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<p><strong>Abstract.</strong> Under Pradhan Mantri Fasal Bima Yojana (PMFBY), a large number of Crop Cutting Experiments (CCEs) were conducted by Odisha State for Kharif Rice in the year 2016 and 2017. The present study was carried out to examine the quality of the performed CCEs using statistical methods and Remote Sensing (RS) technique. Total 24389 and 34725 CCEs were conducted. After removing outliers, 22083 and 26848 CCE points were analyzed for the year 2016 and 2017, respectively. Multi-date RISAT-1 (2016) and Sentinel-1A (2017) satellite data were used for generating the Kharif Rice crop mask, which was used to get NDVI and NDWI values for Rice pixels, from MODIS VI products. The values of these indices were divided into four strata from highest A, followed by B, C, and D (Lowest Value) based on the range (minimum and maximum) of values. The CCE based yield data were then divided into four yield strata of equal proportion. Yield and RS (NDVI+NDWI) based strata were combined to examine whether the CCE Points having high yield fall under good NDVI zone or vice versa. The results showed that there was strong match between CCE strata and the vegetation index strata in both the years. Therefore, it could be be concluded that RS based indices have the capability to assess the quality/accuracy of CCEs. Furthermore, the large variety of information available with CCEs such that crop variety, crop condition, water sources, stress conditions etc., can be used as input parameters to train any model to predict better results.</p>

Indian rivers are facing a severe problem of pollution. Safe discharge of waste water is still a very big problem in every part of the world and mainly in developing countries. The discharge of waste water effects the physiochemical properties of water stream and soils which enter into the food chain and effects agriculture products, human health and animals as well. This study includes the analysis of the Panchaganga river and Ganga river which are highly polluted in the current scenario. The major reason this pollution is disposal of municipal sewage which is not properly treated before letting it out in the rivers.

<p><strong>Abstract.</strong> Early yield assessment at local, regional and national scales is a major requirement for various users such as agriculture planners, policy makers, crop insurance companies and researchers. Current study explored a remote sensing-based approach of predicting the yield of Wheat, Kharif Rice and Rabi Rice at district level, using Vegetation Condition Index (VCI), under the FASAL programme. In order to make the estimates 14-years’ historical database (2003&amp;ndash;2016) of NDVI was used to derive the VCI. The yield estimation was carried out for 335 districts (136 districts of Wheat, 23 districts of Rabi Rice and 159 districts of Kharif Rice) for the period of 2016&amp;ndash;17. NDVI products (MOD-13A2) of MODIS instrument on board Terra satellite at 16-day interval from first fortnight of peak growing period of crop were used to calculate the VCI. Stepwise regression technique was used to develop empirical models between VCI and historical yield of crops. Estimated yields are good in agreement with the actual district level yield with the R² of, 0.78 for Wheat, 0.52 for Rabi Rice and 0.69 for Kharif Rice. For all the districts, the empirical models were found to be statistically significant. A large number of statistical parameters were computed to evaluate the performance of VCI-based models in predicting district-level crop yield. Though there was variation in model performance in different states and crops, overall, the study showed the usefulness of VCI, which can be used as an input for operational crop yield forecasting, at district level.</p>

In the last few years, remote sensing technique has emerged as a viable technology for crop acreage estimation. Under the FASAL project, the jute acreage estimation was carried out in the last 6 years by using both microwave SAR data (2012-13 to 2016-17) and high resolution optical multi-spectral data . In the assessment using SAR data, hierarchical decision rule classification technique and for optical data hybrid classification approach was used. Yield was estimated using, agro-meteorological parameter based statistical models. In the present study, different statistical parameters such as correlation coefficient (r) and RMSE were used for evaluating and comparing the results of the last 6 years (2012-13 to 2017-18) with DES (government) estimates. The RMSE values over the years were found to be 7-20%and 5-13% for area and production, respectively. The correlation coefficient (r) over the years between DES and FASAL estimates ranging between 0.995 to 1.00 and 0.996 to 1.00 in acreage and production estimates respectively. At district level, the correlation coefficient (r) values for the area and production were 0.967 and 0.962 respectively. On the basis of statistical criteria used in this study, FASAL estimates were close to DES estimates and improved over the years. The FASAL jute production estimates could be called better than DES ones in terms of good accuracy, timely reporting and low labour intensive. Thus, the FASAL estimates can be continued for policy purposes as far as jute production forecasts are concerned in India.