Landsat-like Images Research Articles

Towards a Robust Approach for Multitemporal Landcover Dataset: 3 Decades of Landcover Changes in Piauí, Brazil

Mapping the changes of land use and cover through the classification of satellite images is one of the essential sources to investigate and monitor the Earth´s surface. When performed in a multitemporal perspective, this approach requires specific procedures to match the images used. Considering that parkland/grassland savanna patches could increase over time due to different uses, this research aims to present a suitable method for processing Landsat-like images to investigate land cover dynamics. The study area covers seven municipalities in the Brazilian state of Piauí, Cerrado biome, which has been substantially affected by deforestation due to extensive agricultural projects in the past decades. The semi-automatic satellite imaging georegistration and the object-oriented classification of Landsat 5 and 8 satellites over the last 30 years in decadal periods are among the methodological procedures used. Findings demonstrate the semi-automatic image registration process as an effective method for the geometric correction of Landsat scenes, and the object-based classification procedures are appropriate for multitemporal studies allowing comparative metrics of landcover class changes by period. Regarding the remaining natural landcover within the study area, the results showed a substantial decrease of woodland savanna patches from 73% in 1986 to 43% in 2016, while agricultural fields increased from 4% to 25% in 30 years.

Evaluation of Landsat 8-like Land Surface Temperature by Fusing Landsat 8 and MODIS Land Surface Temperature Product

High-spatiotemporal-resolution land surface temperature (LST) is a crucial parameter in various environmental monitoring. However, due to the limitation of sensor trade-off between the spatial and temporal resolutions, such data are still unavailable. Therefore, the generation and verification of such data are of great value. The spatiotemporal fusion algorithm, which can be used to improve the spatiotemporal resolution, is widely used in Landsat and MODIS data to generate Landsat-like images, but there is less exploration of combining long-time series MODIS LST and Landsat 8 LST product to generate Landsat 8-like LST. The purpose of this study is to evaluate the accuracy of the long-time series Landsat 8 LST product and the Landsat 8-like LST generated by spatiotemporal fusion. In this study, based on the Landsat 8 LST product and MODIS LST product, Landsat 8-like LST is generated using Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Enhanced STARFM (ESTARFM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) algorithm, and tested and verified in the research area located in Gansu Province, China. In this process, Landsat 8 LST product was verified based on ground measurements, and the fusion results were comprehensively evaluated based on ground measurements and actual Landsat 8 LST images. Ground measurements verification indicated that Landsat 8 LST product was highly consistent with ground measurements. The Root Mean Square Error (RMSE) was 2.862 K, and the coefficient of determination R2 was 0.952 at All stations. Good fusion results can be obtained for the three spatiotemporal algorithms, and the ground measurements verified at All stations show that R2 was more significant than 0.911. ESTARFM had the best fusion result (R2 = 0.915, RMSE = 3.661 K), which was better than STARFM (R2 = 0.911, RMSE = 3.746 K) and FSDAF (R2 = 0.912, RMSE = 3.786 K). Based on the actual Landsat 8 LST images verification, the fusion images were highly consistent with actual Landsat 8 LST images. The average RMSE of fusion images about STARFM, ESTARFM, and FSDAF were 2.608 K, 2.245 K, and 2.565 K, respectively, and ESTARFM is better than STARFM and FSDAF in most cases. Combining the above verification, the fusion results of the three algorithms were reliable and ESTARFM had the highest fusion accuracy.

