Moderate Resolution Imaging Spectroradiometer Images Research Articles

Mapping the fractional cover of non-photosynthetic vegetation and its spatiotemporal variations in the Xilingol grassland using MODIS imagery (2000−2019)

Obtaining accurate information on the fractional cover of non-photosynthetic vegetation (NPV) (f NPV) on grasslands is essential for monitoring soil erosion risk, assessing grassland productivity and managing grassland ecosystems. However, few studies have monitored f NPV in the Xilingol grassland region (XGR) and its spatiotemporal variations based on Moderate Resolution Imaging Spectroradiometer (MODIS) images. In this study, we determined the upper dead fuel index (DFI) threshold for NPV in the XGR. Then, a remote-sensing inversion model for f NPV was established based on the ground-measured f NPV and the DFI derived from MODIS images. Based on the inversion model, we obtained the spatial distribution and spatiotemporal variations in f NPV from 2000 to 2019. The results indicated that the DFI can reflect the NPV in the XGR with an upper threshold of 27.2. The DFI-f NPV linear regression model showed a good performance, with a coefficient of determination (R2) of 0.60 and a root mean square error of leave-one-out cross-validation (RMSECV) of 0.1574. Furthermore, the spatial distribution of f NPV exhibited significant heterogeneity, and f NPV decreased from the northeastern XGR to the southwestern XGR. The overall trend of the interannual f NPV in the XGR increased in a fluctuating manner during 2000–2019. The f NPV increased in 66.92% of the XGR and decreased in a relatively small proportion (18.21%) of the XGR.

Snow Cover Estimation Underneath the Clouds Based on Multitemporal Correlation Analysis in Historical Time-Series Imagery

The estimation of a snow-covered area (SCA) is often achieved by classification of imagery acquired by passive optical sensors aboard satellite platforms with high revisit frequencies [e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)] required by various applications. The extraction of the SCA from optical imagery is inevitably hindered by the presence of clouds, where the surface labels (i.e., snow and no-snow) remain unknown. In the bulk of existing research, cloud pixels are either masked out without any further processing or assigned to snow/no-snow classes by performing spatial or temporal filters. The current approaches to deal with the cloud-obscuration problem are subjected to sizable uncertainties. They mostly neglect or only partially account for the temporal correlation, which undermines the full potential of long time series. We propose a novel method for estimating snow/no-snow labels beneath the clouds that leverages the multitemporal correlation between the presence/absence of snow and environmental factors including the topographical elevation, the date of acquisition (and thus the season), and the cloud-obscuration duration. The proposed method is built upon analyzing the long time series of maps derived from the single date classification of images in order to estimate the conditional probabilities of transition between the snow and no-snow classes. The probabilities are estimated as a function of the aforementioned environmental and multitemporal factors, which allow for the prediction of labels beneath the clouds in either archive or new acquisitions. Validation results on a four-year time series of daily MODIS images acquired over the Euregio region in Italian and Austrian Alps prove the effectiveness and robustness of the proposed method in assigning labels beneath the clouds.

Chemical Composition and Light Absorption of PM2.5 Observed at Two Sites near a Busy Road during Summer and Winter

