Quality‐assured long‐term satellite‐based leaf area index product
Jian PengSimon BlessingRalf GieringBenjamin MüllerNadine GobronJoanne NightingaleK. F. BoersmaJan‐Peter Müller
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Numerous global satellite-based leaf area index (LAI) products have been generated and widely used for a number of applications such as crop assessment in agro-meteorology or to achieve a better understanding of soil-vegetation-atmosphere interactions and modeling BioGeochemical Cycles (eg, Forzieri, Alkama, Miralles, & Cescatti, 2017; Morton et al., 2014; Spracklen, Arnold, & Taylor, 2012; Zeng et al., 2017). The theoretical and physical uncertainties of these products have been quantified for certain areas and temporal periods (eg, Chen et al., 2002; Garrigues et al., 2008; Morisette et al., 2006). Furthermore, Jiang et al. (2017) comprehensively evaluated the consistency of the existing long-term (≥30 years) global satellite-based LAI products, in terms of uncertainty variations, trends and inter-annual variabilities. Their results indicate that these long-term LAI products are not internally consistent over time and also not consistent with each other. None of them are suitable for serving as a reference dataset in long-term global change research. These inconsistencies among these products are likely due to the different definitions of LAI (eg, effective LAI vs. green leaf LAI), spectral responsivity differences, satellite orbit drift, as well as the different methods of retrieval. Therefore, there is a strong need for a quality-assured long-term LAI product, which means reliable, traceable and understandable quality information is provided. These datasets need to refer to comprehensive details of the processing algorithm (eg, in the form of an ATBD), undergo independent traceable ongoing and globally explicit validation, and contain estimates of uncertainties from the propagation through the processing algorithms. The Quality Assurance for Essential Climate Variables (QA4ECV) project (funded by the European Union's Seventh Framework Programme (FP7/2007-2013) under QA4ECV grant agreement no. 607405) is developing a prototype international quality assurance framework between data producers, national metrology and standards organizations and data users to capture and provide understandable and traceable quality information for ECVs (http://www.qa4ecv.eu). This framework will be further developed for operational application within the European Copernicus Climate Change Service and will ensure that long-term ECV data products, such as LAI, are provided with full uncertainty metrics in a format that can be readily used by end users (in netCDF4-CM format). One such product is the effective LAI that is produced using the Two-stream Inversion Package (Clerici et al., 2010; Voßbeck et al., 2010), based on the Two-Stream Model developed by Pinty et al. (2006), which implements the two-stream approximation of radiative transfer for a homogeneous canopy (1D-canopy). The 1D radiative transfer model is consistent with large-scale climate and Earth system models and does not require assumptions about other factors such as biome type. This LAI product will provide the uncertainties of LAI for each pixel, which have been propagated through the whole processing chain, also taking into account uncertainty correlations in an enhancement of the LAI product presented in Disney et al. (2016). This product will add greater transparency and openness between ECV producers and end users, and facilitate the application of a long-term LAI product for global change research. This research has been supported by the FP7 Project Quality Assurance for Essential Climate Variables (QA4ECV), grant No. 607405.Индекс листовой площади (LAI) и фитомасса являются основными определителями первичной чистой продукции, которые могут быть определены методами дистанционного зондирования кроны растительности. Индекс LAI отражает взаимосвязь кроны растений с оптической радиацией Солнца иявляется довольно значимым показателем для определения обмена СО2, H2O, и энергии между растениями и атмосферой. LAI также является количественным показателем сезонных изменений кроны а такжефенологии растений, которые признаны в качестве интегрированных показателей реакции растений наклиматические изменения. Этот индекс количественно может быть определен как с помощью методовбортовых измерений, так и методов наземных валидационных измерений. Вместе с тем, информация,добываемая с помощи отраженной от растительности радиации, зависит от таких показателей, как уголсолнечного освещения, фоновое отражение, угол обзора, собственные показатели фитомассы и индексLAI. Статья посвящена исследованию влияния небесной фоновой радиации на значение предлагаемогомодифицированного индекса LAI. Проведенное модельное исследование показало, что неопределенностьпредлагаемого модифицированного индекса листовой площади MLAI минимальна при убывающем видефункции зависимости FAPAR от небесной фоновой радиации, т.е. при больших зенитных углах Солнца.На основании этого сделан вывод о том, что вновь введенный индекс MLAI наиболее устойчив при больших зенитных углах Солнца. Следовательно, при наличии данных о значении небесной фоновой радиацииβ, величину LAI желательно вычислять по индексу MLAI при больших зенитных углах Солнца.
