cuSTARFM

Version 1.0

Overview

MODIS and Landsat surface reflectance products have complementary characteristics in terms of spatial and temporal resolutions. To fully exploit these datasets, the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) was developed by Gao et al. (2006). The STARFM approach blends the high-frequency temporal information from MODIS and the high-resolution spatial information from Landsat to generate synthetic surface reflectance products at 30m spatial resolution and daily temporal resolution. STARFM uses one or more pairs of Landsat-MODIS images collected on the same dates to predict the surface reflectance at Landsat resolution on other MODIS observation dates. However, the computational performance of STARFM has been a bottleneck for mass production, even with the parallel option in STARFM v1.2 (Geo, et al., 2015; Gao, et al., 2017).

To overcome the computational barrier and support mass production of large-size images, we designed and implemented a GPU-enabled STARFM program based on the Compute Unified Device Architecture (CUDA), called cuSTARFM. By taking advantages of the large amount of concurrent computing threads of a GPU, cuSTARFM can greatly reduce the computing time and improve the computational performance. Experiments showed that cuSTARFM achieved a speedup of 342 using a Nvidia Tesla K40 GPU, compared with a sequential STARFM program running on an Intel Xeon E3-1226 CPU.

Key features of cuSTARFM:

• Supports a wide range of CUDA-enabled GPUs (https://developer.nvidia.com/cuda-gpus)
  • Automatic setting of the numbers of threads and thread blocks according to the GPU’s available computing resources (e.g., memory, streaming multiprocessors, and warp)
  • Adaptive cyclic task assignment to achieve better load balance
  • Optimized use of registers to improve the computational performance
  • Adaptive data decomposition when the size of images exceeds the GPU’s memory
  • All above are completely transparent to users
• Intakes any number of pairs of Landsat-MODIS images as the input
• Outputs any number of prediction images
• Supports a wide range of image formats (see http://gdal.org/formats_list.html)
• Supports both Windows and Linux/Unix operating systems

References

• Gao, F.; Masek, J.; Schwaller, M. and Hall, F. On the Blending of the Landsat and MODIS Surface Reflectance: Predict Daily Landsat Surface Reflectance, IEEE Transactions on Geoscience and Remote Sensing. 2006, 44(8):2207-2218.
• Gao, F.; Hilker, T.; Zhu, X.; Anderson, M. A.; Masek, J.; Wang, P. and Yang, Y. Fusing Landsat and MODIS data for vegetation monitoring, IEEE Geoscience and Remote Sensing Magazine. 2015, 3(3):47-60.
• Gao, F.; Anderson, M.; Zhang, X.; Yang, Z.; Alfieri, J.; Kustas, W.; Mueller, R.; Johnson, D. and Prueger, J. Toward mapping crop progress at field scales through fusion of Landsat and MODIS imagery. Remote Sensing of Environment. 2017, 188:9-25

To Cite cuSTARFM in Publications

• Please cite the following reference:
  Gao, H., Zhu, X., Guan, Q., Yang, X., Yao, Y., Zeng, W., Peng, X., 2021. cuFSDAF: An Enhanced Flexible Spatiotemporal Data Fusion Algorithm Parallelized Using Graphics Processing Units. IEEE Transactions on Geoscience and Remote Sensing. https://doi.org/10.1109/TGRS.2021.3080384

Compilation

• Requirements:
  • A computer with a CUDA-enabled GPU (https://developer.nvidia.com/cuda-gpus)
  • A C/C++ compiler (e.g., Microsoft Visual Studio for Windows, and gcc/g++ for Linux/Unix) installed and tested
  • Nvidia CUDA Toolkit (https://developer.nvidia.com/cuda-downloads) installed and tested
  • Geospatial Data Abstraction Library (GDAL, http://gdal.org) installed and tested
• For the Windows operating system (using MS Visual Studio as an example)
  1. Open all the source codes in Visual Studio
  2. Click menu Project -> Properties -> VC++ Directories -> Include Directories, and add the “include” directory of GDAL (e.g., C:\GDAL\include)
  3. Click menu Project -> Properties -> VC++ Directories -> Lib Directories, and add the “lib” directory of GDAL (e.g., C:\GDAL\lib)
  4. Click menu Build -> Build Solution
     Once successfully compiled, an executable file, cuSTARFM.exe, is created.
• For the Linux/Unix operating system (using the CUDA compiler --- nvcc)
  In a Linux/Unix terminal, type in:
  • $ cd /the-directory-of-source-codes/
  • $ nvcc -o cuSTARFM kernel.cu cuLayer.cpp cuSTARFM_util.cpp fusion.cpp -lgdal
    Once successfully compiled, an executable file, cuSTARFM, is created.

Usage

• Before running the program, make sure that all Landsat and MODIS images have been pre-processed and co-registered. They must have:
  • the same spatial resolution (i.e., Landsat resolution --- 30m)
  • the same image size (i.e., numbers of rows and columns)
  • the same map projection
• A text file must be manually created to specify the input and output images, and other parameters for the STARFM model.
  Example (# for comments):

STARFM_PARAMETER_START

#The number of input pairs of Landsat-MODIS images (>=1)
NUM_IN_PAIRS = 1

#The pf band of Landsat
The_pf_band_of_Landsat_for_calculating = 3

#The pc band of MODIS
The_pc_band_of_MODIS_for_calculating = 1

#The input MODIS images
#When NUM_IN_PAIRS > 1, multiple images must be given
#File names are separated by space
IN_PAIR_MODIS_FNAME = D:\data\newdata\MODO9A1.A2009249.dat

#The input Landsat images
#When NUM_IN_PAIRS > 1, multiple images must be given
#File names are separated by space
IN_PAIR_LANDSAT_FNAME = D:\data\newdata\2009-249-flaash.dat

#The MODIS images for the prediction dates
#Multiple images can be given
#File names are separated by space
IN_PDAY_MODIS_FNAME = D:\data\newdata\MODO9A1.A2009329-0.dat D:\data\newdata\MODO9A1.A2009329-0.dat

#The output synthetic prediction images
#Multiple images can be given
#File names are separated by space
OUT_PDAY_LANDSAT_FNAME = D:\data\newdata\2009-329new-flaash-test2.tif D:\data\newdata\2009-329new-flaash-test3.tif

#The_width of searching_window
The_width_of_searching_window = 31

#Assumed_number of classifications
Assumed_number_of_classifications = 4

#The relative importance of space distance
The_relative_importance_of_space_distance = 25

#Landsat sensor error
Landsat_sensor_error = 20

#MODIS sensor error
MODIS_sensor_error = 50

#Output image format (optional)
#Will be used when the extension of the output files
#is not given
G_Type = GTIff

STARFM_PARAMETER_END

Note: MODIS and Landsat images use different band number sequence. You can relate them using the following table:

Landsat(pf)	MODIS(pc)
1	3
2	4
3	1
4	2
5	6
7	7

• The program runs as a command line. You may use the Command (i.e., cmd) in Windows, or a terminal in Linux/Unix.

  • For the Windows version:
      $ cuSTARFM.exe parameters.txt
  • For the Linux/Unix version:
    $ ./cuSTARFM parameters.txt

• Note: The computational performance of cuSTARFM largely depends on the GPU. The more powerful is the GPU, the better performance.