Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data

Kouadio, Louis; Duveiller, Grégory; Djaby, Bakary; Jarroudi, Moussa El; Defourny, Pierre; Tychon, Bernard

doi:10.1016/j.jag.2012.01.009

Cited by 39 publications

(23 citation statements)

References 33 publications

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“…Higher spatial resolution satellites are usually only available a few times per year for a given location [31]. This is due to the lower revisit frequency and cloud cover frequency [32,33]. NDVI data derived from Landsat sensors (e.g., TM/ETM + OLI with 30-m resolution at visible and near infrared bands) can successfully capture the spatial details and alleviate the spectral mixture issue to some extent [34,35].…”

Section: Modis Time Series Of Ndvimentioning

confidence: 99%

The Dynamic Change of Vegetation Cover and Associated Driving Forces in Nanxiong Basin, China

Yan

Kašanin‐Grubin

et al. 2017

Sustainability

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Natural climate change and human activities are the main driving forces associated with vegetation coverage change. Nanxiong Basin is a key ecosystem-service area at the national level with a dense population and highly representative of red-bed basins, which are considered as fragile ecological units in humid regions. In this study, the authors aimed to determine the trends in vegetation cover change over past two decades and the associated driving forces in this study area. The Normalized Difference Vegetation Index (NDVI) of 2000-2015, derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing dataset along with the application of statistical methods and GIS (geographic information system) techniques were used to quantify vegetation cover change. The results show that human-induced factors can explain most variations at sites with significant cover change. That is to say that human activities are the main drivers of vegetation dynamics in this study area, which shows a significant reduction trend in vegetation cover during the industrialization and urbanization processes of the study period and noticeable recovery trend in 2000-2015 under the plantation and enclosed forest policy.

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Section: Modis Time Series Of Ndvimentioning

confidence: 99%

The Dynamic Change of Vegetation Cover and Associated Driving Forces in Nanxiong Basin, China

Yan

Kašanin‐Grubin

et al. 2017

Sustainability

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“…En concreto, autores como Boken y Shaykewich (2002), Reeves et al (2005), Salazar et al (2007), Wall et al (2008), Huang et al (2015) o Huang et al (2016) llevaron a cabo estudios en algunas comarcas agrarias de Estados Unidos, Canadá y China. En Europa destacan los trabajos sobre caracterización de la cosecha de trigo realizados por Mika et al (2002), Dente et al (2008), Kouadio et al (2012) Boissard et al (1989) o Padilla et al (2012, centrados en Hungría, Italia, Bélgica, Francia o España, respectivamente. Todos estos estudios tienen un elemento en común: se fundamentan en la relación existente entre la radiancia espectral de las cubiertas vegetales (medida a través de sensores) y parámetros biofísicos del cultivo, excepto Padilla et al (2012) que la relaciona con el estado fenológico.…”

Section: Introductionunclassified

Estimación de la cosecha de trigo en Andalucía usando series temporales de MERIS Terrestrial Chlorophyll Index (MTCI)

