Can We Use Satellite-Based FAPAR to Detect Drought?

Peng, Jian; Müller, Jan-Peter; Blessing, Simon; Giering, Ralf; Danne, Olaf; Gobron, Nadine; Kharbouche, Saïd; Ludwig, Ralf; Müller, Ben; Leng, Guoyong; You, Qinglong; Duan, Zheng

doi:10.3390/s19173662

Cited by 19 publications

(11 citation statements)

References 76 publications

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“…Monthly products at 0.5 • × 0.5 • between SeaWiFS and QA4ECV were also benchmarked, and we demonstrated that a monthly linear correction can be used for correcting the entire QA4ECV time series, from 1982 onwards, for correcting the stability performance. Recently, Giering, et al (2019) proposed a framework to establish fundamental satellite data series for climate applications [52]. Here, we applied a simple approach that can bring a solution for merging different sensor products.…”

Section: Discussionmentioning

confidence: 99%

“…The properties of terrestrial surfaces thus concern a large number of users in such applications including agriculture, forestry, environmental monitoring, etc. [8][9][10]. Since plant canopies significantly affect the spectral and directional reflectance of solar radiation, the analysis of these reflectances leads to a better understanding of fundamental processes controlling the biosphere.…”

Section: Introductionmentioning

confidence: 99%

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Can We Use the QA4ECV Black-sky Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) using AVHRR Surface Reflectance to Assess Terrestrial Global Change?

et al. 2019

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NOAA platforms provide the longest period of terrestrial observation since the 1980s. The progress in calibration, atmospheric corrections and physically based land retrieval offers the opportunity to reprocess these data for extending terrestrial product time series. Within the Quality Assurance for Essential Climate Variables (QA4ECV) project, the black-sky Joint Research Centre (JRC)-fraction of absorbed photosynthetically active radiation (FAPAR) algorithm was developed for the AVHRR sensors on-board NOAA-07 to -16 using the Land Surface Reflectance Climate Data Record. The retrieval algorithm was based on the radiative transfer theory, and uncertainties were included in the products. We proposed a time and spatial composite for providing both 10-day and monthly products at 0.05º × 0.05º. Quality control and validation were achieved through benchmarking against third-party products, including Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) datasets produced with the same retrieval algorithm. Past ground-based measurements, providing a proxy of FAPAR, showed good agreement of seasonality values over short homogeneous canopies and mixed vegetation. The average difference between SeaWiFS and QA4ECV monthly products over 2002–2005 is about 0.075 with a standard deviation of 0.091. We proposed a monthly linear bias correction that reduced these statistics to 0.02 and 0.001. The complete harmonized long-term time series was then used to address its fitness for the purpose of analysis of global terrestrial change.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can We Use the QA4ECV Black-sky Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) using AVHRR Surface Reflectance to Assess Terrestrial Global Change?

et al. 2019

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“…Rojas et al, 2011;Vicente-Serrano et al, 2013Törnros and Menzel, 2014). The Global Inventory Monitoring and Modeling System (GIMMS) NDVI was generated based on Advanced Very-High-Resolution Radiometer (AVHRR) observations and has accounted for various deleterious effects, such as orbital drift, calibration loss and volcanic eruptions (Beck et al, 2011;Pinzon and Tucker, 2014). For the current study, the latest version of GIMMS NDVI (3g.v1) was used, which covers the time period from 1981 to 2015 at biweekly temporal resolution and 8 km spatial resolution (Pinzon and Tucker, 2014).…”

Section: Gimms Ndvimentioning

confidence: 99%

“…Impacts include crop failure, food shortage, famine, epidemics and even mass migration (Wilhite et al, 2007;Ding et al, 2011;Zhou et al, 2018). In recent years, severe events have occurred across the world, such as the 2003 central Europe drought (García-Herrera et al, 2010), the 2010 Russian drought (Spinoni et al, 2015), the 2011 Horn of Africa drought (Nicholson, 2014), the 2000 drought in southeastern Australia (van Dijk et al, 2013;Peng et al, 2019c), the 2013-2014 California drought (Swain et al, 2014), the 2014 North China drought (Wang and He, 2015) and the 2015-2017 southern Africa drought (Baudoin et al, 2017;Muller, 2018). Widespread negative effects of these droughts on natural and socioeconomic systems have been reported afterwards (Wegren, 2011;Arpe et al, 2012;Griffin and Anchukaitis, 2014;Mann and Gleick, 2015;Dadson et al, 2019;Marvel et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A pan-African high-resolution drought index dataset

