Processing of GAC NDVI data for yield forecasting in the Sahelian region

Maselli, F.; Romanelli, Stefano; Bottai, Lorenzo; Maracchi, Giampiero

doi:10.1080/014311600750037525

Cited by 45 publications

(36 citation statements)

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“…Linear regression models relating NDVI to crop yield have, for example, been developed by Rasmussen [51] and Groten [52] for Burkina Faso and by Maselli et al [24] for Niger. The same and other investigations showed that yield forecasting can be obtained by the use of NDVI data of specific periods which depend on the eco-climatic conditions of the areas and the types of crop grown [53][54][55].…”

Section: Use Of Remotely Sensed Indicators For Crop Yield Predictionmentioning

confidence: 99%

“…For this reason several authors have used NDVI to predict final crop production directly [25,53] or to estimate the fraction of NDVI inter-annual variability due to changes in crop area [55]. In general, a direct NDVI/production regression makes only sense under specific conditions, such as a stable crop area over the observed period.…”

Section: Use Of Remotely Sensed Indicators For Crop Yield Predictionmentioning

confidence: 99%

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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: Use Of Remotely Sensed Indicators For Crop Yield Predictionmentioning

confidence: 99%

Section: Use Of Remotely Sensed Indicators For Crop Yield Predictionmentioning

confidence: 99%

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

et al. 2013

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“…It is generally agreed that the net primary production (NPP) of a given area, or pixel, can be estimated by using the temporally integrated vegetation index; iNDVI (e.g. Rasmussen, 1998;Hielkema et al, 1987;Ricotta et al, 1999;Maselli et al, 2000). The calibration from iNDVI to NPP is closely linked to the photosynthetic active radiation (PAR), which in turn is linked to latitude, cloudiness and the time of the year (Runnström, 2000).…”

Section: Methodsmentioning

confidence: 99%

Government policies and land degradation in the Middle East

Nielsen

Adriansen

2005

Land Degrad Dev

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The majority of the Middle East is characterized as arid or semiarid and thus susceptible to degradation or even desertification. This paper evaluates long-term trends in vegetation productivity in the Middle East by means of satellite imagery and discusses the effects of government policies on these trends. Based on yearly estimates of vegetation productivity derived from NDVI, a temporal linear regression model is fitted, pixel by pixel, and the results express the temporal development of vegetation productivity in the region. The results indicate that large-scale land degradation is not the overall impression in the study area. Approximately 3 per cent of the total area was found to be affected by a marked decline, but only approximately 1 per cent of the total area is affected by significant decreases in vegetation productivity. Three different areas are selected for an in-depth analysis of the effect of government policies for the trends seen. The three cases-the newly claimed desert lands in Egypt, the marshes in Iraq, and the restructuring process in Israel-are all areas that have undergone significant changes in terms of vegetation productivity over the last twenty years. The results show that these changes are an outcome of government policies rather than local land use practices and these policies can increase or decrease degradation.

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“…Numerous studies have demonstrated that Net Primary Production and crop yield could be estimated from NDVI (Tucker et al, 1986;Groten, 1993;Maselli et al, 1993Maselli et al, , 2000. Forecasts of NDVI, such as the ones proposed in the current study have been demonstrated to be a valuable tool for forecasting crop yields (Rasmussen, 1997) and grain potential prices movement (Brown et al, 2008), planning resources for livestock for the dry season (Wylie et al, 1991).…”

Section: Introductionmentioning

confidence: 97%

Forecasting the vegetation photosynthetic activity over the Sahel: a Model Output Statistics approach

Philippon

Martiny

Camberlin

2008

Intl Journal of Climatology

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ABSTRACT:The predictability of the mean August-September photosynthetic activity of vegetation over the Sahel for the period 1982-2002 is explored through a Model Output Statistics approach using ECHAM4.5 retrospective forecasts. Given the poor ability of Atmospheric General Circulation Models (AGCMs) to correctly simulate rainfall over the Sahel, the stress is put on using atmospheric dynamics alone. The mean July-September predicted fields of zonal wind at 600 hPa, and humidity flux at 850 hPa, are selected because of their key role in the West African Monsoon system and their consistency in AGCMs. Coupled modes of NDVI/atmospheric dynamics are extracted using Canonical Correlation Analyses performed in leave-one-out cross-validation. The most relevant modes (using NCEP/DOE 2 or ECHAM4.5 atmospheric dynamics) associate enhanced greenness to a weakened African Easterly Jet displaced northward, and strengthened moisture fluxes from the Guinean Gulf. They are linked with increased rainfall over the Sahel and positive (negative) SST anomalies over the Mediterranean (the eastern equatorial Pacific).Used as predictors in a Multiple Linear Regression (MLR) model, the third cross-validated canonical coefficient (CC) derived from ECHAM4.5 simulations added to the August-September NDVI value of the previous year enable to explain 30% of the variance of a Sahelian regional index with a 2-month lead time. Applied at an 8-km spatial resolution, the statistical model possesses a usable skill (>.5) for 24% of the pixels analysed. The NDVI of pixels covered by open grassland appears as the most predictable.

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Processing of GAC NDVI data for yield forecasting in the Sahelian region

Cited by 45 publications

References 0 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

Government policies and land degradation in the Middle East

Forecasting the vegetation photosynthetic activity over the Sahel: a Model Output Statistics approach

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