scholarly journals Evaluation of the Consistency of Simultaneously Acquired Sentinel-2 and Landsat 8 Imagery on Plastic Covered Greenhouses

2020 ◽  
Vol 12 (12) ◽  
pp. 2015 ◽  
Author(s):  
Manuel Ángel Aguilar ◽  
Rafael Jiménez-Lao ◽  
Abderrahim Nemmaoui ◽  
Fernando José Aguilar ◽  
Dilek Koc-San ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Remote Sensing Techniques ◽  
Image Pairs ◽  
Study Sites ◽  
High Level ◽  
Sentinel 2

Remote sensing techniques based on medium resolution satellite imagery are being widely applied for mapping plastic covered greenhouses (PCG). This article aims at testing the spectral consistency of surface reflectance values of Sentinel-2 MSI (S2 L2A) and Landsat 8 OLI (L8 L2 and the pansharpened and atmospherically corrected product from L1T product; L8 PANSH) data in PCG areas located in Spain, Morocco, Italy and Turkey. The six corresponding bands of S2 and L8, together with the normalized difference vegetation index (NDVI), were generated through an OBIA approach for each PCG study site. The coefficient of determination (r2) and the root mean square error (RMSE) were computed in sixteen cloud-free simultaneously acquired image pairs from the four study sites to evaluate the coherence between the two sensors. It was found that the S2 and L8 correlation (r2 > 0.840, RMSE < 9.917%) was quite good in most bands and NDVI. However, the correlation of the two sensors fluctuated between study sites, showing occasional sun glint effects on PCG roofs related to the sensor orbit and sun position. Moreover, higher surface reflectance discrepancies between L8 L2 and L8 PANSH data, mainly in the visible bands, were always observed in areas with high-level aerosol values derived from the aerosol quality band included in the L8 L2 product (SR aerosol). In this way, the consistency between L8 PANSH and S2 L2A was improved mainly in high-level aerosol areas according to the SR aerosol band.

scholarly journals Combined Use of Multi-Temporal Landsat-8 and Sentinel-2 Images for Wheat Yield Estimates at the Intra-Plot Spatial Scale

Agronomy ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 327 ◽  
Author(s):  
Remy Fieuzal ◽  
Vincent Bustillo ◽  
David Collado ◽  
Gerard Dedieu
Keyword(s):  
Relative Error ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Wheat Yield ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Optical Images ◽  
Multi Temporal ◽  
Input Variables ◽  
Sentinel 2

The objective of this study is to address the capabilities of multi-temporal optical images to estimate the fine-scale yield variability of wheat, over a study site located in southwestern France. The methodology is based on the Landsat-8 and Sentinel-2 satellite images acquired after the sowing and before the harvest of the crop throughout four successive agricultural seasons, the reflectance constituting the input variables of a statistical algorithm (random forest). The best performances are obtained when the Normalized Difference Vegetation Index (NDVI) is combined with the yield maps collected during the crop rotation, the agricultural season 2014 showing the lower level of performances with a coefficient of determination (R2) of 0.44 and a root mean square error (RMSE) of 8.13 quintals by hectare (q.h−1) (corresponding to a relative error of 12.9%), the three other years being associated with values of R2 close or upper to 0.60 and RMSE lower than 7 q.h−1 (corresponding to a relative error inferior to 11.3%). Moreover, the proposed approach allows estimating the crop yield throughout the agricultural season, by using the successive images acquired from the sowing to the harvest. In such cases, early and accurate yield estimates are obtained three months before the end of the crop cycle. At this phenological stage, only a slight decrease in performance is observed compared to the statistic obtained just before the harvest.

scholarly journals Spatio-temporal variability of biophysical parameters of irrigated maize using orbital remote sensing

2021 ◽  
Vol 42 (4) ◽  
pp. 2181-2202
Author(s):  
Taiara Souza Costa ◽  
◽  
Robson Argolo dos Santos ◽  
Rosângela Leal Santos ◽  
Roberto Filgueiras ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Crop Coefficient ◽  
Coefficient Of Determination ◽  
Landsat 8 ◽  
Crop Evapotranspiration ◽  
Biophysical Parameters ◽  
Planting Dates ◽  
Maize Crop ◽  
Spatio Temporal

