Landsat Enhanced Thematic Mapper Research Articles

Cross-Comparative Analysis of GF-1 Wide Field View and Landsat-7 Enhanced Thematic Mapper Plus Data

The wide field view (WFV) sensor on-board GF-1 satellite can acquire multi-spectral data with moderate spatial resolution, which holds great potential for monitoring the Earth's surface. This study assesses WFV data through cross-comparison of spectral band reflectances and vegetation indices with Landsat-7 Enhanced Thematic Mapper plus (ETM+) data. The four vegetation indices considered in this study are the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the ratio vegetation index (RVI), and the soil adjusted vegetation index (SAVI). The R2 between the WFV and ETM+ data were 0.82, 0.89, 0.92, and 0.80 for the blue, green, red, and near-infrared bands reflectance, and 0.90, 0.84, 0.83, and 0.91 for NDVI, EVI, RVI, and SAVI, respectively. The results displayed a high correlation between the spectral reflectances and vegetation indices of the two sensors’ data, which indicated the reliability of the WFV data. Furthermore, the WFV data were better than the ETM+ data with regards to spatial and temporal resolutions.

Spatial-Temporal Characteristics of Glacier Velocity in the Central Karakoram Revealed with 1999–2003 Landsat-7 ETM+ Pan Images

The situation of stable and slightly advancing glaciers in the Karakoram is called the “Karakoram anomaly”. Glacier surface velocity is one of the key parameters of glacier dynamics and mass balance, however, the response of glacier motion to this regional anomaly is not fully understood. Here, we characterize the spatial-temporal variations in glacier velocity over the Central Karakoram from 1999–2003. The inter-annual glacier velocity fields were retrieved using a cross-correlation-based algorithm applied to four Landsat-7 Enhanced Thematic Mapper Plus (ETM+) panchromatic image pairs. We find that most of the glaciers on the southern slope flowed faster than those on the northern slope, which might be attributed to the differences in glacier sizes. Furthermore, ice motion observations over four years reveal that most of the glaciers were quasi-stable or experienced small fluctuations of flow velocity during our study period. We identify a new surging event for the South Skamri Glacier in the study period by investigating the glacier frontal changes and the longer-term time series of surface velocities between 1996 and 2006. From the transverse velocity profiles of seven typical glaciers, we infer that basal sliding is the predominant motion mechanism of the middle and upper glaciers, whereas internal deformation dominates closest to the glacier terminus.

The use of earth observation methods for estimating regional crop evapotranspiration and yield for water footprint accounting

AbstractRemote sensing can efficiently support the quantification of crop water requirements included in the goal of assessing water footprints, which is to analyse how human activities or specific products relate to issues of water scarcity and pollution and identify how activities and products can become more sustainable from a water perspective. Remote sensing techniques have become popular in estimating actual crop evapotranspiration and hence crop water requirements in recent decades due to the advantages they offer to users, e.g. low cost, regional data and use of maps instead of point measurements as well as saving time. The use of earth observation data supports models’ accuracy in the procedure for assessing water footprint, since no average values are used: instead, users have real values for the specific parameters.The present study provides two examples of how remote sensing techniques are used essentially for estimating evapotranspiration along with crop yield, two basic parameters, for assessing water footprint. Two different case studies have been illustrated to define the methodology proposed, which refers to Mediterranean conditions and can be applied after inferring the necessary field data of each crop. The first case study refers to the application of Surface Energy Balance Algorithm for Land (SEBAL) for estimating evapotranspiration, while the second refers to the Crop Yield prediction. Both elements, such as evapotranspiration and crop yield, are vital for water footprint accounting. Firstly, the SEBAL was adopted, under the essential adaptations for local soil and meteorological conditions for estimating groundnut water requirements. Landsat-5 TM, Landsat-7 Enhanced Thematic Mapper+ and Landsat 8 OLI images were used to retrieve the required spectral data. The SEBAL model is enhanced with empirical equations regarding crop canopy factors, in order to increase the accuracy of crop evapotranspiration estimation. Maps were created for evapotranspiration (ET) using the SEBAL modified model for the area of interest. The results were compared with measurements from an evaporation pan, used as a reference. Statistical comparisons showed that the modified SEBAL can predict ETc in a very effective and accurate way and provide water footprint modellers with high-level crop water data. Yield prediction plays a vital role in calculating water footprint. Having real values rather than taking reference (or averaged) values from FAO is an advantage that Earth Observation means can provide. This is very important in econometric or any other prediction models used for estimating water footprint because using average data reduces accuracy. In this context, crop and soil parameters along with remotely sensed data can be used to develop models that can provide users with accurate yield estimations. In a second step, crop and soil parameters along with the normalized difference vegetation index were correlated to examine whether crop yield can be predicted and to define the actual time-window to predict the yield. Statistical and remote sensing techniques were then applied to derive and map a model that can predict crop yield. The algorithm developed for this purpose indicates that remote sensing observations can predict crop yields effectively and accurately. Using the statistical Student's t test, it was found that there was no statistically significant difference between predicted and real values for crop yield.