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Betula platyphylla
Specific leaf area
Plant litter
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å¶é¢ç§¯ææ°LAIï¼Leaf Area Indexï¼æ¯è¡¨å¾æ¤è¢«å ä½ç»æåçé¿ç¶æçéè¦çç©ç©çåæ°ï¼ä¹æ¯é表è¿ç¨æ¨¡åçéè¦è¾å ¥åæ°ï¼å¦ä½è·åé«ç²¾åº¦LAIä¸ç´å¤åå ³æ³¨ãè¿å¹´æ¥ï¼éçé¥ææ°æ®çä¸æ丰å¯ï¼LAIé¥æä¼°ç®ç®æ³å¾å°äºå¿«éåå±ï¼å ¨ç尺度çLAI产å已被广æ³åºç¨äºæ°åä¸çæç¯å¢ååç 究ãç¶èï¼å½å主æµçLAIé¥æ产åçæç®æ³åºæ¬ä¸åºäºå¹³å¦å°è¡¨å设è忽ç¥äºå°å½¢çå½±åï¼å æ¤å¨å°å½¢å¤æçå°åºç²¾åº¦è¾å·®ãè¿æ¯å 为å¨å±±å°ä¸å´å²çå°è¡¨ä¸ä» ä¼å¯¼è´ä¸¥éçè¾å°å¤±çç°è±¡ï¼è¿ä¼å é»è¿çå°å½¢å¯¹å°ç©ç®æ é æé®æ¡ï¼å æ¤æ£®æå¤æ ·çå å±ç»æåå±±å°å¤æå°å½¢çç¸äºå½±åç»LAIé¥æåæ¼å¸¦æ¥äºè¾å¤§çä¸ç¡®å®æ§ãå±±å°ä½ä¸ºä¸ç§ç¹æ®çå°è²ï¼çº¦å å ¨çéå°è¡¨é¢ç1/4ï¼å¨ä¸å½å äºè¿2/3ï¼å¨è¿äºå¤æåºåä¸ä¼°ç®LAIèèå°å½¢å ç´ ååå¿ è¦ãå¨æ¬æä¸ï¼æ们é¦å ç³»ç»å°æ»ç»äºç°æLAIåæ¼ç®æ³åå ¨çé¥æ产åçå辨çã精度çä¿¡æ¯ï¼å¹¶è®¨è®ºäºå°è¿äºç®æ³å产ååºç¨äºå´å²å°å½¢LAIåæ¼ç主è¦ææãç¶åï¼é对山å°æ¤è¢«åºæ¯ä¸åå¨çå°å½¢æåºã尺度æåºï¼æ»ç»åºå±±å°æ¤è¢«å å±LAIåæ¼ççç¥ä¸»è¦å æ¬å°å½¢æ ¡æ£æ¹æ³åå±±å°è¾å°ä¼ è¾æ¨¡åï¼å¹¶è®¨è®ºäºä¸åçç¥çä¼ç¼ºç¹ãæ¥çï¼æç« è®¨è®ºäºéå¤è§æµçLAIæ°æ®å¨å´å²å°å½¢ä¸åå¨çå°å½¢æåºå尺度æåºï¼ä»¥åè¿äºæåºå¯¹åæ¼ç»æéªè¯çå½±åç¨åº¦ãæåï¼ç»¼åæ»ç»åå±æ表æï¼é¥æè§æµãå±±å°è¾å°ä¼ è¾å»ºæ¨¡ãæºå¨å¦ä¹ ææ¯çæ¹é¢çåè°ä½¿ç¨å°æ¥å¯ä»¥ä¸ºå´å²å°è¡¨çLAIç²¾åä¼°ç®åå¯é éªè¯æä¾äºä¸æ¡æå¸æçéå¾ã
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Different optical instruments are currently available for measuring LAI such as LAI 2000 Plant Canopy Analyser (LAI-2000), Tracing Radiation and Architecture of Canopies (TRAC) and Digital Hemispherical Photography (DHP). Their applicability varies in different ecosystems. This study was devoted to compare LAI measured using four different methods (LAI measured by DHP, LAI measured by TRAC, LAI calculated using effective LAI measured by LAI-2000 and clumping index measured by DHP, and LAI calculated using effective LAI measured by LAI-2000 and clumping index measured by TRAC) in the Maoershan experimental forest farm of Northeast Forestry University located in Shangzhi city of Heilongjiang province. Methods used to measure LAI have considerable effects on observed LAI. The means of LAI measured by four different methods are 3.15, 4.73, 3.97, and 4.24 and corresponding standard deviations are 1.54, 2.39, 1.82, and 1.75, respectively. According to previous studies, the combination of LAI-2000 with TRAC can give the most reliable measurements of LAI. Therefore, DHP tends to underestimate LAI at this area, especially for dense canopies while TRAC tends to overestimate slightly LAI for dense canopies. The fitting of LAI measured using four different methods with normalized difference vegetation index (NDVI) and reduced simple ratio (RSR) calculated from TM data acquired on June 24, 2009 indicated that RSR is a better predictor of LAI than NDVI in this study area. The agreements between measured and estimated LAI using the best fitted models are 58%, 70%, 57% and 68% for these four different methods. Corresponding root mean square errors (RMSE) are 0.80, 0.85, 0.88, and 0.75, respectively. The regional means of LAI retrieved using the empirical models established on the basis of RSR and LAI measured with four different methods are 3.47, 5.26, 4.31, and 4.68, respectively, indicating that if DHP is used as a surrogate of TRAC and LAI-2000, LAI might be underestimated by about 25.7% in this area.
TRAC
