Evaluation of the SPA Algorithm and the Feasibility of using MM5 Model Output to Estimate the Cloud Gaps in MODIS LST Images

جغرافیا و مخاطرات محیطی

Article 8, Volume 8, Issue 2 - Serial Number 30, October 2018, Pages 133-147   

Document Type: Research Article

DOI: 10.22067/geo.v0i0.65717

Authors

Negar Siabi; Seyed Hossein Sanaeinejad^* ; Bijan Ghahraman

Ferdowsi university of Mashhad

Abstract

1 Introduction
Land Surface Temperature (LST) is an important parameter in controlling surface heat and water exchange with the atmosphere (Li, Tang, Wu, Ren, Yan, Wan, Trigo, & Sobrino, 2013). Remote sensing images are now one of the most important data sources for estimating LST (Hengl, Heuvelink, Perˇcec Tadi´c, & Pebesma, 2012). But the presence of pollutants in the air, cloudiness and failure of the sensors result in the huge data loss, which is called image gaps. So far, several methods have been proposed to estimate the missing values. These methods are divided into three categories: spatial, temporal or spatio-temporal. Time-based methods for estimating missing data are mathematical calculations, the most famous of which are Savitzky and Golay filters (1964). In addition to simple solutions, more sophisticated methods such as Brooks, Thomas, Wynne, and Coulston )2012) and harmonic analysis of Zhou, Jia, and Menenti )2015) were also introduced and implemented on various remote sensing products. Simple interpolation methods such as the nearest neighborhood, SP-Line method, and Inverse Distance Weighing (IDW) are spatial methods. In most of these algorithms, the weighted average is used. Compared to the simple interpolation methods, the approaches that utilize auxiliary data are more of a researcher's interest. Chen, Zhu, Vogelmann, Gao, and Jin (2011) proposed a neighborhood Similar Pixel Interpolation method (NSPI). Zhu, Liu, and Chen (2012) presented the improved NSPI method named Geostatistical Neighborhood Similarity Pixel Interpolation using geostatistics. Gerber, de Jong, Schaepman, Schaepman-Strub, and Furrer (2018) proposed a spatio-temporal algorithm based on subsetting method for estimating the missing values of MODIS NDVI data. The estimation results are influenced by factors such as the structure of the algorithm, the used scenarios, the type of variable, and the region of study. Finding the right fitting image on cloudy days or providing a long time series are also among the most important factors influencing the results of these methods Kandasamy, Baret, Verger, Neveux, and Weiss (2013). To solve this problem, time series from other satellites are mostly used. These images may differ with the target images in terms of spectral structure and have negative effect on the algorithms performance. Only a few researches like Jang, Kang, Kim, Lee, Kim, Kim, Hirata (2010) have used numerical prediction models outputs in producing continuous spatial-temporal remote sensing data. They concluded that the outputs of numerical prediction models could be used on cloudy days.
The aim of this study is to evaluate the approach proposed by Gerber, de Jong, Schaepman, Schaepman-Strub, and Furrer (2018) to reconstruct MODIS LST images and also to study the feasibility of using MM5 model outputs as an auxiliary data for estimating missing values of the images.
2 Materials and Methods
The study area is North Khorasan, Khorasan Razavi, and Southern Khorasan provinces, northeast of Iran, located between 55 to 61 degrees east and 30 to 38 degrees north. The total area of the region is 313,000 square kilometers and the overall climate is semi-arid to dry Ahmadian, Sheibani, Araqi, Shirmohammadi, and Mojarad (2001).
Two types of data were used in this study. 1- LST images, which are produced by Level 3 MODIS (MOD11A2) with a spatial resolution of one square kilometer and a time interval of 8 days. 2- MM5 model Output data, which were images with spatial resolution of 0.5 × 0.5 degrees for the period of 2000-2010. The data was prepared based on the latitude and longitude of the study area from NASA and NOAA internet pages. In the present study, the Subset-Predict Algorithm (SPA) proposed by Gerber et al. (2018) was selected as the basic method. They used the spatio-temporal approach to estimate the missing values of remote sensing images. This approach  is suitable for a data set with a four-dimensional array structure. In this approach, the missing values are predicted in two main steps: 1. Subset, 2. Forecasting lost values based on subsets. This approach was implemented for MODIS LST images. MATLAB software was used to do this. The inputs of the algorithm were an original image and a series of auxiliary images. In this research, two types of tests were designed to estimate missing values. The first test was performed using time series information of LST MODIS and the second test using MM5 output data. In this study, the Root Mean Square Error (RMSE), Mean Difference (AD), and Determination Coefficient (R2) were used to evaluate the performance of the SPA approach in two different situations.
3 Results and Discussion
The results of simulations show that the SPA method has a good accuracy. The average value of the obtained error is 1.487 degrees Celsius. Meanwhile, Kilibarda, Hengl, Heuvelink, Gräler, Pebesma, Perčec Tadić, and Bajat (2014) reported a mean error of ± 2.5 degrees Celsius in reconstruction of LST images of 2011. Implementing SPA algorithm with the MM5 outputs is less accurate than test 1. This can be due to the uncertainty of the MM5 model in predicting the surface temperature. The visual test of the images showed that the spatial pattern of the LST trend was preserved in estimating the missing values, and the algorithm did not impose artificial pattern on the images. This algorithm has been able to easily reset missing values in most places by maintaining a spatial pattern on the edges and also inside the gaps. Only in the quartile section of the right and above the gap area, the temperature pattern was different from the original LST image. Moreover, in this case, using the MM5 model output, the spatial pattern reflects the temperature trend better than the remote sensing time series.
The spatial distribution map of the mean error in the gap region showed that in most pixels, the error value is in the range of 0 to 2 degrees Celsius. Also, an error of more than 6 degrees Celsius is seen in a small number of pixels in the center of the gap. This may be due to the structure of the method and the subsetting in the neighborhoods or because of the extreme changes in the topography in the area. In contrast to the approach proposed by Chan and Shen (2001), SPA method was accurately simulating missing values at both edges. Given that the maximum error location is in both the center of the gap center, there is likely to be a structural problem in the SPA algorithm that needs further investigation.
Validation of the method revealed that test 1 RMSE value is less than test 2. This means that the accuracy of the algorithm is greater in test 1. According to the AD index, in both cases, the SPA algorithm underestimated the missing values. Also, the implementation of the algorithm with the test 1 scenarios with the correlation coefficient was 0.8% more than test 2.
4 Conclusion
In this study, the spatio-temporal SPA method proposed by Gerber et al. (2018) was used to estimate the missing values of MODIS LST and image reconstruction in the years 2000-2010 in the north east of Iran. The results of the SPA method in both cases showed that the chosen method was able to accurately estimate the missing values. They also showed that the obtained error values were within the acceptable range in the temperature data (Ferguson & Wood, 2010). Implementing the SPA algorithm was also less accurate than test 1 with the help of MM5 output maps. The algorithm did not impose an artificial pattern on the images. This algorithm has been able to retrieve missing values by maintaining a spatial pattern on the edges and also inside the gap. On the contrary, many of the existing documentation methods, such as Chen et al. (2004), can't estimate all of the missing pixels.
Error spatial distribution maps show that the highest simulation error relates to a number of pixels in the central region of the gap. The results of this study showed that in the absence of sufficient information for temperature in a region, data from the MM5 model can be used to fill in the missing data pixels and maintain the spatio-temporal continuity of the remote sensing images.

