Growing period and drought early warning in Africa using satellite data

Henricksen, B. L.; Durkin, J.W.

doi:10.1080/01431168608948955

Cited by 91 publications

(45 citation statements)

References 10 publications

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“…With the increasing popularity of low resolution satellite images for large geographic areas, an early warning of water stress as indicator for lowered final productivity became a well-established practice [2,23,24]. Both at national and regional level, experimental crop monitoring systems were put in place starting in the late 70s in the US with the Large Area Crop Inventory Experiment (LACIE), and continuing in the 80s in the EU with the Monitoring Agriculture with Remote Sensing (MARS) project.…”

Section: Introduction and Short Historymentioning

confidence: 99%

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

et al. 2013

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Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing number of operational systems. The combination of their high temporal frequency with their extended geographical coverage generally associated with low costs per area unit makes these images a convenient choice at both national and regional scales. Several qualitative and quantitative approaches can be clearly distinguished, going from the use of low resolution satellite imagery as the main predictor of final crop yield to complex crop growth models where remote sensing-derived indicators play different roles, depending on the nature of the model and on the availability of data measured on the ground. Vegetation performance anomaly detection with low resolution images continues to be a fundamental component of early warning and drought monitoring systems at the regional scale. For applications at more detailed scales, the limitations created by the mixed nature of low resolution pixels are being progressively reduced by the higher resolution offered by new sensors, while the continuity of existing systems remains crucial for ensuring the OPEN ACCESS Remote Sens. 2013, 5 1705 availability of long time series as needed by the majority of the yield prediction methods used today.

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Section: Introduction and Short Historymentioning

confidence: 99%

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

et al. 2013

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“…Remote sensing data has been playing a significant role in monitoring drought-related vegetation condition (Henricksen and Durkin, 1986;Jain et al, 2009). Many vegetation indices have been developed to reflect vegetation characteristics, and the most widely used vegetation index is the Normalized Difference Vegetation Index (NDVI) (Tucker, 1979).…”

Section: Remote Sensing Datamentioning

confidence: 99%

Responses of vegetation growth to climate change in china

Zhou

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Global warming-related climate changes have significantly impacted the growth of terrestrial vegetation. Quantifying the spatiotemporal characteristic of the vegetation's response to climate is crucial for assessing the potential impacts of climate change on vegetation. In this study, we employed the normalized difference vegetation index (NDVI) and the standardized precipitation evapotranspiration index (SPEI) that was calculated for various time scales (1 to 12 months) from monthly records of mean temperature and precipitation totals using 511 meteorological stations in China to study the response of vegetation types to droughts. We separated the NDVI into 12 time series (one per month) and also used the SPEI of 12 droughts time scales to make the correlation. The results showed that the differences exist in various vegetation types. For needle-leaved forest, broadleaf forest and shrubland, they responded to droughts at long time scales (9 to 12 months). For grassland, meadow and cultivated vegetation, they responded to droughts at short time scales (1 to 5months). The positive correlations were mostly found in arid and sub-arid environments where soil water was a primary constraining factor for plant growth, and the negative correlations always existed in humid environments where temperature and radiation played significant roles in vegetation growth. Further spatial analysis indicated that the positive correlations were primarily found in northern China, especially in northwestern China, which is a region that always has water deficit, and the negative correlations were found in southern China, especially in southeastern China, that is a region has water surplus most of the year. The disclosed patterns of spatiotemporal responses to droughts are important for studying the impact of climate change to vegetation growth.

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“…We observe growing international interest in droughts during the last years [3] due to their increasing frequency triggered by climate change and resulting negative impacts on environment and livelihoods [4]. Drought is also a priority concern for most countries of the African continent [3,5,6]. Especially during the last decades, South Africa has been affected by droughts several times.…”

Section: Introductionmentioning

confidence: 99%

Drought Dynamics and Vegetation Productivity in Different Land Management Systems of Eastern Cape, South Africa—A Remote Sensing Perspective

et al. 2017

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Eastern Cape Province in South Africa has experienced extreme drought events during the last decade. In South Africa, different land management systems exist belonging to two different land tenure classes: commercial large scale farming and communal small-scale subsistence farming. Communal lands are often reported to be affected by land degradation and drought events among others considered as trigger for this process. Against this background, we analyzed vegetation response to drought in different land management and land tenure systems through assessing vegetation productivity trends and monitoring the intensity, frequency and distribution of the drought hazard in grasslands and communal and commercial croplands during drought and non-drought conditions.

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Growing period and drought early warning in Africa using satellite data

Cited by 91 publications

References 10 publications

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection

Responses of vegetation growth to climate change in china

Drought Dynamics and Vegetation Productivity in Different Land Management Systems of Eastern Cape, South Africa—A Remote Sensing Perspective

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