Evaluation of Terra-MODIS C6 and C6.1 Aerosol Products against Beijing, XiangHe, and Xinglong AERONET Sites in China during 2004-2014

In this study, Terra-MODIS (Moderate Resolution Imaging Spectroradiometer) Collections 6 and 6.1 (C6 & C6.1) aerosol optical depth (AOD) retrievals with the recommended high-quality flag (QF = 3) were retrieved from Dark-Target (DT), Deep-Blue (DB) and merged DT and DB (DTB) level–2 AOD products for verification against Aerosol Robotic Network (AERONET) Version 3 Level 2.0 AOD data obtained from 2004–2014 for three sites located in the Beijing-Tianjin-Hebei (BTH) region. These are: Beijing, located over mixed bright urban surfaces, XiangHe located over suburban surfaces, and Xinglong located over hilly and vegetated surfaces. The AOD retrievals were also validated over different land-cover types defined by static monthly NDVI (Normalized Difference Vegetation Index) values obtained from the Terra-MODIS level-3 product (MOD13A3). These include non-vegetated surfaces (NVS, NDVI < 0.2), partially vegetated surfaces (PVS, 0.2 ≤ NDVI ≤ 0.3), moderately vegetated surfaces (MVS, 0.3 < NDVI < 0.5) and densely vegetated surfaces (DVS, NDVI ≥ 0.5). Results show that the DT, DB, and DTB-collocated retrievals achieve a high correlation coefficient of ~ 0.90–0.97, 0.89–0.95, and 0.86–0.95, respectively, with AERONET AOD. The DT C6 and C6.1 collocated retrievals were comparable at XiangHe and Xinglong, whereas at Beijing, the percentage of collocated retrievals within the expected error (↔EE) increased from 21.4% to 35.5%, the root mean square error (RMSE) decreased from 0.37 to 0.24, and the relative percent mean error (RPME) decreased from 49% to 27%. These results suggest significant relative improvement in the DT C6.1 product. The percentage of DB-collocated AOD retrievals ↔EE was greater than 70% at Beijing and Xinglong, whereas less than 66% was observed at XiangHe. Similar to DT AOD, DTB AOD retrievals performed well at XiangHe and Xinglong compared with Beijing. Regionally, DB C6 and C6.1-collocated retrievals performed better than DT and DTB in terms of good quality retrievals and relatively small errors. For diverse vegetated surfaces, DT-collocated retrievals reported small errors and good quality retrievals only for NVS and DVS, whereas larger errors were reported for PVS. MVS. DB contains good quality AOD retrievals over PVS, MVS, and DVS compared with NVS. DTB C6.1 collocated retrievals were better than C6 over NVS, PVS, and DVS. C6.1 is substantially improved overall, compared with C6 at local and regional scales, and over diverse vegetated surfaces.

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Validation and Accuracy Assessment of MODIS C6.1 Aerosol Products over the Heavy Aerosol Loading Area

Atmosphere ◽

10.3390/atmos10090548 ◽

2019 ◽

Vol 10 (9) ◽

pp. 548 ◽

Cited By ~ 7

Author(s):

Xinpeng Tian ◽

Zhiqiang Gao

Keyword(s):

Urban Areas ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Accuracy Assessment ◽