To examine the difference in the major chemical composition of fine particulate matter (PM2.5) between two roadway sites, 24 h integrated PM2.5 samples were simultaneously collected both 15 m (Buk-Ku District Office (BKO) site) and 150 m (Chonnam National University campus (CNU) site) away from busy roads during the summer and winter periods; these samples were taken to determine the concentrations of organic and elemental carbon (OC and EC), water-soluble organic carbon (WSOC), and water-soluble inorganic species. In addition, the real-time aerosol light absorption coefficients (Abs) were measured using a dual-spot seven-wavelength aethalometer at the CNU site to evaluate the influence of traffic and biomass burning (BB) emissions on the concentrations of organic aerosol particles. The hourly NO2 concentration was also observed at an air pollution monitoring network that is about 2 km away from the CNU site. During summer, 24 h PM2.5 concentrations (PM2.5 episode) which exceeded the Korean PM2.5 standard (35 μg/m3) were linked to increases in organic matter (OM) and SO42− concentrations that accounted for on average 35–41% and 26–30%, respectively, of the PM2.5 at the two sites. The increased SO42− concentration was most likely attributable to the inflow of long-range transported aerosols, rather than local production, as demonstrated by both the MODIS (Moderate Resolution Imaging Spectroradiometer) images and transport pathways of air masses reaching the sites. On the other hand, the OM, WSOC, and EC concentrations were directly attributable to traffic emissions at the sampling sites, as supported by the tight correlation between the OC and EC. A small difference between the absorption Ångström exponent (AAE) values calculated at wavelengths of 370–950 nm (AAE370–950nm) and 370–520 nm (AAE370–520nm), and the poor correlation of absorption coefficient by brown carbon (BrC) at 370 nm (AbsBrC370nm) with K+ (R2 = 0.00) also suggest a significant contribution of traffic emissions to OM. However, the wintertime PM2.5 episode was strongly related to the enhanced OM and NO3− concentrations, which contributed 26–28% and 22–23% of the PM2.5 concentration, respectively. It is interesting to note that there were two distinct OC/EC ratios in winter: a lower OC/EC (~3.0), which indicates a significant contribution of traffic emissions to the OC and EC, and a higher OC/EC (~6.5), which suggests an additional influence of BB emissions as well as traffic emissions at the sites. Strong correlations between the OC and EC (R2 = 0.72–0.83) and the enhanced AAE370–520nm values compared to the AAE370–950nm support that BB emissions were also an important contributor to the wintertime OM concentrations as well as traffic emissions at the two sites. A good correlation between the gaseous NO2 and NO3− and meteorological conditions (e.g., low wind speed and high relative humidity) suggest that the heterogeneous oxidation of NO2 on moist particles could be an important contributor to wintertime particulate NO3− formation at the sites. The OC concentrations during summer and winter were higher at the BKO site, with a higher traffic flow and a shorter distance from the roadway than at the CNU site. However, there were slight differences in the concentrations of secondary inorganic species (NO3−, SO42−, and NH4+) between the sites during summer and winter.

Understanding the Land Surface Phenology and Gross Primary Production of Sugarcane Plantations by Eddy Flux Measurements, MODIS Images, and Data-Driven Models

Sugarcane (complex hybrids of Saccharum spp., C4 plant) croplands provide cane stalk feedstock for sugar and biofuel (ethanol) production. It is critical for us to analyze the phenology and gross primary production (GPP) of sugarcane croplands, which would help us to better understand and monitor the sugarcane growing condition and the carbon cycle. In this study, we combined the data from two sugarcane EC flux tower sites in Brazil and the USA, images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor, and data-driven models to study the phenology and GPP of sugarcane croplands. The seasonal dynamics of climate, vegetation indices from MODIS images, and GPP from two sugarcane flux tower sites (GPPEC) reveal the temporal consistency in sugarcane phenology (crop calendar: green-up dates and harvesting dates) as estimated by the vegetation indices and GPPEC data. The Land Surface Water Index (LSWI) is shown to be useful to delineate the phenology of sugarcane croplands. The relationship between the sugarcane GPPEC and the Enhanced Vegetation Index (EVI) is stronger than the relationship between the GPPEC and the Normalized Difference Vegetation Index (NDVI). We ran the Vegetation Photosynthesis Model (VPM), which uses the light use efficiency (LUE) concept and is driven by climate data and MODIS images, to estimate the daily GPP at the two sugarcane sites (GPPVPM). The seasonal dynamics of the GPPVPM and GPPEC at the two sites agreed reasonably well with each other, which indicates that VPM is a powerful tool for estimating the GPP of sugarcane croplands in Brazil and the USA. This study clearly highlights the potential of combining eddy covariance technology, satellite-based remote sensing technology, and data-driven models for better understanding and monitoring the phenology and GPP of sugarcane croplands under different climate and management practices.

