Current Science

R. N. Sahoo ¹, S. S. Ray ², K. R. Manjunath ³

Affiliations
1 Indian Agricultural Research Institute, New Delhi 110 012, IN
2 Mahalanobis National Crop Forecast Centre, Pusa Campus, New Delhi 110 012, IN
3 Space Applications Centre, ISRO, Ahmedabad 380 015, IN

Abstract

Remote sensing is being increasingly used in different agricultural applications. Hyperspectral remote sensing in large continuous narrow wavebands provides significant advancement in understanding the subtle changes in biochemical and biophysical attributes of the crop plants and their different physiological processes, which otherwise are indistinct in multispectral remote sensing. This article describes spectral properties of vegetation both in the optical and thermal range of the electromagnetic spectrum as affected by its attributes. Different methods have been discussed to reduce data dimension and minimize the information redundancy. Potential applications of hyperspectral remote sensing in agriculture, i.e. spectral discrimination of crops and their genotypes, quantitative estimation of different biophysical and biochemical parameters through empirical and physical modelling, assessing abiotic and biotic stresses as developed by different researchers in India and abroad are described.

Keywords

Agriculture, Biotic And Abiotic Stress, Hyperspectral Remote Sensing, Spectral Reflectance.

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B. S. Das ¹, M. C. Sarathjith ¹, P. Santra ², R. N. Sahoo ³, R. Srivastava ⁴, A. Routray ¹, S. S. Ray ⁵

Affiliations
1 Indian Institute of Technology Kharagpur, Kharagpur 721 302, IN
2 Central Arid Zone Research Institute, Jodhpur 342 003, IN
3 Indian Agricultural Research Institute, Pusa, New Delhi 110 012, IN
4 National Bureau of Soil Survey and Land Use Planning, Nagpur 440 033, IN
5 Mahalanobis National Crop Forecast Centre, Pusa Campus, New Delhi 110 012, IN

Abstract

Rapid and reliable assessment of soil characteristics is an important step in agricultural and natural resource management. Over the last few decades, diffuse reflectance spectroscopy (DRS) has emerged as a new tool to obtain both qualitative and quantitative information on soil in a non-invasive manner. The DRS approach is attractive because both the proximal and remote mode of measurements may be adopted to estimate multiple attributes of soil such as physical and chemical soil properties and nutrient contents from a single reflectance spectrum. Hyperspectral imaging cameras onboard remote sensing platforms are already providing hundreds of narrow, contiguous bands of reflectance values and the technology is becoming popular as the hyperspectral remote sensing (HRS) approach. The main objective of this review is to summarize the preparedness and opportunities for using the HRS approach for soil assessment in India. Detailed literature review suggests that the HRS approach requires large spectral databases and robust spectral algorithms in addition to the capability to interpret HRS images. Over the last decade, few efforts have been made to create spectral libraries for Indian soils. However, most of these libraries are very small, precluding the development of robust spectral algorithms. Specifically, the availability of HRS data and robust retrieval algorithms for soil properties from HRS data through unmixing procedures require special attention. With several global initiatives to make HRS data available, coordinated efforts are needed in India to build comprehensive spectral libraries, algorithms and create trained human resources to take full advantage of this emerging technology. Specifically, a dedicated spaceborne mission will provide quality hyperspectral data for the effective application of HRS for soil assessment in India.

Keywords

Hyperspectral Remote Sensing, Reflectance Spectroscopy, Soil Assessment, Spectral Databases and Algorithms.

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K. K. Bandyopadhyay ¹, R. N. Sahoo ¹, Ravender Singh ¹, S. Pradhan ¹, S. Singh ¹, Gopal Krishna ¹, S. Pargal ¹, S. K. Mahapatra ²

Affiliations
1 Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, IN
2 National Bureau of Soil Survey and Land Use Planning, Delhi Centre, New Delhi 110 012, IN

Abstract

Rice is the principal crop during kharif (rainy) season in eastern India, which occupies 26.8 M ha accounting for 63.3% of the total rice-growing areas of the country. However, this area is not fully utilized for crop production in the subsequent rabi (post-rainy) season and kept fallow due to a number of biotic, abiotic and socio-economic constraints. If this rabi fallow area can be effectively utilized, it will help in improving the economy of this region, which is yet to be benefited from the green revolution. The objectives of the present study include: (i) delineation of rabi fallow areas of eastern India using remote sensing and GIS technique; (ii) characterization of soil resources of the rabi fallow regions, and (iii) suggesting site-specific crop planning for this region. It was estimated that about 12.54 M ha area in the rabi season is left fallow in eastern India. The soil properties like soil texture, soil moisture retention at field capacity and permanent wilting point, saturated hydraulic conductivity, soil pH, electrical conductivity, soil organic carbon, etc. were determined at the representative profiles distributed in different agro-ecological sub-regions (AESRs) of this region and mapped in a GIS environment. Using water balance studies, site-specific crop planning based on available residual soil moisture has been suggested. In most of the AESRs, pulses and oilseeds like green gram, black gram, Sesamum and mustard can be grown successfully on residual soil moisture content. Crops which suffer from water deficit during maturity stages can also be grown during rabi season with one or two supplemental irrigations, wherever possible. If the site-specific constraints to crop production can be alleviated and these fallow lands can be brought under cultivation through proper crop planning as suggested, poverty in this resourceful region can be eradicated to a great extent.

Keywords

Crop Planning, Rabi Fallow, Remote Sensing and GIS, Water Balance.

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Sujan Adak ¹, K. K. Bandyopadhyay ¹, R. N. Sahoo ¹, N. Mridha ², M. Shrivastava ³, T. J. Purakayastha ⁴

Affiliations
1 Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi 110 012, IN
2 National Institute of Research on Jute and Allied Fibre Technology, Kolkata 700 040, IN
3 Centre for Environment Sciences and Climate Resilient Agriculture, Indian Agricultural Research Institute, New Delhi 110 012, IN
4 Division of Soil Science and Agricultural Chemistry, Indian Agricultural Research Institute, New Delhi 110 012, IN

Abstract

Effect of tillage, residue mulch and nitrogen manage-ment on canopy spectral reflectance indices and their potential to predict the grain and biomass yield of wheat in advance were studied in a field experiment conducted at the Indian Agricultural Research Institute, New Delhi during 2016–17 and 2017–18. The canopy reflectance was measured using a hand-held ASD FieldSpec spectroradiometer at booting, milking and dough stage of wheat. Then 38 hyperspectral structural indices were recorded using the spectral reflectance data and correlated with wheat yield. It was observed that correlation of these indices with wheat grain and biomass yield was maximum for the booting stage. Among the 38 indices recorded at the booting stage, 13 showed significantly higher correlation with grain yield and 10 indices with biomass yield of wheat (r³0.8). Regression models were developed between grain and biomass yield of wheat with these identified spectral indices recorded at booting stage for 2016–17. Validation of these regression models during 2017–18 showed that normalized difference red edge index (NDREI)-based model performed best for grain and biomass prediction. It could account for maximum 76.4% and 84.3% variation in the observed grain and biomass yield of wheat with ischolar_main mean square error of 37.8% and 50.5% of the corresponding mean values respectively. Thus the regression models based on NDREI recorded at booting stage can be successfully used for the prediction of grain and biomass yield of wheat in advance.

Keywords

Canopy Reflectance, Regression Models, Spectral Indices, Wheat, Yield Prediction.

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