Aerosol Loading ◽

Deep Blue ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Terra Modis ◽

Reflecting Surface

The aim of this study is to evaluate the accuracy of MODerate resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) products over heavy aerosol loading areas. For this analysis, the Terra-MODIS Collection 6.1 (C6.1) Dark Target (DT), Deep Blue (DB) and the combined DT/DB AOD products for the years 2000–2016 are used. These products are validated using AErosol RObotic NETwork (AERONET) data from twenty-three ground sites situated in high aerosol loading areas and with available measurements at least 500 days. The results show that the numbers of collections (N) of DB and DT/DB retrievals were much higher than that of DT, which was mainly caused by unavailable retrieval of DT in bright reflecting surface and heavy pollution conditions. The percentage falling within the expected error (PWE) of the DT retrievals (45.6%) is lower than that for the DB (53.4%) and DT/DB (53.1%) retrievals. The DB retrievals have 5.3% less average overestimation, and 25.7% higher match ratio than DT/DB retrievals. It is found that the current merged aerosol algorithm will miss some cases if it is determined only on the basis of normalized difference vegetation index. As the AOD increases, the value of PWE of the three products decreases significantly; the undervaluation is suppressed, and the overestimation is aggravated. The retrieval accuracy shows distinct seasonality: the PWE is largest in autumn or winter, and smallest in summer. The most severe overestimation and underestimation occurred in the summer. Moreover, the DT, DB and DT/DB products over different land cover types still exhibit obvious deviations. In urban areas, the PWE of DB product (52.6%) is higher than for the DT/DB (46.3%) and DT (25.2%) products. The DT retrievals perform poorly over the barren or sparsely vegetated area (N = 52). However, the performance of three products is similar over vegetated area. On the whole, the DB product performs better than the DT product over the heavy aerosol loading area.

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High Efficiency of Solar Cell Model Based on Two Types of Nanoparticles

Jordan Journal of Physics ◽

10.47011/13.1.9 ◽

2020 ◽

Vol 13 (1) ◽

pp. 87-92

Keyword(s):

Solar Cell ◽

High Efficiency ◽

Cell Model ◽

Deep Blue ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Terra Modis ◽

Level 2

Climatology of aerosols, their trends and classification based on the long-term Moderate Resolution Imaging Spectroradiometer (MODIS) measurements (from February 2000 to July 2015) of aerosol optical depths at 550 nm (τ550) and Angstrom exponent (α470-660) using the wavelengths of 470 and 660nm in Nairobi, Skukuza and Ilorin AERONET stations were analyzed in this work. The level-2 collection-6 Deep Blue (L2 C006 DB) of the parameters listed above from the aqua- (MYD04) and terra- (MOD04) MODIS of the study area were statistically analyzed using SPSS. To be able to understand the temporal variation in the characteristics of aerosols in the three stations and during each season separately, MODIS measurements of τ, retrieved for the study area, were compared with AERONET τ. Overall, aqua-MODIS τ corroborate the AERONET measurements well in Nairobi and Ilorin stations with underestimation of 29.80 % and overestimation of 2.90 % respectively, whereas Skukuza station has terra-MODIS τ as the best representation of the AERONET measurements with underestimation of 1.90 %. ....

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New Regression Method to Merge Different MODIS Aerosol Products Based on NDVI Datasets

Atmosphere ◽

10.3390/atmos10060303 ◽

2019 ◽

Vol 10 (6) ◽

pp. 303 ◽

Cited By ~ 2

Author(s):

Weiwei Xu ◽

Wei Wang ◽

Lixin Wu

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Global Scale ◽

Regression Method ◽

Official Method ◽

Deep Blue ◽

Moderate Resolution ◽

Data Coverage ◽

Level 2 ◽

Better Than

The moderate resolution and imaging spectroradiometer (MODIS) level 2 operational aerosol products that are based on the dark target (DT) method over vegetated regions and the enhanced deep blue (DB) algorithms over bright pixels provide daily global aerosol optical depth (AOD). However, increasing the data coverage by merging the DT and DB merged AOD product has recently become the focus of research. Therefore, this study aims to improve the merged AOD performance by introducing a new regression method (DTBRG), depending on the normalized difference vegetation index values when DT and DB AOD are valid. The DTBRG AOD is validated on a global scale while using aerosol robot network AOD measurements. Merged AOD550s from the MODIS official method and Bilal’s customized methods are evaluated for the same period for comparison. The inter-comparison of merged AOD550s from different methods with an equal number of coincident observations demonstrates that the DTBRG method performs better than the MODIS official algorithm with increased expected error (83% versus 76%), R (0.92 versus 0.90), and decreased bias (−0.001 versus 0.012). Therefore, it can be operationally used for global merged aerosol retrievals.