Spatial-Temporal Variation Characteristics of Vertical Dust Flux Simulated by WRF-Chem Model with GOCART and AFWA Dust Emission Schemes (Case Study: Central Plateau of Iran)

Dust and sand storms are among the major threats to central Iran. These phenomena pose irreparable risks to natural ecosystems and human societies, including effects on health. In this study, the spatial and temporal pattern of vertical dust flux (VDF) was used to identify dust sources as well as areas with high potential for dust generation. To simulate VDF, two intense dust storms, from 21 February 2015 and 14 February 2018, were selected using synoptic data and Moderate Resolution Imaging Spectroradiometer (MODIS) images. These dust storms were identified as responsible for a reduction of horizontal visibility to less than 1000 m, using remote sensing tools and Ackerman Dust Index. MODIS images show that these two storms covered most of Central Plateau of Iran. The Weather Research and Forecasting model with chemistry (WRF-Chem) was used to simulate the storms, with either the Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) or Air Force Weather Agency (AFWA) scheme to calculate VDF. Modeled vertical dust fluxes in both events indicate that the Arabian deserts in Saudi Arabia and in southwestern Iran can be identified as main sources of the dust in the central Iranian plateau. The other source of dust is the Hirmand Basin, located in the country of Afghanistan and in the southeast of Iran. The results of VDF simulations indicate that central southeast Iran could be the main dust source of internal origin. Additionally, over seasonal wetlands in Iran, the amount of VDF was simulated to be sometimes over 4000 μg/(m2s), an indication that these areas are sensitive to wind erosion in dry conditions and can be a source of dust. The WRF-Chem results were compared with the horizontal visibility measured in synoptic stations in the area. The results showed that the coefficients of determination of GOCART results with the measured horizontal visibility on 21 February 2015 and 14 February 2018 were 0.72 and 0.76, respectively, while the coefficient values from the simulations with AFWA scheme on 21 February 2015 and 14 February 2018 with the measured horizontal visibility were lower, 0.44 and 0.50, respectively. Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA2) re-analysis data also showed timing of peak dust levels consistent with the GOCART scheme.

Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method

Lakes have been identified as an important indicator of climate change and a finer lake area can better reflect the changes. In this paper, we propose an effective unsupervised deep gradient network (UDGN) to generate a higher resolution lake area from remote sensing images. By exploiting the power of deep learning, UDGN models the internal recurrence of information inside the single image and its corresponding gradient map to generate images with higher spatial resolution. The gradient map is derived from the input image to provide important geographical information. Since the training samples are only extracted from the input image, UDGN can adapt to different settings per image. Based on the superior adaptability of the UDGN model, two strategies are proposed for super-resolution (SR) mapping of lakes from multispectral remote sensing images. Finally, Landsat 8 and MODIS (moderate-resolution imaging spectroradiometer) images from two study areas on the Tibetan Plateau in China were used to evaluate the performance of UDGN. Compared with four unsupervised SR methods, UDGN obtained the best SR results as well as lake extraction results in terms of both quantitative and visual aspects. The experiments prove that our approach provides a promising way to break through the limitations of median-low resolution remote sensing images in lake change monitoring, and ultimately support finer lake applications.

