The potential for integrating Sentinel 2 MSI with SPOT 5 HRG and Landsat 8 OLI imagery for monitoring semi-arid savannah woody cover

2017 ◽  
Vol 38 (17) ◽  
pp. 4888-4913 ◽  
Author(s):  
C. Munyati
Keyword(s):  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Woody Cover ◽  
Spot 5 ◽  
Semi Arid ◽  
Sentinel 2

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 Suitability Evaluation of Typical Drought Index in Soil Moisture Retrieval and Monitoring Based on Optical Images

2020 ◽  
Vol 12 (16) ◽  
pp. 2587
Author(s):  
Yan Nie ◽  
Ying Tan ◽  
Yuqin Deng ◽  
Jing Yu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Arid Regions ◽  
Drought Index ◽  
Dynamic Monitoring ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Rapid Acquisition ◽  
Semi Arid ◽  
Soil Moisture Inversion

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

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 GEOTECNOLOGIAS COMO SUPORTE PARA ANÁLISE DA VEGETAÇÃO NATURAL NA SUB-BACIA HIDROGRÁFICA DO RIO GAVIÃO (1988 A 2015)

2020 ◽  
Vol 15 (01) ◽  
pp. 98-113
Author(s):  
Carlos Magno Santos Clemente ◽  
Pablo Santana Santos
Keyword(s):  
Hydrological Cycle ◽  
Natural Vegetation ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Palabras Clave ◽  
El Sistema ◽  
History Of ◽  
Main Canal ◽  
Del Rio ◽  
Semi Arid

O histórico de ocupação da sub-bacia do rio Gavião passou por transformações socioeconômicas expressivas nos últimos 30 anos. Desse modo,preocupações com preservação ou recuperação da cobertura vegetal influência, positivamente, na manutenção do ciclo hidrológico da sub-bacia. A presente pesquisa teve como objetivo analisar a modificação da vegetal natural entre os anos de 1988a 2015 na sub-bacia hidrográfico do rio Gavião (semiárido brasileiro). Foram utilizados as técnicas sensoriamento remoto e Processamento Digital de Imagens - PDI para aquisição e processamento dos produtos orbitais (satélites landsat5 TM e landsat 8 OLI). E o Sistema de Informações Geográficas – SIG para armazenamento e análise do banco de dados alfanumérico georreferenciado. Os resultados indicam redução da cobertura vegetal de 751,69 km², entre os anos de 1988 a 2015. Também, manchas de desmatamento em áreas de nascentes, na parte alta da rede de drenagem e no dessegue do canal principal. Assim, a presente pesquisa chama atenção para os efeitos da mudança da vegetação natural para outros usos da terra (solo exposto, plantio, entre outros), a concentração do desmatamento em áreas de fragilidade ambiental. Palavras-chave: Landsat; Desmatamento; Semiárido brasileiro.   GEOTECHNOLOGIES AS SUPPORT FOR ANALYSIS OF NATURAL VEGETATION IN THE HYDROGRAPHIC BASIN OF HAWK RIVER (1988 A 2015) Abstract  The occupation history of the Hawk River sub-basin underwent significant socioeconomic transformations in the last 30 years. Thus, concerns for preservation or recovery of vegetation cover positively influence the maintenance of the sub-basin's hydrological cycle. The present research had as objective to analyze the modification of the natural vegetal between the years of 1988 to 2015 in the hydrographic sub-basin of the river Gavião (semi-arid Brazilian).The techniques of remote sensing and Digital Image Processing (PDI) were used for the acquisition and processing of orbital products (landsat 5 TM and landsat 8 OLI satellites). The Geographic Information System - GIS for storage and analysis of the georeferenced alphanumeric database. The results indicate a reduction of the vegetal cover of 751,69 km ², between the years of 1988 to 2015. In addition, deforestation patches in areas of springs, in the upper part of the drainage network and in the main canal deregulation. Thus, the present research draws attention to the effects of changing natural vegetation to other land uses (exposed soil, planting, among others), the concentration of deforestation in areas of environmental fragility.  Keywords: Landsat; deforestation; Brazilian semi-arid.   GEOTECNOLOGÍA COMO SOPORTE PARA EL ANÁLISIS DE VEGETACIÓN NATURAL DE LA SUBCUENCA DEL RÍO GAVILÁN (1988 A 2015) Resumen La historia de laocupación de lasub-cuencadelrío Gavião fue sometido a importantes cambios socioeconómicos enlos últimos 30 años. De este modo, preocupación por lapreservación o restauración de lacubierta vegetal influencia positiva enelmantenimientodel ciclo hidrológico de lasubcuenca. Esta investigacióntuvo como objetivo analizarlamodificación de lavegetación natural entre losaños 1988-2015 enlasubcuenca hidrográfica delrío Gavião (semiárido brasileño). Como apoyo técnico, lateledetección y la técnica de imagen digital se utiliza Procesamiento - PDI para laadquisición y procesamiento de productosorbitales (satélites Landsat 5 y Landsat TM 8 OLI). Y el Sistema de Información Geográfica - SIG para elalmacenamiento y análisis de la base de datos alfanuméricos georeferenciada. Los resultados indicanlareducción de lacubierta vegetal de 751.69 km², entre losaños 1988-2015. Tambiénlas manchas de deforestaciónenlascabecerasenla parte superior del sistema de drenaje y dessegue el canal principal. Así, estainvestigaciónllamalaatención sobre losefectosdelcambio de lavegetación natural a otros usos de latierra (sueloexpuesto, ,plantación, etc.), laconcentración de ladeforestaciónen áreas ambientalmente frágiles. Palabras clave: Landsat; deforestación; semiárido brasileño.