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An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

Remote Sensing ◽

10.3390/rs13040719 ◽

2021 ◽

Vol 13 (4) ◽

pp. 719

Author(s):

Xiuxia Li ◽

Shunlin Liang ◽

Huaan Jin

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Continuous Data ◽

Landsat Data ◽

Area Index ◽

Free Data ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Resolution Data

Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics.

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Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2016000700009 ◽

2016 ◽

Vol 51 (7) ◽

pp. 858-868

Author(s):

Marcos Cicarini Hott ◽

Luis Marcelo Tavares de Carvalho ◽

Mauro Antonio Homem Antunes ◽

Polyanne Aguiar dos Santos ◽

Tássia Borges Arantes ◽

...

Keyword(s):

Vegetative Growth ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

High Growth ◽

Growth Index ◽

Data Series ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Very High

Abstract: The objective of this work was to analyze the development of grasslands in Zona da Mata, in the state of Minas Gerais, Brazil, between 2000 and 2013, using a parameter based on the growth index of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (Modis) data series. Based on temporal NDVI profiles, which were used as indicators of edaphoclimatic conditions, the growth index (GI) was estimated for 16-day periods throughout the spring season of 2012 to early 2013, being compared with the average GI from 2000 to 2011, used as the reference period. Currently, the grassland areas in Zona da Mata occupy approximately 1.2 million hectares. According to the used methods, 177,322 ha (14.61%) of these grassland areas have very low vegetative growth; 577,698 ha (45.96%) have low growth; 433,475 ha (35.72%) have balanced growth; 39,980 ha (3.29%) have high growth; and 5,032 ha (0.41%) have very high vegetative growth. The grasslands had predominantly low vegetative growth during the studied period, and the NDVI/Modis series is a useful source of data for regional assessments.

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Development of a remote sensing-based rice yield forecasting model

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2016143-8347 ◽

2016 ◽

Vol 14 (3) ◽

pp. e0907 ◽

Cited By ~ 14

Author(s):

Mostafa K. Mosleh ◽

Quazi K. Hassan ◽

Ehsan H. Chowdhury

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Rice Yield ◽

Decision Makers ◽

Coefficient Of Determination ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Boro Rice

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

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Comparison of MODIS 250 m products for early corn yield predictions: a case study in Vojvodina, Serbia

Open Geosciences ◽

10.1515/geo-2016-0070 ◽

2016 ◽

Vol 8 (1) ◽

Cited By ~ 3

Author(s):

Miro Govedarica ◽

Dušan Jovanović ◽

Filip Sabo ◽

Mirko Borisov ◽

Milan Vrtunski ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Corn Yield ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

AbstractThe aim of the paper is to compare Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (

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Development and Demonstration of a Method for GEO-to-LEO NDVI Transformation

Remote Sensing ◽

10.3390/rs13204085 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4085

Author(s):

Kenta Obata ◽

Kenta Taniguchi ◽

Masayuki Matsuoka ◽

Hiroki Yoshioka

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Middle Latitude ◽

Transformation Method ◽

Low Earth Orbit ◽

Linear Mixture Model ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Index Time Series

This study presents a new method that mitigates biases between the normalized difference vegetation index (NDVI) from geostationary (GEO) and low Earth orbit (LEO) satellites for Earth observation. The method geometrically and spectrally transforms GEO NDVI into LEO-compatible GEO NDVI, in which GEO’s off-nadir view is adjusted to a near-nadir view. First, a GEO-to-LEO NDVI transformation equation is derived using a linear mixture model of anisotropic vegetation and nonvegetation endmember spectra. The coefficients of the derived equation are a function of the endmember spectra of two sensors. The resultant equation is used to develop an NDVI transformation method in which endmember spectra are automatically computed from each sensor’s data independently and are combined to compute the coefficients. Importantly, this method does not require regression analysis using two-sensor NDVI data. The method is demonstrated using Himawari 8 Advanced Himawari Imager (AHI) data at off-nadir view and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at near-nadir view in middle latitude. The results show that the magnitudes of the averaged NDVI biases between AHI and MODIS for five test sites (0.016–0.026) were reduced after the transformation (<0.01). These findings indicate that the proposed method facilitates the combination of GEO and LEO NDVIs to provide NDVIs with smaller differences, except for cases in which the fraction of vegetation cover (FVC) depends on the view angle. Further investigations should be conducted to reduce the remaining errors in the transformation and to explore the feasibility of using the proposed method to predict near-real-time and near-nadir LEO vegetation index time series using GEO data.

