Performance Of Landsat Research Articles

Performance of Landsat 8 OLI and Sentinel 2 MSI Images Based on MNF Versus PCA Algorithms and Convolution Operators for Automatic Lithuanian Coastline Extraction

Performance of Landsat 8 OLI and Sentinel 2 MSI Images Based on MNF Versus PCA Algorithms and Convolution Operators for Automatic Lithuanian Coastline Extraction

Estimation of Forest Stock Volume Using Sentinel-2 MSI, Landsat 8 OLI Imagery and Forest Inventory Data

Forest stock volume (FSV) is a key indicator for measuring forest quality, evaluating forest management capabilities, and the main factor for evaluating forest carbon sequestration levels. In this study, to achieve an accurate estimation of FSV, we used Ninth Beijing Forest Inventory data (FID), and Landsat 8 OLI and Sentinel-2 MSI imagery to establish FSV models. The performance of Landsat 8 and Sentinel-2 imagery data in estimating forest volume in Huairou District, Beijing, China was compared. The combination of Landsat 8 and Sentinel-2 satellite data was employed to create a new data source. Two variable selection methods, linear stepwise regression (LSR) and recursive feature elimination (RFE), were used to extract feature variables. The multiple linear regression(MLR) models, Back Propagation (BP) neural network models, and Random Forest (RF) models were employed to estimate forest volume in the study area based on the feature variables obtained from both data sources. The research results indicate (1) the Sentinel-2-based model achieved higher accuracy compared to the same model based on the Landsat 8 factor set. The correlation between the red-edge band of Sentinel-2 imagery and FSV is more significant than that of other characteristic variables used. Variables derived from the red-edge band have the potential to reduce model errors; (2) the estimation accuracy of the model can be significantly improved by using the RFE (Recursive Feature Elimination) method to select remote sensing feature variables. RFE is based on the importance ranking of all feature variables and selects the feature variables that contribute the most to the model. In the variable group selected by RFE, the texture features and the derived features from the red-edge band, such as SenB5, SenRVI, SenmNDVIre, and SenB5Mean, contribute the most to the improvement of model accuracy. Furthermore, in the optimal Landsat 8–Sentinel-2 RFE-RF model, where texture features are involved, the rRMSE is greatly reduced by 3.7% compared to the joint remote sensing RFE-RF model without texture features; (3) the MLR, BP, and RF models based on the modeling factor set established on Sentinel-2 have accuracy superior to the model accuracy established based on the modeling factor set of Landsat 8. Among them, the Random Forest (RF) method inverted by the recursive feature elimination (RFE) method using Sentinel-2A image has the best inversion accuracy effect (R2 = 0.831, RMSE = 12.604 m3 ha−1, rRMSE = 36.411%, MAE = 9.366 m3 ha−1). Comparing the performance of the models on the test set, the ranking is as follows, Random Forest (RF) model > Back Propagation (BP) neural network model > multiple linear regression (MLR) model. The feature variable screening based on the Random Forest’s recursive feature elimination (RFE) method is better than the linear stepwise regression (LSR). Therefore, the RFE-RF method based on the joint variables from Landsat 8 and Sentinel-2 satellite data to establish a new remote sensing data source provides the possibility to improve the estimation accuracy of FSV and provides reference for forest dynamic monitoring.

Estimation of Actual Evapotranspiration in Panam Canal Command Using Remote Sensing and Geographical Information System (GIS)

Estimation of reference evapotranspiration (ET0) and actual evapotranspiration (ETc) is a key factor for estimation of crop water requirement, water balance and irrigation scheduling. The FAO-56 Penman–Monteith equation has been accepted universally for estimating of reference evapotranspiration (ET0). Considering the high spatial variation of meteorological phenomenon and limited availability of such dense network for data collection, application of remote sensing and GIS has gained momentum for estimation of ET0 and ETc over the larger area with accurately and efficiently. For estimation of ET0 and ETc, the most widely applied MOD16 remote sensing images as well as Landsat 8 remote sensing images are applied in this study of Panam canal command area which is located in the semi-arid middle Gujarat region. Initially, FAO-56 PM method is used to estimate ET0 and MOD16 data is used to estimate ET0, whereas Landsat 8 is used to estimate land surface temperature and then by using the regression equation to estimate maximum and minimum temperature to find out ET0 for the study area. Based on result obtained, it was found that Landsat 8 remote sensing-based data have better capacity to estimate actual evapotranspiration compared to the MOD16 remote sensing data. The better performance of Landsat 8 data compared to MOD16 data is due to the reason that it has better spatial resolution(30m) compared to MOD16 (1 km) remote sensing image and can represent the actual field conditions of farm fields which are generally smaller.