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 Assessment of Leaf Area Index Models Using Harmonized Landsat and Sentinel-2 Surface Reflectance Data over a Semi-Arid Irrigated Landscape

2020 ◽  
Vol 12 (19) ◽  
pp. 3121
Author(s):  
Roya Mourad ◽  
Hadi Jaafar ◽  
Martha Anderson ◽  
Feng Gao
Keyword(s):  
Linear Regression ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Above Ground Biomass ◽  
Area Index ◽  
Ground Biomass ◽  
Semi Arid ◽  
Sentinel 2

Leaf area index (LAI) is an essential indicator of crop development and growth. For many agricultural applications, satellite-based LAI estimates at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions, Sentinel 2 (ESA) and Landsat 8 (NASA), provides this opportunity. In this study, we evaluated the leaf area index generated from three methods, namely, existing vegetation index (VI) relationships applied to Harmonized Landsat-8 and Sentinel-2 (HLS) surface reflectance produced by NASA, the SNAP biophysical model, and the THEIA L2A surface reflectance products from Sentinel-2. The intercomparison was conducted over the agricultural scheme in Bekaa (Lebanon) using a large set of in-field LAIs and other biophysical measurements collected in a wide variety of canopy structures during the 2018 and 2019 growing seasons. The major studied crops include herbs (e.g., cannabis: Cannabis sativa, mint: Mentha, and others), potato (Solanum tuberosum), and vegetables (e.g., bean: Phaseolus vulgaris, cabbage: Brassica oleracea, carrot: Daucus carota subsp. sativus, and others). Additionally, crop-specific height and above-ground biomass relationships with LAIs were investigated. Results show that of the empirical VI relationships tested, the EVI2-based HLS models statistically performed the best, specifically, the LAI models originally developed for wheat (RMSE:1.27), maize (RMSE:1.34), and row crops (RMSE:1.38). LAI derived through European Space Agency’s (ESA) Sentinel Application Platform (SNAP) biophysical processor underestimated LAI and provided less accurate estimates (RMSE of 1.72). Additionally, the S2 SeLI LAI algorithm (from SNAP biophysical processor) produced an acceptable accuracy level compared to HLS-EVI2 models (RMSE of 1.38) but with significant underestimation at high LAI values. Our findings show that the LAI-VI relationship, in general, is crop-specific with both linear and non-linear regression forms. Among the examined indices, EVI2 outperformed other vegetation indices when all crops were combined, and therefore it can be identified as an index that is best suited for a unified algorithm for crops in semi-arid irrigated regions with heterogeneous landscapes. Furthermore, our analysis shows that the observed height-LAI relationship is crop-specific and essentially linear with an R2 value of 0.82 for potato, 0.79 for wheat, and 0.50 for both cannabis and tobacco. The ability of the linear regression to estimate the fresh and dry above-ground biomass of potato from both observed height and LAI was reasonable, yielding R2: ~0.60.

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