Keywords

Algorithm; Cloudiness; LST; Missing data; MM5

References

Addink, E.A., 1999. A comparison of conventional and geostatistical methods to replace clouded pixels in NOAA–AVHRR images. International Journal of Remote Sens, 20: 961– 977.

Ahmadian, J., Sheibani, D., Iraqi, H., Shirmohammadi, R., & Mojarad, M., 2002. agricultural Classification of climate for sustainable water resources management in developing countries. P. 593-605. Eleventh Meeting of the National Committee on Irrigation and Drainage, January 2002, Tehran, Iran.

Boegh, E., Soegaard, H., Christensen, J.H., Hasager, C.B., Jensen, N.O., Nielsen, N.W. and Rasmussen, M.S., 2004. Combining weather prediction and remote sensing data for the calculation of evapotranspiration rates: application to Denmark. International Journal of Remote Sensing, 25(13): 2553-2574.

Brooks, E.B., Thomas, V.A., Wynne, R.H. and Coulston, J.W., 2012. Fitting the multitemporal curve: A Fourier series approach to the missing data problem in remote sensing analysis. IEEE Transactions on Geoscience and Remote Sensing, 50(9): 3340-3353.

Chan, T., and Shen, J., 2001. Nontexture inpainting by curvature-driven diffusions. Journal of Visual Communication and Image Representation, 4: 436-449

Chen, J., Jönsson, P., Tamura, M., Gu, Z., Matsushita, B. and Eklundh, L., 2004. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote sensing of Environment, 91(3): 332-344.

Chen, J., Zhu, X., Vogelmann, J.E., Gao, F. and Jin, S., 2011. A simple and effective method for filling gaps in Landsat ETM+ SLC-off images. Remote Sensing of Environment, 115(4): 1053-1064

Ferguson, C.R. and Wood, E.F., 2010. An evaluation of satellite remote sensing data products for land surface hydrology: Atmospheric infrared sounder. Journal of Hydrometeorology, 11(6): 1234-1262.

Gao, Y. and Mas, J.F., 2008. A comparison of the performance of pixel-based and object-based classifications over images with various spatial resolutions. Online journal of earth sciences, 2(1): 27-35.