A boundary finding-based spatiotemporal fusion model for vegetation index

ABSTRACT The spatiotemporal image fusion technique is regarded as an effective and efficient remedy to blend two or more geometrically registered remote sensing images of the same scene acquired from different sensors into a single image with both high temporal and high spatial resolutions. A number of existing fusion methods developed for generating Landsat-like images from the Moderate Resolution Imaging Spectroradiometer (MODIS) have successfully solved limitations on vegetation dynamics monitoring. However, these methods either treat the weighting parameters as constants or adopt insufficient linear regression coefficients (considering only spatial, temporal or spectral weights), which may contribute to blocky artefacts and dissimilar pixels in the predicted image. In this study, a boundary finding-based vegetation index spatiotemporal fusion model (BESFM) is newly proposed to generate high spatial and high temporal resolutions Enhanced Vegetation Index (EVI) images in homogeneous and heterogeneous regions with rapid vegetation changes. The cloud-free Landsat-8 Operational Land Imager (OLI) and the MODIS (MOD09GA) data acquired from two study sites, located in Queensland and New South Wales and Australia, were selected to evaluate the performance of the proposed method. Compared with the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and flexible spatiotemporal data fusion (FSDAF), the BESFM outperforms in both visible image quality (reducing the spatial distortion and blocky artefacts in prediction caused by abrupt land cover changes) and quantitative indices (with RSME minimum to 0.011 and R2 maximum to 0.956). Since the BESFM method not only uses adjustable linear regression assumptions for heterogeneous and homogeneous regions differently but also implements boundary detection-based spatial weight with temporal and spectral weights together to enhance the predicted spatial details, it has the potential to increase the availability of fine spatial resolution data.

Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach

Multi-sensor remote sensing data fusion technologies have been developed and widely applied in recent years, providing a feasible and economical solution to increase the availability of high spatial and temporal resolution data. These methods, however, have been challenging to apply in highly heterogeneous areas, especially in complex agricultural landscapes where there are rapid changes at small scales, while features at larger scales change more slowly. In this study, we developed a novel method to reconstruct daily 30 m Normalized Difference Vegetation Index (NDVI) using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat and Landsat-like platforms, and the Cropland Data Layer (CDL). This method utilizes a crop reference curve (CRC) approach, in which a set of NDVI time series are extracted from pure MODIS pixels (250 m resolution) identified using the CDL, and then used to fit Landsat-like observations (30 m). The CRC based method was applied over a complex agricultural landscape in the Choptank River watershed on the Eastern Shore of Maryland. Landsat data from 2013 and 2014 and Harmonized Landsat and Sentinel-2 (HLS) data from 2018 were used to reconstruct 30 m daily NDVI maps for major crop types. Results show that the relative error (RE) in reconstructed NDVI is around 6–8% during periods of rapid crop growth, and 3–5% during peak periods when growth is slow. The accuracy of the CRC method outperforms a standard image pair-based data fusion algorithm (Spatial and Temporal Adaptive Reflectance Fusion Model; STARFM), which yields RE of 4–9% in slow-growth periods and 10–16% in fast-growth periods when clear Landsat images are scarce. The CRC method was also compared with time-series data fusion methods, including a harmonic fitting model and the SaTellite dAta IntegRation (STAIR) model. The results show that CRC gives similar results when the Landsat-like image availability is high (around 27 images per year), but outperforms other methods when availability is limited (less than 15 images per year). The reconstructed NDVI time series for corn, soybean, winter wheat/soybean and forest at 30-m resolution show clear phenological patterns at the sub-field scale. The resulting 30-m NDVI timeseries data provide useful information for mapping crop phenology and monitoring crop condition in complex agricultural landscapes, especially for complex double-cropping areas. However, the input requirement of an accurate 30-m crop classification map constrains its application to areas and periods where classifications are available.

Remote Sensing Image Spatiotemporal Fusion Using a Generative Adversarial Network

Due to technological limitations and budget constraints, spatiotemporal fusion is considered a promising way to deal with the tradeoff between the temporal and spatial resolutions of remote sensing images. Furthermore, the generative adversarial network (GAN) has shown its capability in a variety of applications. This article presents a remote sensing image spatiotemporal fusion method using a GAN (STFGAN), which adopts a two-stage framework with an end-to-end image fusion GAN (IFGAN) for each stage. The IFGAN contains a generator and a discriminator in competition with each other under the guidance of the optimization function. Considering the huge spatial resolution gap between the high-spatial, low-temporal (HSLT) resolution Landsat imagery and the corresponding low-spatial, high-temporal (LSHT) resolution MODIS imagery, a feature-level fusion strategy is adopted. Specifically, for the generator, we first super-resolve the MODIS images while also extracting the high-frequency features of the Landsat images. Finally, we integrate the features from the MODIS and Landsat images. STFGAN is able to learn an end-to-end mapping between the Landsat-MODIS image pairs and predicts the Landsat-like image for a prediction date by considering all the bands. STFGAN significantly improves the accuracy of phenological change and land-cover-type change prediction with the help of residual blocks and two prior Landsat-MODIS image pairs. To examine the performance of the proposed STFGAN method, experiments were conducted on three representative Landsat-MODIS data sets. The results clearly illustrate the effectiveness of the proposed method.