Estimating Fractional Vegetation Cover From Landsat-7 ETM+ Reflectance Data Based on a Coupled Radiative Transfer and Crop Growth Model

Fractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (~1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data ( $R^{\mathrm {\mathbf {2}}} = 0.6942$ , RMSE =0.0884), compared with the widely used dimidiate pixel model ( $R^{\mathrm {\mathbf {2}}} = 0.7034$ , RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale.

Forest canopy structure and reflectance in humid tropical Borneo: A physically-based interpretation using spectral invariants

South East Asia region is a triple hotspot of carbon, biodiversity, and forest degradation. The latter is leading to heterogeneous forest landscapes with predominant and varying intensity of human modification. Optical Earth observation data offers the most feasible and accessible means of mapping and monitoring the forest cover heterogeneity in this region. Further, difficulties in ground data collection in this region present the need for more robust physically-based interpretation of satellite data which extends beyond simple empirical correlations of reflectance data with forest characteristics. We present an application of forest reflectance parameterization based on spectral invariants in lowland humid tropical forests of Malaysian Borneo. Our framework combined field measurements (hemispherical photos) of canopy structure, radiative transfer modelling at leaf (PROSPECT) and canopy scale (PARAS), and assessments using satellite data (Landsat-7 Enhanced Thematic Mapper Plus, ETM+). Field data were collected representing a gradient of forest degradation within a large-scale experimental landscape. Results showed limited ability of Landsat-class satellites to uncover the full gradient of forest degradation in this biome due to similarity in canopy structure as characterized by effective canopy cover, leaf area index, and angular profile of canopy gap fraction. Our findings, however, showed strong expression of leaf optical properties at canopy level. This supports the feasibility to remotely measure canopy biochemistry in dense humid tropical rainforests at medium spatial resolution using freely available Landsat-class satellites, and forthcoming hyperspectral satellite missions.

Combining Multi-Source Remotely Sensed Data and a Process-Based Model for Forest Aboveground Biomass Updating.

Monitoring and understanding the spatio-temporal variations of forest aboveground biomass (AGB) is a key basis to quantitatively assess the carbon sequestration capacity of a forest ecosystem. To map and update forest AGB in the Greater Khingan Mountains (GKM) of China, this work proposes a physical-based approach. Based on the baseline forest AGB from Landsat Enhanced Thematic Mapper Plus (ETM+) images in 2008, we dynamically updated the annual forest AGB from 2009 to 2012 by adding the annual AGB increment (ABI) obtained from the simulated daily and annual net primary productivity (NPP) using the Boreal Ecosystem Productivity Simulator (BEPS) model. The 2012 result was validated by both field- and aerial laser scanning (ALS)-based AGBs. The predicted forest AGB for 2012 estimated from the process-based model can explain 31% (n = 35, p < 0.05, RMSE = 2.20 kg/m2) and 85% (n = 100, p < 0.01, RMSE = 1.71 kg/m2) of variation in field- and ALS-based forest AGBs, respectively. However, due to the saturation of optical remote sensing-based spectral signals and contribution of understory vegetation, the BEPS-based AGB tended to underestimate/overestimate the AGB for dense/sparse forests. Generally, our results showed that the remotely sensed forest AGB estimates could serve as the initial carbon pool to parameterize the process-based model for NPP simulation, and the combination of the baseline forest AGB and BEPS model could effectively update the spatiotemporal distribution of forest AGB.

