Pub Date : 2019-11-01DOI: 10.1016/J.JAG.2019.101928
J. Hall, R. Zhang, W. Schroeder, Chengquan Huang, L. Giglio
{"title":"Validation of GOES-16 ABI and MSG SEVIRI active fire products","authors":"J. Hall, R. Zhang, W. Schroeder, Chengquan Huang, L. Giglio","doi":"10.1016/J.JAG.2019.101928","DOIUrl":"https://doi.org/10.1016/J.JAG.2019.101928","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74072679","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-11-01DOI: 10.1016/J.JAG.2019.101930
Waqas Ahmad, Dongkyun Kim
{"title":"Estimation of flow in various sizes of streams using the Sentinel-1 Synthetic Aperture Radar (SAR) data in Han River Basin, Korea","authors":"Waqas Ahmad, Dongkyun Kim","doi":"10.1016/J.JAG.2019.101930","DOIUrl":"https://doi.org/10.1016/J.JAG.2019.101930","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76931706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-08-16DOI: 10.1016/J.JAG.2019.101933
M. Hosseini, H. Mcnairn, S. Mitchell, Laura Dingle Robertson, Andrew A. Davidson, Saeid Homayouni
{"title":"Synthetic aperture radar and optical satellite data for estimating the biomass of corn","authors":"M. Hosseini, H. Mcnairn, S. Mitchell, Laura Dingle Robertson, Andrew A. Davidson, Saeid Homayouni","doi":"10.1016/J.JAG.2019.101933","DOIUrl":"https://doi.org/10.1016/J.JAG.2019.101933","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"90 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80428039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Miranda, R. Nóbrega, M. Moura, S. Raghavan, J. Galvíncio
Abstract Leaf Area Index (LAI) models that consider all phenological stages have not been developed for the Caatinga, the largest seasonally dry tropical forest in South America. LAI models that are currently used show moderate to high covariance when compared to in situ data, but they often lack accuracy in the whole spectra of possible values and do not consider the impact that the stems and branches have over LAI estimates, which is of great influence in the Caatinga. In this study, we develop and assess PAI (Plant Area Index) and LAI models by using ground-based measurements and satellite (Landsat) data. The objective of this study was to create and test new empirical models using a multi-year and multi-source of reflectance data. The study was based on measurements of photosynthetic photon flux density (PPFD) from above and below the canopy during the periods of 2011–2012 and 2016–2018. Through iterative processing, we obtained more than a million candidate models for estimating PAI and LAI. To clean up the small discrepancies in the extremes of each interpolated series, we smoothed out the dataset by fitting a logarithmic equation with the PAI data and the inverse contribution of WAI (Wood Area Index) to PAI, that is the portion of PAI that is actually LAI ( L A I C ). L A I C can be calculated as follows: L A I C = 1 - W A I / P A I ). We subtracted the WAI values from the PAI to develop our in situ LAI dataset that was used for further analysis. Our in situ dataset was also used as a reference to compare our models with four other models used for the Caatinga, as well as the MODIS-derived LAI products (MCD15A3H/A2H). Our main findings were as follows: (i) Six models use NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and EVI (Enhanced Vegetation Index) as input, and performed well, with r2 ranging from 0.77 to 0.79 (PAI) and 0.76 to 0.81 (LAI), and RMSE with a minimum of 0.41 m2 m−2 (PAI) and 0.40 m2 m−2 (LAI). The SAVI models showed values 20% and 32% (PAI), and 21% and 15% (LAI) smaller than those found for the models that use EVI and NDVI, respectively; (ii) the other models (ten) use only two bands, and in contrast to the first six models, these new models may abstract other physical processes and components, such as leaves etiolation and increasing protochlorophyll. The developed models used the near-infrared band, and they varied only in relation to the inclusion of the red, green, and blue bands. (iii) All previously published models and MODIS-LAI underperformed against our calibrated models. Our study was able to provide several PAI and LAI models that realistically represent the phenology of the Caatinga.