Spatiotemporal Fusion of Remote Sensing Image Based on Deep Learning

High spatial and temporal resolution remote sensing data play an important role in monitoring the rapid change of the earth surface. However, there is an irreconcilable contradiction between the spatial and temporal resolutions of the remote sensing image acquired from a same sensor. The spatiotemporal fusion technology for remote sensing data is an effective way to solve the contradiction. In this paper, we will study the spatiotemporal fusion method based on the convolutional neural network, which can fuse the Landsat data with high spatial but low temporal resolution and MODIS data with low spatial but high temporal resolution, and generate time series data with high spatial resolution. In order to improve the accuracy of spatiotemporal fusion, a residual convolution neural network is proposed. MODIS image is used as the input to predict the residual image between MODIS and Landsat, and the sum of the predicted residual image and MODIS data is used as the predicted Landsat-like image. In this paper, the residual network not only increases the depth of the superresolution network but also avoids the problem of vanishing gradient due to the deep network structure. The experimental results show that the prediction accuracy by our method is greater than that of several mainstream methods.

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.

An enhanced spatiotemporal fusion method – Implications for coal fire monitoring using satellite imagery

Many real-world applications require remotely sensed images at both high spatial and temporal resolutions. This requirement, however, is generally not met by single satellite system. A number of spatiotemporal fusion models have been developed to overcome this constraint. Landsat and Visible Infrared Imaging Radiometer Suite (VIIRS) data have been extensively used for detection and monitoring of active fires at different scales. Fusing the data obtained from these sensors will, therefore, significantly contribute to the satellite-based monitoring of fires. Among the available spatiotemporal fusion methods, the spatial and temporal adaptive reflectance fusion model (STARFM) and enhanced STARFM (ESTARFM) algorithms have been widely used for studying the land surface dynamics in the homogeneous and heterogeneous regions. The present study explores the applicability of STARFM and ESTARFM algorithms for fusing the high spatial resolution Landsat-8 OLI data with high temporal resolution VIIRS data in the context of active surface coal fire monitoring. Further, a modified version of ESTARFM algorithm, referred as modified-ESTARFM, is developed to improve the performance of the fusion model. Jharia coalfield (India), known for widespread occurrences of coal fires, is taken as the study area. The qualitative and quantitative assessments of the predicted (synthetic) Landsat-like images from different algorithms (STARFM, modified-STARFM, ESTARFM, modified-ESTARFM) indicate that the modified-ESTARFM outperforms the other fusion approaches used in this study. Considering the advantages, limitations and performance of the algorithms used, modified-ESTARFM along with STARFM can be used for surface coal fire monitoring. The study will not only contribute to remote sensing based coal fire studies but also to other applications, such as forest fires, crop residue burning, land cover and land use change, vegetation phenology, etc.

Mapping Paddy Rice Planting Area in Northeastern China Using Spatiotemporal Data Fusion and Phenology-Based Method

Accurate paddy rice mapping with fine spatial detail is significant for ensuring food security and maintaining sustainable environmental development. In northeastern China, rice is planted in fragmented and patchy fields and its production has reached over 10% of the total amount of rice production in China, which has brought the increasing need for updated paddy rice maps in the region. Existing methods for mapping paddy rice are often based on remote sensing techniques by using optical images. However, it is difficult to obtain high quality time series remote sensing data due to the frequent cloud cover in rice planting area and low temporal sampling frequency of satellite imagery. Therefore, paddy rice maps are often developed using few Landsat or time series MODIS images, which has limited the accuracy of paddy rice mapping. To overcome these limitations, we presented a new strategy by integrating a spatiotemporal fusion algorithm and phenology-based algorithm to map paddy rice fields. First, we applied the spatial and temporal adaptive reflectance fusion model (STARFM) to fuse the Landsat and MODIS data and obtain multi-temporal Landsat-like images. From the fused Landsat-like images and the original Landsat images, we derived time series vegetation indices (VIs) with high temporal and high spatial resolution. Then, the phenology-based algorithm, considering the unique physical features of paddy rice during the flooding and transplanting phases/open-canopy period, was used to map paddy rice fields. In order to prove the effectiveness of the proposed strategy, we compared our results with those from other three classification strategies: (1) phenology-based classification based on original Landsat images only, (2) phenology-based classification based on original MODIS images only and (3) random forest (RF) classification based on both Landsat and Landsat-like images. The validation experiments indicate that our fusion-and phenology-based strategy could improve the overall accuracy of classification by 6.07% (from 92.12% to 98.19%) compared to using Landsat data only, and 8.96% (from 89.23% to 98.19%) compared to using MODIS data, and 4.66% (from93.53% to 98.19%) compared to using the RF algorithm. The results show that our new strategy, by integrating the spatiotemporal fusion algorithm and phenology-based algorithm, can provide an effective and robust approach to map paddy rice fields in regions with limited available images, as well as the areas with patchy and fragmented fields.

Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series

The paradox between environmental conservation and economic development is a challenge for Brazil, where there is a complex and dynamic agricultural scenario. This reinforces the need for effective methods for the detailed mapping of agriculture. In this work, we employed land surface phenological metrics derived from dense satellite image time series to classify agricultural land in the Cerrado biome. We used all available Landsat images between April 2013 and April 2017, applying a weighted ensemble of Radial Basis Function (RBF) convolution filters as a kernel smoother to fill data gaps such as cloud cover and Scan Line Corrector (SLC)-off data. Through this approach, we created a dense Enhanced Vegetation Index (EVI) data cube with an 8-day temporal resolution and derived phenometrics for a Random Forest (RF) classification. We used a hierarchical classification with four levels, from land cover to crop rotation classes. Most of the classes showed accuracies higher than 90%. Single crop and Non-commercial crop classes presented lower accuracies. However, we showed that phenometrics derived from dense Landsat-like image time series, in a hierarchical classification scheme, has a great potential for detailed agricultural mapping. The results are promising and show that the method is consistent and robust, being applicable to mapping agricultural land throughout the entire Cerrado.

An Improved Spatial and Temporal Reflectance Unmixing Model to Synthesize Time Series of Landsat-Like Images

The trade-off between spatial and temporal resolution limits the acquisition of dense time series of Landsat images, and limits the ability to properly monitor land surface dynamics in time. Spatiotemporal image fusion methods provide a cost-efficient alternative to generate dense time series of Landsat-like images for applications that require both high spatial and temporal resolution images. The Spatial and Temporal Reflectance Unmixing Model (STRUM) is a kind of spatial-unmixing-based spatiotemporal image fusion method. The temporal change image derived by STRUM lacks spectral variability and spatial details. This study proposed an improved STRUM (ISTRUM) architecture to tackle the problem by taking spatial heterogeneity of land surface into consideration and integrating the spectral mixture analysis of Landsat images. Sensor difference and applicability with multiple Landsat and coarse-resolution image pairs (L-C pairs) are also considered in ISTRUM. Experimental results indicate the image derived by ISTRUM contains more spectral variability and spatial details when compared with the one derived by STRUM, and the accuracy of fused Landsat-like image is improved. Endmember variability and sliding-window size are factors that influence the accuracy of ISTRUM. The factors were assessed by setting them to different values. Results indicate ISTRUM is robust to endmember variability and the publicly published endmembers (Global SVD) for Landsat images could be applied. Only sliding-window size has strong influence on the accuracy of ISTRUM. In addition, ISTRUM was compared with the Spatial Temporal Data Fusion Approach (STDFA), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), the Hybrid Color Mapping (HCM) and the Flexible Spatiotemporal DAta Fusion (FSDAF) methods. ISTRUM is superior to STDFA, slightly superior to HCM in cases when the temporal change is significant, comparable with ESTARFM and a little inferior to FSDAF. However, the computational efficiency of ISTRUM is much higher than ESTARFM and FSDAF. ISTRUM can to synthesize Landsat-like images on a global scale.