On the Value of Available MODIS and Landsat8 OLI Image Pairs for MODIS Fractional Snow Cover Mapping Based on an Artificial Neural Network

This article investigates how to select the optimal Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 OLI image pairs for MODIS fractional snow cover (FSC) mapping using an artificial neural network (ANN). Four issues are discussed, including date selection, location selection, priority of date and location, and global and regional monitoring of MODIS FSC with ANNs. We propose using the histogram quadratic distance to define the similarity between the ANN training and the target test scene, which was used to quantify the representativeness of the training samples. We use the case study of MODIS FSC mapping of North Xinjiang, China, in the 2014–2015 snow season as an example. Thirty-eight experiments were designed. The experimental results demonstrate that the ANN-based FSC estimation accuracy outperformed the MODIS FSC product, with an average RMSE of 0.17, ${R}$ exceeds 0.8, and the total snow cover area was estimated more accurately in most cases. For a target test scene, we preliminarily inferred that the best method is to develop an ANN using image pairs of another location with the highest similarity in the same acquisition time, using historical image pairs of the target scene with the highest similarity is the second choice, and using historical image pairs from another location with a high similarity is the third choice. For global- and regional-scale MODIS FSC mapping with ANNs, we formulated the strategy of initially determining a reasonable location and subsequently selecting the acquisition date of the image pairs to guarantee that the training data set represents the entire study area well.

Extraction of Bohai Sea ice from MODIS data based on multi-constraint endmembers and linear spectral unmixing

ABSTRACT A Linear Spectral Unmixing method based on Multi-Constraint Endmembers (LSU-MCE) is proposed to extract the sea ice in the Bohai Sea using the Moderate Resolution Imaging Spectroradiometer (MODIS) images. The method first extracts the spectral features, texture features, shape features, and temperature features of the sea ice and sea water, and then constructs the rules using the features to purify the endmembers, and finally decomposes the pixels using the linear spectral unmixing method. The method can properly detect sea ice including thin ice, drift ice, and melt ponds. Besides, the turbid sea water is separated from sea water as a separate endmember. So the proposed method can eliminate the disturbance caused by suspended sediment and successfully detect sea ice in high sediment waters. The method has good robustness to MODIS images of different satellites (Terra and Aqua) and the stripe noise. The method has been successfully applied to time series data of MODIS and the results show that it has high accuracy for sea ice detection in the Bohai Sea, with better visual results, and higher overall accuracy and kappa coefficient ().

Fusion of MODIS and Landsat-Like Images for Daily High Spatial Resolution NDVI

One of the obstacles in monitoring agricultural crops is the difficulty in understanding and mapping rapid changes of these crops. With the purpose of addressing this issue, this study aimed to model and fuse the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) using Landsat-like images to achieve daily high spatial resolution NDVI. The study was performed for the period of 2017 on a commercial farm of irrigated maize-soybean rotation in the western region of the state of Bahia, Brazil. To achieve the objective, the following procedures were performed: (i) Landsat-like images were upscaled to match the Landsat-8 spatial resolution (30 m); (ii) the reflectance of Landsat-like images was intercalibrated using the Landsat-8 as a reference; (iii) Landsat-like reflectance images were upscaled to match the MODIS sensor spatial resolution (250 m); (iv) regression models were trained daily to model MODIS NDVI using the upscaled Landsat-like reflectance images (250 m) of the closest day as the input; and (v) the intercalibrated version of the Landsat-like images (30 m) used in the previous step was used as the input for the trained model, resulting in a downscaled MODIS NDVI (30 m). To determine the best fitting model, we used the following statistical metrics: coefficient of determination (r2), root mean square error (RMSE), Nash–Sutcliffe efficiency index (NSE), mean bias error (MBE), and mean absolute error (MAE). Among the assessed regression models, the Cubist algorithm was sensitive to changes in agriculture and performed best in modeling of the Landsat-like MODIS NDVI. The results obtained in the present research are promising and can enable the monitoring of dynamic phenomena with images available free of charge, changing the way in which decisions are made using satellite images.