考虑所有物候阶段的叶面积指数(LAI)模型尚未在南美最大的季节性干燥热带森林Caatinga建立。与原位数据相比，目前使用的LAI模型显示出中度至高度的协方差，但它们在可能值的整个谱中往往缺乏准确性，并且没有考虑干和分支对LAI估计值的影响，而这在Caatinga中具有很大的影响。在本研究中，我们利用地面测量和卫星(Landsat)数据开发并评估了PAI(植物面积指数)和LAI模型。本研究的目的是使用多年和多来源的反射率数据创建和测试新的经验模型。该研究基于2011-2012年和2016-2018年期间冠层上下的光合光子通量密度(PPFD)测量。通过迭代处理，我们获得了100多万个用于估计PAI和LAI的候选模型。为了消除每个插值序列极值的微小差异，我们通过拟合PAI数据和WAI(木材面积指数)对PAI的逆贡献的对数方程来平滑数据集，即PAI中实际上是LAI (L a I C)的部分。L A I C的计算公式为:L A I C = 1 - W A I / P A I)。我们从PAI中减去WAI值，以建立用于进一步分析的原位LAI数据集。我们的原位数据集也被用作参考，将我们的模型与Caatinga使用的其他四个模型以及modis衍生的LAI产品(MCD15A3H/A2H)进行比较。结果表明:(1)6个模型均以归一化植被指数(NDVI)、土壤调整植被指数(SAVI)和增强植被指数(EVI)为输入，r2范围分别为0.77 ~ 0.79 (PAI)和0.76 ~ 0.81 (LAI)， RMSE最小值分别为0.41 m2 m−2 (PAI)和0.40 m2 m−2 (LAI)。与使用EVI和NDVI模型相比，SAVI模型的PAI值分别小20%和32%，LAI值分别小21%和15%;(ii)其他模型(10个)仅使用两个波段，与前6个模型相比，这些新模型可能抽象出其他物理过程和成分，如叶片黄化和原叶绿素增加。开发的模型使用近红外波段，它们的变化仅与红、绿、蓝波段的包含有关。(iii)所有先前发表的模型和MODIS-LAI与我们校准的模型相比表现不佳。我们的研究能够提供几个真实反映Caatinga物候的PAI和LAI模型。
{"title":"Realistic and simplified models of plant and leaf area indices for a seasonally dry tropical forest","authors":"R. Miranda, R. Nóbrega, M. Moura, S. Raghavan, J. Galvíncio","doi":"10.31223/osf.io/kj2vc","DOIUrl":"https://doi.org/10.31223/osf.io/kj2vc","url":null,"abstract":"Abstract Leaf Area Index (LAI) models that consider all phenological stages have not been developed for the Caatinga, the largest seasonally dry tropical forest in South America. LAI models that are currently used show moderate to high covariance when compared to in situ data, but they often lack accuracy in the whole spectra of possible values and do not consider the impact that the stems and branches have over LAI estimates, which is of great influence in the Caatinga. In this study, we develop and assess PAI (Plant Area Index) and LAI models by using ground-based measurements and satellite (Landsat) data. The objective of this study was to create and test new empirical models using a multi-year and multi-source of reflectance data. The study was based on measurements of photosynthetic photon flux density (PPFD) from above and below the canopy during the periods of 2011–2012 and 2016–2018. Through iterative processing, we obtained more than a million candidate models for estimating PAI and LAI. To clean up the small discrepancies in the extremes of each interpolated series, we smoothed out the dataset by fitting a logarithmic equation with the PAI data and the inverse contribution of WAI (Wood Area Index) to PAI, that is the portion of PAI that is actually LAI ( L A I C ). L A I C can be calculated as follows: L A I C = 1 - W A I / P A I ). We subtracted the WAI values from the PAI to develop our in situ LAI dataset that was used for further analysis. Our in situ dataset was also used as a reference to compare our models with four other models used for the Caatinga, as well as the MODIS-derived LAI products (MCD15A3H/A2H). Our main findings were as follows: (i) Six models use NDVI (Normalized Difference Vegetation Index), SAVI (Soil-Adjusted Vegetation Index) and EVI (Enhanced Vegetation Index) as input, and performed well, with r2 ranging from 0.77 to 0.79 (PAI) and 0.76 to 0.81 (LAI), and RMSE with a minimum of 0.41 m2 m−2 (PAI) and 0.40 m2 m−2 (LAI). The SAVI models showed values 20% and 32% (PAI), and 21% and 15% (LAI) smaller than those found for the models that use EVI and