A Prediction Smooth Method for Blending Landsat and Moderate Resolution Imagine Spectroradiometer Images

Landsat images have been widely used in support of responsible development of natural resources, disaster risk management (e.g., forest fire, flooding etc.), agricultural production monitoring, as well as environmental change studies due to its medium spatial resolution and rich spectral information. However, its availability and usability are largely constrained by its low revisit frequency. On the other hand, MODIS (Moderate Resolution Imaging Spectroradiometer) images for land studies have much more frequent coverage but with a lower spatial resolution of 250–500 m. To take advantages of the two sensors and expand their availability and usability, during the last decade, a number of image fusion methods have been developed for generating Landsat-like images from MODIS observations to supplement clear-sky Landsat imagery. However, available methods are typically effective or applicable for certain applications. For a better result, a new Prediction Smooth Reflectance Fusion Model (PSRFM) for blending Landsat and MODIS images is proposed. PSRFM consists of a dynamic prediction model and a smoothing filter. The dynamic prediction model generates synthetic Landsat images from a pair of Landsat and MODIS images and another MODIS image, either forward or backward in time. The smoothing filter combines the forward and backward predictions by weighted average based on elapsed time or on the estimated prediction uncertainty. Optionally, the smooth filtering can be applied with constraints based on Normalized Difference Snow Index (NDSI) or Normalized Difference Vegetation Index (NDVI). In comparison to some published reflectance fusion methods, PSRFM shows the following desirable characteristics: (1) it can deal with one pair or two pairs of Landsat and MODIS images; (2) it can incorporate input image uncertainty during prediction and estimate prediction uncertainty; (3) it can track gradual vegetation phenological changes and deal with abrupt land-cover type changes; and (4) for predictions using two pairs of input images, the results can be further improved through the constrained smoothing filter based on NDSI or NDVI for certain applications. We tested PSRFM to generate a Landsat-like image time series by using Landsat 8 OLI and MODIS (MOD09GA) images and compared it to two reflectance fusion algorithms: STARFM (Spatial and Temporal Adaptive Reflectance Fusion Model) and ESTARFM (Enhanced version of STARFM). The results show that the proposed PSRFM is effective and outperforms STARFM and ESTARFM both visually and quantitatively.

Improving the mapping of crop types in the Midwestern U.S. by fusing Landsat and MODIS satellite data

Mapping crop types is of great importance for assessing agricultural production, land-use patterns, and the environmental effects of agriculture. Indeed, both radiometric and spatial resolution of Landsat’s sensors images are optimized for cropland monitoring. However, accurate mapping of crop types requires frequent cloud-free images during the growing season, which are often not available, and this raises the question of whether Landsat data can be combined with data from other satellites. Here, our goal is to evaluate to what degree fusing Landsat with MODIS Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) data can improve crop-type classification. Choosing either one or two images from all cloud-free Landsat observations available for the Arlington Agricultural Research Station area in Wisconsin from 2010 to 2014, we generated 87 combinations of images, and used each combination as input into the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) algorithm to predict Landsat-like images at the nominal dates of each 8-day MODIS NBAR product. Both the original Landsat and STARFM-predicted images were then classified with a support vector machine (SVM), and we compared the classification errors of three scenarios: 1) classifying the one or two original Landsat images of each combination only, 2) classifying the one or two original Landsat images plus all STARFM-predicted images, and 3) classifying the one or two original Landsat images together with STARFM-predicted images for key dates. Our results indicated that using two Landsat images as the input of STARFM did not significantly improve the STARFM predictions compared to using only one, and predictions using Landsat images between July and August as input were most accurate. Including all STARFM-predicted images together with the Landsat images significantly increased average classification error by 4% points (from 21% to 25%) compared to using only Landsat images. However, incorporating only STARFM-predicted images for key dates decreased average classification error by 2% points (from 21% to 19%) compared to using only Landsat images. In particular, if only a single Landsat image was available, adding STARFM predictions for key dates significantly decreased the average classification error by 4 percentage points from 30% to 26% (p<0.05). We conclude that adding STARFM-predicted images can be effective for improving crop-type classification when only limited Landsat observations are available, but carefully selecting images from a full set of STARFM predictions is crucial. We developed an approach to identify the optimal subsets of all STARFM predictions, which gives an alternative method of feature selection for future research.