Subpixel Mapping of Surface Water in the Tibetan Plateau with MODIS Data

This article presents a comprehensive subpixel water mapping algorithm to automatically produce routinely open water fraction maps in the Tibetan Plateau (TP) with the Moderate Resolution Imaging Spectroradiometer (MODIS). A multi-index threshold endmember extraction method was applied to select the endmembers from MODIS images. To incorporate endmember variability, an endmember selection strategy, called the combined use of typical and neighboring endmembers, was adopted in multiple endmember spectral mixture analysis (MESMA), which can assure a robust subpixel water fractions estimation. The accuracy of the algorithm was assessed at both the local scale and regional scale. At the local scale, a comparison using the eight pairs of MODIS/Landsat 8 Operational Land Imager (OLI) water maps demonstrated that subpixels water fractions were well retrieved with a root mean square error (RMSE) of 7.86% and determination coefficient (R2) of 0.98. At the regional scale, the MODIS water fraction map in October 2014 matches well with the TP lake data set and the Global Lake and Wetland Database (GLWD) in both latitudinal and longitudinal distribution. The lake area estimation is more consistent with the reference TP lake data set (difference of −3.15%) than the MODIS Land Water Mask (MOD44W) (difference of −6.39%).

Phenology-Based Rice Paddy Mapping Using Multi-Source Satellite Imagery and a Fusion Algorithm Applied to the Poyang Lake Plain, Southern China

Accurate information about the spatiotemporal patterns of rice paddies is essential for the assessment of food security, management of agricultural resources, and sustainability of ecosystems. However, accurate spatial datasets of rice paddy fields and multi-cropping at fine resolution are still lacking. Landsat observation is the primary source of remote sensing data that has continuously mapped regional rice paddy fields at a 30-m spatial resolution since the 1980s. However, Landsat data used for rice paddy studies reveals some challenges, especially data quality issues (e.g., cloud cover). Here, we present an algorithm that integrates time-series Landsat and MODIS (Moderate-resolution Imaging Spectroradiometer) images with a phenology-based approach (ILMP) to map rice paddy planting fields and multi-cropping patterns. First, a fusion of MODIS and Landsat data was used to reduce the cloud contamination, which added more information to the Landsat time series data. Second, the unique biophysical features of rice paddies during the flooding and open-canopy periods (which can be captured by the dynamics of the vegetation indices) were used to identify rice paddy regions as well as those of multi-cropping. This algorithm was tested for 2015 in Nanchang County, which is located on the Poyang Lake plain in southern China. We evaluated the resultant map of the rice paddy and multi-cropping systems using ground-truth data and Google Earth images. The overall accuracy and kappa coefficient of the rice paddy planting areas were 93.66% and 0.85, respectively. The overall accuracy and kappa coefficient of the multi-cropping regions were 92.95% and 0.89, respectively. In addition, our algorithm was more capable of capturing detailed information about areas with fragmented cropland than that of the National Land Cover Dataset (NLCD) from 2015. These results demonstrated the great potential of our algorithm for mapping rice paddy fields and using the multi-cropping index in complex landscapes in southern China.

Observation of internal waves with OLCI and SRAL on board Sentinel-3

The ocean and land color instrument (OLCI) and synthetic aperture radar altimeter (SRAL) installed aboard the Sentinel-3 satellite have been in orbit for operational uses. In this study, data collected from Sentinel-3 are used to investigate internal waves in the South China Sea. An internal wave is detected using an OLCI image with a resolution of 300 m, and an analysis was performed with a quasi-synchronous moderate-resolution imaging spectroradiometer (MODIS) image. The opposite characteristics of OLCI and MODIS images of the same internal wave are explained by the critical angle in brightness reversals. The unique observational geometry of the OLCI image and its influence on observations of internal waves are discussed. The distribution of σ0 and sea surface height anomalies (SSHAs) induced by internal waves are studied using SRAL records. The σ0 records of SRAL occasionally show less sensitivity to the modulation of internal waves, which may be attributed to the observational geometry, while SSHAs show obvious variations. The synchronous pairing of OLCI images and SRAL records are analyzed to extract the three-dimensional sea surface signatures induced by internal waves. The analysis demonstrates that the profile of SSHAs in the surface shows an opposite phase to the profiles of internal waves in the ocean. The opposite phase relationship, observed in the remote sensing view, is also confirmed with a laboratory experiment.

