scispace - formally typeset
Search or ask a question
Author

K. R. Manjunath

Bio: K. R. Manjunath is an academic researcher from Indian Space Research Organisation. The author has contributed to research in topics: Normalized Difference Vegetation Index & Crop yield. The author has an hindex of 16, co-authored 36 publications receiving 790 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: In this paper, the temporal backscatter of rice crop for a predominant rice-growing region in West Bengal, India was analyzed using a standard beam SAR data of different incidence angle.
Abstract: Temporal RADARSAT Standard beam SAR data of different incidence angle were analysed to study the temporal backscatter of rice crop for a predominant rice-growing region in West Bengal, India Correlation studies of backscatter with crop growth parameters were carried out Second order polynomial was the best fit obtained for crop age and crop height Shallow angle data (> 40°) was found better correlated to crop height than steep angle (23°) data Inversion algorithm was used to generate spatial maps of crop height and age The results validated over a village showed an over all 90% accuracy

108 citations

Journal ArticleDOI
TL;DR: In this article, the source and sinks of CO 2 and CH 4 due to anthropogenic and natural biospheric activities were estimated for the South Asian region (Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka).
Abstract: The source and sinks of carbon dioxide (CO 2 ) and methane (CH 4 ) due to anthropogenic and natural biospheric activities were estimated for the South Asian region (Bangladesh, Bhutan, India, Nepal, Pakistan and Sri Lanka). Flux estimates were based on top-down methods that use inversions of atmospheric data, and bottom-up methods that use field observations, satellite data, and terrestrial ecosystem models. Based on atmospheric CO 2 inversions, the net biospheric CO 2 flux in South Asia (equivalent to the Net Biome Productivity, NBP) was a sink, estimated at −104 ± 150 Tg C yr −1 during 2007–2008. Based on the bottom-up approach, the net biospheric CO 2 flux is estimated to be −191 ± 193 Tg C yr −1 during the period of 2000–2009. This last net flux results from the following flux components: (1) the Net Ecosystem Productivity, NEP (net primary production minus heterotrophic respiration) of −220 ± 186 Tg C yr −1 (2) the annual net carbon flux from land-use change of −14 ± 50 Tg C yr −1 , which resulted from a sink of −16 Tg C yr −1 due to the establishment of tree plantations and wood harvest, and a source of 2 Tg C yr −1 due to the expansion of croplands; (3) the riverine export flux from terrestrial ecosystems to the coastal oceans of +42.9 Tg C yr −1 ; and (4) the net CO 2 emission due to biomass burning of +44.1 ± 13.7 Tg C yr −1 . Including the emissions from the combustion of fossil fuels of 444 Tg C yr −1 for the 2000s, we estimate a net CO 2 land–atmosphere flux of 297 Tg C yr −1 . In addition to CO 2 , a fraction of the sequestered carbon in terrestrial ecosystems is released to the atmosphere as CH 4 . Based on bottom-up and top-down estimates, and chemistry-transport modeling, we estimate that 37 ± 3.7 Tg C yr −1 were released to atmosphere from South Asia during the 2000s. Taking all CO 2 and CH 4 fluxes together, our best estimate of the net land–atmosphere CO 2 -equivalent flux is a net source of 334 Tg C yr −1 for the South Asian region during the 2000s. If CH 4 emissions are weighted by radiative forcing of molecular CH 4 , the total CO 2 -equivalent flux increases to 1148 Tg C yr −1 suggesting there is great potential of reducing CH 4 emissions for stabilizing greenhouse gases concentrations.

105 citations

Journal ArticleDOI
TL;DR: In this article, the spectral properties of vegetation both in the optical and thermal range of the electromagnetic spectrum as affected by its attributes have been discussed to reduce data dimension and minimize the information redundancy.
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.

104 citations

Journal ArticleDOI
TL;DR: OAA levels obtained in this study by ANN and SVM classifiers identify the suitability of these classifiers for tropical vegetation discrimination.