This study proposes to estimate the actual crop evapotranspiration, using the SAFER model, as well as calculate the crop coefficient (Kc) as a function of the normalized difference vegetation index (NDVI) and determine the biomass of an irrigated maize crop using images from the Operational Land Imager (OLI) and Thermal Infrared (TIRS) sensors of the Landsat-8 satellite. Pivots 21 to 26 of a commercial farm located in the municipalities of Bom Jesus da Lapa and Serra do Ramalho, west of Bahia State, Brazil, were selected. Sowing dates for each pivot were arranged as North and South or East and West, with cultivation starting firstly in one of the orientations and subsequently in the other. The relationship between NDVI and the Kc values obtained in the FAO-56 report (KcFAO) revealed a high coefficient of determination (R2 = 0.7921), showing that the variance of KcFAO can be explained by NDVI in the maize crop. Considering the center pivots with different planting dates, the crop evapotranspiration (ETc) pixel values ranged from 0.0 to 6.0 mm d-1 during the phenological cycle. The highest values were found at 199 days of the year (DOY), corresponding to around 100 days after sowing (DAS). The lowest BIO values occur at 135 DOY, at around 20 DAS. There is a relationship between ETc and BIO, where the DOY with the highest BIO are equivalent to the days with the highest ETc values. In addition to this relationship, BIO is strongly influenced by soil water availability.

Evaluating the Persistence of Post-Wildfire Ash: A Multi-Platform Spatiotemporal Analysis

Fire ◽  
2021 ◽  
Vol 4 (4) ◽  
pp. 68
Author(s):  
Sarah A. Lewis ◽  
Peter R. Robichaud ◽  
Andrew T. Hudak ◽  
Eva K. Strand ◽  
Jan U. H. Eitel ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Cost Effective ◽  
Spatiotemporal Analysis ◽  
Short Wave ◽  
Hydrologic Model ◽  
Landsat 8 ◽  
Data Volume ◽  
Sentinel 2

As wildland fires amplify in size in many regions in the western USA, land and water managers are increasingly concerned about the deleterious effects on drinking water supplies. Consequences of severe wildfires include disturbed soils and areas of thick ash cover, which raises the concern of the risk of water contamination via ash. The persistence of ash cover and depth were monitored for up to 90 days post-fire at nearly 100 plots distributed between two wildfires in Idaho and Washington, USA. Our goal was to determine the most ‘cost’ effective, operational method of mapping post-wildfire ash cover in terms of financial, data volume, time, and processing costs. Field measurements were coupled with multi-platform satellite and aerial imagery collected during the same time span. The image types spanned the spatial resolution of 30 m to sub-meter (Landsat-8, Sentinel-2, WorldView-2, and a drone), while the spectral resolution spanned visible through SWIR (short-wave infrared) bands, and they were all collected at various time scales. We that found several common vegetation and post-fire spectral indices were correlated with ash cover (r = 0.6–0.85); however, the blue normalized difference vegetation index (BNDVI) with monthly Sentinel-2 imagery was especially well-suited for monitoring the change in ash cover during its ephemeral period. A map of the ash cover can be used to estimate the ash load, which can then be used as an input into a hydrologic model predicting ash transport and fate, helping to ultimately improve our ability to predict impacts on downstream water resources.

scholarly journals Assessing Spatial Limits of Sentinel-2 Data on Arable Crops in the Context of Checks by Monitoring

2020 ◽  
Vol 12 (14) ◽  
pp. 2195 ◽  
Author(s):  
Blanka Vajsová ◽  
Dominique Fasbender ◽  
Csaba Wirnhardt ◽  
Slavko Lemajic ◽  
Wim Devos
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Policy Framework ◽  
Pixel Size ◽  
Control Approach ◽  
Continuous State ◽  
Ndvi Time Series ◽  
High Level ◽  
Sentinel 2

The availability of large amounts of Sentinel-2 data has been a trigger for its increasing exploitation in various types of applications. It is, therefore, of importance to understand the limits above which these data still guarantee a meaningful outcome. This paper proposes a new method to quantify and specify restrictions of the Sentinel-2 imagery in the context of checks by monitoring, a newly introduced control approach within the European Common Agriculture Policy framework. The method consists of a comparison of normalized difference vegetation index (NDVI) time series constructed from data of different spatial resolution to estimate the performance and limits of the coarser one. Using similarity assessment of Sentinel-2 (10 m pixel size) and PlanetScope (3 m pixel size) NDVI time series, it was estimated that for 10% out of 867 fields less than 0.5 ha in size, Sentinel-2 data did not provide reliable evidence of the activity or state of the agriculture field over a given timeframe. Statistical analysis revealed that the number of clean or full pixels and the proportion of pixels lost after an application of a 5-m (1/2 pixel) negative buffer are the geospatial parameters of the field that have the highest influence on the ability of the Sentinel-2 data to qualify the field’s state in time. We specified the following limiting criteria: at least 8 full pixels inside a border and less than 60% of pixels lost. It was concluded that compliance with the criteria still assures a high level of extracted information reliability. Our research proved the promising potential, which was higher than anticipated, of Sentinel-2 data for the continuous state assessment of small fields. The method could be applied to other sensors and indicators.