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A field study was conducted at the International Rice Research Institute (IRRI), Philippines during the dry seasons of 1997 and 1998 under irrigated conditions. The objectives of this study were to quantify the critical leaf area index (LAI c ) at which tillering stops based on the relationship between tillering rate and LAI, and to determine the effect of nitrogen (N) on LAI c in irrigated rice ( Oryza sativa L.) crop. Results showed that the relative tillering rate (RTR) decreased exponentially as LAI increased at a given N input level. The coefficient of determination for the equation quantifying the RTR-LAI relationship ranged from 0·87 to 0·99. The relationship between RTR and LAI was affected by N input level, but not by planting density. The N input level had a significant effect on LAI c with a high N input level causing an increase in LAI c . Tillering stopped at LAI of 3·36 to 4·11 when N was not limiting. Under N limited conditions LAI c reduced to as low as 0·98. Transplanting spacing and number of seedlings per hill had little effect on LAI c . Results from this study suggest that LAI and plant N status are two major factors that influence tiller production in rice crops. The possibility that LAI influences tillering by changing light intensity and/or light quality at the base of the canopy where tiller buds and young tillers are located is discussed.
Tiller (botany)
Transplanting
Growing season
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Interception
Specific leaf area
Spatial heterogeneity
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Measurement of leaf area index (LAI) is critical to understanding many aspects of crop development, growth, and management. Availability of portable meters to estimate LAI non‐destructively has greatly increased our ability to determine this parameter during the cropping season. However, with several devices on the market, each with an independent set of protocols for gathering accurate estimates of LAI, it is necessary for scientists to have comparisons of these meters under field conditions before selecting one for purchase and use. The objective of our study was to compare the LAI estimates by three meters (AccuPAR, LAI‐2000, and SunScan) to LAI measured by destructive sampling. Leaf area index of two corn ( Zea mays L.) hybrids, grown on a Pachic Haplustoll, was measured at the R2 stage by the four methods before and after successive thinning of plant stands. Destructively sampled LAI ranged from 4.59 to 1.25 for the initial stand to the most severe thinning. Hybrids did not differ in LAI. All meters underestimated LAI compared with destructive sampling. When all data from all rings of the LAI‐2000 meter were included in the calculations, LAI‐2000 estimates of LAI differed from those of the other two meters. However, when data from Ring 5 was removed from the calculations, estimates of LAI for the LAI‐2000 improved and were indistinguishable from the other meters. The relationship between LAI estimated destructively and by each of the meters was described by a unique linear equation for each hybrid. Results of this study, and experience with use of the meters, suggest that users should consider protocols for operating each meter before deciding which device best suits their application.
