Enhancing MODIS land cover product with a spatial–temporal modeling algorithm

Cai, Shanshan; Liu, Desheng; Sulla‐Menashe, Damien; Friedl, M. A.

doi:10.1016/j.rse.2014.03.012

Cited by 78 publications

(51 citation statements)

References 34 publications

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“…With the rapid progress of remote sensing techniques, the need for remotely sensed images with high temporal, spatial, and spectral resolution has increased [1][2][3]. In particular, remotely sensed images with high spatial resolution and frequent coverage are needed in the monitoring of global biophysical processes, which change quickly during growing season.…”

Section: Introductionmentioning

confidence: 99%

A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China

Liu

et al. 2018

Remote Sensing

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Satellite data for studying surface dynamics in heterogeneous landscapes are missing due to frequent cloud contamination, low temporal resolution, and technological difficulties in developing satellites. A modified spatiotemporal fusion algorithm for predicting the reflectance of paddy rice is presented in this paper. The algorithm uses phenological information extracted from a moderate-resolution imaging spectroradiometer enhanced vegetation index time series to improve the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). The algorithm is tested with satellite data on Yueyang City, China. The main contribution of the modified algorithm is the selection of similar neighborhood pixels by using phenological information to improve accuracy. Results show that the modified algorithm performs better than ESTARFM in visual inspection and quantitative metrics, especially for paddy rice. This modified algorithm provides not only new ideas for the improvement of spatiotemporal data fusion method, but also technical support for the generation of remote sensing data with high spatial and temporal resolution.

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Section: Introductionmentioning

confidence: 99%

A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China

Liu

et al. 2018

Remote Sensing

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“…Its effects are wide-reaching on both human and natural systems (Turner et al, 2007). To better understand this trend, we require multi-temporal land cover products that are detailed, accurate, and spatialtemporally consistent Cai et al, 2014). The current generation of products (Table 1) does not fulfill these requirements because it was largely created using methods that are designed for the classification of single date imagery and typically created using coarse resolution data sources.…”

Section: Introductionmentioning

confidence: 99%

A spatial–temporal contextual Markovian kernel method for multi-temporal land cover mapping

Wehmann

Liu

2015

ISPRS Journal of Photogrammetry and Remote Sensing

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“…The index t * can be simulated by drawing a random number from { } 1,2,...,n . In addition, the simulated land-cover changes were assumed to obey the rules of a logical transition as defined in Cai et al [31]. For instance, the simulated time series represent landcover changes such as forest disturbance (e.g., forest → grassland) or human activities (e.g., grassland → cropland).…”

Section: Croplands/natural Vegetation Mosaicmentioning

confidence: 99%

Detecting Change Dates from Dense Satellite Time Series Using a Sub-Annual Change Detection Algorithm

Cai

Liu

2015

Remote Sensing

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Multi-temporal satellite images are available at very high revisit frequency, allowing the characterization of land-cover transitions and trajectories in greater detail. However, most change detection methods aim to capture a snapshot of land cover, for instance on an annual scale, which do not describe changes that occurred between the annual time points. In this study, we present a sub-annual change detection (SCD) approach to detect change dates from dense satellite time series. SCD estimates change dates by analyzing differences between two consecutive annual segments in two steps. To validate the proposed method, SCD was applied to real and simulated time series of MODIS 16-day NDVI from 2000 to 2012 for MODIS tile h11v05 and a sub-area in southeast Ohio, USA. The results show that SCD can successfully detect from dense time series the dates of changes representing land-cover transitions among various vegetated land covers. In addition, SCD can achieve comparable accuracy on the date of abrupt land-cover changes for the real time series tested in the study area when compared with the trend and seasonal changes identified by a Breaks for Additive Seasonal and Trend (BFAST) method. Future effort will be given to apply the proposed approach to various remotely sensed time series for other areas with extreme climates.

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Enhancing MODIS land cover product with a spatial–temporal modeling algorithm

Cited by 78 publications

References 34 publications

A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China

A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China

A spatial–temporal contextual Markovian kernel method for multi-temporal land cover mapping

Detecting Change Dates from Dense Satellite Time Series Using a Sub-Annual Change Detection Algorithm

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