Gerber, F., Furrer, R., Schaepman-Strub, G., de Jong, R. and Schaepman, M.E., 2016. Predicting missing values in spatio-temporal satellite data. arXiv preprint arXiv:1605.01038.

Grell G A, Dudhia J, Staurer F R. 1995. A description of the fifth-generation Penn State/NCAR mesoscale model (MM5). NCAR Tech Note NCAR/TN- 398 +STR. 122, Boulder, Colorado [EB/OL]. http://www.mmm.ucar.edu/mm5/doc1.html/2003-07-19

Hengl, T.G.B., Heuvelink, M. Perˇcec Tadi´c, and E.J. Pebesma. 2012. Spatio-temporal prediction of daily temperatures using time-series of MODIS LST images. Journal of Theor. Appl. Climatol, 107: 265–277.

Jang, K., Kang, S., Kim, J., Lee, C.B., Kim, T., Kim, J., Hirata, R. and Saigusa, N., 2010. Mapping evapotranspiration using MODIS and MM5 four-dimensional data assimilation. Remote Sensing of Environment, 114(3): 657-673.

Kandasamy, S., Baret, F., Verger, A., Neveux, P. and Weiss, M., 2013. A comparison of methods for smoothing and gap filling time series of remote sensing observations–application to MODIS LAI products. Biogeosciences, 10(6): 4055-4071.

Kilibarda, M., Hengl, T., Heuvelink, G., Gräler, B., Pebesma, E., Perčec Tadić, M. and Bajat, B., 2014. Spatio‐temporal interpolation of daily temperatures for global land areas at 1 km resolution. Journal of Geophysical Research: Atmospheres, 119(5):2294-2313.

Li, Z.L., Tang, B.H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I.F. and Sobrino, J.A., 2013. Satellite-derived land surface temperature: Current status and perspectives. Remote Sensing of Environment, 131: 14-37.

Lloyd, C.D. and Atkinson, P.M., 2002. Deriving DSMs from LiDAR data with kriging. International Journal of Remote Sensing, 23(12): 2519-2524.

Maxwell, S., Schmidt, G., and Storey, J., 2007. A multi-scale segmentation approach to filling gaps in Landsat ETM+SLC-off images. International Journal of Remote Sensing, 28:5339-5356.

McMillen, D.P., 2012. Quantile regression for spatial data. Springer Science & Business Media.

Meij, A.D., Gzella, A., Cuvelier, C., Thunis, P., Bessagnet, B., Vinuesa, J.F., Menut, L. and Kelder, H.M., 2009. The impact of MM5 and WRF meteorology over complex terrain on CHIMERE model calculations. Atmospheric Chemistry and Physics, 9(17): 6611-6632.

Mobasheri, M.R., Sadeghi Naeini, A., 2007. Using IRS Products to Recover 7ETM+ Defective Images. American Journal of Applied Science. 5(6): 618-625.

Mohammdy, M., Moradi, H.R., Zeinivand, H., Temme, A.J.A.M., Pourghasemi, H.R. and Alizadeh, H., 2014. Validating gap-filling of Landsat ETM+ satellite images in the Golestan Province, Iran. Arabian Journal of Geosciences, 7(9): 3633-3638.

Salomonson, V.V., Guenther, B. and Masuoka, E., 2001. A summary of the status of the EOS Terra Mission Moderate Resolution Imaging Spectroradiometer (MODIS) and attendant data product development after one year of on-orbit performance. In Geoscience and Remote Sensing Symposium, 2001. IGARSS'01. IEEE 2001 International, 3: 1197-1199. IEEE.

Savitzky, A. and Golay, M.J., 1964. Smoothing and differentiation of data by simplified least squares procedures. Analytical chemistry, 36(8): 1627-1639.

Wan, Z., Zhang, Y., Zhang, Q. and Li, Z.L., 2002. Validation of the land-surface temperature products retrieved from Terra Moderate Resolution Imaging Spectroradiometer data. Remote sensing of Environment, 83(1): 163-180.

Wan, Z., Zhang, Y., Zhang, Q., Li, Z., 2004. Quality assessment and validation of the MODIS global land surface temperature. International Journal of Remote Sensing, 25(1): 261–274.

Zeng, C., Shen, H. and Zhang, L., 2013. Recovering missing pixels for Landsat ETM+ SLC-off imagery using multi-temporal regression analysis and a regularization method. Remote Sensing of Environment, 131: 182-194.

Zhou, J., Jia, L. and Menenti, M., 2015. Reconstruction of global MODIS NDVI time series: Performance of harmonic analysis of time series (HANTS). Remote Sensing of Environment, 163: 217-228.

Zhu, X., Liu, D., and Chen, J., 2012. A new geostatistical approach for filling gaps in Landsat ETM + SLC-off images. Remote Sensing of Environment, 124: 49–60.