Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe

Funk, Chris; Budde, Michael

doi:10.1016/j.rse.2008.08.015

Cited by 214 publications

(141 citation statements)

References 44 publications

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“…Key phenological variables are derived from the analysis of FAPAR (Fraction of Absorbed Photosynthetically Active Radiation) data from SPOT-VEGETATION instrument for the period of 1998-2012. The cumulative FAPAR value (CFAPAR) over the growing season is used as an indicator of biomass production, as CFAPAR appears to be a suitable proxy of GPP, biomass production [38,[45][46][47][48][49][50] and of crop yield (e.g., [51][52][53]). The correlation between CFAPAR and the key phenological variables and an indicator of the maximum productivity attained during the growing season is then mapped over the Sahel.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

et al. 2014

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Abstract:In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation productivity is directly related to phenology, most approaches to estimate such risks do not explicitly take into account the actual timing of vegetation growth and development. The date of the start of the season (SOS) or of the peak canopy density can be assessed by remote sensing techniques in a timely manner during the growing season. However, there is limited knowledge about the relationship between vegetation biomass production and these variables at the regional scale. This study describes the first attempt to increase our understanding of such a relationship through the analysis of phenological variables retrieved from SPOT-VEGETATION time series of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Two key phenological variables (growing season length (GSL); timing of SOS) and the OPEN ACCESSRemote Sens. 2014, 6 5869 maximum value of FAPAR attained during the growing season (Peak) are analyzed as potentially related to a proxy of biomass production (CFAPAR, the cumulative value of FAPAR during the growing season). GSL, SOS and Peak all show different spatial patterns of correlation with CFAPAR. In particular, GSL shows a high and positive correlation with CFAPAR over the whole Sahel (mean r = 0.78). The negative correlation between delays in SOS and CFAPAR is stronger (mean r = −0.71) in the southern agricultural band of the Sahel, while the positive correlation between Peak FAPAR and CFAPAR is higher in the northern and more arid grassland region (mean r = 0.75). The consistency of the results and the actual link between remote sensing-derived phenological parameters and biomass production were evaluated using field measurements of aboveground herbaceous biomass of rangelands in Senegal. This study demonstrates the potential of phenological variables as indicators of biomass production. Nevertheless, the strength of the relation between phenological variables and biomass production is not universal and indeed quite variable geographically, with large scattered areas not showing a statistically significant relationship.

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Section: Introductionmentioning

confidence: 99%

Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

et al. 2014

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“…As population increases and food supplies become more constrained, the need to monitor agricultural production even in the least productive regions will grow (Funk & Brown, 2009;Funk & Budde, 2009). The amount of food produced locally often interacts with global commodity prices to determine the price of food on the market, affecting the ability of millions of poor urban and rural Africans to access food (Brown et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

The response of African land surface phenology to large scale climate oscillations

Brown

Beurs

Vrieling

2010

Remote Sensing of Environment

130

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Variations in agricultural production due to rainfall and temperature fluctuations are a primary cause of food insecurity on the African continent. Analysis of changes in phenology can provide quantitative information on the effect of climate variability on growing seasons in agricultural regions. Using a robust statistical methodology, we describe the relationship between phenology metrics derived from the 26 year AVHRR NDVI record and the North Atlantic Oscillation index (NAO), the Indian Ocean Dipole (IOD), the Pacific Decadal Oscillation (PDO), and the Multivariate ENSO Index (MEI). We map the most significant positive and negative correlation for the four climate indices in Eastern, Western and Southern Africa between two phenological metrics and the climate indices. Our objective is to provide evidence of whether climate variability captured in the four indices has had a significant impact on the vegetative productivity of Africa during the past quarter century. We found that the start of season and cumulative NDVI were significantly affected by large scale variations in climate. The particular climate index and the timing showing highest correlation depended heavily on the region examined. In Western Africa the cumulative NDVI correlates with PDO in September-November. In Eastern Africa the start of the June-October season strongly correlates with PDO in March-May, while the PDO in December-February correlates with the start of the FebruaryJune season. The cumulative NDVI over this last season relates to the MEI of March-May. For Southern Africa, high correlations exist between SOS and NAO of September-November, and cumulative NDVI and MEI of March-May. The research shows that climate indices can be used to anticipate late start and variable vigor in the growing season of sensitive agricultural regions in Africa.Published by Elsevier Inc.

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“…The studies of Kogan (1997), Unganai and Kogan (1998), and Ramesh et al (2003) concluded that AVHRR NDVI is one of the best tools to monitor/assess the large area agricultural droughts. Wan et al (2004), Knight et al (2006) and Funk et al (2009) used NDVI derived from MODIS to understand the crop stages and long term disasters at finer resolution level. MODIS TERRA provide NDVI at 250 m resolution level from which one can make studies from a particular location point where ever required.…”

Section: Introductionmentioning

confidence: 99%

Variability in MODIS NDVI in Relation to Southwest Monsoon over Western Ghats, India

Kumar¹,

Uma²,

Rao³

et al. 2016

J ENVIRON INFORM

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ABSTRACT. Eleven years (2000 to 2010) of Normalized Difference Vegetation Index (NDVI) data, derived from Moderate Imaging Spectroradiometer (MODIS) Terra with 250 m resolution is used in the present study to discuss the changes in the vegetal cover over Western Ghats, India. The NDVI over Western Ghats (number of test sites are 16) showed increasing tendency and the pronounced changes in NDVI have been studied in the context of southwest monsoon's distribution and activity. The NDVI progression is observed from June with a minimum value of 0.179 and yielded to a maximum at 0.565 during September/October, on an average. The study then relates NDVI with different rainy events to understand the connection between the ground vegetation and the south west monsoon. The results of the study inferred are as follows: i) NDVI, Antecedent Precipitation Index (API) are in good agreement throughout the monsoon which is evidenced by correlation; ii) NDVI maintained good correlation with a number of Light and Moderate Rainy days alternatively but not with the number of Heavy Rainy days; iii) Relation of NDVI with Isolated, Scattered distributions and active monsoons is substantial; and iv) Rate of Green Up is captured by the satellite during the crop growing season over the Western Ghats.

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Phenologically-tuned MODIS NDVI-based production anomaly estimates for Zimbabwe

Cited by 214 publications

References 44 publications

Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

The response of African land surface phenology to large scale climate oscillations

Variability in MODIS NDVI in Relation to Southwest Monsoon over Western Ghats, India

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