scholarly journals USABILITY, STRENGTH AND PRACTICALITY OF THE UPCOMING HYSPIRI IN DETECTING MAIZE GRAY LEAFY SPOT IN RELATION TO SENTINEL-2 MSI, VENμS AND LANDSAT 8 OLI SPECTRAL BAND-SETTINGS

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 1015-1022
Author(s):  
M. Sibanda ◽  
O. Mutanga ◽  
T. Dube ◽  
J. Odindi ◽  
P. L. Mafongoya
Keyword(s):  
Disease Progression ◽  
Precision Agriculture ◽  
Spectral Band ◽  
Maize Yield ◽  
Gray Leaf Spot ◽  
Added Value ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
To Come ◽  
Sentinel 2

Abstract. Considering the high maize yield loses that are caused by diseases incidences as well as incomprehensive monitoring initiatives in the crop farming sector of agriculture, there is a need to come up with spatially explicit, cheap, fast and consistent approaches for monitoring as well as forecasting food crop diseases, such as maize gray leaf spot. This study, therefore, we sought to investigate the usability, strength and practicality of the forthcoming HyspIRI in detecting disease progression of Maize Gray leafy spot infections in relation to the Sentinel-2 MSI, Landsat 8 OLI spectral configurations. Maize Gray leafy spot disease progression that were discriminated based on partial least squares –discriminant analysis (PLS-DA) algorithm were (i) healthy, (ii) intermediate and (ii) severely infected maize crops. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93 and 0.89 exhibited by Sentinel-2 MSI, VENμS and Landsat 8 OLI sensor sensors, respectively. Further, the results showed that the visible section the red-edge and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray leafy spot infections. These findings underscore the added value and potential scientific breakthroughs likely to be brought about by the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop disease epidemics to ensure food security.

scholarly journals The Utility of the Upcoming HyspIRI’s Simulated Spectral Settings in Detecting Maize Gray Leafy Spot in Relation to Sentinel-2 MSI, VENµS, and Landsat 8 OLI Sensors

Agronomy ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 846
Author(s):  
Mbulisi Sibanda ◽  
Onisimo Mutanga ◽  
Timothy Dube ◽  
John Odindi ◽  
Paramu L. Mafongoya
Keyword(s):  
Food Security ◽  
Precision Agriculture ◽  
Leaf Spot ◽  
Gray Leaf Spot ◽  
Economic Losses ◽  
Leaf Spot Disease ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Maize Crop ◽  
Sentinel 2

Considering the high maize yield loses caused by incidences of disease, as well as incomprehensive monitoring initiatives in crop farming, there is a need for spatially explicit, cost-effective, and consistent approaches for monitoring, as well as for forecasting, food-crop diseases, such as maize Gray Leaf Spot. Such approaches are valuable in reducing the associated economic losses while fostering food security. In this study, we sought to investigate the utility of the forthcoming HyspIRI sensor in detecting disease progression of Maize Gray Leaf Spot infestation in relation to the Sentinel-2 MSI and Landsat 8 OLI spectral configurations simulated using proximally sensed data. Healthy, intermediate, and severe categories of maize crop infections by the Gray Leaf Spot disease were discriminated based on partial least squares–discriminant analysis (PLS-DA) algorithm. Comparatively, the results show that the HyspIRI’s simulated spectral settings slightly performed better than those of Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor. HyspIRI exhibited an overall accuracy of 0.98 compared to 0.95, 0.93, and 0.89, which were exhibited by Sentinel-2 MSI, VENµS, and Landsat 8 OLI sensor sensors, respectively. Furthermore, the results showed that the visible section, red-edge, and NIR covered by all the four sensors were the most influential spectral regions for discriminating different Maize Gray Leaf Spot infections. These findings underscore the potential value of the upcoming hyperspectral HyspIRI sensor in precision agriculture and forecasting of crop-disease epidemics, which are necessary to ensure food security.

scholarly journals Exploring the utility of Sentinel-2 MSI and Landsat 8 OLI in burned area mapping for a heterogenous savannah landscape

PLoS ONE ◽  
2020 ◽  
Vol 15 (5) ◽  
pp. e0232962 ◽  
Author(s):  
Fiona Ngadze ◽  
Kudzai Shaun Mpakairi ◽  
Blessing Kavhu ◽  
Henry Ndaimani ◽  
Monalisa Shingirayi Maremba
Keyword(s):  
Burned Area ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Burned Area Mapping ◽  
Sentinel 2

scholarly journals Comparative Evaluation of the Spectral and Spatial Consistency of Sentinel-2 and Landsat-8 OLI Data for Igneada Longos Forest

2019 ◽  
Vol 8 (2) ◽  
pp. 56 ◽  
Author(s):  
Maliheh Arekhi ◽  
Cigdem Goksel ◽  
Fusun Balik Sanli ◽  
Gizem Senel
Keyword(s):  
Vegetation Index ◽  
Data Retrieval ◽  
Forest Monitoring ◽  
Landsat 8 ◽  
Spring Season ◽  
Landsat 8 Oli ◽  
Atmospheric Effect ◽  
Spatial Consistency ◽  
The Mean ◽  
Sentinel 2