Compositing the Minimum NDVI for Daily Water Surface Mapping

Capturing high frequency water surface dynamics via optical remote sensing is important for understanding hydro-ecological processes over seasonally flooded wetlands. However, it is a difficult task due to the presence of clouds on satellite images. This study proposed the MODerate-resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) Minimum Value Composite (MinVC) algorithm to generate daily water surface data at a 250-m resolution. The algorithm selected pixelwise minimum values from the combined daily Terra and Aqua MODIS NDVI data within a 15-day moving window. Consisting mainly of cloud and water surface information, the MinVC NDVI data were segmented for water surfaces over the Poyang Lake, China (2000–2017) by using an edge detection model. The water surface mapping result was strongly correlated with the Landsat based result (R2 = 0.914, root mean square error, RMSE = 223.7 km2), the cloud free MODIS image based result (R2 = 0.824, RMSE = 356.7 km2), the recent Landsat-MODIS image fusion based result (R2 = 0.765, RMSE = 403 km2), and the hydrodynamic modeling result (R2 = 0.799). Compared to the equivalent eight-day MOD13 NDVI based on the Constraint View-Angle Maximum Value Composite (CV-MVC) algorithm, the daily MinVC NDVI highlighted water bodies by generating spatially homogenous water surface information. Consequently, the algorithm provided spatially and temporally continuous data for calculating water submersion times and trends in water surface area, which contribute to a better understanding of hydro-ecological processes over seasonally flooded wetlands. Within the framework of sensor intercalibration, the algorithm can be extended to incorporate multiple sensor data for improved water surface mapping.

MODIS and Landsat satellite images comparison for modeling near surface air temperature

Near surface air temperature (T air) is a descriptor of terrestrial environment conditions and one of climatic variables that most widely used in climate change studies. In this search, the worker suggested a high-resolution air temperature estimation from integrating Iraqi Agrometeorological network daily (T air) and land surface temperature (LST) that derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) or from Landsat8. The objective of this search is to find model to estimate high spatial resolution air temperature for any region based on satellite images and climate datasets. The data of air temperature used for modelling is collected by four weather stations located in Babylon Governorate from December 2016 to March 2017. The standard error for MODIS winter model is 1.55 and RMSE for landsat8 winter model is 1.3.

Kalman filter method for generating time-series synthetic Landsat images and their uncertainty from Landsat and MODIS observations

The Landsat program, since its commencement in 1972, has acquired millions of images of our planet. Those images are one of the most valuable Earth Observation resources for local, regional and global land surface monitoring and study due to their moderate spatial resolution and rich spectral information. However, their applications are impeded largely by their relatively low revisit frequency and cloud contamination on images. In order to improve their usability, a number of studies have been conducted to blend Landsat images with Moderate Resolution Imaging Spectroradiometer (MODIS) images to take merits of the two sensors. All blending models reported that they can predict synthetic Landsat images with various degrees of accuracy. However, only a couple of models reported that they can explicitly estimate uncertainty for their blended images.In this study, we propose a new surface reflectance blending model based on a Kalman Filter algorithm (Kalman Filter Reflectance Fusion Model - KFRFM) to predict time-series synthetic Landsat images from Landsat and MODIS images, and simultaneously to estimate uncertainty of the predicted synthetic images to quantify the quality of the synthetic images. Using the model, we predicted a time-series of 38 synthetic Landsat images with a temporal interval of 4 days for a vegetation growing season spanning about 6 months from Nadir Bi-directional Reflectance Distribution Function adjusted MODIS product (MCD43A4), and their corresponding uncertainties. From this time-series, we calculated five vegetation indices involving all the spectral bands of the synthetic images, and compared them to those from Landsat observations. The results demonstrated that the proposed method is able to produce high quality synthetic Landsat images to meet various application demands for higher spatial and temporal resolution images. Uncertainty analysis reveals that cropland has the largest uncertainty followed by grassland while forests have the smallest uncertainties among the seven vegetation land cover types of the study area. For performance evaluation, we compared KFRFM to several published models. The comparison results reveal that KFRFM performs the best based on the assessed image quality indices.

