Volume XXXIX-B3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 379-383, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-379-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XXXIX-B3, 379-383, 2012
https://doi.org/10.5194/isprsarchives-XXXIX-B3-379-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.

  31 Jul 2012

31 Jul 2012

RETRIEVING SURFACE SOIL MOISTURE FROM MODIS AND AMSR-E DATA: A CASE STUDY IN TAIWAN

C. F. Chen1,2, Y. J. Lin2, L. Y. Chang2, and N. T. Son1 C. F. Chen et al.
  • 1Center for Space and Remote Sensing Research, National Central University, Jhongli, Taiwan
  • 2Department of Civil Engineering, National Central University, Jhongli, Taiwan

Keywords: MODIS data, Surface soil moisture, Taiwan

Abstract. Soil moisture is a key factor that controls the exchange of water between land surface evaporation and plant transpiration. Information on surface soil moisture variations in both time and spatial domains is important for numerous applications, especially agricultural and environmental monitoring. This study aimed at retrieving surface soil moisture from daily MODIS and AMSR-E (Advanced Microwave Scanning Radiometer – Earth Observing System) data. A case study was conducted in Taiwan for 2009. Data were processed using the Temperature Vegetation Dryness Index (TVDI). This index is developed based on an empirical analysis of the relationship between land surface temperature (LST) and normalized difference vegetation index (NDVI) data. The comparison between the TVDI results and the daily precipitation data collected from meteorological stations throughout the study area indicated that there were close relationships between the two datasets. The TDVI results (values range from 0 to 1) were converted to the same unit with the AMSR-E soil moisture data (i.e., g cm-3) by linear regression analysis between these two datasets. The results achieved by this analysis were soil moisture maps that had a better spatial resolution (1 km × 1 km) than the AMSE-E soil moisture data (25 km × 25 km). The soil moisture achieved by TVDI – AMSR-E regression analysis showed the comparable spatial patterns with those from the AMSR-E soil moisture data. A quantitative analysis between the soil moisture (deduced from TVDI-AMSR-E analysis) and the AMSR-E soil moisture data also reaffirmed significant correlations between the two datasets. This study has demonstrated a method of surface soil moisture retrieval from MODIS and AMSR-E data.