Remote Sensing Based Yield Estimation in a Stochastic Framework — Case Study of Durum Wheat in Tunisia

Meroni, Michele; Marinho, Eduardo; Sghaier, Nabil; Verstrate, Michel M.; Léo, Oberdan

doi:10.3390/rs5020539

Cited by 54 publications

(32 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Although in this study we lack multi-season field data on forage biomass across the large spatial extent covered, other studies have demonstrated that the temporal integration of vegetation indices over the growing season strongly relates to the vegetation's seasonal biomass productivity (Jung et al 2008;Rigge et al 2013) or derivatives like crop yield (Funk and Budde 2009;Meroni et al 2013). For that reason, we have a high level of confidence that a forage scarcity index based on seasons derived from phenological analysis provide a better measure of forage scarcity as compared to the former LRLD/SRSD definitions.…”

Section: Defining the Forage Production Seasonmentioning

confidence: 99%

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

Vrieling

Meroni

Mude

et al. 2016

Remote Sensing of Environment

Self Cite

View full text Add to dashboard Cite

Section: Defining the Forage Production Seasonmentioning

confidence: 99%

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

Vrieling

Meroni

Mude

et al. 2016

Remote Sensing of Environment

Self Cite

View full text Add to dashboard Cite

“…More particularly, this information brings fine-scale information on the spatial pattern of drought, hardly precisely assessed from rough interpolations of meteorological variables [68], particularly when rain gauges are sparse, as in Tunisia [69] and more generally in Africa [70], and still uncertain from global-scale remote sensing [71][72][73].…”

Section: Regional Analysis Of Ewtcanmentioning

confidence: 99%

Regional Equivalent Water Thickness Modeling from Remote Sensing across a Tree Cover/LAI Gradient in Mediterranean Forests of Northern Tunisia

2015

View full text Add to dashboard Cite

Abstract:The performance of vegetation indexes derived from moderate resolution imaging spectroradiometer (MODIS) sensors is explored for drought monitoring in the forests of Northern Tunisia; representing a transition zone between the Mediterranean Sea and the Sahara Desert. We investigated the suitability of biomass and moisture vegetation indexes for vegetation water content expressed by the equivalent water thickness (EWT) in a Mediterranean forest ecosystem with contrasted water budgets and desiccation rates. We proposed a revised EWT at canopy level (EWTCAN) based on weekly field measurements of fuel moisture in seven species during the 2010 dry period, considering the mixture of plant functional types for water use (trees, shrubs and herbaceous layers) and a varying vegetation cover. MODIS vegetation indexes computed and smoothed over the dry season were highly correlated with the EWTCAN. The performances of moisture indexes Normalized Difference Infrared Index (NDII6 and NDII7) and Global Moisture Vegetation Index (GVMI6 and GVMI7) were comparable, whereas for biomass vegetation indexes, Normalized Difference 1938Vegetation Index (NDVI), Modified Soil Adjusted Vegetation Index (MSAVI) and Adjusted Normalized Difference Vegetation Index (ANDVI) performed better than Enhanced Vegetation Index (EVI) and Soil Adjusted Vegetation Index (SAVI). We also identified the effect of Leaf Area Index (LAI) on EWTCAN monitoring at the regional scale under the tree cover/LAI gradient of the region from relatively dense to open forest. Statistical analysis revealed a significant decreasing linear relationship; indicating that for LAI less than two, the greater the LAI, the less responsive are the vegetation indexes to changes in EWTCAN; whereas for higher LAI, its influence becomes less significant and was not considered in the inversion models based on vegetation indexes. The EWTCAN time-course from LAI-adapted inversion models based on significantly-related vegetation indexes to EWTCAN showed close profiles resulting from the inversion models using NDVI, ANDVI, MSAVI and NDII6 applied during the dry season. The developed EWTCAN model from MODIS vegetation indexes for the study region was finally tested for its ability to capture the topo-climatic effects on the seasonal and the spatial patterns of desiccation/rewetting for keystone periods of Mediterranean vegetation functioning. Implications for further use in scientific developments or management are discussed.

show abstract

“…Ren et al [33] used 10-day MODIS NDVI composites to forecast the yield of winter wheat for a sub-region of Shandong Province in China, and the results were within 5% of official statistics. Meroni et al [34] examined the performance of spectral parameters derived from SPOT-VEGETATION data for wheat yield forecasting in Tunisia and, for NDVI, achieved an r-squared value of 0.75 between modeled and observed yield. Becker-Reshef et al [24] used a daily surface reflectance MODIS dataset to develop an empirical, regression-based model for forecasting wheat yield for the U.S. state of Kansas.…”

Section: Vegetation Indicesmentioning

confidence: 99%

Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

et al. 2014

View full text Add to dashboard Cite

Policy makers, government planners and agricultural market participants in Pakistan require accurate and timely information about wheat yield and production. Punjab Province is by far the most important wheat producing region in the country. The manual collection of field data and data processing for crop forecasting by the provincial government requires significant amounts of time before official reports can be released. Several studies have shown that wheat yield can be effectively forecast using satellite remote sensing data. In this study, we developed a methodology for estimating wheat yield and area for Punjab Province from freely available Landsat and MODIS satellite imagery approximately six weeks before harvest. Wheat yield was derived by regressing reported yield values against time series of four different peak-season MODIS-derived vegetation indices. We also tested deriving wheat area from the same MODIS time series using a regression-tree approach. Among the four evaluated indices, WDRVI provided more consistent and accurate yield forecasts compared to NDVI, EVI2 and saturation-adjusted normalized difference vegetation index (SANDVI). The lowest RMSE values at the district level for forecast versus reported yield were found when using six or more years of training data. 9654 reported values. Absolute deviations of wheat area and production forecasts from reported values were slightly greater compared to using the previous year's or the three-or six-year moving average values, implying that 250-m MODIS data does not provide sufficient spatial resolution for providing improved wheat area and production forecasts.

show abstract

Remote Sensing Based Yield Estimation in a Stochastic Framework — Case Study of Durum Wheat in Tunisia

Cited by 54 publications

References 27 publications

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

Regional Equivalent Water Thickness Modeling from Remote Sensing across a Tree Cover/LAI Gradient in Mediterranean Forests of Northern Tunisia

Wheat Yield Forecasting for Punjab Province from Vegetation Index Time Series and Historic Crop Statistics

Contact Info

Product

Resources

About