Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information

Franch, B.; Vermote, Éric; Becker‐Reshef, Inbal; Claverie, Martin; Huang, Jianxi; Zhang, J.; Justice, Chris; Sobrino, José A.

doi:10.1016/j.rse.2015.02.014

Cited by 171 publications

(106 citation statements)

References 47 publications

Supporting

Mentioning

102

Contrasting

Unclassified

Order By: Relevance

“…The TA values obtained using this method can be employed as input data in crop growth simulation models to monitor the crop growth, predict the timing of crop development stages and forecast the crop yield at the regional scale [45]. Combined with indicators of a potential agricultural disaster such as extreme temperature, these data can improve the ability to predict the development and spatial distribution of damage caused by cold [5,46], freezing [7] or high temperatures [6].…”

Section: Discussionmentioning

confidence: 99%

Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data

et al. 2015

View full text Add to dashboard Cite

Air temperature is one of the most important factors in crop growth monitoring and simulation. In the present study, we estimated and mapped daily mean air temperature using daytime and nighttime land surface temperatures (LSTs) derived from TERRA and AQUA MODIS data. Linear regression models were calibrated using LSTs from 2003 to 2011 and validated using LST data from 2012 to 2013, combined with meteorological station data. The results show that these models can provide a robust estimation of measured daily mean air temperature and that models that only accounted for meteorological data from rural regions performed best. Daily mean air temperature maps were generated from each of four MODIS LST products and merged using different strategies that combined the four MODIS products in different orders when data from one product was unavailable for a pixel. The annual average spatial coverage increased from 20.28% to 55.46% in 2012 and 28.31% to 44.92% in 2013.The root-mean-square and mean absolute errors (RMSE and MAE) for the optimal image merging strategy were 2.41 and 1.84, respectively. Compared with the OPEN ACCESS Remote Sens. 2015, 7 8729 least-effective strategy, the RMSE and MAE decreased by 17.2% and 17.8%, respectively. The interpolation algorithm uses the available pixels from images with consecutive dates in a sliding-window mode. The most appropriate window size was selected based on the absolute spatial bias in the study area. With an optimal window size of 33 × 33 pixels, this approach increased data coverage by up to 76.99% in 2012 and 89.67% in 2013.

show abstract

Section: Discussionmentioning

confidence: 99%

Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Usually, remote sensing derived indicators are connected to crop yield using empirical regression-based models. Traditionally, vegetation indices such as Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Vegetation Health Index (VHI) are used as input parameters into empirical models (Becker-Reshef et al, 2010;Franch et al, 2015;Kogan et al, 2013a;Kowalik et al, 2014;Salazar et al, 2008). Recently, however, more attention has been brought to the usage of biophysical parameters such as leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (fAPAR) (Camacho et al, 2013;Shelestov et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

Kolotii

Kussul

Shelestov

et al. 2015

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

View full text Add to dashboard Cite

ABSTRACT:Winter wheat crop yield forecasting at national, regional and local scales is an extremely important task. This paper aims at assessing the efficiency (in terms of prediction error minimization) of satellite and biophysical model based predictors assimilation into winter wheat crop yield forecasting models at different scales (region, county and field) for one of the regions in central part of Ukraine. Vegetation index NDVI, as well as different biophysical parameters (LAI and fAPAR) derived from satellite data and WOFOST crop growth model are considered as predictors of winter wheat crop yield forecasting model. Due to very short time series of reliable statistics (since 2000) we consider single factor linear regression. It is shown that biophysical parameters (fAPAR and LAI) are more preferable to be used as predictors in crop yield forecasting regression models at each scale. Correspondent models possess much better statistical properties and are more reliable than NDVI based model. The most accurate result in current study has been obtained for LAI values derived from SPOT-VGT (at 1 km resolution) on county level. At field level, a regression model based on satellite derived LAI significantly outperforms the one based on LAI simulated with WOFOST.

show abstract

“…As a demonstration of the utility of the LCDR, we apply the methods developed by [7,8] to test the performance of the AVHRR data to monitor wheat yield. These methods are based on the …”

Section: Agriculture Applicationmentioning

confidence: 99%

“…The main objective of GEOGLAM is to strengthen global agricultural monitoring by improving the use of remote sensing tools for crop production projections and weather forecasting. In this context we demonstrate the performance of the LCDR, by applying the yield model described in [7,8] to the V4 series of the AVHRR data records.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A 30+ Year AVHRR Land Surface Reflectance Climate Data Record and Its Application to Wheat Yield Monitoring

et al. 2017

View full text Add to dashboard Cite

Abstract:The Advanced Very High Resolution Radiometer (AVHRR) sensor provides a unique global remote sensing dataset that ranges from the 1980s to the present. Over the years, several efforts have been made on the calibration of the different instruments to establish a consistent land surface reflectance time-series and to augment the AVHRR data record with data from other sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS). In this paper, we present a summary of all the corrections applied to the AVHRR surface reflectance and NDVI Version 4 Product, developed in the framework of the National Oceanic and Atmospheric Administration (NOAA) Climate Data Record (CDR) program. These corrections result from assessment of the geolocation, improvement of cloud masking, and calibration monitoring. Additionally, we evaluate the performance of the surface reflectance over the AERONET sites by a cross-comparison with MODIS, which is an already validated product, and evaluation of a downstream leaf area index (LAI) product. We demonstrate the utility of this long time-series by estimating the winter wheat yield over the USA. The methods developed by Becker-Reshef et al. (2010) and Franch et al. (2015) are applied to both the MODIS and AVHRR data. Comparison of the results from both sensors during the MODIS-era shows the consistency of the dataset with similar errors of 10%. When applying the methods to AVHRR historical data from the 1980s, the results have errors equivalent to those derived from MODIS.

show abstract

Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information

Cited by 171 publications

References 47 publications

Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data

Mapping of Daily Mean Air Temperature in Agricultural Regions Using Daytime and Nighttime Land Surface Temperatures Derived from TERRA and AQUA MODIS Data

Comparison of biophysical and satellite predictors for wheat yield forecasting in Ukraine

A 30+ Year AVHRR Land Surface Reflectance Climate Data Record and Its Application to Wheat Yield Monitoring

Contact Info

Product

Resources

About