Muhammad Jehanzeb Masud Cheema

Evaluation of MODIS and Landsat multiband vegetation indices used for wheat yield estimation in irrigated Indus Basin

Crop yield estimation for food security and management plans.Prediction performance between MODIS and LANDSAT 8 for yield estimation.SAVI exhibited strong relationship in cropping area of Irrigated Indus Basin.Wheat yield estimated by Landsat SAVI has strong relationship rather than MODIS. Crop yield estimation has significant importance for policy makers to make timely dicisions on import/export of particular crop. Traditionally, in Pakistan crop yield estimation is being carried out by Village Master Sampling (VMS) that is laborious and time-consuming. Satellite imagery is also being used as an alternative to estimate vegetation health and yield. Various vegetation indices are being used for the purpose however, their efficiency to estimate yield has not been tested. In this study, a comparison was performed among various satellite-based vegetation indices e.g. Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI) Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), to evaluate most appropriate index that performs better in cropping area of irrigated Indus Basin (a complex basin with spatially heterogeneous land use). A stepwise regression based model was developed for remotely sensed crop (i.e. Wheat) using multi-band MODIS and Landsat 8 products based on Land use and Land cover map developed by Semi-Supervised Classification. The results revealed that SAVI showed a fairly acceptable association with reported yield data as compared to other indices. The correlation coefficient (R2) was estimated at 0.60. Yield estimated by SAVI obtained from Landsat 8 showed good results with R2 and Pearson correlation (r), estimated at 0.74 and 0.88 as compared to SAVI obtained from MODIS with 0.63 and 0.79 respectively. The results support that SAVI vegetation indices is reliable for quick and efficient wheat area mapping under Pakistanis farm conditions.

Rainfall Extremes: a Novel Modeling Approach for Regionalization

The rainfall events of extreme magnitude over the past few decades have caused destructive damages to lives and properties, especially in the subcontinent (e.g. Pakistan, India, Bangladesh etc). Rainfall hazard maps for these areas can be of great practical and theoretical interests. In our work, we used extreme value analysis and spatial interpolation techniques to provide such maps through a combination of the Tropical Rainfall Measuring Mission Precipitation (TRMM) 3B42 product and raingauge data. This mixed approach takes advantage of both the long time series available at a limited number of stations, and the large spatial coverage of the satellite data which, instead, has a poor temporal extent. The methodology is implemented by (1) creating a unique growth curve for the homogeneous region by utilizing in-situ rainfall data and (2) mapping the parameters of intensity-duration functions for the entire length of the study area by using TRMM 3B42 product. The regional results obtained by using mixed approach and TRMM 3B42 are compared with the estimates obtained by using in-situ data. The comparison showed that the overall output of mixed approach is more consistent with what transpired by in-situ data for a pre-defined return period.