scholarly journals Comparison of Different Multispectral Sensors for Photosynthetic and Non-Photosynthetic Vegetation-Fraction Retrieval

2020 ◽  
Vol 12 (1) ◽  
pp. 115 ◽  
Author(s):  
Cuicui Ji ◽  
Xiaosong Li ◽  
Huaidong Wei ◽  
Sike Li
Keyword(s):  
Mixture Model ◽  
Near Infrared ◽  
Scale Effects ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Wide Field ◽  
Vegetation Fraction ◽  
Spectral Mixture ◽  
Multispectral Sensors

It is very difficult and complex to acquire photosynthetic vegetation (PV) and non-PV (NPV) fractions (fPV and fNPV) using multispectral satellite sensors because estimations of fPV and fNPV are influenced by many factors, such as background-noise interference of pixel-, spatial-, and spectral-scale effects. In this study, comparisons between Sentinel-2A Multispectral Instrument (S2 MSI), Landsat-8 Operational Land Imager (L8 OLI), and GF1 Wide Field View (GF1 WFV) sensors for retrieving sparse photosynthetic and non-photosynthetic vegetation coverage are presented. The analysis employed a linear spectral-mixture model (LSMM) and nonlinear spectral-mixture model (NSMM) to unmix pixels with different spectral and spatial resolution images based on field endmembers; the estimated endmember fractions were later validated with reference to fraction measurements. The results demonstrated that: (1) with higher spatial and spectral resolution, the S2 MSI sensor had a clear advantage for retrieving PV and NPV fractions compared to L8 OLI and GF1 WFV sensors; (2) through incorporating more red edge (RE) and near-infrared (NIR) bands, the accuracy of NPV fraction estimation could be greatly improved; (3) nonlinear spectral mixing effects were not obvious on the 10–30 m spatial scale for desert vegetation; (4) in arid regions, a shadow endmember is a significant factor for sparse vegetation coverage estimated with remote-sensing data. The estimated NPV fractions were especially affected by the shadow effects and could increase root mean square by 50%. The utilized approaches in the study could effectively assess the performance of major multispectral sensors to extract fPV and fNPV through the novel method of spectral-mixture analysis.

scholarly journals Alpine Cold Vegetation Response to Climate Change in the Western Nyainqentanglha Range in 1972–2009

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Xu Wang ◽  
Ziyong Sun ◽  
Ai-Guo Zhou
Keyword(s):  
Climate Change ◽  
Tibetan Plateau ◽  
Remote Sensing Data ◽  
Vegetation Coverage ◽  
Ecosystem Change ◽  
The Tibetan Plateau ◽  
Mountain Areas ◽  
Vegetation Fraction ◽  
The Past ◽  
Spectral Mixture

The Tibetan Plateau is regarded as one of the most climatic-sensitive regions all over the world. Long-term remote sensing data enable us to monitor spatial-temporal change in this area. The vegetation changes of the western Nyainqentanglha region were detected by using RS and GIS techniques. And the vegetation coverage was derived by the NDVI-SMA (spectral mixture analysis) methods. An incensement of vegetation was observed in the mountain areas during 1972–2009 with a mean vegetation coverage of 24.87%, 35.89%, and 42.88% in 30/09/1972, 14/09/1991, and 30/08/2009, respectively. The vegetation fraction increased by 18% in the period of 1972–2009. The bin with the elevation between 4400 and 5200 m had the highest vegetation coverage. This may be the result of the mountain effect. Alpine vegetation had a trend to increase and expand to higher altitudes with the climate change in the past 40 years. The variation appears to be associated with an increase in mean temperature of 0.05°C per year and an increase in precipitation of 1.83 mm per year in the growing season of the past four decades. The results provide further evidence of alpine ecosystem change due to climate change in the central Tibetan Plateau.

scholarly journals Relationship between remote sensing data and field-observed interril erosion

2018 ◽  
Vol 53 (3) ◽  
pp. 332-341 ◽  
Author(s):  
André Geraldo de Lima Moraes ◽  
Daniel Fonseca de Carvalho ◽  
Mauro Antonio Homem Antunes ◽  
Marcos Bacis Ceddia
Keyword(s):  
Remote Sensing ◽  
Prediction Models ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Exponential Model ◽  
Landsat 8 ◽  
Sensing Data ◽  
Linear Spectral Mixture Analysis ◽  
Spectral Mixture ◽  
The Relationship

