Fusing Landsat and MODIS Data for Vegetation Monitoring

Gao, Feng; Hilker, Thomas; Zhu, Xiaolin; Anderson, Martha C.; Masek, Jeffrey G.; Wang, Peijuan; Yang, Yun

doi:10.1109/mgrs.2015.2434351

Cited by 239 publications

(130 citation statements)

References 35 publications

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“…Landsat data are almost only available during the dry season, when vegetation and land surface in general have a significantly different appearance than during the rainy season. Previous studies indicated the importance of using Landsat input scenes that are temporally as close as possible to the prediction dates to minimize errors in the ESTARFM prediction [3,32]. Our results confirmed this assumption and additionally showed the value of Landsat input scenes from the same season and that abrupt events such as fires or floods between input and prediction date can decrease the accuracy.…”

Section: Added Value Of the Estarfm Framework For Cloud-prone Areassupporting

confidence: 86%

“…First of all, the availability of Landsat input data has a great influence on the quality of the fusion. Minimizing the temporal distance of the shoulder pairs to the target scene is crucial for a successful prediction [3,32] and it became evident that using at least one shoulder pair from the same phenological stage as the prediction date has even higher positive influence (Figure 12).…”

Section: Uncertainties In the Prediction With Estarfmmentioning

confidence: 99%

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An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes

et al. 2016

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Abstract:Monitoring the spatio-temporal development of vegetation is a challenging task in heterogeneous and cloud-prone landscapes. No single satellite sensor has thus far been able to provide consistent time series of high temporal and spatial resolution for such areas. In order to overcome this problem, data fusion algorithms such as the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) have been established and frequently used in recent years to generate high-resolution time series. In order to make it applicable to larger scales and to increase the input data availability especially in cloud-prone areas, an ESTARFM framework was developed in this study introducing several enhancements. An automatic filling of cloud gaps was included in the framework to make best use of available, even partly cloud-covered Landsat images. Furthermore, the ESTARFM algorithm was enhanced to automatically account for regional differences in the heterogeneity of the study area. The generation of time series was automated and the processing speed was accelerated significantly by parallelization. To test the performance of the developed ESTARFM framework, MODIS and Landsat-8 data were fused for generating an 8-day NDVI time series for a study area of approximately 98,000 km 2 in West Africa. The results show that the ESTARFM framework can accurately produce high temporal resolution time series (average MAE (mean absolute error) of 0.02 for the dry season and 0.05 for the vegetative season) while keeping the spatial detail in such a heterogeneous, cloud-prone region. The developments introduced within the ESTARFM framework establish the basis for large-scale research on various geoscientific questions related to land degradation, changes in land surface phenology or agriculture.

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Section: Added Value Of the Estarfm Framework For Cloud-prone Areassupporting

confidence: 86%

Section: Uncertainties In the Prediction With Estarfmmentioning

confidence: 99%

An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes

et al. 2016

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“…Soil tillage intensity maps for the South Fork watershed by year ([a, d] 2009, [b, e, g] 2010, and [c, f, h] (Gao et al 2010(Gao et al , 2015. The fused Landsat-MODIS images capture temporal variations possible with MODIS and show spatial details that can only be observed at Landsat spatial resolution.…”

Section: Figurementioning

confidence: 99%

Multispectral satellite mapping of crop residue cover and tillage intensity in Iowa

