<p><strong>Abstract.</strong> Lowland rice in tropical and subtropical regions can be detected precisely and its crop growth can be tracked effectively through Synthetic Aperture Radar (SAR) imagery, especially where cloud cover restricts the use of optical imagery. Parameterised classification with multi-temporal features derived from regularly acquired, C-band, VV and VH polarized Sentinel-1A SAR imagery was used for mapping rice area. A fully automated processing chain in MAPscape-Rice software was used to convert the multi-temporal SAR data into terrain-geocoded &amp;sigma;⁰ values, which included strip mosaicking, co-registration of images acquired with the same observation geometry and mode, time-series speckle filtering, terrain geocoding, radiometric calibration and normalization. Further Anisotropic non-linear diffusion (ANLD) filtering was done to smoothen homogeneous targets, while enhancing the difference between neighbouring areas. Multi-Temporal Features viz., max, min, mean, max date, min date and span ratio were extracted from VV and VH polarizations to classify rice pixels. Rice detection was based on the analysis of temporal signature from SAR backscatter in relation to crop stages. About sixty images across four footprints covering 16 <i>samba</i> (<i>Rabi</i>) rice growing districts of Tamil Nadu, India were obtained between August 2017 and January 2018. In-season site visits were conducted across 280 monitoring locations in the footprints for classification purposes and more than 1665 field observations were made for accuracy assessment. A total rice area of 1.07 million ha was mapped with classification accuracy from 90.3 to 94.2 per cent with Kappa values ranging from 0.81 to 0.88. Using ORYZA2000, a weather driven process based crop growth simulation model developed by IRRI, yield estimates were made by integrating remote sensing products viz., seasonal rice area, start of season and backscatter time series. By generating average backscatter for each time series and dB stack for each SoS, LAI values were estimated. The model has generated rice yield estimate for each hectare which were aggregated at administrative boundary level and compared against CCE yield. Yield Simulation accuracy of more than 86&amp;ndash;91% at district level and 82&amp;ndash;97% at block level from the study indicates the suitability of these products for policy decisions. SAR products and yield information were used to meet the requirements of PMFBY crop insurance scheme in Tamil Nadu and helped in identifying or invoking prevented/failed sowing in 529 villages and total crop failure in 821 villages. In total 303703 farmers were benefitted by this technology in getting payouts of INR 9.94 billion through crop insurance. The satellite technology as an operational service has helped in getting quicker payouts.</p>

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<p><strong>Abstract.</strong> A research study was conducted to map maize area in Ariyalur and Perambalur districts of Tamil Nadu, India using multi-temporal features extracted from time-series Sentinel 1A SAR data. Multi-temporal Sentinel 1A GRD data at VV and VH polarizations and SLC products were acquired for the study area at 12 days interval and processed using MAPscape-RICE software. Multi-temporal Sentinel 1A data was used to identify the backscattering dB curve of maize crop. Analysis of temporal signatures of the crop showed minimum values at sowing period and maximum during the tasseling stage, which decreased during maturity stage of the crop. The maximum increase in the signature was observed during seedling to vegetative growth period. The signature derived from dB values for maize crop expressed a significant temporal behavior with the range of &amp;minus;21.26 to &amp;minus;13.18 in VH polarization and &amp;minus;14.05 to &amp;minus;6.54 in VV polarization. Considering the accuracy of SAR data to phenological variations of maize growing period, Multi-Temporal Features were extracted from multi-temporal dB images of VV and VH polarization and coherence images. Multi-Temporal Features viz., max, min, mean, max date, min date and span ratio were extracted from VV and VH polarizations of Sentinel 1A GRD and SLC data to classify maize pixels in the study area using parameterized classification approach. The overall classification accuracy was 91 percent with the kappa score of 0.82.</p>

Rice cultivation has been recognized as one of the major anthropogenic source for CH4 emissions. Methane emission from rice fields is a microbial mediated anaerobic activity, mainly favoured by the flooded condition. SAR-based operational mapping of rice crop across a diverse range of environments is possible with the increasing availability of multi-temporal SAR satellite data. Precise estimation of methane emission from rice fields at regional scale depends on accurate assessment of rice area and the corresponding time of flooding in those fields with IPCC emission factor. Start of Season (SoS) map was derived from satellite data showing rice emergence dates in Tiruchirapalli district recording 87 to 121 days of flooding during rice growth period. The rate of methane emission based on IPCC factor ranged from 37.4 to 45.74 kg/ha for a period of 87 to 121 days of flooding. The total methane emission from Tiruchirapalli district was 1.57Gg during Samba season 2015-16.