Desouza Blaise , Nirmala D. Desouza , Amarpreet Singh
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This phase was the strongest sink and coincided with the highest CO<sub>2</sub> uptake, followed by the flowering and square formation phases. The cotton crop was a C source during the initial vegetative phase and after the boll opening. Overall, the cotton crop was a sink for atmospheric CO<sub>2</sub> with an average NEE value of −189.6 g C m<sup>−2</sup> under irrigated and −245.6 g C m<sup>−2</sup> in rainfed cotton. Higher ecosystem respiration in irrigated cotton resulted in lower C sink strength than rainfed cotton. Our studies indicate that the SMAP L4_C product model estimates can be used to obtain information on C fluxes in real-world situations. 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Satellite-based remote sensing techniques have made it possible to obtain NEE and component carbon (C) fluxes. One-third of the world's cotton area is in India, but the information on NEE is limited. We used the Level 4 Carbon (L4_C) product from the Soil Moisture Active Passive (SMAP) mission to estimate C fluxes based on satellite-derived soil moisture, weather, and vegetation data. For our study (2018-19 to 2020-21), we chose two ecosystems (rainfed central India vs. irrigated northern India). Seasonal variations were observed in C fluxes. Gross primary productivity was the highest during the boll formation phase. This phase was the strongest sink and coincided with the highest CO<sub>2</sub> uptake, followed by the flowering and square formation phases. The cotton crop was a C source during the initial vegetative phase and after the boll opening. 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引用次数: 0
摘要
二氧化碳(CO2)观测网络数量少,设备成本过高,限制了对生态系统二氧化碳净交换量(NEE)的估算。卫星遥感技术使获取净生态系统二氧化碳交换量和碳通量成为可能。印度的棉花面积占世界棉花面积的三分之一,但有关 NEE 的信息却很有限。我们利用土壤水分主动被动(SMAP)任务的第四级碳(L4_C)产品,根据卫星获得的土壤水分、天气和植被数据估算碳通量。在我们的研究中(2018-19 年至 2020-21 年),我们选择了两个生态系统(印度中部的雨水灌溉系统与印度北部的灌溉系统)。我们观察到了碳通量的季节性变化。在棉铃形成阶段,总初级生产力最高。这个阶段是最强的吸收汇,同时也是二氧化碳吸收量最高的阶段,其次是开花期和方格形成期。棉花作物在无性繁殖初期和棉铃开放后是碳源。总体而言,棉花作物是大气二氧化碳的吸收汇,灌溉棉花的平均 NEE 值为 -189.6 g C m-2,雨浇棉花为 -245.6 g C m-2。与雨浇棉花相比,灌溉棉花的生态系统呼吸作用更强,导致碳汇强度更低。我们的研究表明,SMAP L4_C 产品模型估计值可用于获取实际情况下的碳通量信息。此外,这种基于卫星的遥感技术将实现对不同种植系统的大规模环境监测,并为政策制定提供支持。
Satellite-based measurements of temporal and spatial variations in C fluxes of irrigated and rainfed cotton grown in India
The small number of carbon dioxide (CO2) observation networks and the prohibitively high equipment cost restrict the estimation of net ecosystem CO2 exchange (NEE). Satellite-based remote sensing techniques have made it possible to obtain NEE and component carbon (C) fluxes. One-third of the world's cotton area is in India, but the information on NEE is limited. We used the Level 4 Carbon (L4_C) product from the Soil Moisture Active Passive (SMAP) mission to estimate C fluxes based on satellite-derived soil moisture, weather, and vegetation data. For our study (2018-19 to 2020-21), we chose two ecosystems (rainfed central India vs. irrigated northern India). Seasonal variations were observed in C fluxes. Gross primary productivity was the highest during the boll formation phase. This phase was the strongest sink and coincided with the highest CO2 uptake, followed by the flowering and square formation phases. The cotton crop was a C source during the initial vegetative phase and after the boll opening. Overall, the cotton crop was a sink for atmospheric CO2 with an average NEE value of −189.6 g C m−2 under irrigated and −245.6 g C m−2 in rainfed cotton. Higher ecosystem respiration in irrigated cotton resulted in lower C sink strength than rainfed cotton. Our studies indicate that the SMAP L4_C product model estimates can be used to obtain information on C fluxes in real-world situations. Moreover, such satellite-based remote sensing techniques will enable large-scale environmental monitoring with different cropping systems and support policymaking.
期刊介绍:
The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems