Exploring potential of C band synthetic aperture radar imagery to investigate rice crop growth mechanism and productivity.

Development of satellite technology over decades established unique approach to analyze rice crop phenological parameters and supervise growth and production. Advancement in technology leads to the development of microwave remote sensing that is operational round the clock irrespective of weather conditions. An attempt has been carried out in the present study to classify and map phenological stages, namely, transplanting stage, heading stage, and harvesting stage of rice crop using Sentinel-1, MODIS Enhanced vegetation index (EVI) data. Puddling stage, transplanting stage, heading stage, and harvesting stages are identified on 05th and 15th January, 28th February, and 27th March of the year 2017, respectively. Field visits are performed frequently at sampling locations for an effective study on rice phenological stages, rice yield estimation, and mapping large-scale area using regression analysis. Estimated yield is compared with ground truth data; average yield produced from study area is 3.03 tons/acre. Methodology adopted reflected satisfactory performance with minimal error for mapping of rice phenological stages, and it is suggested to experiment with other agricultural crops.

Identification of paddy crop phenological parameters using dual polarized SCATSAT-1 (ISRO, India) scatterometer data.

Paddy crop is one of the foremost food crops in the world. Human consumption accounts for 85% of total production of paddy. Paddy delivers 21% of human per capita energy and 15% of per capita protein. The present study focused on estimating the crop phenological parameters. The phenological parameters were estimated using soil moisture active passive (SMAP), MODIS NDVI, and SCATSAT-1 scatterometer data. The statistical models adopted in the study are two-parameter Gaussian distribution and two-parameter logistic distributions. The puddling stage is the first phenological stage, and it is estimated by the application of soil wetness index (SWI) and anomaly method. The transplanting stage is estimated using the anomaly method. The heading stages are estimated using statistical models, and it is found that Gaussian distribution is the best-fitted model. The harvesting stage is identified using SCATSAT-1 scatterometer and MODIS NDVI data. A chi-square test and degrees of freedom are used to identify the performance and comparison of statistical models. Chi-square test measure is equal to 80.561 and corresponding tabulated chi-square value with N-K-1 degrees of freedom that is equal to 117 is 151.929. The null hypothesis is not rejected.