Recovering the large gaps in Landsat 7 SLC-off imagery using weighted multiple linear regression (WMLR)

Since 2003, the permanent failure of the scan line corrector (SLC) of the Landsat Enhanced Thematic Mapper Plus (ETM+) sensor has seriously limited the scientific applications and usability of ETM+ data. While a number of methods have been conducted to fill the regular un-scanned locations in ETM+ SLC-off images, only a few researches have been developed to recover the large gap areas in such images. In this study, an innovative gap filling method has been introduced to reconstruct the large gap locations in SLC-off images via multi-temporal auxiliary fill images. A correlation is established between the corresponding pixels in the target SLC-off image and two auxiliary fill images in parallel using the multiple linear regression (MLR) model in two successive steps. In the first step, almost half the gap locations have been recovered using the MLR model, then in the second step a weighted multiple linear regression (WMLR) algorithm is proposed to recover the remaining missing values. The simulated and actual case studies show that the proposed approach may provide a powerful tool for recovering the large gaps in SLC-off images, especially when there is a long time interval between the auxiliary fill images and the target SLC-off image.

The Role of Human Activities in Streambank Stability: Lower Sakarya River (NW Turkey)

The main objective of this study is to determine historic and current human impacts on streambank stability in Lower Sakarya River. Remote sensing and Geographical Information System techniques with conjunction field works were performed to identify the impact of human alteration on streambank stability in the riverine environment of the eastern portion of Sakarya province. LULC (land use/cover) and historical streambank changes were analyzed from Landsat 1-5 Multispectral Scanner (MSS), Landsat-7 Enhanced Thematic Mapper Plus (ETM+), and Google Earth images between 1995 and 2016. As results, a significant LULC changes have been observed along the buffered zone due to population growth. Recently, change in LULC type from agricultural to urban usage has changed river equilibrium. The stream channel also became more stable and straight as man-made modifications including a hydropower (HES) dam constructed in 2010, which primarily reduced flood frequency, water velocity, stream power, shear stress on sediment particles temporarily deposited along the streambank. After the year of 2010, downstream portion of the dam had experienced narrowing and expanding mid-channel bars. Moreover, the channel has been slightly moved towards east especially along urbanized and sinuous courses. The streambank displacement ranged from 2.9 m to 36 m in the region. Instream mining activities and bridge constructions in the region also disturb active streambanks, which raise a concern about instability of streambank and potential damage to infrastructures. Such studies are extremely important for understanding basic mechanisms of streambank evolution for further river restoration practices.

Urban Land-Cover Dynamics in Arid China Based on High-Resolution Urban Land Mapping Products

Rapid urbanization has occurred in northwestern China, threatening the sustainability of its fragile dryland ecosystems. A lack of precise urban land-cover information has limited our understanding on the urbanization in the dryland. Here, we examined urban land-cover changes from 2000 to 2014 in 21 major cities that comprise over 50% of the developed land in arid China, using Landsat Enhanced Thematic Mapper Plus and Operational Land Imager data, and a hybrid classification method. The 15-m resolution urban land-cover products (including impervious surfaces, vegetation, bare soil, and water bodies) had an overall accuracy of 90.37%. Based on these new land use products, we found the urbanization in arid China was characterized by the dramatic expansion of impervious surface (+13.23%) and reduction of bare soil (−13.41%), while the proportions of vegetation (+0.27%) and water (−0.10%) remained stable. The observed dynamic equilibrium of vegetated ratio implies an increasing harmonization of urbanization and greening, which was particularly important for the sustainability of fragile urban ecosystems in arid regions. From an economic perspective, gross domestic product and population were significantly correlated with impervious surfaces, and oasis cities displayed a stronger ability to attract new residents than desert cities.