et al. 2018

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Resumen: Existe una relación entre la producción primaria neta del trigo y los índices de vegetación obtenidos de imágenes de satélite. Con frecuencia se utiliza el NDVI (Normalized Difference Vegetation Index) para la estimación de producción y rendimiento de trigo y otros cultivos. Sin embargo, hay pocas investigaciones que utilicen el índice MTCI (MERIS Terrestrial Chlorophyll Index) para conocer el rendimiento y la producción de los cultivos a una escala regional posiblemente debido a la falta de continuidad del sensor MERIS. No obstante, la posibilidad del cálculo de MTCI a partir de Sentinel 2 abre nuevas oportunidades para su aplicación e investigación. En esta investigación se han generado dos modelos empíricos de estimación de producción y rendimiento de trigo en Andalucía. Para ello, se ha empleado la serie temporal completa (imágenes semanales de 2006 a 2011) del índice de vegetación MTCI del sensor satelital MERIS (Medium Resolution Imaging Spectrometer) asociada a los datos de producción y rendimiento del Anuario de estadísticas agrarias y pesqueras de Andalucía (AEAP). Para la creación de estos modelos ha sido necesaria la identificación del periodo óptimo del desarrollo de la planta, la agregación temporal de los valores MTCI usando ese momento óptimo como referencia, relacionar ese índice con observaciones directas de producción y rendimiento a través de agregaciones espaciales mediante la utilización de coberturas SIGPAC y las solicitudes de ayudas PAC, caracterizar la variación del índice en función del año de cultivo y relacionarlo con los datos estadísticos. Los resultados obtenidos indican una correlación estadísticamente significativa (p-valor < 0,05) entre el índice MTCI y los datos de producción y rendimiento recogidos por AEAP (R 2 =0,81 y 0,57, respectivamente).Palabras clave: teledetección, MTCI, modelo, trigo, cosecha, series temporales. Wheat yield prediction in Andalucía using MERIS Terrestrial Chlorophyll Index (MTCI) time seriesAbstract: There is a relationship between net primary production of wheat and vegetation indices obtained from satellite imaging. Most wheat production studies use the Normalised Difference Vegetation Index (NDVI) to estimate the production and yield of wheat and other crops. On the one hand, few studies use the MERIS Terrestrial Chlorophyll Index (MTCI) to determine crop yield and production on a regional level. This is possibly due to a lack of continuity of MERIS. On the other hand, the emergence of Sentinel 2 open new possibilities for the research and application of MTCI. This study has built two empirical models to estimate wheat production and yield in Andalusia. To this end, the study used the complete times series (weekly images from 2006-2011) of the MTCI vegetation index from the Medium Resolution Imaging Spectrometer (MERIS) sensor associated with the Andalusian yearbook for agricultural and fishing statistics (AEAP-Anuario de estadísticas agrarias y pesqueras de Andalucía). In order to build these models, the optimal development period for the ...

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“…Although these changes will positively affect crop production in some regions, crop production for food, feed, and fodder will be negatively affected in other regions [3,4], aggravating all dimensions of food security. Timely and reliable information on crop growth and yield at the regional, national and global scales is essential for food security and trade policies [5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Using MODIS Data to Predict Regional Corn Yields

et al. 2016

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A simple approach was developed to predict corn yields using the MoDerate Resolution Imaging Spectroradiometer (MODIS) data product from two geographically separate major corn crop production regions: Illinois, USA and Heilongjiang, China. The MOD09A1 data, which are eight-day interval surface reflectance data, were obtained from day of the year (DOY) 89 to 337 to calculate the leaf area index (LAI). The sum of the LAI from early in the season to a given date in the season (end of DOY (EOD)) was well fitted to a logistic function and represented seasonal changes in leaf area duration (LAD). A simple phenology model was derived to estimate the dates of emergence and maturity using the LAD-logistic function parameters b 1 and b 2 , which represented the rate of increase in LAI and the date of maximum LAI, respectively. The phenology model predicted emergence and maturity dates fairly well, with root mean square error (RMSE) values of 6.3 and 4.9 days for the validation dataset, respectively. Two simple linear regression models (Y P and Y F ) were established using LAD as the variable to predict corn yield. The yield model Y P used LAD from predicted emergence to maturity, and the yield model Y F used LAD for a predetermined period from DOY 89 to a particular EOD. When state/province corn yields for the validation dataset were predicted at DOY 321, near completion of the corn harvest, the Y P model, including the predicted phenology, performed much better than the Y F model, with RMSE values of 0.68 t/ha and 0.66 t/ha for Illinois and Heilongjiang, respectively. The Y P model showed similar or better performance, even for the much earlier pre-harvest yield prediction at DOY 257. In addition, the model performance showed no difference between the two study regions with very different climates and cultivation methods, including cultivar and irrigation management. These results suggested that the approach described in this paper has potential for application to relatively wide agroclimatic regions with different cultivation methods and for extension to the other crops. However, it needs to be examined further in tropical and sub-tropical regions, which are very different from the two study regions with respect to agroclimatic constraints and agrotechnologies.

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Estimating regional wheat yield from the shape of decreasing curves of green area index temporal profiles retrieved from MODIS data

Cited by 39 publications

References 33 publications

The Dynamic Change of Vegetation Cover and Associated Driving Forces in Nanxiong Basin, China

The Dynamic Change of Vegetation Cover and Associated Driving Forces in Nanxiong Basin, China

Estimación de la cosecha de trigo en Andalucía usando series temporales de MERIS Terrestrial Chlorophyll Index (MTCI)

Using MODIS Data to Predict Regional Corn Yields

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