High spatial-and temporal-resolution NDVI produced by the assimilation of MODIS and HJ-1 data

Cai, Wenju; Song, Jinling; Wang, Jindi; Xiao, Zhiqiang

doi:10.5589/m12-004

Cited by 18 publications

(6 citation statements)

References 2 publications

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“…The prior information forms a constant NDVI time series for each land-cover type, but there are substantial variations within the same land-cover type at different locations due to the diverse genetic traits of vegetation and environmental variation [21]. This is why the CC method [16], which uses the prior information directly as background values, can seldom obtain high accuracy. Thus, from this perspective, using the unmixed estimators to update the prior information has a significant effect.…”

Section: Discussionmentioning

confidence: 99%

“…For example, a Kalman filter-based method was proposed to generate continuous time series of Landsat images and further used for regional winter wheat yield estimation [30,31]. Moreover, a previous study [16] presented by our research group took the multi-year average NDVI calculated from MODIS "pure" (homogeneous) pixels for each land-cover type within the application region as the background value, and the Landsat NDVI as the observations, where NDVI time series images with Landsat spatial details and MODIS temporal resolution could be generated by applying a Continuous Correction (CC) method. The background value used in the CC method was stable and independent of the MODIS NDVI on the prediction date, thereby contributing to better results compared with other models when the MODIS NDVI acquired on the prediction date were of poor quality, but normally, this also causes significant losses of detailed information contained in the MODIS NDVI on the prediction date.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI

et al. 2016

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Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM) for accurately and effectively building frequent high spatial resolution Landsat-like NDVI datasets by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat NDVI. Experimental comparisons with the results obtained using other popular methods (i.e., 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 (FSDAF) method) showed that our proposed method has the following advantages: (1) it can obtain more accurate estimates; (2) it can retain more spatial detail; (3) its prediction accuracy is less dependent on the quality of the MODIS NDVI on the specific prediction date; and (4) it produces smoother NDVI time series profiles. All of these advantages demonstrate the strengths and the robustness of the proposed NDVI-BSFM in providing reliable high spatial and temporal resolution NDVI datasets to support other land surface process studies.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI

et al. 2016

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“…Moreover, the application of Ref-BSFM is not restricted to OLI and MODIS sensors. It was shown that using this method to fuse MODIS and Sentinel-2 MSI sensors images provides good results [41][42][43][44][45][46][47][48][49][50][51]; therefore, Ref-BSFM can be extended for use on a variety of remote sensing satellite platforms.…”

Section: Discussionmentioning

confidence: 99%

Reconstruction of High-Temporal- and High-Spatial-Resolution Reflectance Datasets Using Difference Construction and Bayesian Unmixing

Yang

Song

Han³

et al. 2020

Remote Sensing

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High-temporal- and high-spatial-resolution reflectance datasets play a vital role in monitoring dynamic changes at the Earth’s land surface. So far, many sensors have been designed with a trade-off between swath width and pixel size; thus, it is difficult to obtain reflectance data with both high spatial resolution and frequent coverage from a single sensor. In this study, we propose a new Reflectance Bayesian Spatiotemporal Fusion Model (Ref-BSFM) using Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance, which is then used to construct reflectance datasets with high spatiotemporal resolution and a long time series. By comparing this model with other popular reconstruction methods (the Flexible Spatiotemporal Data Fusion Model, the Spatial and Temporal Adaptive Reflectance Fusion Model, and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model), we demonstrate that our approach has the following advantages: (1) higher prediction accuracy, (2) effective treatment of cloud coverage, (3) insensitivity to the time span of data acquisition, (4) capture of temporal change information, and (5) higher retention of spatial details and inconspicuous MODIS patches. Reflectance time-series datasets generated by Ref-BSFM can be used to calculate a variety of remote-sensing-based vegetation indices, providing an important data source for land surface dynamic monitoring.

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“…According to Phat et al (2004), the 21st century brought new challenges to forest management and forest ecosystems. This is potentially an extremely important tool for dealing with climate change, in addition to improving human actions (Cai et al, 2011). Policymakers and scientists need to know the spatial dimensions of land use and land use consistently in order to be sufficiently prepared to make informed decisions on land resources.…”

Section: Introductionmentioning

confidence: 99%

Land Use/Land Cover Changes of Ago-Owu Forest Reserve, Osun State, Nigeria Using Remote Sensing Techniques

Aderele¹,

Bola²,

Oke³

2020

OJF

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Remote sensing (RS) and GIS are important methods for land use assessment and land cover transition. In this study, land use/land cover changes in the Ago-Owu Forest Reserve, Osun State, Nigeria have been assessed. Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI were acquired for 1986, 2002 and 2017 respectively. The three scenes corresponded to path 190 and row 055 of WRS-2 (Worldwide Reference System). The processing of the imagery was preceded by the clipping of the study area from the satellite image. The boundary of the reserve was carefully digitized and used to clip the imagery to produce an image map of the forest reserve. Using the supervised image classification procedure, training sites were used to produce land use/land cover maps. The same classification scheme was used for the 1986, 2002 and 2017 images to facilitate the detection of change. The differences in the area covered by the different polygons between the three sets of images were measured in km 2. The results show that during 1986 and 2017, there is a dramatic increase of build-up areas with a change of 55.65 km 2 and sparse vegetation (farmland and grassland) with a change of 53.97 km 2 , while a dramatic decrease of dense vegetation (forest areas) with a change of 109.61 km 2. The consequence of these results is that over the years, the population of people living in the forest reserve has increased and many of them are engaged in farming, leading to an increase in farmland. In addition, logging activities continued unabated in the forest reserve, as demonstrated by a sharp increase in the deforested area within the reserve. The maps produced in this study will serve as a planning tool for the Osun State Forestry Department to plan reforestation activities for the forest reserve.

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High spatial-and temporal-resolution NDVI produced by the assimilation of MODIS and HJ-1 data

Cited by 18 publications

References 2 publications

Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI

Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI

Reconstruction of High-Temporal- and High-Spatial-Resolution Reflectance Datasets Using Difference Construction and Bayesian Unmixing

Land Use/Land Cover Changes of Ago-Owu Forest Reserve, Osun State, Nigeria Using Remote Sensing Techniques

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