Propensity for erosion and deposition in a deltaic wetland complex: Implications for river management and coastal restoration

The Mississippi River Delta is one of the most rapidly changing area on Earth, with large areas experiencing land loss and smaller areas experiencing loss. While some of the drivers of these changes are well known (high rates of relative sea level rise, reduced sediment inputs, canal dredging), debate exists about other drivers. One area that has received substantial attention is the role of, “river diversions,” areas where sediments and water are diverted from the Mississippi River into degrading wetlands with the hope of reinitiating deltaic land building processes. Some authors have argued that diversions lead to reduced shear strengths of wetland soils that make them more vulnerable to storm driven erosion, while other authors have argued that sediments from river diversions will develop stable land. This study examined this controversy in the Cubits Gap Subdelta, an analogue for a large (>1420m3s−1) river diversion by testing the hypothesis that areas of land gain, and/or resilience to erosion occurred in areas that actively received river sediments and as a result had mineral rich soils with high shear strength. To accomplish this, a Normalized Difference Water Index (NDWI) was developed for Landsat-7 Enhanced Thematic Mapper (ETM+) and Landsat-8 Operational Land Imager (OLI) images. The NDWI was calculated from (Blue−SWIR)/(Blue+SWIR), where SWIR is the shorter wavelength, and yielded land/water boundary maps with 30m resolution. Results indicate that land gain occurred predominantly in the riverside section of this subdelta where sediments were imported from Mississippi River crevasses and/or dredging. Land loss typically occurred in the distal regions of the subdelta, which had lower levels of sediment supply and greater wave exposure. Sediment geotechnical analyses revealed land loss pixels generally correlated sediments with to high organic contents (9.0±1.9%), water contents (54.8±3.7%) and salinity (6.5±2.0PSU), with low shear strengths (5.7±0.8kNm−2) and low bulk density (0.6±0.8g cm−3), whereas land gain pixels generally correlate with low organic content (3.9±0.6%), water content (38.1±4.2%) high shear strength (10.9±4.1kNm−2) and bulk density (1.00±0.1gcm−3). This study suggests plans to restore the region by partially diverting the flow of the Mississippi River will be most successful if they carry high loads of sediment, and that concerns about the integrity of fresh marsh may be unwarranted if those marshes are sediment rich.

Surface Reflectance Climate Data Records (CDRs) is a Reliable Landsat ETM+ Source to Study Chlorophyll Content in Pecan Orchards

We evaluated the relationships among three Landsat Enhanced Thematic Mapper (ETM+) datasets, top-of-atmosphere (TOA) reflectance, surface reflectance climate data records (surface reflectance-CDR) and atmospherically corrected images using Fast Line-of-Sight atmospheric analysis of Spectral Hypercubes model (surface reflectance-FLAASH) and their linkto pecan foliar chlorophyll content(chl-cont). Foliar chlorophyll content as determined with a SPAD meter, and remotely-sensed data were collected from two mature pecan orchards (one grown in a sandy loam and the other in clay loam soil) during the experimental period. Enhanced vegetation index derived from remotely sensed data was correlated to chl-cont. At both orchards, TOA reflectance was significantly lower than surface reflectance within the 550–2400 nm wavelength range. Reflectance from atmospherically corrected images (surface reflectance-CDR and surface reflectance-FLAASH) was similar in the shortwave infrared (SWIR: 1550–1750 and 2080–2350 nm) and statistically different in the visible (350–700 nm). Enhanced vegetation index derived from surface reflectance-CDR and surface reflectance-FLAASH had higher correlation with chl-cont than TOA. Accordingly, surface reflectance is an essential prerequisite for using Landsat ETM+ data and TOA reflectance could lead to miss-/or underestimate chl-cont in pecan orchards. Interestingly, the correlation comparisons (Williams t test) between surface reflectance-CDR and chl-cont was statistically similar to the correlation between chl-cont and commercial atmospheric correction model. Overall, surface reflectance-CDR, which is freely available from the earth explorer portal, is a reliable atmospherically corrected Landsat ETM+ image source to study foliar chlorophyll content in pecan orchards.