Abstract: The objective of this work was to evaluate the relationship between different remote sensing data, derived from satellite images, and interrill soil losses obtained in the field by using a portable rainfall simulator. The study was carried out in an area of a hydrographic basin, located in Médio Paraíba do Sul, in the state of Rio de Janeiro - one of the regions most affected by water erosion in Brazil. Evaluations were performed for different vegetation indices (NDVI, Savi, EVI, and EVI2) and fraction images (FI), derived from linear spectral mixture analysis (LSMA), obtained from RapidEye, Sentinel2A, and Landsat 8 OLI images. Vegetation indices are more adequate to predict soil loss than FI, highlighting EVI2, whose exponential model showed R2 of 0.74. The best prediction models are generated from the RapidEye image, which shows the highest spatial resolution among the sensors evaluated.

scholarly journals Field Scale Assessment of the TsHARP Technique for Thermal Sharpening of MODIS Satellite Images Using VENµS and Sentinel-2-Derived NDVI

2021 ◽  
Vol 13 (6) ◽  
pp. 1155
Author(s):  
Lorena N. Lacerda ◽  
Yafit Cohen ◽  
John Snider ◽  
Hanna Huryna ◽  
Vasileios Liakos ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Near Infrared ◽  
Irrigation Management ◽  
Superior Performance ◽  
Landsat 8 ◽  
Stress Monitoring ◽  
Field Scale ◽  
Crop Monitoring ◽  
Vegetation Fraction ◽  
Sentinel 2

Remotely sensed-based surface temperature is an important tool for crop monitoring and has great potential for improving irrigation management. However, current thermal satellite platforms do not display the fine spatial resolution required for identifying crop water status patterns at the field scale. The thermal sharpening (TsHARP) utility provides a technique for downscaling coarse thermal images to match the finer resolution of images acquired in the visible and near infrared bandwidths. This sharpening method is based on the inverse linear relationship between vegetation fraction calculated from the normalized difference vegetation index (NDVI) and land surface temperature (LST). The current study used the TsHARP method to sharpen low-resolution thermal data from the Moderate Resolution Imaging Spectrometer MODIS (1 km) to the finer resolution of Sentinel-2 (10 m) and Vegetation and Environment New micro-Spacecraft (VENµS) (5 m) visible-near infrared images. The sharpening methodology was evaluated at scene and field scales in southern Georgia and northern Mississippi, USA. A comparison of sharpened temperature was made with reference temperatures from Landsat-8 Operational Land Imager (OLI) in four different spatial resolutions (30, 60, 120, and 240 m) for method validation. Coarse resolution comparison on the dates in which imagery from both sensors were acquired on the same day resulted in average observed mean absolute error (MAE) of 1.63 °C, and R2 variation from 0.34 to 0.74. Temperature errors at the field scale ranged from 0.25 to 3.11 °C using both Sentinel-2 and VENµS. Sharpened maps at 120 and 60 m resolution showed the highest consistency for all fields and dates. Maps sharpened using VENµS images showed comparable or higher accuracy than maps sharpened using Sentinel-2. The superior performance coupled with the better revisit time indicates that the VENµS platform has high potential for frequent in-season crop monitoring. Further research with ground data collection is needed to explore field use limitations of this methodology, but these results give useful insights of potential benefits of implementing the TsHARP technique as a tool for crop stress monitoring.

scholarly journals VEGETATION COVERAGE AND IMPERVIOUS SURFACE AREA ESTIMATED BASED ON THE ESTARFM MODEL AND REMOTE SENSING MONITORING

2018 ◽  
Vol IV-3 ◽  
pp. 113-119
Author(s):  
Rongming Hu ◽  
Shu Wang ◽  
Jiao Guo ◽  
Liankun Guo
Keyword(s):  
Remote Sensing ◽  
Surface Area ◽  
Temporal Resolution ◽  
Remote Sensing Data ◽  
Impervious Surface ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Spatiotemporal Resolution ◽  
Impervious Surface Area ◽  
Sensing Data