61 citations

Journal ArticleDOI
TL;DR: In this article, the authors used four-date data acquired in the 24-day repeat cycle between January 2 and March 15, 1997 was used to study the temporal backscatter characteristics of these crops in relation to the growth stages.
Abstract: The Canadian satellite RADARSAT launched in November 1995 acquires C-band HH polarisation Synthetic Aperture Radar (SAR) data in various incident angles and spatial resolutions. In this study, the Standard Beam S7 SAR data with 45°–49° incidence angle has been used to discriminate rice and potato crops grown in the Gangetic plains of West Bengal state. Four-date data acquired in the 24-day repeat cycle between January 2 and March 15, 1997 was used to study the temporal backscatter characteristics of these crops in relation to the growth stages. Two, three and four-date data were used to classify the crops. The results show that the backscatter was the lowest during puddling of rice fields and increased as the crop growth progressed. The backscatter during this period changed from −18 dB to −8 dB. This temporal behaviour was similar to that observed in case of ERS-SAR data. The classification accuracy of rice areas was 94% using four-date data. Two-date data, one corresponding to pre-field preparation and the other corresponding to transplantation stage, resulted in 92% accuracy. The last observation is of particular interest as one may estimate the crop area as early as within 20–30 days of transplantation. Such an early estimate is not feasible using optical remote sensing data or ERS-SAR data. The backscatter of potato crop varied from −9 dB to −6 dB during the growth phase and showed large variations during early vegetative stage. Two-date data, one acquired during 40–45 days of planting and another at maturing stage, resulted in 93% classification accuracy for potato. All other combinations of two-date data resulted in less than 90% classification accuracy for potato.

59 citations


Cited by
More filters
Book ChapterDOI
01 Jan 2014
TL;DR: For base year 2010, anthropogenic activities created ~210 (190 to 230) TgN of reactive nitrogen Nr from N2 as discussed by the authors, which is at least 2 times larger than the rate of natural terrestrial creation of ~58 Tg N (50 to 100 Tg nr yr−1) (Table 6.9, Section 1a).
Abstract: For base year 2010, anthropogenic activities created ~210 (190 to 230) TgN of reactive nitrogen Nr from N2. This human-caused creation of reactive nitrogen in 2010 is at least 2 times larger than the rate of natural terrestrial creation of ~58 TgN (50 to 100 TgN yr−1) (Table 6.9, Section 1a). Note that the estimate of natural terrestrial biological fixation (58 TgN yr−1) is lower than former estimates (100 TgN yr−1, Galloway et al., 2004), but the ranges overlap, 50 to 100 TgN yr−1 vs. 90 to 120 TgN yr−1, respectively). Of this created reactive nitrogen, NOx and NH3 emissions from anthropogenic sources are about fourfold greater than natural emissions (Table 6.9, Section 1b). A greater portion of the NH3 emissions is deposited to the continents rather than to the oceans, relative to the deposition of NOy, due to the longer atmospheric residence time of the latter. These deposition estimates are lower limits, as they do not include organic nitrogen species. New model and measurement information (Kanakidou et al., 2012) suggests that incomplete inclusion of emissions and atmospheric chemistry of reduced and oxidized organic nitrogen components in current models may lead to systematic underestimates of total global reactive nitrogen deposition by up to 35% (Table 6.9, Section 1c). Discharge of reactive nitrogen to the coastal oceans is ~45 TgN yr−1 (Table 6.9, Section 1d). Denitrification converts Nr back to atmospheric N2. The current estimate for the production of atmospheric N2 is 110 TgN yr−1 (Bouwman et al., 2013).

1,967 citations

Journal ArticleDOI
Marielle Saunois1, Philippe Bousquet1, Ben Poulter2, Anna Peregon1, Philippe Ciais1, Josep G. Canadell3, Edward J. Dlugokencky4, Giuseppe Etiope5, David Bastviken6, Sander Houweling7, Greet Janssens-Maenhout, Francesco N. Tubiello8, Simona Castaldi, Robert B. Jackson9, Mihai Alexe, Vivek K. Arora, David J. Beerling10, Peter Bergamaschi, Donald R. Blake11, Gordon Brailsford12, Victor Brovkin13, Lori Bruhwiler4, Cyril Crevoisier14, Patrick M. Crill, Kristofer R. Covey15, Charles L. Curry16, Christian Frankenberg17, Nicola Gedney18, Lena Höglund-Isaksson19, Misa Ishizawa20, Akihiko Ito20, Fortunat Joos21, Heon Sook Kim20, Thomas Kleinen13, Paul B. Krummel3, Jean-Francois Lamarque22, Ray L. Langenfelds3, Robin Locatelli1, Toshinobu Machida20, Shamil Maksyutov20, Kyle C. McDonald23, Julia Marshall13, Joe R. Melton, Isamu Morino18, Vaishali Naik24, Simon O'Doherty25, Frans-Jan W. Parmentier26, Prabir K. Patra27, Changhui Peng28, Shushi Peng1, Glen P. Peters29, Isabelle Pison1, Catherine Prigent30, Ronald G. Prinn31, Michel Ramonet1, William J. Riley32, Makoto Saito20, Monia Santini, Ronny Schroeder23, Ronny Schroeder33, Isobel J. Simpson11, Renato Spahni21, P. Steele3, Atsushi Takizawa34, Brett F. Thornton, Hanqin Tian35, Yasunori Tohjima20, Nicolas Viovy1, Apostolos Voulgarakis36, Michiel van Weele37, Guido R. van der Werf38, Ray F. Weiss39, Christine Wiedinmyer22, David J. Wilton10, Andy Wiltshire18, Doug Worthy40, Debra Wunch41, Xiyan Xu32, Yukio Yoshida20, Bowen Zhang35, Zhen Zhang2, Qiuan Zhu42 
TL;DR: The Global Carbon Project (GCP) as discussed by the authors is a consortium of multi-disciplinary scientists, including atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions.
Abstract: . The global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (∼ biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003–2012 decade, global methane emissions are estimated by top-down inversions at 558 Tg CH4 yr−1, range 540–568. About 60 % of global emissions are anthropogenic (range 50–65 %). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 Tg CH4 yr−1, range 596–884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (∼ 64 % of the global budget, The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30–40 % on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center ( http://doi.org/10.3334/CDIAC/GLOBAL_METHANE_BUDGET_2016_V1.1 ) and the Global Carbon Project.

