In order to monitor vegetation growth and development over the districts and land covers of Tamil Nadu, India during the crop growing season viz., Khairf and Rabi of 2017, Moderate Resolution Imaging Spectroradiometer (MODIS) derived surface reflectance product (MOD09A1) which is available at 500 m resolution and 8-day temporal period was used to derive a time series based Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) for monitoring and mapping terrestrial vegetation trend analysis which showed areas in Tamil Nadu having vegetation greening and vegetation browning. The regression slope values derived from the trend analysis was utilized and the NDVI and NDWI seasonal trend showed majority of area in Tamil Nadu falling under positive trend during the Kharif season (86.52 per cent for NDVI and 90.29 per cent for NDWI). While irrespective of land cover classes, NDVI and NDWI during Kharif season showed a greater positive trend (greening) with least negative trend (browning) for vegetation growth over the land covers whereas during Rabi season it was observed to have a mix of positive trend and negative trend over the land covers. This study was carried out to show that a systematic study can be done for understanding changes over the landscape through the use of high spatial resolution satellite dataset such as MODIS, which provides detailed spatial and temporal description at regional scale. While a trend analysis using regression slope values can be considered for demonstrating the spatial and temporal consistency on land and vegetation dynamics.

<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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Timely and accurate medium range weather information is critical to conquer the impact of highly dynamic next few days’ weather on the farming. Advances in weather forecast models, as well as their increased resolution, have resulted in more accurate and realistic forecasts. An attempt was made during 2019 – 2021 at Agro Climate Research Centre, Tamil Nadu Agricultural University, Coimbatore to develop cluster of village level (@ 3km resolution) Medium Range Weather Forecast (MRWF) for Tamil Nadu with higher accuracy. In this study, Weather Research and Forecast Model (WRF v4.2.1) with four microphysics viz., Kessler, WSM3, WSM5, WSM6 schemes were tested for Tamil Nadu during CWP, HWP, SWM and NEM 2020. The MRWF generated from the WRF model v4.2.1 with WSM3 had better BSF, higher Forecast Accuracy Index (FAI) and Forecast Usability Percent (FUP) for Tamil Nadu followed by Kessler scheme. The WSM5 and WSM6 were poor performer during the study. In general, CWP had higher FAI followed by HWP, NEM & SWM. The FAI from WSM3 was 0.65 - 0.74 during NEM and 0.55 - 0.69 during SWM. Among the season, the MRWF generated during SWM were over forecasted the rainfall quantity, where the NEM and HWP had better rainfall forecast nearing actuals. The FUP was higher in NEM followed by CWP, SWM & HWP, which was 57 – 88 per cent during NEM and 46 – 82 per cent during SWM. A decreasing trend in the quantitative FUP was observed with increase in lead times, irrespective of the microphysics and seasons. Finally, the study concluded that the accuracy of village level medium range rainfall forecasts from WRF model v4.2.1 varied temporally by season and the WSM3 microphysics option having superiority in all seasons.

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