Assessment of Complementary Medium-Resolution Satellite Imageries for Nearshore Bathymetry Estimation

This paper focuses on utilizing Sentinel 2 MSI datasets to generate satellite-derived bathymetry (SDB) maps at a resolution of 10 m for two temporally varying datasets of the study region of Ameland Inlet, located in The Netherlands, by using support vector regression (SVR) technique. The relative performance of Landsat 8 OLI (30 m) datasets with SVR technique is also assessed to demonstrate the complementary nature of these freely available medium-resolution imageries. Further, the root mean square error and mean absolute error between the retrieved and measured bathymetries are estimated and reported to evaluate the capability of SVR in estimating depths. It is evident that the SDBs thus generated using this machine learning approach provide dependable estimations of depths that can further be utilized for various coastal engineering studies.

Evaluating Landsat 8 evapotranspiration for water use mapping in the Colorado River Basin

Abstract Evapotranspiration (ET) mapping at the Landsat spatial resolution (100 m) is essential to fully understand water use and water availability at the field scale. Water use estimates in the Colorado River Basin (CRB), which has diverse ecosystems and complex hydro-climatic regions, will be helpful to water planners and managers. Availability of Landsat 8 images, starting in 2013, provides the opportunity to map ET in the CRB to assess spatial distribution and patterns of water use. The Operational Simplified Surface Energy Balance (SSEBop) model was used with 528 Landsat 8 images to create seamless monthly and annual ET estimates at the inherent 100 m thermal band resolution. Annual ET values were summarized by land use/land cover classes. Croplands were the largest consumer of “blue” water while shrublands consumed the most “green” water. Validation using eddy covariance (EC) flux towers and water balance approaches showed good accuracy levels with R2 ranging from 0.74 to 0.95 and the Nash–Sutcliffe model efficiency coefficient ranging from 0.66 to 0.91. The root mean square error (and percent bias) ranged from 0.48 mm (13%) to 0.60 mm (22%) for daily (days of satellite overpass) ET and from 7.75 mm (2%) to 13.04 mm (35%) for monthly ET. The spatial and temporal distribution of ET indicates the utility of Landsat 8 for providing important information about ET dynamics across the landscape. Annual crop water use was estimated for five selected irrigation districts in the Lower CRB where annual ET per district ranged between 681 mm to 772 mm. Annual ET by crop type over the Maricopa Stanfield irrigation district ranged from a low of 384 mm for durum wheat to a high of 990 mm for alfalfa fields. A rainfall analysis over the five districts suggested that, on average, 69% of the annual ET was met by irrigation. Although the enhanced cloud-masking capability of Landsat 8 based on the cirrus band and utilization of the Fmask algorithm improved the removal of contaminated pixels, the ability to reliably estimate ET over clouded areas remains an important challenge. Overall, the performance of Landsat 8 based ET compared to available EC datasets and water balance estimates for a complex basin such as the CRB demonstrates the potential of using Landsat 8 for annual water use estimation at a national scale. Future efforts will focus on (a) use of consistent methodology across years, (b) integration of multiple sensors to maximize images used, and (c) employing cloud-computing platforms for large scale processing capabilities.

Performance of Landsat 8 Operational Land Imager for Mapping Ice Sheet Velocity.

Landsat imagery has long been used to measure glacier and ice sheet surface velocity, and this application has increased with increased length and accessibility of the archive. The radiometric characteristics of Landsat sensors, however, have limited these measurements generally to only fast-flowing glaciers with high levels of surface texture and imagery with high sun angles and cloud-free conditions, preventing wide-area velocity mapping at the scale achievable with synthetic aperture radar (SAR). The Operational Land Imager (OLI) aboard the newly launched Landsat 8 features substantially improves radiometric performance compared to preceding sensors: enhancing performance of automated Repeat-Image Feature Tracking (RIFT) for mapping ice flow speed. In order to assess this improvement, we conduct a comparative study of OLI and the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) performance for measuring glacier velocity in a range of surface and atmospheric conditions. To isolate the effects of radiometric quantization and noise level, we construct a model for simulating ETM+ imagery from OLI and compare RIFT results derived from each. We find that a nonlinearity in the relationship between ETM+ and OLI radiances at higher brightness levels results in a particularly large improvement in RIFT performance over the low-textured interior of the ice sheets, as well as improved performance in adverse conditions such as low sun-angles and thin clouds. Additionally, the reduced noise level in OLI imagery results in fewer spurious motion vectors and improved RIFT performance in all conditions and surfaces. We conclude that OLI imagery should enable large-area ice sheet and glacier mapping so that its coverage is comparable to SAR, with a remaining limitation being image geolocation.