NDVI, respectively; (ii) the other models (ten) use only two bands, and in contrast to the first six models, these new models may abstract other physical processes and components, such as leaves etiolation and increasing protochlorophyll. The developed models used the near-infrared band, and they varied only in relation to the inclusion of the red, green, and blue bands. (iii) All previously published models and MODIS-LAI underperformed against our calibrated models. Our study was able to provide several PAI and LAI models that realistically represent the phenology of the Caatinga.","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88706626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1016/J.JAG.2019.03.003
R. Darvishzadeh, A. Skidmore, H. Abdullah, Elias Cherenet, A. Ali, Tiejun Wang, W. Nieuwenhuis, M. Heurich, A. Vrieling, B. O'Connor, M. Paganini
{"title":"Mapping leaf chlorophyll content from Sentinel-2 and RapidEye data in spruce stands using the invertible forest reflectance model","authors":"R. Darvishzadeh, A. Skidmore, H. Abdullah, Elias Cherenet, A. Ali, Tiejun Wang, W. Nieuwenhuis, M. Heurich, A. Vrieling, B. O'Connor, M. Paganini","doi":"10.1016/J.JAG.2019.03.003","DOIUrl":"https://doi.org/10.1016/J.JAG.2019.03.003","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"42 6","pages":"58-70"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91500658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1016/J.JAG.2019.03.002
Yaqian Long, B. Rivard, Derek M. Rogge, Minghua Tian
{"title":"Hyperspectral band selection using the N-dimensional Spectral Solid Angle method for the improved discrimination of spectrally similar targets","authors":"Yaqian Long, B. Rivard, Derek M. Rogge, Minghua Tian","doi":"10.1016/J.JAG.2019.03.002","DOIUrl":"https://doi.org/10.1016/J.JAG.2019.03.002","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"27 1","pages":"35-47"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82642673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1016/J.JAG.2019.02.011
D. Yan, Xiaoyang Zhang, S. Nagai, Yunyue Yu, T. Akitsu, K. Nasahara, R. Ide, T. Maeda
{"title":"Evaluating land surface phenology from the Advanced Himawari Imager using observations from MODIS and the Phenological Eyes Network","authors":"D. Yan, Xiaoyang Zhang, S. Nagai, Yunyue Yu, T. Akitsu, K. Nasahara, R. Ide, T. Maeda","doi":"10.1016/J.JAG.2019.02.011","DOIUrl":"https://doi.org/10.1016/J.JAG.2019.02.011","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"11 1","pages":"71-83"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87136254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2019-07-01DOI: 10.1016/J.JAG.2019.02.007
D. Mandal, Vineet Kumar, H. Mcnairn, A. Bhattacharya, Y. S. Rao
{"title":"Joint estimation of Plant Area Index (PAI) and wet biomass in wheat and soybean from C-band polarimetric SAR data","authors":"D. Mandal, Vineet Kumar, H. Mcnairn, A. Bhattacharya, Y. S. Rao","doi":"10.1016/J.JAG.2019.02.007","DOIUrl":"https://doi.org/10.1016/J.JAG.2019.02.007","url":null,"abstract":"","PeriodicalId":13664,"journal":{"name":"Int. J. Appl. Earth Obs. Geoinformation","volume":"9 1","pages":"24-34"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88770744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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