Gap-Filling of a MODIS Normalized Difference Snow Index Product Based on the Similar Pixel Selecting Algorithm: A Case Study on the Qinghai–Tibetan Plateau

Li, Muyi; Zhu, Xiufang; Li, Nan; Pan, Yaozhong

doi:10.3390/rs12071077

Cited by 15 publications

(4 citation statements)

References 76 publications

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“…The ability to produce gap-free Landsat time series in a simple and efficient way can significantly improve remote-sensing applications for land-surface characterization, analysis, and monitoring [1]. Although there are several image reconstruction methods proposed to fill Landsat 7 SLC-off [73] and CCS gaps [74,75], methods that are simple to tune on a global scale, fast in computation, and robust in producing a time series of gap-free imagery are still scant [76,77]. The newly published method, Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM), has demonstrated high performance in predicting large-area gaps with respect to spatially heterogeneous land-cover areas and has the potential to generate gap-free Landsat time series [18].…”

Section: Discussionmentioning

confidence: 99%

Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series

et al. 2021

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The number of Landsat time-series applications has grown substantially because of its approximately 50-year history and relatively high spatial resolution for observing long term changes in the Earth’s surface. However, missing observations (i.e., gaps) caused by clouds and cloud shadows, orbit and sensing geometry, and sensor issues have broadly limited the development of Landsat time-series applications. Due to the large area and temporal and spatial irregularity of time-series gaps, it is difficult to find an efficient and highly precise method to fill them. The Missing Observation Prediction based on Spectral-Temporal Metrics (MOPSTM) method has been proposed and delivered good performance in filling large-area gaps of single-date Landsat images. However, it can be less practical for a time series longer than one year due to the lack of mechanics that exclude dissimilar data in time series (e.g., different phenology or changes in land cover). To solve this problem, this study proposes a new gap-filling method, Spectral Temporal Information for Missing Data Reconstruction (STIMDR), and examines its performance in Landsat reflectance time series. Two groups of experiments, including 2000 × 2000 pixel Landsat single-date images and Landsat time series acquired from four sites (Kenya, Finland, Germany, and China), were performed to test the new method. We simulated artificial gaps to evaluate predicted pixel values with real observations. Quantitative and qualitative evaluations of gap-filled images through comparisons with other state-of-the-art methods confirmed the more robust and accurate performance of the proposed method. In addition, the proposed method was also able to fill gaps contaminated by extreme cloud cover for a period (e.g., winter in high-latitude areas). A down-stream task of random forest supervised classification through both gap-filled simulated datasets and the original valid datasets verified that STIMDR-generated products are relevant to the user community for land cover applications.

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Section: Discussionmentioning

confidence: 99%

Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series

et al. 2021

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“…Microwave products are weather-independent but have lower spatial resolution, while visible light products offer higher resolution but are weather-sensitive. Among these, MODIS snow products are favored for their accuracy, high spatial and temporal resolution, free availability, and global coverage [13,14].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

Pu,

Zhou,

Sun

et al. 2024

Water

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The variations in snow cover on the Tibetan Plateau play a pivotal role in comprehending climate change patterns and governing hydrological processes within the region. This study leverages daily snow cover data and the NASA Digital Elevation Model (DEM) from 2003 to 2020 to analyze spatiotemporal snow cover days and assess their responsiveness to climatic shifts by integrating meteorological data. The results reveal significant spatial heterogeneity in snow cover across the Plateau, with a slight decreasing trend in annual average snow cover duration. Snow cover is predominantly observed during the spring and winter seasons, constituting approximately 32% of the total snow cover days annually. The onset and cessation of snow cover occur within a range of 120–220 days. Additionally, an increasing trend in snow cover duration below 5000 m altitude was observed, in addition to a decreasing trend above 5000 m altitude. Sub-basin analysis delineates the Tarim River Basin as exhibiting the lengthiest average annual snow cover duration of 83 days, while the Yellow River Basin records the shortest duration of 31 days. The decreasing trend in snow cover duration closely aligns with climate warming trends, characterized by a warming rate of 0.17 ± 0.54 °C per decade, coupled with a concurrent increase in precipitation at a rate of 3.09 ± 3.81 mm per year. Temperature exerts a more pronounced influence on annual snow cover duration variation compared to precipitation, as evidenced by a strong negative correlation (CC = −0.67). This study significantly augments the comprehension of hydrological cycle dynamics on the Tibetan Plateau, furnishing essential insights for informed decision-making in water resource management and ecological conservation efforts.

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“…Remote Sens. 2023, 15, 5122 2 of 16 Gap-filling approaches can be divided into two categories: (i) temporal approaches [10][11][12] and (ii) spatial approaches [13][14][15][16]. Temporal approaches are based on looking for the time series corresponding to a selected pixel in a satellite image and using this information to estimate the unknown pixel value.…”

Section: Introductionmentioning

confidence: 99%

A Modified Version of the Direct Sampling Method for Filling Gaps in Landsat 7 and Sentinel 2 Satellite Imagery in the Coastal Area of Rhone River

Farhat,

Manakos,

Sylaios

et al. 2023

Remote Sensing

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Earth Observation (EO) data, such as Landsat 7 (L7) and Sentinel 2 (S2) imagery, are often used to monitor the state of natural resources all over the world. However, this type of data tends to suffer from high cloud cover percentages during rainfall/snow seasons. This has led researchers to focus on developing algorithms for filling gaps in optical satellite imagery. The present work proposes two modifications to an existing gap-filling approach known as the Direct Sampling (DS) method. These modifications refer to ensuring the algorithm starts filling unknown pixels (UPs) that have a specified minimum number of known neighbors (Nx) and to reducing the search area to pixels that share similar reflectance as the Nx of the selected UP. Experiments were performed on images acquired from coastal water bodies in France. The validation of the modified gap-filling approach was performed by imposing artificial gaps on originally gap-free images and comparing the simulated images with the real ones. Results indicate that satisfactory performance can be achieved for most spectral bands. Moreover, it appears that the bi-layer (BL) version of the algorithm tends to outperform the uni-layer (UL) version in terms of overall accuracy. For instance, in the case of B04 of an L7 image with a cloud percentage of 27.26%, accuracy values for UL and BL simulations are, respectively, 64.05 and 79.61%. Furthermore, it has been confirmed that the introduced modifications have indeed helped in improving the overall accuracy and in reducing the processing time. As a matter of fact, the implementation of a conditional filling path (minNx = 4) and a targeted search (n2 = 200) when filling cloud gaps in L7 imagery has contributed to an average increase in accuracy of around 35.06% and an average gain in processing time by around 78.18%, respectively.

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Gap-Filling of a MODIS Normalized Difference Snow Index Product Based on the Similar Pixel Selecting Algorithm: A Case Study on the Qinghai–Tibetan Plateau

Cited by 15 publications

References 76 publications

Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series

Spectral Temporal Information for Missing Data Reconstruction (STIMDR) of Landsat Reflectance Time Series

Spatiotemporal Variations in Snow Cover on the Tibetan Plateau from 2003 to 2020

A Modified Version of the Direct Sampling Method for Filling Gaps in Landsat 7 and Sentinel 2 Satellite Imagery in the Coastal Area of Rhone River

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