This study provides a baseline quality check on provisional Landsat Surface Reflectance (SR) products as generated by the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center using Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) software. Characterization of the Landsat SR products leveraged comparisons between aerosol optical thickness derived from LEDAPS and measured by Aerosol Robotic Network (AERONET), as well as reflectance correlations with field spectrometer and Moderate Resolution Imaging Spectroradiometer (MODIS) data. Results consistently indicated similarity between LEDAPS and alternative data products in longer wavelengths over vegetated areas with no adjacent water, while less reliable performance was observed in shorter wavelengths and sparsely vegetated areas. This study demonstrates the strengths and weaknesses of the atmospheric correction methodology used in LEDAPS, confirming its successful implementation to generate Landsat SR products.

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The assessment of direct radiative forcing due to atmospheric aerosols (ADRF) in the Indo Gangetic Plain (IGP), which is a food basket of south Asia, is important for measuring the effect of atmospheric aerosols on the terrestrial ecosystem and for assessing the effect of aerosols on crop production in the region. Existing comprehensive analytical models to estimate ADRF require a large number of input parameters and high processing time. In this context, here, we develop a simple model to estimate daily ADRF at any location on the surface of the IGP through multiple regressions of AErosol RObotic NETwork (AERONET) aerosol optical depth (AOD) and atmospheric water vapour using data from 2002 to 2015 at 10 stations in the IGP. The goodness of fit of the model is indicated by an adjusted R2 value of 0.834. The Jackknife method of deleting one group (station data) was employed to cross validate and study the stability of the regression model. It was found to be robust with an adjusted R2 fluctuating between 0.813 and 0.842. In order to use the year-round ADRF model for locations beyond the AERONET stations in the IGP, AOD, and atmospheric water vapour products from MODIS Aqua and Terra were compared against AERONET station data and they were found to be similar. Using MODIS Aqua and Terra products as input, the year-round ADRF regression was evaluated at the IGP AERONET stations and found to perform well with Pearson correlation coefficients of 0.66 and 0.65, respectively. Using ADRF regression model with MODIS inputs allows for the estimation of ADRF across the IGP for assessing the aerosol impact on ecosystem and crop production.

Cold waves are considered one of the important extreme weather events affecting winter crop production in the Indo-Gangetic Plain (IGP). In spite of media coverage of extreme cold events in the Terai area of Nepal (Nepal section of IGP) in recent years, few studies on this topic were found. This study investigates cold waves and their impact on agriculture during winter in the Terai region of Nepal. Historical daily maximum and minimum temperature data from six stations in the Terai (Dhangadhi, Nepalgunj, Bhairahawa, Simara, Janakpur, and Biratnagar) during 1971–2015 were analyzed to study the occurrence of cold days, cold nights, extreme cold days, extreme cold nights, cold wave days, and extreme cold wave days in the Terai. The average number of cold days per annum ranges from 15.6 to 17.9 days and the extreme cold days per annum ranges from 3.2 to 3.6 days in the Terai. Except for Nepalgunj, all the Terai stations show statistically significant increasing trends in the frequency of cold days and extreme cold days over the last four decades. Similarly, the average number of cold wave days varies from 9.2 to 13.8 per annum and the average number of extreme cold wave days varies from 1.4 to 3.8 days in the Terai region of Nepal. By comparing the co-occurrence of foggy days and cold and extreme cold wave days at Biratnagar, Simara, Bhairahawa, and Nepalgunj airport, it is also observed that most of the cold and extreme cold wave days are also foggy days. The perception of farmers regarding the effect of fog and cold wave events was explored through focus group discussions at Dhanusha and Sunsari districts of Nepal and found that the fog and cold events have significantly affected their winter crops, livestock, and their day-to-day life.

Abstract. Integration of Sentinel-2 and Landsat-8 imagery is a key factor to provide earth observation data at a global scale with higher temporal resolution. Integration of data from two sensors is possible with the consistent harmonized data framed in common reference and processing, which can be used for comparing geophysical surface characteristics. This study focuses on the analysis of the atmospheric correction methods available for both Landsat-8 and Sentinel-2 products to convert the top of the atmosphere to the bottom of atmosphere reflectance. Other investigations (De Keukelaere, 2018) carried out similar analyses focusing on data acquired over water, while this study emphasises the analyses over land covers. Two processing algorithms iCOR and Sen2COR are utilized to perform atmospheric corrections, and results are statistically and visually compared. Comparisons based on same images processed with different algorithms show very strong correlation for some classes (urban: 0.99), while correlation values around 0.85 were achieved between images from different sensors.