An Algorithm for Subpixel Snow Mapping: Extraction of a Fractional Snow-Covered Area Based on Ten-Day Composited AVHRR\/2 Data of the Qinghai-Tibet Plateau

Zhu, Ji; Shi, Jiancheng

doi:10.1109/mgrs.2018.2850963

Cited by 9 publications

(8 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As clouds identified in the QA index of AVHRR/2 5 km data would be overestimated [34], the MESMA and TEM algorithms could not select effective snow endmembers. Therefore, Hao's [33] cloud identification algorithm and relevant thresholds were selected for cloud identification.…”

Section: Results and Evaluation In High Mountain Asiamentioning

confidence: 99%

“…Therefore, Hao's [33] cloud identification algorithm and relevant thresholds were selected for cloud identification. In addition, there is no corresponding cloud identification product for AVHHRR/2 1 km data, so the cloud identification algorithm and relevant thresholds of Zhu [34] were selected for cloud identification. To better reflect the spatial distribution of the three fractional-snow-cover retrieval algorithm results at different scales, one AVHRR/2 scene with a large snow-cover area and less cloud cover was selected as an example.…”

Section: Results and Evaluation In High Mountain Asiamentioning

confidence: 99%

“…The MESMA method is a highly accurate method for estimating fractional snow cover in MODIS [30,33,34], which selects vegetation and soil/rock endmembers from the spectral database obtained from laboratory and field measurements and extracts snow endmembers with different grain sizes and incident-observation geometries using radiative transfer models. This algorithm uses the multi-endmember spectral mixture analysis method to calculate the fractional snow cover, which has high precision but requires many computations.…”

Section: Non-snow/snow Two Endmember Model (Tem) Algorithmmentioning

confidence: 99%

“…Shi [35] proposed an automatic endmember extraction algorithm based on MODIS reflectivity images, which automatically selects endmembers from images using NDVI, NDSI, and other indices. Zhu [34] improved this method by using typical or neighbouring endmembers and solving them according to a two-endmember hybrid analysis model to achieve faster, high-precision, automated fractional-snow-cover calculation. Since the AVHRR/2 1 km data did not have reflectivity data in the 3.75 µm band, Zhu [30] could use only the two-endmember hybrid analysis model.…”

Section: Non-snow/snow Two Endmember Model (Tem) Algorithmmentioning

confidence: 99%

“…Metsamaki [16,31] used a model of snow reflectance in forest areas, which was an improvement over the linear interpolation algorithm. Zhu [34] improved an automatic endmember extraction algorithm proposed by Shi [35], based on MODIS reflectivity imagery, and applied it to AVHRR/2 1 km data. This algorithm automatically selects endmembers from images using indices such as NDVI and NDSI and retrieves the fractional snow cover using a linear spectral mixture analysis algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Retrieval of Fractional Snow Cover over High Mountain Asia Using 1 km and 5 km AVHRR/2 with Simulated Mid-Infrared Reflective Band

et al. 2022

View full text Add to dashboard Cite

Accurate long-term snow-covered-area mapping is essential for climate change studies and water resource management. The NOAA AVHRR/2 provides a unique data source for long-term, large-spatial-scale monitoring of snow-covered areas at a daily scale. However, the value of AVHRR/2 in mapping snow-covered areas is limited, due to its lack of a shortwave infrared band for snow/cloud discrimination. We simulated the reflectance in the 3.75 µm mid-infrared band with a radiative transfer model and then developed three fractional-snow-cover retrieval algorithms for AVHRR/2 imagery at 1 km and 5 km resolutions. These algorithms are based on the multiple endmember spectral mixture analysis algorithm (MESMA), snow index (SI) algorithm, and non-snow/snow two endmember model (TEM) algorithm. Evaluation and comparison of these algorithms were performed using 313 scenarios that referenced snow-cover maps from Landsat-5/TM imagery at 30 m resolution. For all the evaluation data, the MESMA algorithm outperformed the other two algorithms, with an overall accuracy of 0.84 (0.85) and an RMSE of 0.23 (0.21) at the 1 km (5 km) scale. Regarding the effect of land cover type, we found that the three AVHRR/2 fractional-snow-cover retrieval algorithms have good accuracy in bare land, grassland, and Himalayan areas; however, the accuracy decreases in forest areas due to the shading of snow by the canopy. Regarding the topographic effect, the accuracy evaluation indices showed a decreasing and then increasing trend as the elevation increased. The accuracy was worst in the 4000–5000 m range, which was due to the severe snow fragmentation in the High Mountain Asia region; the early AVHRR/2 sensors could not effectively monitor the snow cover in this region. In this study, by increasing the number of bands of AVHRR/2 1 km data for fractional-snow-cover retrieval, a good foundation for subsequent long time series kilometre- resolution snow-cover monitoring has been laid.

show abstract

Section: Results and Evaluation In High Mountain Asiamentioning

confidence: 99%

Section: Results and Evaluation In High Mountain Asiamentioning

confidence: 99%