Impervious surface area and vegetation coverage are important biophysical indicators of urban surface features which can be derived from medium-resolution images. However, remote sensing data obtained by a single sensor are easily affected by many factors such as weather conditions, and the spatial and temporal resolution can not meet the needs for soil erosion estimation. Therefore, the integrated multi-source remote sensing data are needed to carry out high spatio-temporal resolution vegetation coverage estimation. Two spatial and temporal vegetation coverage data and impervious data were obtained from MODIS and Landsat 8 remote sensing images. Based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the vegetation coverage data of two scales were fused and the data of vegetation coverage fusion (ESTARFM FVC) and impervious layer with high spatiotemporal resolution (30 m, 8 day) were obtained. On this basis, the spatial variability of the seepage-free surface and the vegetation cover landscape in the study area was measured by means of statistics and spatial autocorrelation analysis. The results showed that: 1) ESTARFM FVC and impermeable surface have higher accuracy and can characterize the characteristics of the biophysical components covered by the earth's surface; 2) The average impervious surface proportion and the spatial configuration of each area are different, which are affected by natural conditions and urbanization. In the urban area of Xi'an, which has typical characteristics of spontaneous urbanization, landscapes are fragmented and have less spatial dependence.

scholarly journals Novel Vegetation Indices for Cotton Boll Opening Status Estimation Using Sentinel-2 Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1712 ◽  
Author(s):  
Yu Ren ◽  
Yanhua Meng ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
Yuxing Han ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Regional Scale ◽  
Vegetation Indices ◽  
Great Influence ◽  
Remote Sensing Data ◽  
Area Ratio ◽  
Wide Field ◽  
Entire Area ◽  
Sentinel 2

The application of chemical harvest aids to defoliate leaves and ripen bolls plays a significant role in the once-over machine harvest of cotton (Gossypium hirsutum L.) fields. The boll opening rate (BOR) is a key indicator for the determination of harvest aid spraying times. However, the most commonly used method to determine BOR is manual investigation, which is subjective and cannot have a holistic judgment of the entire area. Remote sensing can be employed to overcome these limitations, due to a wide field of vision, acceptably spatial and temporal resolution, and rich spectral information beyond the perception of the human eye. The reflectance of open cotton bolls is relatively high in the visible and near-infrared bands. High reflectance of open bolls has a great influence on the reflectance of the mixed pixels on remote sensing imagery. Therefore, it is an effective method to detect boll opening status by constructing vegetation indices with the sensitive spectral bands of imagery. In this study, we proposed two new vegetation indices based on Sentinel-2 remote sensing data, namely, the boll area ratio index (BARI) and the boll opening rate index (BORI), in order to estimate the boll opening status on a regional scale. The proposed indices were strongly correlated with the boll area ratio (BAR) and BOR. In particular, BARI exhibited the most accurate and robust performance with BAR in the prediction (R2 = 0.754, RMSE = 2.56%) and validation (R2 = 0.706, RMSE = 5.00%) among all the indices, including published indices we chose. Furthermore, when comparing to all other indices, BORI demonstrated the best and satisfactory estimation with BOR in the prediction (R2 = 0.675, RMSE = 7.96%) and validation (R2 = 0.616, RMSE = 2.79%). Meanwhile, an exponential growth relationship between BOR and BAR was identified, and the underlying mechanisms behind this phenomenon were discussed. Overall, through our study, we provided convenient and accurate vegetation indices for the investigation of boll opening status in a cotton-producing area by accessible and free Sentinel-2 imagery.

scholarly journals Examining long-term natural vegetation dynamics in the Aral Sea Basin applying the linear spectral mixture model

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10747
Author(s):  
Yiting Su ◽  
Dongchuan Wang ◽  
Shuang Zhao ◽  
Jiancong Shi ◽  
Yanqing Shi ◽  
...  
Keyword(s):  
Mixture Model ◽  
Vegetation Cover ◽  
Vegetation Dynamics ◽  
Natural Vegetation ◽  
Aral Sea ◽  
Vegetation Coverage ◽  
Cultivated Land ◽  
Aral Sea Basin ◽  
Spectral Mixture