771 citations

Journal ArticleDOI
TL;DR: In this paper, the authors reviewed the global impacts of climate change on livestock production, the contribution of livestock production to climate change, and specific climate change adaptation and mitigation strategies in the livestock sector.

741 citations

Journal ArticleDOI
TL;DR: In this article, a spatial assessment of heat stress risk at a global level for four key crops, wheat, maize, rice and soybean, using the FAO/IIASA Global Agro-Ecological Zones Model (GAEZ) is presented.

616 citations

Journal ArticleDOI
TL;DR: In this article, a suite of nine dynamic global vegetation models and four ocean biogeochemical general circulation models were used to estimate trends driven by global and regional climate and atmospheric CO2 in land and oceanic CO2 exchanges with the atmosphere over the period 1990-2009, to attribute these trends to underlying processes in the models, and to quantify the uncertainty and level of inter-model agreement.
Abstract: . The land and ocean absorb on average just over half of the anthropogenic emissions of carbon dioxide (CO2) every year. These CO2 "sinks" are modulated by climate change and variability. Here we use a suite of nine dynamic global vegetation models (DGVMs) and four ocean biogeochemical general circulation models (OBGCMs) to estimate trends driven by global and regional climate and atmospheric CO2 in land and oceanic CO2 exchanges with the atmosphere over the period 1990–2009, to attribute these trends to underlying processes in the models, and to quantify the uncertainty and level of inter-model agreement. The models were forced with reconstructed climate fields and observed global atmospheric CO2; land use and land cover changes are not included for the DGVMs. Over the period 1990–2009, the DGVMs simulate a mean global land carbon sink of −2.4 ± 0.7 Pg C yr−1 with a small significant trend of −0.06 ± 0.03 Pg C yr−2 (increasing sink). Over the more limited period 1990–2004, the ocean models simulate a mean ocean sink of −2.2 ± 0.2 Pg C yr−1 with a trend in the net C uptake that is indistinguishable from zero (−0.01 ± 0.02 Pg C yr−2). The two ocean models that extended the simulations until 2009 suggest a slightly stronger, but still small, trend of −0.02 ± 0.01 Pg C yr−2. Trends from land and ocean models compare favourably to the land greenness trends from remote sensing, atmospheric inversion results, and the residual land sink required to close the global carbon budget. Trends in the land sink are driven by increasing net primary production (NPP), whose statistically significant trend of 0.22 ± 0.08 Pg C yr−2 exceeds a significant trend in heterotrophic respiration of 0.16 ± 0.05 Pg C yr−2 – primarily as a consequence of widespread CO2 fertilisation of plant production. Most of the land-based trend in simulated net carbon uptake originates from natural ecosystems in the tropics (−0.04 ± 0.01 Pg C yr−2), with almost no trend over the northern land region, where recent warming and reduced rainfall offsets the positive impact of elevated atmospheric CO2 and changes in growing season length on carbon storage. The small uptake trend in the ocean models emerges because climate variability and change, and in particular increasing sea surface temperatures, tend to counter\-act the trend in ocean uptake driven by the increase in atmospheric CO2. Large uncertainty remains in the magnitude and sign of modelled carbon trends in several regions, as well as regarding the influence of land use and land cover changes on regional trends.

607 citations