JUTE CROP PRODUCTION ESTIMATION IN MAJOR STATES OF INDIA: A COMPARATIVE STUDY OF LAST 6 YEARS’ FASAL AND DES ESTIMATES

<p><strong>Abstract.</strong> 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&amp;ndash;13 to 2016&amp;ndash;17) and high resolution optical multi-spectral data (2017&amp;ndash;18). 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&amp;ndash;13 to 2017&amp;ndash;18) with DES (government) estimates. The RMSE values over the years were found to be 7&amp;ndash;20% and 5&amp;ndash;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.</p>

RICE (KHARIF) PRODUCTION ESTIMATION USING SAR DATA OF DIFFERENT SATELLITES AND YIELD MODELS: A COMPARATIVE ANALYSIS OF THE ESTIMATES GENERATED UNDER FASAL PROJECT

<p><strong>Abstract.</strong> Rice is the most important food crop of India. Majority of Rice is sown in kharif season in the country. This is monsoon season for the country where cloud cover poses a major problem for optical remote sensing. Therefore, for these states rice acreage estimation is being done using Synthetic Aperture Radar (SAR) data operationally in India since 1998. A case study is presented in this paper for analysis of past 6 years’ (2012&amp;ndash;13 to 2017&amp;ndash;18) estimations. Multi temporal Radarsat-2 (HH), RISAT-1 ScanSAR (HH) and Sentinel-1 (VV) data was used in years 2012, 2013&amp;ndash;2016, and 2017, respectively for paddy identification. Hierarchal Decision Rule based classification (HDRC) approach was used to identify rice areas under sample segments. Extensive ground truth collected by state remote sensing departments and agriculture departments was utilized in setting the limits of HDRC models and accuracy assessment. Yield was estimated using weather based and remote sensing-based models. Area, production and yield estimates were made and compared with those given by DES. RMSE and R² were used as statistical measures to assess the accuracy of results. The RMSE % ranged from 2.3 to 4.3; 0.84 to 1.35; 0.24 to 0.27 for area, production and yield respectively. The coefficient of determination (R²) ranged from 0.62 to 0.92; 0.75 to 0.91; 0.5 to 0.83 for area, production and yield respectively. The study showed that use of multi temporal SAR data (both HH and VV) is quite useful for paddy acreage estimation, especially during monsoon.</p>

DISCRIMINATION OF SUGARCANE CROP AND CANE YIELD ESTIMATION USING LANDSAT AND IRS RESOURCESAT SATELLITE DATA

<p><strong>Abstract.</strong> The objective of this research work aims at crop acreage estimation at mill catchment level, derivation of sugarcane phenology and yield estimation at field level. The study was carried out in Kisan Sahkari Chini Mill catchment, Nanauta, Saharanpur, Uttar Pradesh. Extensive and systematic field sampling was carried out for ground-truth observations, biophysical measurements (LAI and above/below canopy PAR) and mill-able cane yield through crop cutting experiments. Major emphasis were laid on sugarcane crop discrimination, biophysical parameter estimation, generation of phenological metrics and yield model development for sugarcane crop at mill catchment level. Sugarcane crop discrimination and its acreage estimation was done using multi-sensor satellite data. The sugarcane classification accuracies were &amp;gt;&amp;thinsp;92% for LISS-IV, &amp;gt;&amp;thinsp;86% for Landsat-8 and &amp;gt;&amp;thinsp;83% for LISS-III classified image. The sugarcane phenological matrices at field level derived using time-series of NDVI for a period of 2015&amp;ndash;2016 through TIMESAT software. To retrieve the biophysical parameters particularly leaf area index, best predictive function developed with vegetation indices (EVI, NDVI, SAVI) through correlation and regression analysis along this cane yield estimation attempted with multi-date (eight-day) NDVI from Landsat OLI. Yield models developed for ratoon cane and planted cane explained variance in yield significantly with coefficient of determination (R²) values equal to 0.83 and 0.69, respectively. Similar predictive functions were also established with monthly composite dataset for village-level yield estimates with step wise regression (R²&amp;thinsp;=&amp;thinsp;0.83) (P&amp;thinsp;=&amp;thinsp;0.00001), Multi linear regression (MLR) (R²&amp;thinsp;=&amp;thinsp;0.792) (P&amp;thinsp;=&amp;thinsp;0.00081) and Random forest regression (R²&amp;thinsp;=&amp;thinsp;0.466) (P&amp;thinsp;=&amp;thinsp;0.038).</p>