Multi‐Season Atmospheric Normalization of NOAA AVHRR Derived NDVI for Crop Yield Modeling

Potdar, M. B.; Manjunath, K. R.; Purohit, N. L.

doi:10.1080/10106049908542128

Cited by 11 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Potdar et al [65] observed that the spatio-temporal rainfall distribution needs to be incorporated into crop yield models, in addition to vegetation indices deduced from remote sensing data, to predict crop yield of different cereal crops grown in rain-fed conditions. Such hybrid models show higher correlation and predictive capability than the models using remote sensing indicators only [66,67] as the input variables complement each other.…”

Section: Concomitant Use Of Remotely Sensed Indicators Together With mentioning

confidence: 99%

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

et al. 2013

View full text Add to dashboard Cite

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.

show abstract

Section: Concomitant Use Of Remotely Sensed Indicators Together With mentioning

confidence: 99%

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

et al. 2013

View full text Add to dashboard Cite

show abstract

“…Consequently, rainfall distribution parameters in space and time are often incorporated into crop yield models with vegetation indices deduced from remote sensing because such hybrid models show a higher correlation and predictive capability than simple models (Potdar et al 1999, Manjunath andPotdar 2002). In Rajasthan state in India, for example, Manjunath and Potdar (2002) found that the incorporation of monthly rainfall into their regression yield models, in addition to NDVI, improved performance significantly.…”

Section: Yield Forecasting Measuresmentioning

confidence: 97%

The early explanatory power of NDVI in crop yield modelling

Wall¹,

Larocque

Léger

2008

International Journal of Remote Sensing

View full text Add to dashboard Cite

“…Remote sensing products alone have been used in different parts of the world to estimate crop yield (Lewis, et al, 1998;Hochheim andBarber, 1998, Wang, et al, 2005). Potdar et al (1999) observed for some cereal crops grown in rain-fed conditions that rainfall distribution parameters in space and time need to be incorporated into crop yield models in addition to vegetation indices deduced from remote sensing data. Such hybrid models show higher correlation and predictive capability than the simple models (Manjunath and Potdar, 2002).…”

Section: Introductionmentioning

confidence: 99%

Operational maize yield model development and validation based on remote sensing and agro‐meteorological data in Kenya

Rojas

2007

International Journal of Remote Sensing

View full text Add to dashboard Cite

ABSTRACT:Remote sensing (RS) data acquired by satellite have wide scope for agricultural applications owing to their synoptic and repetitive coverage. On the one hand, spectral indices deduced from visible and near-infrared RS data have been extensively used for crop characterization, biomass estimation and crop yield monitoring and forecasting. On the other hand, extensive research has been conducted using agrometerological models to estimate soil moisture to produce indicators of plant-water stress. This paper reports the development of an operational spectro-agrometeorological yield model for maize using a spectral index, the Normalized Difference Vegetation Index (NDVI) derived from SPOT-VEGETATION, meteorological data obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) model and crop-water status indicators estimated by the Crop Specific Water Balance model (CSWB). Official figures produced by the Government of Kenya (GoK) on crop yield, area planted and production were used in the model. The statistical multiple regression linear model has been developed for six large maize-growing provinces in Kenya. The spectro-agrometerological yield model was validated by comparing the predicted province-level yields with those estimated by GoK. The performance of the NDVI and land cover weighted NDVI (CNDVI) on the yield model was tested. Using CNDVI instead of NDVI in the model reduces 26% of the unknown variance. Of the output indicators of the CSWB model, the actual evapotranspiration (ETA) performs best. CNDVI and ETA in the model explain 83% of the maize crop yield variance with a root square mean error (RMSE) of 0.3298 t/ha. Very encouraging results were obtained when the Jack-knife re-sampling technique was applied proving the validity of the forecast capability of the model (r 2 = 0.81 and RMSE= 0.359 t/ha). The optimal prediction capability of the independent variables is 20 days and 30 days for the short and long maize crop cycles respectively. The national maize production during the first crop season for the years 1998 to 2003 was estimated with a RMSE of 185 060 tons and coefficient of variation of 9 %.

show abstract

Multi‐Season Atmospheric Normalization of NOAA AVHRR Derived NDVI for Crop Yield Modeling

Cited by 11 publications

References 5 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

The early explanatory power of NDVI in crop yield modelling

Operational maize yield model development and validation based on remote sensing and agro‐meteorological data in Kenya

Contact Info

Product

Resources

About