A novel method for urban area extraction from VIIRS DNB and MODIS NDVI data: a case study of Chinese cities

ABSTRACTMapping urban areas at the regional and global scales has been used in ecology, environment, sociology, and other subjects. Recently, it has become increasingly popular to extract urban areas from night-time light remote-sensing data. In this article, we reported an alternative method to extract information of urban areas from VIIRS Day/Night Band (DNB) and MODIS normalized differential vegetation index (NDVI) data based on the adaptive mutation particle swarm optimization (AMPSO) algorithm and the Support Vector Machine (SVM) classification algorithm. This method was validated using the urban areas of nine Chinese cities classified from Landsat Enhanced Thematic Mapper (ETM+) images by object-oriented image classification technology. We demonstrated that this new method for urban area extraction had a good classification coherency with the Landsat8 OLI result. In addition, it is more robust than other classification methods, and can be used to characterize the inter-urban texture as well.

An Improved Aerosol Optical Depth Retrieval Algorithm for Moderate to High Spatial Resolution Optical Remotely Sensed Imagery

To extract quantitative land information accurately and monitor the air pollution at city scale from moderate to high spatial resolution (MHSR) with a resolution no coarser than 30 m, optical remotely sensed imagery and aerosol parameters, especially aerosol optical depth (AOD), are a necessary step. In this paper, we introduce a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from moderate to high spatial resolution imagery under general atmosphere and land surface conditions. This algorithm has been improved in the following three aspects: (i) it has been developed for most of the moderate to high spatial resolution remotely sensed imagery; (ii) it can be applied to all kinds of land surface types including bright surface; and (iii) it is completely automatic. This algorithm is therefore suitable for operational applications. The derived AOD in Beijing from Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+), and Huan Jing 1 (HJ-1/CCD) data is validated with AErosol Robotic NETwork (AERONET) ground measurements at Beijng and Xianghe stations.

Application of SWAT model for predicting soil erosion and sediment yield

Hirakud, the longest earthen dam in Asia on the river Mahanadi, has been affected by sedimentation problems which affect reservoir storage capacities and different reservoir operations. This article presents the hydrological modeling of the Mahanadi catchment up to Hirakud, located in the states of Chhattisgarh and Odisha in India, using the modified version of Soil and Water Assessment Tool (SWAT) with an interface of ArcView Geographic Information System software to predict soil erosion and sediment transport to the reservoir for improving its useful life. The data sources used in the study were: terrain data 90 m resolution of Shuttle Radar Topography Mission, Land Use and Land Cover derived from Landsat Enhanced Thematic Mapper Plus, soil map published by National Bureau of Soil Survey and Land Use Planning, India and meteorological data collected from India Meteorological Department, India. The study involves the analysis of sensitive parameters of the simulated data (discharge 15, sediment load 6); 5-year calibration and 3-year validation on daily basis to predict the discharge and sediment load. The sensitivity analysis was carried out using the Latin Hypercube and One-factor-At-a-Time. The results also show that the prediction of sediment yield is highly sensitive to the sizes of different sub-basins due to the sensitivity of topographic factors used in the Modified Universal Soil Loss Equation. The graphical as well as statistical results achieved in this study indicate that the model is capable to identify critical erosion-prone areas within the watershed on the scale of sub-watershed which helps the hydrologists to introduce effective management practices at the lowest cost. The study shows that about 34% area of total catchment falls under high or above soil erosion zones with combination of coarse loamy type of soil and agricultural type of land use and land cover condition. The results of this study also identify regions of high sediment yield and sediment delivery ratio. To enhance the SWAT model performance, it is recommended to use sub-daily data and to prioritize soil erosion at the Hydrological Response Units level for improvement of watershed management.

