In this study, an attempt has been made to assess the crop situation in India, before the COVID-19 pandemic lockdown, during the lockdown and after the lockdown. In India, the lockdown was imposed from 24 March to 31 May 2020. That was the period of harvesting of Rabi (winter) season crop and sowing of Zaid (summer) season crop. However, the government announced a large number of measures to provide relaxation to agricultural activities during the lockdown. A satellite remote sensing-based assessment was carried out to see the impact of the lockdown and government measures. Normalized Difference Vegetation Index (NDVI) time composite all-India product derived from Resourcesat-2/2A Advanced Wide Field Sensor for February, May, and July 2020 was used to assess the crop situation, representing three crop seasons, i.e. Rabi, Zaid and Kharif (rainy). NDVI images of 2020 were compared with corresponding images of 2019. Change images were generated, and state-level NDVI values were computed. The state-level cropped area proportion was also mapped using the NDVI thresholding approach. The crop sown area statistics, crop cutting experiment (CCE) numbers and rainfall data were also compared for both the years. It was observed that the differences were very low between the NDVI of 2019 and 2020 in February month. However, the differences were high in many states during May and in most of the states during July. A statistical test of significance (paired t test) was carried out for state-level NDVI and crop area values, which validated this result. This NDVI change was mostly due to increased crop area during Zaid and Kharif (rainy) seasons and higher rainfall from May to July. The satellite and other data (crop area and CCE numbers) analysis also showed the sowing and harvesting operations in major parts of the country went on smoothly, during the lockdown period.
<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&ndash;13 to 2017&ndash;18) estimations. Multi temporal Radarsat-2 (HH), RISAT-1 ScanSAR (HH) and Sentinel-1 (VV) data was used in years 2012, 2013&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 R<sup>2</sup> 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 (R<sup>2</sup>) 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>
India is one of the world's largest producers of rice, accounting for 20% of all world rice production. Rice crop occupies nearly 27.6% of the India's arable land with average consumption per capita/year was ~68.2 kg milled rice. Being a staple food it is crucially important for policy makers, planners and researchers to have an accurate estimate before the harvest of crop. Timely and accurate statistics helps planners, and decision makers in formulating policies in regard to import/export in the event of shortfall and/or surplus. In the present study it is tried to evaluate the applicability of the remote sensing for yield estimation of major rice growing states of India. Recent advances on the resolutions (i.e., spectral, spatial, radiometric, and temporal) and availability of remote sensing imagery allowed us timely collection of information. This study developed an intermediate method called semiphysical method using remote sensing and the physiological concepts such as the Photo-synthetically Active Radiation and the fraction of PAR absorbed by the crop. Net Primary Product was computed using the Monteith model. Rice yield was computed using the actual NPP, Radiation use efficiency and Harvest index. The study was carried in kharif season 2018-19. Although model gives slight difference of yield with respect to actual and the estimated yield and DES yields within the range of ± 10%, which confirms the utility of model and can be used for the operational estimates of rice crop.
<p><strong>Abstract.</strong> Agricultural drought is concerned with the soil moisture deficiency in relation to meteorological droughts and climatic factors and their impacts on agricultural production and economic profitability. Present study is based on two years <i>kharif</i> seasons i.e. 2018 and 2017, comparison of drought assessment using remote sensing, soil moisture indices, rainfall and crop sown area as per the New Drought Manual, December, 2016. The drought assessment was carried out at district and sub-district level under National Agricultural Drought Assessment and Monitoring System (NADAMS) project. Drought trigger-1 is checked with rainfall deviation and dry spell. During 2017, the final drought categories were defined on the basis of Rainfall, Moisture and Vegetation Condition Index. During 2018, the final district level drought categories are defined using 3 indicators, where sown area upto end of August was also considered. Based on the approach defined in the New Drought Manual, analysis was carried out at district level for 17 major agricultural drought prone states of the country. State wise Rainfall deviation, dry spell, NDVI/NDWI situation was compared for both the years. Remote sensing based vegetation and water indices are important impact indicator out of 4 because it gives an idea of crop profile and surface wetness condition respectively. Thus the present study is an attempt to compare the drought situation in <i>kharif</i> season of years 2017 and 2018 on the basis of different impact indicators.</p>
<p><strong>Abstract.</strong> Rapeseed-mustard (<i>Brassica</i> spp.) is the major <i>rabi</i> oilseed crop of India. India is fourth largest contributor of oilseeds and Rapeseed-mustard contributing to around 11% of world’s total production and about 28.6% in total oilseeds production of the country. More than 85% Rapeseed-mustard production comes from 5 States viz. Rajasthan [48%], Haryana [12%], MP [10%], UP [9%] and West Bengal [7%]. In the previous few years, remote sensing technique has been progressively more considered for evolving as an alternative, standardized, possibly cheaper and faster technology for crop acreage estimation. Furthermore, satellite remote sensing data have strong advantages in comparison with other monitoring techniques because it provides timely, synoptic and latest information of crop at various stages over large scales. Therefore, under FASAL project, cloud free crop season’s images of different satellites (Sentinel-2, Resourcesat-2 and Landsat-8) were used and mustard crop was discriminated using Maximum Likelihood Classifier (MLC). Yield was estimated using different methods such as remote sensing derived NDVI, Agrometeorological yield model and Semi-Physical Model. The RMSE values for state level were found to be 4&ndash;17%, 8&ndash;19% and 13&ndash;23% for area, yield and production, respectively. The correlation coefficient (r) between DES and FASAL estimates were close to 0.9 in all the cases. The results of t-test at 5% level of significance inferred that FASAL and DES results were not significantly different. These results show that RS and weather-based techniques can be effectively used for pre-harvest acreage, yield and production estimation of mustard crop at district, state and national level.</p>
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