A New Neighboring Pixels Method for Reducing Aerosol Effects on the NDVI Images

Wang, Dandan; Chen, Yunhao; Wang, Mengjie; Quan, Jingling; Jiang, Tao

doi:10.3390/rs8060489

Cited by 3 publications

(2 citation statements)

References 46 publications

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“…Previous studies have pointed out that the observed NDVI signal could be greatly affected by the aerosol optical depth (AOD), which could significantly underestimate the amount of vegetation [42,43]. Apart from the AOD, atmospheric water vapor, clouds, and differences in spectral wavelengths could also cause differences in NDVI values [44,45].…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Assessment of NDVI Products Derived from Fengyun Satellites across China

Wang,

Han,

Fang

et al. 2024

Remote Sensing

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NDVI data are crucial for agricultural and environmental research. The Fengyun-3 (FY-3) series satellites are recognized as primary sources for retrieving NDVI products on a global scale. To apply FY-3 NDVI data for long-term studies, such as climate change, this study conducted a thorough evaluation to detect the potentials of the FY-3B and FY-3D satellites for generating a long time series NDVI dataset. For this purpose, the spatiotemporal consistency between the FY-3B and FY-3D satellites was evaluated, and their performances were compared. Then, a grey relational analysis (GRA) method was applied to detect the factors influencing the consistency among the different satellites, and a gradient boosting regression (GBR) model was constructed to create a long-term FY-3 NDVI product. The results indicate an overall high consistency between the FY-3B and FY-3D NDVIs, suggesting that they could be used as complementary datasets for generating a long-term NDVI dataset. The correlations between the FY-3D NDVI and the MODIS NDVI, as well as the leaf area index (LAI) measurements, were both higher than those of FY-3B, which indicates a better performance of FY-3D in retrieving NDVI data. The grey correlation degrees between the NDVI differences and four parameters, which were land cover (LC), DEM, latitude (LAT) and longitude (LON), were calculated, revealing that the LC was the most related to the NDVI differences. Finally, a GBR model with FY-3B NDVI, LC, DEM, LAT and LON as the input variables and FY-3D NDVI as the target variable was established and achieved a robust performance. The R values between the GBR-estimated NDVI and FY-3D NDVI reached 0.947, 0.867 and 0.829 in the training, testing and validation datasets, respectively, indicating the feasibility of the established model for generating long time series NDVI data by combining data from the FY-3B and FY-3D satellites.

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Section: Discussionmentioning

confidence: 99%

Comprehensive Assessment of NDVI Products Derived from Fengyun Satellites across China

Wang,

Han,

Fang

et al. 2024

Remote Sensing

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“…33,34 To reducing this effect, the atmospherically resistant vegetation index (ARVI) 35 and the enhanced vegetation index 36 have been developed to account for impacts of aerosol on NDVI. 37 Similarly, uncertainty in WV will induce uncertainty in NIR reflectance and vegetation water indices, since absorption features of liquid water and WV overlap. 38,39 NDWI using SWIR water absorption band was developed to be less sensitive to atmospheric effect through radiative transfer (RT) simulation.…”

Section: Introductionmentioning

confidence: 99%

Atmospherically resistant vegetation water indices using the 970-nm water absorption feature

Jiao

Liu

et al. 2020

J. Appl. Rem. Sens.

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Atmospheric correction can introduce errors in surface spectral reflectance, and hence induces errors in plant water estimation from remote sensing water indices. We intend to develop water indices that are less impacted by atmospheric effects for plant water content estimation based on the 970-nm water absorption feature. A simulation study using the PROSAIL and 6S models showed that uncertainty in atmospheric water vapor (WV) content can induce large variation in existing 970-nm water indices, such as WI, NWI-1, and NWI-3. An attempt was made to incorporate atmospheric WV absorption at 940 nm to correct for the perturbation due to atmospheric WV variability, leading to the development of improved indices, named as ARWI, NARWI-1, and NARWI-3. The performance of these indices was evaluated using the simulated and field spectral reflectance data, as well as Hyperion and GF5 satellite data. Results showed that the new indices were resistant to uncertainty of WV and could be used to deliver improved estimation of canopy water content, with a smaller root-mean-square-error (ARWI: 7.4 mg∕cm 2 , NARWI-1: 8.3 mg∕cm 2 , and NARWI-3: 8.8 mg∕cm 2) compared to that obtained using the traditional water indices (WI: 8.9 mg∕cm 2 , NWI-1: 9.4 mg∕cm 2 , and NWI-3: 16.6 mg∕cm 2). The water indices developed in this study, although needing further assessment in wide application scenarios, have great potential for monitoring of vegetation water status using satellite hyperspectral data with reflectance measurement around 970 nm. © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Spatio-temporal variation indicators for landscape structure dynamics monitoring using dense normalized difference vegetation index time series

Meng

Liu

et al. 2019

Ecological Indicators

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A New Neighboring Pixels Method for Reducing Aerosol Effects on the NDVI Images

Cited by 3 publications

References 46 publications

Comprehensive Assessment of NDVI Products Derived from Fengyun Satellites across China

Comprehensive Assessment of NDVI Products Derived from Fengyun Satellites across China

Atmospherically resistant vegetation water indices using the 970-nm water absorption feature

Spatio-temporal variation indicators for landscape structure dynamics monitoring using dense normalized difference vegetation index time series

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