An effective ensemble classification framework using random forests and a correlation based feature selection technique

AbstractAccurate classification of heterogeneous land surfaces with homogeneous land cover classes is a challenging task as satellite images are characterized by a large number of features in the spectral and spatial domains. The identifying relevance of a feature or feature set is an important task for designing an effective classification scheme. Here, an ensemble of random forests (RF) classifiers is realized on the basis of relevance of features. Correlation‐based Feature Selection (CFS) was utilized to assess the relevance of a subset of features by studying the individual predictive ability of each feature along with the degree of redundancy between them. Predictability of RF was greatly improved by random selection of the relevant features in each of the splits. An investigation was carried out on different types of images from the Landsat Enhanced Thematic Mapper Plus (Landsat ETM+) and QuickBird sensors. It has been observed that the performance of the RF classifier was significantly improved while using the optimal set of relevant features compared with a few of the most advanced supervised classifiers such as maximum likelihood classifier (MLC), Navie Bayes, multi‐layer perception (MLP), support vector machine (SVM) and bagging.

Spatio-temporal analysis of the relationship between 2D/3D urban site characteristics and land surface temperature

This paper focuses on the relationship between remotely-sensed urban site characteristics (USCs) and land surface temperature (LST). Particular emphasis is put on an extensive comparison of two-dimensional (2D) and three-dimensional (3D) USCs as potential indicators of the surface urban heat island (UHI) effect and as potential predictors for thermal sharpening applications. Both widely-used as well as more recently proposed metrics of the urban remote sensing literature are investigated within a single experiment. While some of these USCs have already been used earlier, others have never been analyzed before in the context of urban temperature studies. In addition to the comparison of 2D and 3D USCs, the spatio-temporal dependencies of their relation to LST are examined. To this end, the experimental setup of this work includes two study areas, 26 USCs, and 16 LST scenes covering four seasons. Use is made of a comprehensive database compiled for the cities of Berlin and Cologne, Germany. After data preparation, very high resolution (VHR) multi-spectral and height data are employed to map fine-scale urban land cover (LC). The resulting LC maps are then used in conjunction with the height information to compute 2D and 3D USCs. Subsequently, multi-temporal LST images are retrieved from Landsat Enhanced Thematic Mapper Plus (ETM+) scenes. The spatio-temporal investigation of the USC–LST connection constitutes the final stage of the workflow and is achieved in the framework of a dedicated correlation analysis. The results of this study highlight that the linkage between USCs and LST sensed at small scan angles is not stronger when 3D parameters are considered. Even though they may offer more holistic representations of the urban landscape, 3D USCs are consistently outperformed by some of the most widely-used 2D metrics. The analysis of spatial dependencies reveals that the USC–LST interplay does not only differ between, but also within the two test sites. This is due to their distinct geographies, with urban form and compactness, green spaces and street trees, and the structural composition of LC elements being some of the determining factors. The examination of temporal dependencies yielded that the association between USCs and LST is fairly stable over time but can be subject to larger inter- and intra-season variations for different reasons, including the season of acquisition, vegetation phenology, and meteorological conditions. Since previous research was based on the analysis of a single study area, a limited number of (mainly 2D) USCs, and/or only a few LST scenes acquired in specific seasons, it is concluded that the findings of this study provide researchers and practitioners with a more complete picture of the USC–LST relationship.

Mapping plastic greenhouse with medium spatial resolution satellite data: Development of a new spectral index

Plastic greenhouses (PGs) are an important agriculture development technique to protect and control the growing environment for food crops. The extensive use of PGs can change the agriculture landscape and affects the local environment. Accurately mapping and estimating the coverage of PGs is a necessity to the strategic planning of modern agriculture. Unfortunately, PG mapping over large areas is methodologically challenging, as the medium spatial resolution satellite imagery (such as Landsat data) used for analysis lacks spatial details and spectral variations. To fill the gap, the paper proposes a new plastic greenhouse index (PGI) based on the spectral, sensitivity, and separability analysis of PGs using medium spatial resolution images. In the context of the Landsat Enhanced Thematic Mapper Plus (ETM+) imagery, the paper examines the effectiveness and capability of the proposed PGI. The results indicate that PGs in Landsat ETM+ image can be successfully detected by the PGI if the PG fraction is greater than 12% in a mixed pixel. A kappa coefficient of 0.83 and overall accuracy of 91.2% were achieved when applying the proposed PGI in the case of Weifang District, Shandong, China. These results show that the proposed index can be applied to identifying transparent PGs in atmospheric corrected Landsat image and has the potential for the digital mapping of plastic greenhouse coverage over a large area.

