Current Science

Swapnil S. Vyas ¹, Bimal K. Bhattacharya ¹, Rahul Nigam ¹

Affiliations
1 Earth Ocean Atmosphere Planetary Sciences and Applications Area, Space Applications Centre, ISRO, Ahmedabad 380 015, IN

Abstract

Quantification of assured solar energy potential is essential to select locations for solar photovoltaic, thermal power plants and to quantify solar power potential. The use of remote sensing observations from geostationary satellite sensors is ideal to capture space-time variability of surface insolation. The annual clear solar energy exposure over India was determined using three years' insolation data at 8 km spatial resolution from Kalpana-1 satellite. High density solar energy pockets were diagnosed in western, central and southern India including Gujarat, Rajasthan, Madhya Pradesh, Karnataka, Tamil Nadu and Chhattisgarh states with annual solar energy exposure ranging from 2500 to 3500 kW h m^-2 yr^-1.

Keywords

Geostationary Satellite, Renewable Energy, Remote Sensing.

Full Text

   

Rahul Nigam ¹, Rojalin Tripathy ¹, Sujay Dutta ¹, Nita Bhagia ¹, Rohit Nagori ¹, K. Chandrasekar ², Rajsi Kot ³, Bimal K. Bhattacharya ¹, Susan Ustin ⁴

Affiliations
1 Agriculture and Land Eco-system Division, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre (ISRO), Ahmedabad 380 015, IN
2 National Remote Sensing Centre (ISRO), Hyderabad 500 037, IN
3 M.G. Science Institute, Ahmedabad 380 009, IN
4 Environmental and Resource Sciences, University of California, Davis, CA 95616, US

Abstract

Advancements in hyperspectral remote sensing technology have opened new avenues to explore innovative ways to map crops in terms of area and health. To study precise mapping of agriculture and horticulture crops along with biophysical and biochemical constituents at field scale, an airborne AVIRIS-NG hyperspectral imaging has been conducted in various agro-climatic regions representing diverse agricultural types of India. Crop classification with available and developed algorithms has been applied over homogeneous and heterogeneous agriculture and horticulture cropped areas. The spectral angle mapper and maximum likelihood algorithms showed classification accuracy of 77%–94% for AVIRI-NG and 42%–55% for LISS IV. The customized deep neural network and maximum noise function (MNF)-based classification schemes showed an accuracy of 93% and 86% for mapping of agriculture and horticulture crops respectively. The forward and inversion of canopy radiative transfer model protocol was developed for retrieval of crop parameters such as leaf area index (LAI) and chlorophyll content (C_ab) using AVIRIS-NG narrow bands. The retrieved LAI and C_ab showed 19%–27% and 23%–29% deviation from measured mean for homogeneous and heterogeneous agricultural areas respectively. Red edge position index-based empirical model and multivariate linear regression of multiple indices showed maximum correlation of 0.62 and 0.93 respectively, to map leaf nitrogen content. Water condition index was developed using vegetation and water indices to distinguish crop water-based abiotic stress. Wheat yellow rust disease has been identified at field scale using absorption band depth analysis at 662–702 and 2155–2175 nm, and further applied to AVIRIS-NG data to detect biotic stress at spatial scale. This study establishes that such missions have the potential to boost accurate mapping of economically valuable minor crops and generate health indicators to distinguish biotic and abiotic stresses at field scale.

Keywords

Assessment, Biotic and Abiotic Stress, Crop Classification, Health, Hyperspectral Imaging.

Full Text

   

Bhubanananda Adhikari ¹, Radhakrushna Senapati ², Minati Mohapatra ³, Laxminarayan Mohapatra ³, Rahul Nigam ⁴, Shyamaranjan Das Mohapatra ²

Affiliations
1 ICAR-National Rice Research Institute, Cuttack 753 006, India; Odisha University of Agriculture and Technology, Bhubaneswar 751 003, India, IN
2 ICAR-National Rice Research Institute, Cuttack 753 006, India, IN
3 Odisha University of Agriculture and Technology, Bhubaneswar 751 003, India, IN
4 Space Application Centre, Indian Space Research Organisation, Ahmedabad 380 015, India, IN

Abstract

Hyperspectral remote sensing is a useful technique for detecting spatio-temporal changes in crop morphological and physiological health. In order to identify the pest-sensitive bands for rice leaf folder (RLF), the ground-based hyperspectral data were recorded at varying damage levels. The first- and second-order derivatives were correlated with correlation coefficient r and per cent leaf damage. The common region identified were recognized as sensitive regions (508–529, 671–680, 721–759, 779–786 and 804–820 nm). The absorption dips were also found using Sensitivity and Continuum Removal Analysis. Combining all, a total of nine spectral bands (518, 549, 661, 674, 678, 731, 789, 816 and 898 nm) were identified as pest-sensitive bands for RLF. The feature-selection method was employed using RELIEFF algorithm to find out the band combinations and bands 518, 661 and 731 nm yielded maximum accuracy of 81.67%

Keywords

Hyperspectral sensing, rice leaf folder, sen-sitive spectral bands, spectroradiometer.

Full Text