Peng

Dadson

Hirpa

et al. 2020

Earth Syst. Sci. Data

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Abstract. Droughts in Africa cause severe problems, such as crop failure, food shortages, famine, epidemics and even mass migration. To minimize the effects of drought on water and food security on Africa, a high-resolution drought dataset is essential to establish robust drought hazard probabilities and to assess drought vulnerability considering a multi- and cross-sectional perspective that includes crops, hydrological systems, rangeland and environmental systems. Such assessments are essential for policymakers, their advisors and other stakeholders to respond to the pressing humanitarian issues caused by these environmental hazards. In this study, a high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset is presented to support these assessments. We compute historical SPEI data based on Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) precipitation estimates and Global Land Evaporation Amsterdam Model (GLEAM) potential evaporation estimates. The high-resolution SPEI dataset (SPEI-HR) presented here spans from 1981 to 2016 (36 years) with 5 km spatial resolution over the whole of Africa. To facilitate the diagnosis of droughts of different durations, accumulation periods from 1 to 48 months are provided. The quality of the resulting dataset was compared with coarse-resolution SPEI based on Climatic Research Unit (CRU) Time Series (TS) datasets, Normalized Difference Vegetation Index (NDVI) calculated from the Global Inventory Monitoring and Modeling System (GIMMS) project and root zone soil moisture modelled by GLEAM. Agreement found between coarse-resolution SPEI from CRU TS (SPEI-CRU) and the developed SPEI-HR provides confidence in the estimation of temporal and spatial variability of droughts in Africa with SPEI-HR. In addition, agreement of SPEI-HR versus NDVI and root zone soil moisture – with an average correlation coefficient (R) of 0.54 and 0.77, respectively – further implies that SPEI-HR can provide valuable information for the study of drought-related processes and societal impacts at sub-basin and district scales in Africa. The dataset is archived in Centre for Environmental Data Analysis (CEDA) via the following link: https://doi.org/10.5285/bbdfd09a04304158b366777eba0d2aeb (Peng et al., 2019a).

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“…The EVI index of the Moderate-resolution Imaging Spectroradiometer (MODIS) was used in Germany in 2018 to identify potential regional differences in order to examine drought effects in various land cover types [19]. The Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) can reflect the greenness and health conditions of vegetation and has been used to monitor and assess the impact of drought [20]. In particular, FAPAR has been chosen and operationally used in the formula of the Combined Drought Indicator (CDI) for monitoring drought in the European Drought Observatory (EDO) within the Copernicus Emergency Management Service [21].…”

Section: Introductionmentioning

confidence: 99%

Drought Model DISS Based on the Fusion of Satellite and Meteorological Data under Variable Climatic Conditions

et al. 2020

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The use of effective methods for large-area drought monitoring is an important issue; hence, there have been many attempts to solve this problem. In this study, the Drought Information Satellite System (DISS) index is presented, based on the synergistic use of meteorological data and information derived from satellite images. The index allows us to monitor drought phenomena in various climatic and environmental conditions. The approach utilizes two indices for constructing a drought index: (1) the hydrothermal coefficient (HTC), which characterizes meteorological conditions across the study area over a long-term period; and (2) the temperature condition index (TCI) derived from Moderate-resolution Imaging Spectroradiometer (MODIS) data, which refers instantaneous land surface temperature (LST) to long-term extreme values. The model for drought assessment based on the DISS index was applied for generating drought index maps for Poland for the 2001–2019 vegetation seasons. The performance of the index was verified through comparison of the extent of agricultural drought to the reduction in cereal and maize yield. Analysis of variance revealed a significant relationship between the area of drought determined by the drought index and the decrease in cereal yield due to unfavorable growth conditions. The presented study proves that the proposed drought index can be an effective tool for large-area drought monitoring under variable environmental conditions.

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Can We Use Satellite-Based FAPAR to Detect Drought?

Cited by 19 publications

References 76 publications

Can We Use the QA4ECV Black-sky Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) using AVHRR Surface Reflectance to Assess Terrestrial Global Change?

Can We Use the QA4ECV Black-sky Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) using AVHRR Surface Reflectance to Assess Terrestrial Global Change?

A pan-African high-resolution drought index dataset

Drought Model DISS Based on the Fusion of Satellite and Meteorological Data under Variable Climatic Conditions

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