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ANALISIS TUTUPAN LAHAN DENGAN MENGGUNAKAN METODE BREAK FOR ADDITIVE SEASONAL AND TREND (BFAST) BERDASARKAN INDEKS VEGETASI

10.31227/osf.io/yeba6 ◽

2017 ◽

Author(s):

Seminar Nasional Multidisiplin Ilmu 2017 ◽

Ramos Lumban Tobing

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Trend Data ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Metode penginderaan jarak jauh (Remote Sensing) telah banyak digunakan dalam berbagai bidang termasuk diantaranya bidang tutupan lahan/vegetasi termasuk perkebunan. Produk dari penginderaan jauh tersebut banyak tersedia diantaranya NDVI (Normalized Difference Vegetation Index) dan EVI (Enhanced Vegetation Indeks) yang merupakan indikator proxy dari suatu lokasi atau kondisi tutupan lahan lokasi tersebut. Dari beberapa penilitian, NDVI telah banyak digunakan namun EVI masih belum banyak digunakan. Kami membandingkan pengaruh dari penggunaan NDVI dan EVI pada jumlah dan waktu perubahan yang terekam dengan menggunakan metode BFAST (Breaks For Additive Seasonal and Trend). Data yang digunakan adalah MODIS (Moderate Resolution Imaging Spectroradiometer)16 harian NDVI dan EVI berupa gambar komposit (06 April 2000 s.d. 16 November 2014) dari empat piksel (pixel 293,294,295 dan 296) disekitar menara fluks Aek Loba.Hasil penelitian menunjukkan bahwa EVI untuk pemantauan tutupan lahan di kawasan perkebunan tropis yang ditutupi oleh awan intens lebih baik dari NDVI itu. Meskipun demikian, penelitian lebih lanjut dengan meningkatkan resolusi spasial dari citra satelit untuk aplikasi NDVI sangat dianjurkan

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TVDI Obtido de Imagens OLI/TIRS e MODIS

Revista Brasileira de Meteorologia ◽

10.1590/0102-7786344070 ◽

2019 ◽

Vol 34 (4) ◽

pp. 573-583

Author(s):

Lucimara Wolfarth Schirmbeck ◽

Denise Cybis Fontana ◽

Juliano Schirmbeck ◽

Carolina Bremm

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Thermal Infrared ◽

Landsat 8 ◽

Infrared Sensor ◽

Moderate Resolution ◽

Dryness Index ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Resumo O objetivo do estudo foi analisar a variabilidade no TVDI (Temperature-Vegetation Dryness Index) obtido de sensores orbitais com resoluções distintas, em região agrícola no sul do Brasil. Utilizou-se três imagens OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) do satélite Landsat 8, e 12 imagens MODIS (Moderate Resolution Imaging Spectroradiometer) do satélite Terra. Dados coletados em campo serviram como base para classificação de imagem OLI/TIRS e mapeamento de áreas de arroz, soja, campos naturais, mata ciliar e solo exposto. O TVDI foi obtido por duas parametrizações em períodos distintos, utilizando as dispersões entre Temperatura de Superfície (TS) e NDVI (Normalized Difference Vegetation Index). O TVDI obtido para ambos sensores apresentou padrão similar possibilitando diferenciar os alvos. Na média de todas as datas e classes, o TVDI obtido das imagens MODIS foi superior em 0,128 unidades ao TVDI obtido com o OLI/TIRS. Quando utilizado OLI/TIRS há um melhor detalhamento espacial das condições hídricas, mas com menor repetição ao longo da safra; já utilizando o TVDI-MODIS é possível monitorar as condições hídricas em escala regional, com menor detalhamento espacial, mas com maior repetitividade no tempo. O TVDI estimado pelos sensores OLI/TIRS e MODIS, pode ser utilizado de forma conjunta, trazendo informações complementares.

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