Rice is the most important food security crop in Asia. Information on its seasonal extent forms part of the national accounting of many Asian countries. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland rice, especially in tropical and subtropical regions, where pervasive cloud cover in the rainy seasons precludes the use of optical imagery. Here, we present a simple, robust, rule-based classification for mapping rice area with regularly acquired, multi-temporal, X-band, HH-polarized SAR imagery and site-specific parameters for classification. The rules for rice detection are based on the well-studied temporal signature of rice from SAR backscatter and its relationship with crop stages. We also present a procedure for estimating the parameters based on "temporal feature descriptors" that concisely characterize the key information in the rice signatures in monitored field locations within each site. We demonstrate the robustness of the approach on a very large dataset. A total of 127 images across 13 footprints in six countries in Asia were obtained between October 2012, and April 2014, covering 4.78 m ha. More than 1900 in-season site visits were conducted across 228 monitoring locations in the footprints for classification purposes, and more than 1300 field observations were made for accuracy assessment. Some 1.6 m ha of rice were mapped with classification accuracies from 85% to 95% based on the parameters that were closely related to the observed temporal feature descriptors derived for Remote Sens. 2014, 6 10775 each site. The 13 sites capture much of the diversity in water management, crop establishment and maturity in South and Southeast Asia. The study demonstrates the feasibility of rice detection at the national scale using multi-temporal SAR imagery with robust classification methods and parameters that are based on the knowledge of the temporal dynamics of the rice crop. We highlight the need for the development of an open-access library of temporal signatures, further investigation into temporal feature descriptors and better ancillary data to reduce the risk of misclassification with surfaces that have temporal backscatter dynamics similar to those of rice. We conclude with observations on the need to define appropriate SAR acquisition plans to support policies and decisions related to food security.

ABSTRACT:Rice is the most important cereal crop governing food security in Asia. Reliable and regular information on the area under rice production is the basis of policy decisions related to imports, exports and prices which directly affect food security. Recent and planned launches of SAR sensors coupled with automated processing can provide sustainable solutions to the challenges on mapping and monitoring rice systems. High resolution (3m) Synthetic Aperture Radar (SAR) imageries were used to map and monitor rice growing areas in selected three sites in TamilNadu, India to determine rice cropping extent, track rice growth and estimate yields. A simple, robust, rule-based classification for mapping rice area with multi-temporal, X-band, HH polarized SAR imagery from COSMO Skymed and TerraSAR X and site specific parameters were used. The robustness of the approach is demonstrated on a very large dataset involving 30 images across 3 footprints obtained during 2013-14. A total of 318 in-season site visits were conducted across 60 monitoring locations for rice classification and 432 field observations were made for accuracy assessment. Rice area and Start of Season (SoS) maps were generated with classification accuracies ranging from 87-92 per cent. Using ORYZA2000, a weather driven process based crop growth simulation model; yield estimates were made with the inclusion of rice crop parameters derived from the remote sensing products viz., seasonal rice area, SoS and backscatter time series. Yield Simulation accuracy levels of 87 per cent at district level and 85-96 per cent at block level demonstrated the suitability of remote sensing products for policy decisions ensuring food security and reducing vulnerability of farmers in India.

The present investigation was carried out at Sugarcane Research Station, Cuddalore on CoC25 sugarcane variety during the period of 2016-2019. This study was conducted to determine the effect of silicon nutrition on physiology, yield and quality of sugarcane under drought condition. Result showed that soil application of silica solublizer @ 12.5 kg + 50 kg FYM/ha + sett treatment with 0.5% K 2 SiO 3 + 2.5% urea and potash foliar spray on 15 days interval from 60 to 150 DAP showed maximum germination percentage, tiller population, leaf area, millable canes, relative water content, silica solubilizing microbial count, total chlorophyll content, nitrate reductase activity, catalase, proline, plant height, number of nodes, internode length, silicon content, carbon content, potassium content, single cane weight, commercial cane sugar per cent, cane yield and sugar yield under drought condition in both plant and ratoon crop.

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Soil texture is a vital variable that reflects a number of soil properties such as Bulk Density, Particle Density, Infiltration Rate, Hydraulic conductivity, Water holding capacity, nutrient storage and availability as well as transport and binding and stability of soil aggregates. For better tuber development in cassava soil texture plays vital role.  The main objective of this study is to produce kriged maps (Ordinary kriging map and semivariogram) to interpolate the soil texture for Tapioca growing soils of Paramthy block, Namakkal District at unsampled locations. In this study, nearly 54 surface samples were collected covering 19,149 ha of agriculture land with dominant cultivation of Tapioca. This study helps spatial interpolation of unsampled location of soil texture i.e. sand, silt and clay content which rules the soil physical, chemical and hydrological properties. The average standard error for sand and clay are 0.2 and 0.19 respectively. The results such as provided maps and their associated variance can be used as data source for the development and implementation of further land management and soil water conservation plans in the study area.