Mapping land cover in complex Mediterranean landscapes using Landsat: Improved classification accuracies from integrating multi-seasonal and synthetic imagery

Low-intensity farming systems are of great importance for biodiversity in Europe, but they are often affected by soil degradation or economic pressure, leading to either land abandonment or intensification of agriculture. These changes in land use influence local biodiversity patterns and require annual monitoring of land cover. To accurately map land cover in such spatio-temporal complex landscapes, it is important to capture their phenological dynamics and fine spatial heterogeneity. Multi-seasonal analyses using optical sensors with a medium spatial resolution from 10 to 60m (e.g. Landsat) have been used for this task, but data availability can be scarce due to cloud cover, sub-optimal acquisition schedules and data archive access restrictions. Combining coarse spatial resolution data from the MODerate-resolution Imaging Spectroradiometer (MODIS) and Landsat provides opportunities to close these gaps by simulating Landsat-like images at MODIS temporal resolution. In this study, we test whether and by what degree land cover maps of complex Mediterranean landscapes improve by integrating multi-seasonal Landsat imagery, as well as whether STARFM-simulated imagery can be used whenever original multi-seasonal Landsat observations are unavailable. Therefore, we develop different classification scenarios based on seasonally varying data availability and based on original and simulated Landsat data. Results show that multi-seasonal Landsat data from spring and early autumn are crucial for achieving satisfying mapping accuracies (overall accuracy 74.5%). Using synthetic Landsat imagery increases classification accuracy compared to using single-date Landsat data, but accuracies were never as good as a classification based on original data. We conclude that multi-seasonal data is essential for mapping complex Mediterranean landscapes and that STARFM can be used to compensate for missing Landsat observations. However, if Landsat data availability is sufficient to cover all phenologically important dates, we suggest relying solely on Landsat.

Operational Data Fusion Framework for Building Frequent Landsat-Like Imagery

An operational data fusion framework was built to generate dense time-series Landsat-like images by fusing MODIS data products and Landsat imagery. The spatial and temporal adaptive reflectance fusion model (STARFM) was integrated in the framework. Compared with earlier implementations of the STARFM, several improvements have been incorporated in the operational data fusion framework. These include viewing angular correction on the MODIS daily bidirectional reflectance, precise and automated coregistration on MODIS and Landsat paired images, and automatic selection of Landsat and MODIS paired dates. Three tests that use MODIS and Landsat data pairs from the same season of the same year, the same season of two different years, and different seasons from adjacent years were performed over a Landsat scene in northern India using the integrated STARFM operational framework. The results show that the accuracy of the predicted results depends on the data consistency between the MODIS nadir bidirectional-reflectance-distribution-function-adjusted reflectance and Landsat surface reflectance on both the paired dates and the prediction dates. When MODIS and Landsat reflectances were consistent, the maximum difference of the predicted results for all Landsat spectral bands, except the blue band, was about 0.007 (or 5.1% relatively). However, differences were larger (0.026 in absolute and 13.8% in relative, except the blue band) when two data sources were inconsistent. In an extreme case, the difference for blue-band reflectance was as large as 0.029 (or 39.1% relatively). Case studies focused on monitoring vegetation condition in central India and the Hindu Kush Himalayan region. In general, spatial and temporal landscape variation could be identified with a high level of detail from the fused data. Vegetation index trajectories derived from the fused products could be associated with specific land cover types that occur in the study regions. The operational data fusion framework provides a feasible and cost-effective way to build dense time-series images at Landsat spatial resolution for cloudy regions.

Blending MODIS and Landsat images for urban flood mapping

Satellite images provide important data sources for monitoring flood disasters. However, the trade-off between spatial and temporal resolutions of current satellite sensors limits their uses in urban flooding studies. This study applied and compared two data fusion models, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), in generating synthetic flooding images with improved temporal and spatial resolution for flood mapping. The synthetic images are produced in two scenarios: (1) for real-time prediction based on Landsat and MODIS images acquired before the investigated flooding; and (2) for post-disaster prediction based on images acquired after the flooding. The 2005 Hurricane Katrina in New Orleans was selected as a case study. The result shows that the Landsat-like images generated can be successfully applied in flood mapping. Particularly, ESTARFM surpasses STARFM in predicting surface reflectance in both real-time and post-flooding predictions. However, the flood mapping results from the Landsat-like images produced by both STARFM and ESTARFM are similar with overall accuracy around 0.9. Only for the flooding maps of real-time predictions does ESTARFM get a slightly higher overall accuracy than STARFM, indicating that the lower quality of the Landsat-like image generated by STARFM may not affect flood mapping accuracy, due to the marked contrast between land and water. This study suggests great potential of both STARFM and ESTARFM in urban flooding research. Blending multi-sources images could also support other disaster studies that require remotely sensed data with both high spatial and temporal resolution.

Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection

Blending algorithms model land cover change by using highly resolved spatial data from one sensor and highly resolved temporal data from another. Because the data are not usually observed concurrently, unaccounted spatial and temporal variances cause error in blending algorithms, yet, to date, there has been no definitive assessment of algorithm performance against spatial and temporal variances. Our objectives were to: (i) evaluate the accuracy of two advanced blending algorithms (STARFM and ESTARFM) and two simple benchmarking algorithms in two landscapes with contrasting spatial and temporal variances; and (ii) synthesise the spatial and temporal conditions under which the algorithms performed best. Landsat-like images were simulated on 27 dates in total using the nearest temporal cloud-free Landsat–MODIS pairs to the simulation date, one before and one after. RMSD, bias, and r2 estimates between simulated and observed Landsat images were calculated, and overall variance of Landsat and MODIS datasets were partitioned into spatial and temporal components. Assessment was performed over the whole study site, and for specific land covers. Results addressing objective (i) were that: ESTARFM did not always produce lower errors than STARFM; STARFM and ESTARFM did not always produce lower errors than simple benchmarking algorithms; and land cover spatial and temporal variances were strongly associated with algorithm performance. Results addressing objective (ii) indicated ESTARFM was superior where/when spatial variance was dominant; and STARFM was superior where/when temporal variance was dominant. We proposed a framework for selecting blending algorithms based on partitioning variance into the spatial and temporal components and suggested that comparing Landsat and MODIS spatial and temporal variances was a practical method to determine if, and when, MODIS could add value for blending.

A data-model fusion approach for upscaling gross ecosystem productivity to the landscape scale based on remote sensing and flux footprint modelling

Abstract. In order to use the global available eddy-covariance (EC) flux dataset and remote-sensing measurements to provide estimates of gross primary productivity (GPP) at landscape (101–102 km2), regional (103–106 km2) and global land surface scales, we developed a satellite-based GPP algorithm using LANDSAT data and an upscaling framework. The satellite-based GPP algorithm uses two improved vegetation indices (Enhanced Vegetation Index – EVI, Land Surface Water Index – LSWI). The upscalling framework involves flux footprint climatology modelling and data-model fusion. This approach was first applied to an evergreen coniferous stand in the subtropical monsoon climatic zone of south China. The EC measurements at Qian Yan Zhou tower site (26°44´48" N, 115°04´13" E), which belongs to the China flux network and the LANDSAT and MODIS imagery data for this region in 2004 were used in this study. A consecutive series of LANDSAT-like images of the surface reflectance at an 8-day interval were predicted by blending the LANDSAT and MODIS images using an existing algorithm (ESTARFM: Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model). The seasonal dynamics of GPP were then predicted by the satellite-based algorithm. MODIS products explained 60% of observed variations of GPP and underestimated the measured annual GPP (= 1879 g C m−2) by 25–30%; while the satellite-based algorithm with default static parameters explained 88% of observed variations of GPP but overestimated GPP during the growing seasonal by about 20–25%. The optimization of the satellite-based algorithm using a data-model fusion technique with the assistance of EC flux tower footprint modelling reduced the biases in daily GPP estimations from about 2.24 g C m−2 day−1 (non-optimized, ~43.5% of mean measured daily value) to 1.18 g C m−2 day−1 (optimized, ~22.9% of mean measured daily value). The remotely sensed GPP using the optimized algorithm can explain 92% of the seasonal variations of EC observed GPP. These results demonstrated the potential combination of the satellite-based algorithm, flux footprint modelling and data-model fusion for improving the accuracy of landscape/regional GPP estimation, a key component for the study of the carbon cycle.