Background Associated with the significant decrease in water resources, natural vegetation degradation has also led to many widespread environmental problems in the Aral Sea Basin. However, few studies have examined long-term vegetation dynamics in the Aral Sea Basin or distinguished between natural vegetation and cultivated land when calculating the fractional vegetation cover. Methods Based on the multi-temporal Moderate Resolution Imaging Spectroradiometer, this study examined the natural vegetation coverage by introducing the Linear Spectral Mixture Model to the Google Earth Engine platform, which greatly reduces the experimental time. Further, trend line analysis, Sen trend analysis, and Mann–Kendall trend test methods were employed to explore the characteristics of natural vegetation cover change in the Aral Sea Basin from 2000 to 2018. Results Analyses of the results suggest three major conclusions. First, the development of irrigated agriculture in the desert area is the main reason for the decrease in downstream water. Second, with the reduction of water, the natural vegetation coverage in the Aral Sea Basin showed an upward trend of 17.77% from 2000 to 2018. Finally, the main driving factor of vegetation cover changes in the Aral Sea Basin is the migration of cultivated land to the desert.

scholarly journals Selection of Remote Sensing Images And Evapotranspiration Models On Complex Land Surfaces for a Humid Karst Catchment

2021 ◽  
Author(s):  
Rongfei Zhang
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Data ◽  
Regional Level ◽  
Thermal Dissipation ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Karst Water ◽  
Land Surfaces ◽  
Karst Catchment ◽  
Karst Landscapes

Abstract Evapotranspiration (ET) is predominant variable for water management in various types of ecosystems, and ET processes in these ecosystems have been assessed through in-situ measuring and modelling methods. However, it is challenging to measure actual ET and upscale it to regional level. In addition, the accuracy of retrieved parameters from models is usually low for karst landscapes, where the underlying surface is more complex than non-karst landscapes. Due to various porosities and conduits, aquifers in karst landscapes typically show remarkable and rapid responses to precipitation events, leading to serious water stress. Therefore, there is an urgent need to quantify water fluxes to provide reliable evidence for the protection and sustainable management of karst water resources. In this study, five plots were built to observe actual ET based on Thermal Dissipation Probes (TDP), re-designed Ventilated-chamber and Micro-lysimeters in a karst catchment in southwest China. Then, three models (Penman-Monteith-Leurning, PML; Remote Sensing-Priestley and Taylor, RS-PT; and Hargreaves) were selected to upscale ET estimation to the regional level based on Landsant-8 and MODIS data. The results showed that: 1) The PML model performed better than other models (p < 0.01) with higher R2 values (0.72 for MODIS images and 0.87 for Landsat-8 images) and smaller RMSE values (1.4 mm·day-1 and 0.8 mm·day-1 for MODIS and Landsat-8 images, respectively); 2) Daily ET exhibited significant seasonal variability and different spatial distribution; 3) ET had a slightly positive correlation with DEM; however, ground temperature had a negative correlation with ET. By combining remote sensing data and upscaling it to the regional level, this study helps improve the accuracy of measured and estimated ET. It suggests that ET is strongly regulated by vegetation coverage and available energy in subtropical humid karst catchments.

scholarly journals Development of the Statistical Model for Monitoring Salinization in the Mekong Delta of Vietnam Using Remote Sensing Data and In-Situ Measurements

Proceedings ◽  
2018 ◽  
Vol 2 (10) ◽  
pp. 565
Author(s):  
Nguyen Nguyen Vu ◽  
Le Van Trung ◽  
Tran Thi Van
Keyword(s):  
Statistical Model ◽  
Mekong Delta ◽  
Remote Sensing Data ◽  
Principal Component ◽  
Measured Data ◽  
In Situ Measurements ◽  
Landsat 8 ◽  
Spectral Bands ◽  
Component Band

This article presents the methodology for developing a statistical model for monitoring salinity intrusion in the Mekong Delta based on the integration of satellite imagery and in-situ measurements. We used Landsat-8 Operational Land Imager and Thermal Infrared Sensor (Landsat- 8 OLI and TIRS) satellite data to establish the relationship between the planetary reflectance and the ground measured data in the dry season during 2014. The three spectral bands (blue, green, red) and the principal component band were used to obtain the most suitable models. The selected model showed a good correlation with the exponential function of the principal component band and the ground measured data (R2 > 0.8). Simulation of the salinity distribution along the river shows the intrusion of a 4 g/L salt boundary from the estuary to the inner field of more than 50 km. The developed model will be an active contribution, providing managers with adaptation and response solutions suitable for intrusion in the estuary as well as the inner field of the Mekong Delta.

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