scholarly journals Determination of the Normalized Difference Vegetation Index (NDVI) with Top-of-Canopy (TOC) Reflectance from a KOMPSAT-3A Image Using Orfeo ToolBox (OTB) Extension

2020 ◽  
Vol 9 (4) ◽  
pp. 257 ◽  
Author(s):  
Kiwon Lee ◽  
Kwangseob Kim ◽  
Sun-Gu Lee ◽  
Yongseung Kim
Keyword(s):  
Open Source ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Response ◽  
Atmospheric Correction ◽  
Urban Forestry ◽  
Google Earth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Reflectance Data

Surface reflectance data obtained by the absolute atmospheric correction of satellite images are useful for land use applications. For Landsat and Sentinel-2 images, many radiometric processing methods exist, and the images are supported by most types of commercial and open-source software. However, multispectral KOMPSAT-3A images with a resolution of 2.2 m are currently lacking tools or open-source resources for obtaining top-of-canopy (TOC) reflectance data. In this study, an atmospheric correction module for KOMPSAT-3A images was newly implemented into the optical calibration algorithm in the Orfeo Toolbox (OTB), with a sensor model and spectral response data for KOMPSAT-3A. Using this module, named OTB extension for KOMPSAT-3A, experiments on the normalized difference vegetation index (NDVI) were conducted based on TOC reflectance data with or without aerosol properties from AERONET. The NDVI results for these atmospherically corrected data were compared with those from the dark object subtraction (DOS) scheme, a relative atmospheric correction method. The NDVI results obtained using TOC reflectance with or without the AERONET data were considerably different from the results obtained from the DOS scheme and the Landsat-8 surface reflectance of the Google Earth Engine (GEE). It was found that the utilization of the aerosol parameter of the AERONET data affects the NDVI results for KOMPSAT-3A images. The TOC reflectance of high-resolution satellite imagery ensures further precise analysis and the detailed interpretation of urban forestry or complex vegetation features.

scholarly journals ASSESSMENT OF RELATIONSHIP BETWEEN LAND SURFACE TEMPERATURE AND NORMALIZED DIFFERENT VEGETATION INDEX USING LANDSAT IMAGES IN SOME REGIONS OF DIYALA GOVERNORATE

2021 ◽  
Vol 52 (4) ◽  
pp. 793-801
Author(s):  
Al-Jbouri & Al-Timimi
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Conditions ◽  
Landsat 8 ◽  
Landsat Images ◽  
Remote Sensing Techniques ◽  
The Relationship

Agriculture is the most important and most dependent economic activity and influenced by climatic conditions as the climate elements represented by solar radiation, temperature, wind and relative humidity. Therefore, is necessary that analyze and understand the relationship between climate and agriculture. The aim of this study to assessment the relationship between land surface temperature (LST) and normalized difference vegetation index (NDVI) for three regions of Diyala Governorate in Iraq (Al Muqdadya, Baladrooz, and Baquba) by through using of remote sensing techniques and geographic information system (GIS).The Normalized difference vegetation index NDVI and land surface temperature (LST) were used in two of the Landsat-5 ETM + and Landsat-8 OLI satellite imagery during the years 1999 and 2019.  The results showed that increased in NDVI and decreased in LST for 2019, while for 1999 increased in LST and decreased in NDVI for the three regions. Finally, the regression was used to obtain that correlation between LST and NDVI. It was concluded that the correlation coefficient between NDVI and LST is negative, where the strongest correlation was 0.76 for Baquba and weakest correlation was 0.55 for Muqdadyia.

scholarly journals IDENTIFICATION OF DROUGHT EXTENT BASED ON VCI USING SENTINEL DATA: A CASE STUDY OF THE EASTERN OF IAŞI COUNTY

2019 ◽  
Vol 13 (2) ◽  
pp. 179-186
Author(s):  
Paul Macarof ◽  
Florian Statescu ◽  
Cristian Iulian Birlica ◽  
Paul Gherasim
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
European Space Agency ◽  
Condition Index ◽  
Landsat 8 ◽  
Natural Phenomena ◽  
Space Agency ◽  
Vegetation Condition Index ◽  
Sentinel 2

In this study was analyzed zones affected by drought using Vegetation Condition Index (VCI), that is based on Normalized Difference Vegetation Index (NDVI). This fact, drought, is one of the most wide -spread and least understood natural phenomena. In this paper was used remote sensing (RS) data, kindly provided by The European Space Agency (ESA), namely Sentinel-2 (S-2) Multispectral Instrument (MSI) and wellkonwn images Landsat 8 Operational Land Imager (OLI). The RS images was processed in SNAP and ArcMap. Study Area, was considered the eastern of Iasi county. The main purpose of paper was to investigating if Sentinel images can be used for VCI analysis.