Hacia el modelado de material particulado fino en Santiago, Chile, mediante imágenes MODIS

This study explored the capabilities offered by the aerosol optical depth (AOD) product (created on a daily basis from MODIS (Moderate Resolution Imaging Spectroradiometer) images acquired at 10:30 AM and 1:30 PM local time, to predict the at-surface concentrations of fine particulate matter (PM2.5) measured on the monitoring stations of the city of Santiago, Chile, during the summer months of 2016. To accomplish that, linear correlations were performed between remote-based and field-based data sources, both matched in time and space, in order to find the days and stations where MODIS-AOD may offer the best correlation. Based on these cases, a linear regression model was performed in order to explore the predictive power of MODIS-AOD. The best correlations were consistently obtained when MODIS-AOD data created at 1:30 PM were used at the station level for all the sampled days of the study period. When the monitoring stations of Cerro Navia, Quilicura and Pudahuel were grouped, the correlation coefficient was 0.77 and the predictive model explained the 63% of the at-surface MP2.5 concentrations and yielded an accuracy of 69.1%. The findings of this exploratory study provide a first approach to the modeling of the at-surface concentrations of fine particulate matter in the city of Santiago, which could be improved in a future with the aim to anticipate environmental warnings and disease outbreaks related to the exposure of this pollutant.

An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications

Spatiotemporal data fusion is a key technique for generating unified time-series images from various satellite platforms to support the mapping and monitoring of vegetation. However, the high similarity in the reflectance spectrum of different vegetation types brings an enormous challenge in the similar pixel selection procedure of spatiotemporal data fusion, which may lead to considerable uncertainties in the fusion. Here, we propose an object-based spatiotemporal data-fusion framework to replace the original similar pixel selection procedure with an object-restricted method to address this issue. The proposed framework can be applied to any spatiotemporal data-fusion algorithm based on similar pixels. In this study, we modified the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data-fusion model (FSDAF) using the proposed framework, and evaluated their performances in fusing Sentinel 2 and Landsat 8 images, Landsat 8 and Moderate-resolution Imaging Spectroradiometer (MODIS) images, and Sentinel 2 and MODIS images in a study site covered by grasslands, croplands, coniferous forests, and broadleaf forests. The results show that the proposed object-based framework can improve all three data-fusion algorithms significantly by delineating vegetation boundaries more clearly, and the improvements on FSDAF is the greatest among all three algorithms, which has an average decrease of 2.8% in relative root-mean-square error (rRMSE) in all sensor combinations. Moreover, the improvement on fusing Sentinel 2 and Landsat 8 images is more significant (an average decrease of 2.5% in rRMSE). By using the fused images generated from the proposed object-based framework, we can improve the vegetation mapping result by significantly reducing the “pepper-salt” effect. We believe that the proposed object-based framework has great potential to be used in generating time-series high-resolution remote-sensing data for vegetation mapping applications.