This study aims to test the spectral and spatial consistency of Sentinel-2 and Landsat-8 OLI data for the potential of monitoring longos forests for four seasons in Igneada, Turkey. Vegetation indices, including Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI), were generated for the study area in addition to the five corresponding bands of Sentinel-2 and Landsat-8 OLI Images. Although the spectral consistency of the data was interpreted by cross-calibration analysis using the Pearson correlation coefficient, spatial consistency was evaluated by descriptive statistical analysis of investigated variables. In general, the highest correlation values were achieved for the images that were acquired in the spring season for almost all investigated variables. In the spring season, among the investigated variables, the Red band (B4), NDVI and EVI have the largest correlation coefficients of 0.94, 0.92 and 0.91, respectively. Regarding the spatial consistency, the mean and standard deviation values of all variables were consistent for all seasons except for the mean value of the NDVI for the fall season. As a result, if there is no atmospheric effect or data retrieval/acquisition error, either Landsat-8 or Sentinel-2 can be used as a combination or to provide the continuity data in longos monitoring applications. This study contributes to longos forest monitoring science in terms of remote sensing data analysis.

scholarly journals Comparing Spectral Characteristics of Landsat-8 and Sentinel-2 Same-Day Data for Arctic-Boreal Regions

2019 ◽  
Vol 11 (14) ◽  
pp. 1730 ◽  
Author(s):  
Alexandra Runge ◽  
Guido Grosse
Keyword(s):  
Time Series ◽  
Least Squares ◽  
Spectral Band ◽  
Ordinary Least Squares ◽  
Landsat 8 ◽  
Eastern Siberia ◽  
Landscape Changes ◽  
Least Squares Regression ◽  
Spectral Bandpass ◽  
Sentinel 2

The Arctic-Boreal regions experience strong changes of air temperature and precipitation regimes, which affect the thermal state of the permafrost. This results in widespread permafrost-thaw disturbances, some unfolding slowly and over long periods, others occurring rapidly and abruptly. Despite optical remote sensing offering a variety of techniques to assess and monitor landscape changes, a persistent cloud cover decreases the amount of usable images considerably. However, combining data from multiple platforms promises to increase the number of images drastically. We therefore assess the comparability of Landsat-8 and Sentinel-2 imagery and the possibility to use both Landsat and Sentinel-2 images together in time series analyses, achieving a temporally-dense data coverage in Arctic-Boreal regions. We determined overlapping same-day acquisitions of Landsat-8 and Sentinel-2 images for three representative study sites in Eastern Siberia. We then compared the Landsat-8 and Sentinel-2 pixel-pairs, downscaled to 60 m, of corresponding bands and derived the ordinary least squares regression for every band combination. The acquired coefficients were used for spectral bandpass adjustment between the two sensors. The spectral band comparisons showed an overall good fit between Landsat-8 and Sentinel-2 images already. The ordinary least squares regression analyses underline the generally good spectral fit with intercept values between 0.0031 and 0.056 and slope values between 0.531 and 0.877. A spectral comparison after spectral bandpass adjustment of Sentinel-2 values to Landsat-8 shows a nearly perfect alignment between the same-day images. The spectral band adjustment succeeds in adjusting Sentinel-2 spectral values to Landsat-8 very well in Eastern Siberian Arctic-Boreal landscapes. After spectral adjustment, Landsat and Sentinel-2 data can be used to create temporally-dense time series and be applied to assess permafrost landscape changes in Eastern Siberia. Remaining differences between the sensors can be attributed to several factors including heterogeneous terrain, poor cloud and cloud shadow masking, and mixed pixels.

scholarly journals Mapping Winter Wheat with Combinations of Temporally Aggregated Sentinel-2 and Landsat-8 Data in Shandong Province, China

2020 ◽  
Vol 12 (12) ◽  
pp. 2065 ◽  
Author(s):  
Feng Xu ◽  
Zhaofu Li ◽  
Shuyu Zhang ◽  
Naitao Huang ◽  
Zongyao Quan ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Google Earth ◽  
Shandong Province ◽  
Sensor Data ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Multi Temporal ◽  
Wheat Development ◽  
Development Phases ◽  
Sentinel 2

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere.

Exploring the potential of Landsat-8 OLI and Sentinel-2 MSI data for mapping and monitoring Enez Dalyan Lagoon

2020 ◽  
Vol 177 ◽  
pp. 330-337
Author(s):  
Gizem Senel ◽  
Ahmet Ozgur Dogru ◽  
Cigdem Goksel
Keyword(s):  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Sentinel 2

scholarly journals Identification of Continental Wetlands Using Different Orbital Remote Sensors

Terr Plural ◽  
2021 ◽  
Vol 15 ◽  
pp. 1-25
Author(s):  
Isadora Taborda Silva ◽  
Jéssica Rabito Chaves ◽  
Helen Rezende Figueiredo ◽  
Bruno Silva Ferreira ◽  
César Claudio Cáceres Encina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Spectral Band ◽  
Landsat 8 ◽  
Remote Sensors ◽  
False Color ◽  
False Color Composite ◽  
International Importance ◽  
Color Composite ◽  
Shortwave Infrared ◽  
Sentinel 2

This paper evaluates the potential of false-color composite images, from 3 different remote sensing satellites, for the identification of continental wetlands. Landsat 8, Sentinel-2 and CBERS-4 scenes from three different Ramsar sites (i.e., sites designated to be of international importance) two sites located within the Mato-Grossense Pantanal and one within the Sul-mato-grossense were used for analyses. For each site, images from both the dry and rainy seasons were analyzed using Near-Infrared (NIR), Shortwave Infrared (SWIR), and visible (VIS) bands. The results show that false-color composite images from both the Landsat 8 and the Sentinel-2 satellites, with both SWIR 2-NIR-BLUE and NIR-SWIR-RED spectral band combinations, allow the identification of wetlands.

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