Thinning
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Leaf area index (LAI) is a key variable in many land surface models that involve energy and mass exchange between vegetation and the environment. In recent years, extracting vegetation structure parameters from digital photography becomes a widely used indirect method to estimate LAI for its simplicity and ease of use. A Leaf Area Index Sensor (LAIS) system was developed to continuously monitor the growth of crops in several sampling points in Huailai, China. The system applies 3G/WIFI communication technology to remotely collect crop photos in real-time. Then the crop photos are automatically processed and LAI is estimated based on the improved leaf area index of Lang and Xiang (LAILX) algorithm in LAIS. The objective of this study is to primarily verify the LAI estimated from LAIS (Lphoto) through comparing them with the destructive green LAI (Ldest). Ldest was measured across the growing season ntil maximum canopy development while plants are still green. The preliminary verification shows that Lphoto corresponds well with the Ldest (R2=0.975). In general, LAI could be accurately estimated with LAIS and its LAI shows high consistency compared with the destructive green LAI. The continuous LAI measurement obtained from LAIS could be used for the validation of remote sensing LAI products.
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Rapid and reliable estimates of leaf area index (LAI) are important for studies of exchanges of energy and gases in the biosphere-atmosphere continuum. This paper evaluates the field performance of SunScan canopy analysis system for rapid estimation of LAI. Direct and indirect measurements of LAI were made in a maize ( Zea mays L.) field at four phenological stages (emergence, vegetative, flowering and physiological maturity) at a tropical site in Ghana during the Glowa Vota Project field campaign ( www.glowa-volta.de ). Similar measurements were repeated in early and late planting seasons with similar crop management practices. The result showed a generally good performance of this sensor at all the phenological stages. Average LAI from the sensor (LAI S ), ranged from 0.40–4.45, and was consistently higher than the actual LAI, which varied from 0.31–4.22, respectively for both seasons. Regression between LAI and LAI S showed a range of significant correlations with R 2 > 0.74 for all the stages and seasons. With combine d datasets for all stages and the two plantings, a simple regression model was fitted to estimate LAI from LAI S with R 2 = 0.97 and standard error of 0.23 ( P The evaluated sensor yielded a good and reliable LAI estimates under maize canopy. Keywords: SunScan probe, field evaluation, leaf area index, maize, Ghana
Growing season
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Appropriate quantification of leaf area index (LAI) is important for accurate prediction of photosynthetic productivity by crop growth models. Estimation of LAI requires accurate modelling of leaf senescence. Many models use empirical turnover coefficients, the relative leaf-death rate determined from frequent field samplings, to describe senescence during growth. In this paper, we first derive a generic equation for nitrogen-determined photosynthetically active LAI (LAI N ), and then describe a method of using this equation in crop growth models to predict leaf senescence. Based on the theory that leaf-nitrogen at different horizons of a canopy declines exponentially, LAI N , which is counted from the top of the canopy to the depth at which leaf-nitrogen equals the minimum value for leaf photosynthesis, is calculated analytically as a function of canopy leaf-nitrogen content. At each time-step of crop growth modelling, LAI N is compared to an independent calculation of the non-nitrogen-limited LAI assuming no leaf death during that time-step (LAI NLD ). In early stages, LAI N is higher than LAI NLD ; but with the advancement of crop growth, LAI N will become smaller than LAI NLD . The difference between LAI NLD and LAI N , whenever LAI N is smaller than LAI NLD , gives the estimate of leaf area senesced at the time-step; the senesced leaf area divided by specific leaf area (SLA) gives the estimate of senesced leaf mass. The method was incorporated into two crop models and the models adequately accounted for the LAI observed in field experiments for rice and barley. The novel features of the approach are that: (1) it suggests a coherent, biologically reasonable picture of leaf senescence based on the link with photosynthesis and leaf nitrogen content; (2) it avoids the use of empirical leaf-turnover coefficients; (3) it avoids over-sensitivity of LAI prediction to SLA; and (4) it is presumably of sufficient generality as to be applicable to plant types other than crops. The method can be applied to models where leaf-nitrogen is used as an input variable or is simulated explicitly. Copyright 2000 Annals of Botany Company
Specific leaf area
Photosynthetic capacity
Plant canopy
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