scholarly journals Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI

2020 ◽  
Vol 12 (8) ◽  
pp. 1297
Author(s):  
Roberto Filgueiras ◽  
Everardo Chartuni Mantovani ◽  
Elpídio Inácio Fernandes-Filho ◽  
Fernando França da Cunha ◽  
Daniel Althoff ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Regression Models ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Spatial Resolution ◽  
Coefficient Of Determination ◽  
Mean Bias Error ◽  
Bias Error ◽  
Landsat 8 ◽  
Modis Ndvi

One of the obstacles in monitoring agricultural crops is the difficulty in understanding and mapping rapid changes of these crops. With the purpose of addressing this issue, this study aimed to model and fuse the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) using Landsat-like images to achieve daily high spatial resolution NDVI. The study was performed for the period of 2017 on a commercial farm of irrigated maize-soybean rotation in the western region of the state of Bahia, Brazil. To achieve the objective, the following procedures were performed: (i) Landsat-like images were upscaled to match the Landsat-8 spatial resolution (30 m); (ii) the reflectance of Landsat-like images was intercalibrated using the Landsat-8 as a reference; (iii) Landsat-like reflectance images were upscaled to match the MODIS sensor spatial resolution (250 m); (iv) regression models were trained daily to model MODIS NDVI using the upscaled Landsat-like reflectance images (250 m) of the closest day as the input; and (v) the intercalibrated version of the Landsat-like images (30 m) used in the previous step was used as the input for the trained model, resulting in a downscaled MODIS NDVI (30 m). To determine the best fitting model, we used the following statistical metrics: coefficient of determination (r2), root mean square error (RMSE), Nash–Sutcliffe efficiency index (NSE), mean bias error (MBE), and mean absolute error (MAE). Among the assessed regression models, the Cubist algorithm was sensitive to changes in agriculture and performed best in modeling of the Landsat-like MODIS NDVI. The results obtained in the present research are promising and can enable the monitoring of dynamic phenomena with images available free of charge, changing the way in which decisions are made using satellite images.

scholarly journals Evaluation of Vegetation Index-Based Curve Fitting Models for Accurate Classification of Salt Marsh Vegetation Using Sentinel-2 Time-Series

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5551
Author(s):  
Chao Sun ◽  
Jialin Li ◽  
Luodan Cao ◽  
Yongchao Liu ◽  
Song Jin ◽  
...  
Keyword(s):  
Time Series ◽  
Salt Marsh ◽  
Curve Fitting ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Coefficient Of Determination ◽  
Salt Marsh Vegetation ◽  
Marsh Vegetation ◽  
Sentinel 2

The successful launch of the Sentinel-2 constellation satellite, along with advanced cloud detection algorithms, has enabled the generation of continuous time series at high spatial and temporal resolutions, which is in turn expected to enable the classification of salt marsh vegetation over larger spatiotemporal scales. This study presents a critical comparison of vegetation index (VI) and curve fitting methods—two key factors for time series construction that potentially influence vegetation classification performance. To accomplish this objective, the stability of five different VI time series, namely Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Green Normalized Difference Vegetation Index (GNDVI), and Water-Adjusted Vegetation Index (WAVI), was compared empirically; the suitability between three curve fitting methods, namely Asymmetric Gaussian (AG), Double Logistic (DL), and Two-term Fourier (TF), and VI time series was measured using the coefficient of determination, and the salt marsh vegetation separability among different combinations of VI time series and curve fitting methods (i.e., VI time series-based curve fitting model) was quantified using overall the Jeffries–Matusita distance. Six common types of salt marsh vegetation from three typical coastal sites in China were used to validate these findings, which demonstrate: (1) the SAVI performed best in terms of time series stability, while the EVI exhibited relatively poor time series stability with conspicuous outliers induced by the sensitivity to omitted clouds and shadows; (2) the DL method commonly resulted in the most accurate classification of different salt marsh vegetation types, especially when combined with the EVI time series, followed by the TF method; and (3) the SAVI/NDVI-based DL/TF model demonstrated comparable efficiency for classifying salt marsh vegetation. Notably, the SAVI/NDVI-based DL model performed most strongly for high latitude regions with a continental climate, whilst the SAVI/NDVI-based TF model appears to be better suited to mid- to low latitude regions dominated by a monsoonal climate.

scholarly journals Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

2020 ◽  
Vol 12 (17) ◽  
pp. 2708 ◽  
Author(s):  
Qi Wang ◽  
Jiancheng Li ◽  
Taoyong Jin ◽  
Xin Chang ◽  
Yongchao Zhu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Sensors ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Vegetation Indices ◽  
Optical Data ◽  
Landsat 8 ◽  
Different Sources ◽  
Sentinel 2

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

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