Beeson¹,

Daughtry²,

Hunt³

et al. 2016

Journal of Soil and Water Conservation

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Soil tillage intensity can be characterized by the proportion of the soil surface covered by crop residue shortly after planting, with <15% residue cover classified as intensive or conventional tillage, 15% to 30% residue cover as reduced tillage, and >30% residue cover as conservation tillage (CTIC 2009). Accurate assessments of crop residue cover are important for evaluating effectiveness of conservation practices because of the numerous ecosystem services (e.g., decreased soil erosion, increased soil organic matter, improved soil and water quality, and reduced carbon dioxide [CO 2 ] emissions) that crop residues on the soil surface influence (Daughtry et al. 2012;Lal et al. 2004).Accurate estimates of crop residue cover are also important for modeling water qualAbstract: Quantifying crop residue cover is crucial for identifying tillage intensity and evaluating effectiveness of conservation management practices across large geographic areas. Current assessment protocols are labor intensive, time consuming, and costly. Our objective was to assess crop residue cover and soil tillage intensity in a watershed in central Iowa for three years (2009 to 2011) using multispectral satellite images. The watershed is dominated by corn (Zea mays L.) and soybean (Glycine max [L.] Merr.), which are grown on glacial-till derived soils across 85% of the land area. For each year, crop residue cover was measured for a few fields using the line-point transect method or visually estimating surface cover through roadside surveys. Conservation tillage fields had ≥30% residue cover, while more intensively tilled fields had <30% residue cover. Landsat Thematic Mapper (TM), Système Pour l'Observation de la Terre (SPOT) High Resolution Geometrical (HRG), Indian ResourceSat Advanced Wide Field Sensor (AWiFS), and Deimos satellite images were also acquired and analyzed to determine surface cover. SPOT and Landsat images provided similar classification accuracy ranging from 64% to 92%, while AWiFS and Deimos classifications had accuracies ranging from 61% to 73%. Clouds and the revisit interval for each satellite affected the timing of satellite images with respect to field operations, which also influenced classification accuracy. Overall soil tillage intensity varied little from year-to-year. Soil tillage intensity was also mapped as a function of slope, which could be useful for targeting additional conservation practices throughout the watershed. We conclude that satellite imagery is well suited for classifying crop residue cover. Furthermore, two recently launched satellites, Landsat-8 and Sentinel-2, have comparable multispectral sensors and together these satellites should provide frequent opportunities to acquire suitable imagery for assessing crop residue cover and soil tillage intensity over large geographic areas.Key words: conservation agriculture-conservation tillage-maize-remote sensing ity and carbon (C) dynamics within the soil-crop-atmosphere system. Agroecosystem models have been developed for evaluating nutrient losses ...

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“…Hilker et al [8] proposed the STAARCH algorithm to use the tasseled cap transform of both Landsat's Thematic Mapper and Enhanced Thematic Mapper Plus (TM/ETM+) and MODIS reflectance data to identify spatial and temporal changes in the landscape with a high level of detail. Performance of these three methods was compared in [20]. Separately, Wu [9] proposed a spatiotemporal data fusion algorithm (STDFA) by mapping medium-resolution spatial images to extract fractional covers, using least square for fractional cover, and calculating surface reflectance using a surface reflectance calculation model.…”

Section: Methods Of Spatiotemporal Fusionmentioning

confidence: 99%

Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

et al. 2016

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Fusion of remote sensing images with different spatial and temporal resolutions is highly needed by diverse earth observation applications. A small number of spatiotemporal fusion methods using sparse representation appear to be more promising than traditional linear mixture methods in reflecting abruptly changing terrestrial content. However, one of the main difficulties is that the results of sparse representation have reduced expressional accuracy; this is due in part to insufficient prior knowledge. For remote sensing images, the cluster and joint structural sparsity of the sparse coefficients could be employed as a priori knowledge. In this paper, a new optimization model is constructed with the semi-coupled dictionary learning and structural sparsity to predict the unknown high-resolution image from known images. Specifically, the intra-block correlation and cluster-structured sparsity are considered for single-channel reconstruction, and the inter-band similarity of joint-structured sparsity is considered for multichannel reconstruction, and both are implemented with block sparse Bayesian learning. The detailed optimization steps are given iteratively. In the experimental procedure, the red, green, and near-infrared bands of Landsat-7 and Moderate Resolution Imaging Spectrometer (MODIS) satellites are put to fusion with root mean square errors to check the prediction accuracy. It can be concluded from the experiment that the proposed methods can produce higher quality than state-of-the-art methods.

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Fusing Landsat and MODIS Data for Vegetation Monitoring

Cited by 239 publications

References 35 publications

An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes

An ESTARFM Fusion Framework for the Generation of Large-Scale Time Series in Cloud-Prone and Heterogeneous Landscapes

Multispectral satellite mapping of crop residue cover and tillage intensity in Iowa

Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning

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