Estimation of Actual Evapotranspiration in Fragmented Mediterranean Areas by the Spatio-Temporal Fusion of NDVI Data

Actual evapotranspiration $(\text{ET}_{{\rm{A}}})$ is a fundamental component of the land water cycle that can be predicted by the combination of meteorological data and remotely sensed normalized difference vegetation index (NDVI) observations. The proficient application of this approach to the retrospective study of fragmented areas, however, depends on the preliminary use of spatio-temporal fusion (STF) methods capable of integrating different satellite datasets. One of these methods is the Spatial Enhancer of Vegetation Index image Series (SEVIS), which has been recently developed to improve the annual NDVI datasets based on one or a few high spatial resolution images. This STF method is currently applied to moderate resolution imaging spectroradiometer (MODIS) and TM/ETM+/OLI imagery taken over three fragmented areas in Tuscany (Central Italy), representative of different Mediterranean ecosystems, i.e., an urban grassland, a tomato field, and an olive grove. The performance of SEVIS is evaluated by comparing the $\text{ET}_{{\rm{A}}}$ estimates obtained from the original (MODIS) and synthetic (MODIS plus TM/ETM+/OLI) NDVI datasets to ground $\text{ET}_{{\rm{A}}}$ observations. The experimental results indicate that the original MODIS NDVI data cannot properly characterize the seasonal vegetation evolutions of the three study sites, which negatively affects the performance of $\text{ET}_{{\rm{A}}}$ simulation. In contrast, such evolutions are reasonably reproduced by the synthetic NDVI datasets, which improves the accuracy of the $\text{ET}_{{\rm{A}}}$ estimates both in terms of correlation and errors. The improvements are particularly evident during the summer dry period when the MODIS images are incapable of characterizing the actual vegetation response to water stress.

Decomposition of mixed pixels in MODIS data using Bernstein basis functions

The decomposition of mixed pixels in Moderate Resolution Imaging Spectroradiometer (MODIS) images is essential for the application of MODIS data in many fields. Many existing methods for unmixing mixed pixels use principal component analysis to reduce the dimensionality of the image data and require the extraction of endmember spectra. We propose the pixel spectral unmixing index (PSUI) method for unmixing mixed pixels in MODIS images. In this method, a set of third-order Bernstein basis functions is applied to reduce the dimensionality of the image data and characterize the spectral curves of the mixed pixels in a MODIS image, and then the derived PSUIs (i.e., the coefficients of the basis functions) are calibrated by means of the abundance values of the ground features from the Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) classification images corresponding to the date and region of the MODIS image. The proposed method was tested on MODIS and ETM+/OLI images, and it obtained satisfying unmixing results. We compared the PSUI method with conventional methods, including the pixel purity index, the N-finder algorithm, the sequential maximum angle convex cone, and vertex component analysis and found that the PSUI method outperformed the other four methods.

Monitoring crop water content for corn and soybean fields through data fusion of MODIS and Landsat measurements in Iowa

Vegetation water content (VWC) is of vital significance to many applications in agriculture, such as crop yield estimation and precision irrigation. However, previous studies mainly estimate VWC at either high spatial resolution or temporal resolution due to the limitation of space-borne observation systems. In this paper, we target on monitoring daily plant VWC as well as canopy VWC at 30 m high spatial resolution with the fusion of optical measurements from the Land Remote Sensing Satellite (Landsat) Operational Land Imager (OLI) and the Moderate Resolution Imaging Spectroradiometer (MODIS) in an agricultural area. The study area locates in the central of Iowa, the United States (USA), the study period ranges from August 1 to August 17, 2016. Landsat 8 OLI observations are with 16-day revisit cycle and 30-m spatial resolution, MODIS daily surface reflectance product used in this study has 500-m spatial resolution. Six Landsat OLI images and thirty MODIS images were used to estimate daily remotely sensed reflectance data at 30m first. Then, the fused satellite measurements were used to calculate Normalized Difference Water Index (NDWI). Ground-based plant VWC and canopy VWC measurements were collected during the Soil Moisture Active Passive Validation Experiment (SMAPVEX16) to calibrate and validate the model for plant VMC and canopy VWC estimation separately with the NDWI. The results of validation with in-situ measurements showed an R2 of 0.44 for the corn plant VWC, an R2 of 0.66 for the corn canopy VWC; and an R2 of 0.78 for the soybean plant VWC, an R2 of 0.85 for the soybean canopy VWC, respectively.