Spatial Downscaling of Vegetation Optical Depth Using the Modis and Srtm Observations

• Main Authors: Mohite, Jayantrao, Sawant, Suryakant, Pandit, Ankur, Pappula, Srinivasu
• Format: Conference Proceeding
• Language: English
• Subjects: Crops; Land surface temperature; Microwave radiometry; MODIS; Optical imaging; Radar imaging; Remote Sensing; Spatial Down-scaling; SRTM; Time series analysis; Vegetation mapping; Vegetation Optical Depth
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS46834.2022.9884507

The optical satellite observations are affected by cloud cover. Vegetation Optical Depth (VOD) has the potential to provide insights into plant water and vegetation structure. The main objective of this study is to spatially downscale VOD using a Moderate Resolution Imaging Spectrometer (MODIS) and Shuttle Radar Topographic Mission (SRTM) observations. The study considered India geography and post-monsoon cropping season (locally called Rabi season). Data for three different Rabi crop seasons, i.e., 2017-18, 2018-19, and 2019-20, was used in the analysis. The VOD estimates at 25 km scale derived from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and Advanced Microwave Scanning Radiometer 2 (AMSR2) were used for spatial down-scaling. Three day VOD composites were created to cover the study area. MODIS products such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), albedo (black and white sky), and Land Surface Temperature (LST) were similarly acquired at a 3-day interval by constructing a 3-day composite. In addition, SRTM digital elevation model with a spatial resolution of 90 m was used in this study. We carried out regression modeling where VOD was used as a dependent variable, with NDVI, NDWI, Albedo (black and white sky), LST, and elevation as independent variables. We compared three regression algorithms, viz., Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Regression (SVR), using the R-square (R 2 ) as the assessment metric. A comparison between the various regression techniques showed that the SVR outperformed monthly and seasonal models. Further more, a comparison of monthly and seasonal models revealed that the model generated with January data performed best, with an R 2 of 0.85, followed by R 2 of 0.82, 0.80, and 0.78 for March, December, and February, respectively. The R 2 for the seasonal model was 0.83. Finally, for the wheat crop time series of down-scaled VOD and Sentinel 2 based NDVI was compared to gain insights on seasonal variations in VOD. We found that down-scaled VOD and NDVI have a significant agreement.