confidence: 99%

Improved Mapping of Mountain Shrublands Using the Sentinel-2 Red-Edge Band

Bayle

Carlson²,

Thiérion

et al. 2019

Remote Sensing

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Shrub encroachment into grassland and rocky habitats is a noticeable land cover change currently underway in temperate mountains and is a matter of concern for the sustainable management of mountain biodiversity. Current land cover products tend to underestimate the extent of mountain shrublands dominated by Ericaceae (Vaccinium spp. (species) and Rhododendron ferrugineum). In addition, mountain shrubs are often confounded with grasslands. Here, we examined the potential of anthocyanin-responsive vegetation indices to provide more accurate maps of mountain shrublands in a mountain range located in the French Alps. We relied on the multi-spectral instrument onboard the Sentinel-2A and 2B satellites and the availability of red-edge bands to calculate a Normalized Anthocyanin Reflectance Index (NARI). We used this index to quantify the autumn accumulation of anthocyanin in canopies dominated by Vaccinium spp. and Rhododendron ferrugineum and compared the effectiveness of NARI to Normalized Difference Vegetation Index (NDVI) as a basis for shrubland mapping. Photointerpretation of high-resolution aerial imagery, intensive field campaigns, and floristic surveys provided complementary data to calibrate and evaluate model performance. The proposed NARI-based model performed better than the NDVI-based model with an area under the curve (AUC) of 0.92 against 0.58. Validation of shrub cover maps based on NARI resulted in a Kappa coefficient of 0.67, which outperformed existing land cover products and resulted in a ten-fold increase in estimated area occupied by Ericaceae-dominated shrublands. We conclude that the Sentinel-2 red-edge band provides novel opportunities to detect seasonal anthocyanin accumulation in plant canopies and discuss the potential of our method to quantify long-term dynamics of shrublands in alpine and arctic contexts.

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“…Citra ini dipandang layak dan tepat untuk analisis vegetasi karena resolusi tinggi hingga 10 m untuk saluran visible dan inframerah. Penelitian Wachid et al (2017) mendapatkan nilai r 0.7739 untuk korelasi NDVI-kerapatan kanopi mangrove sementara Qiu et al (2017) menemukan peningkatan akurasi analisis vegetasi hingga 1,40% dengan citra Sentinel dibandingkan penggunaan citra Landsat. Indeks tutupan daun (leaf area index) dan kandungan klorofil kanopi yang dianalisis secara penginderaan jauh dengan citra Sentinel-2 akan memiliki korelasi kuat dengan kondisi lapangan dan nilai korelasi lebih kuat akan didapat pada analisis kandungan klorofil daun menggunakan citra tersebut (Frampton et al, 2013) Tabel 2 menunjukkan dominasi vegetasi dengan densitas kanopi rendah di Kecamatan Laweyan (31.3%).…”

Section: Gambar 3 Komposisi Pemanfaatan Lahan DI Kecamatan Laweyan 2017unclassified

A remote sensing study of vegetation cover on the Surakarta urban area 2017 using Sentinel 2A image

Sari

Wiryanto

Setyono

2019

jpsl

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High-resolution imagery Sentinel 2A is one of the media for assessing vegetation density in urban areas. Laweyan District is a Surakarta urban area with a variety and intensity of high anthropogenic activities. The purpose of this study was to determine the vegetation cover of Laweyan Subdistrict as the basic data for determining green open space policy to balance anthropogenic activity in urban areas. The study was conducted by remote sensing method to determine the NDVI value by using ENVI 5.3 and ArcMap 10.4 software. Recording data of Sentenel 2A images on August 7, 2017. The samples used were 30 points and field surveys were conducted on December 24, 2017. The results of this study showed NDVI ranges from-0.15 to 0.7 in Laweyan District. The largest NDVI distribution is in the value range of 0.11-0.2 which refers to the low vegetation category with a composition reaching 31.3%. The level of accuracy of the interpretation of NDVI in this study reached 83.2%. The results of the study indicate the low availability of green open space in Laweyan Sub-district which indicates a lack of carrying capacity of the biotic environment to overcome the effects of urban anthropogenic activities.

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Assessments of Sentinel-2 Vegetation Red-Edge Spectral Bands for Improving Land Cover Classification

Cited by 17 publications

References 6 publications

Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data

Accuracy of carrot yield forecasting using proximal hyperspectral and satellite multispectral data

Improved Mapping of Mountain Shrublands Using the Sentinel-2 Red-Edge Band

A remote sensing study of vegetation cover on the Surakarta urban area 2017 using Sentinel 2A image

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