Radiometric Cross-Calibration of GF-4 in Multispectral Bands

The GaoFen-4 (GF-4), launched at the end of December 2015, is China’s first high-resolution geostationary optical satellite. A panchromatic and multispectral sensor (PMS) is onboard the GF-4 satellite. Unfortunately, the GF-4 has no onboard calibration assembly, so on-orbit radiometric calibration is required. Like the charge-coupled device (CCD) onboard HuanJing-1 (HJ) or the wide field of view sensor (WFV) onboard GaoFen-1 (GF-1), GF-4 also has a wide field of view, which provides challenges for cross-calibration with narrow field of view sensors, like the Landsat series. A new technique has been developed and used to calibrate HJ-1/CCD and GF-1/WFV, which is verified viable. The technique has three key steps: (1) calculate the surface using the bi-directional reflectance distribution function (BRDF) characterization of a site, taking advantage of its uniform surface material and natural topographic variation using Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) imagery and digital elevation model (DEM) products; (2) calculate the radiance at the top-of-the atmosphere (TOA) with the simulated surface reflectance using the atmosphere radiant transfer model; and (3) fit the calibration coefficients with the TOA radiance and corresponding Digital Number (DN) values of the image. This study attempts to demonstrate the technique is also feasible to calibrate GF-4 multispectral bands. After fitting the calibration coefficients using the technique, extensive validation is conducted by cross-validation using the image pairs of GF-4/PMS and Landsat-8/OLI with similar transit times and close view zenith. The validation result indicates a higher accuracy and frequency than that given by the China Centre for Resources Satellite Data and Application (CRESDA) using vicarious calibration. The study shows that the new technique is also quite feasible for GF-4 multispectral bands as a routine long-term procedure.

Comparing Landsat-7 ETM+ and ASTER Imageries to Estimate Daily Evapotranspiration Within a Mediterranean Vineyard Watershed

We compared the capabilities of Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imageries for mapping daily evapotranspiration (ET) within a Mediterranean vineyard watershed. We used Landsat and ASTER data simultaneously collected on four dates in 2007 and 2008, along with the simplified surface energy balance index (S-SEBI) model. We used previously ground-validated good quality ASTER estimates as reference, and we analyzed the differences with Landsat retrievals in light of the instrumental factors and methodology. Although Landsat and ASTER retrievals of S-SEBI inputs were different, estimates of daily ET from the two imageries were similar. This is ascribed to the S-SEBI spatial differencing in temperature, and opens the path for using historical Landsat time series over vineyards.

Landsat-Based Land Cover Change in the Beijing-Tianjin-Tangshan Urban Agglomeration in 1990, 2000 and 2010

Rapid urbanization dramatically changes the local environment. A hybrid classification method is designed and applied to multi-temporal Landsat images and ancillary data to obtain land cover change datasets. A support vector machine (SVM) classifier is used to classify multi-temporal Landsat Enhanced Thematic Mapper Plus (ETM+) images that were collected in 2000 at the pixel level. These images are also segmented with the mean shift method. The impervious surface is refined based on a combination of the segmented objects and the SVM classification results. The changed areas in 1990 and 2010 are determined by comparing the Thematic Mapper (TM) and ETM+ images via the re-weighted multivariate alteration detection transformation method. The TM images that were masked as changed areas in 1990 and 2000 are input into the SVM classifier. Land cover maps for 1990 and 2010 are produced by combining the unchanged area in 2000 with the new classes of the changed areas in 1990 and 2010. Land cover change has continuously accelerated since 1990. Remarkably, arable land decreased, while the impervious surface area significantly increased.