Crop growth modelling and crop yield forecasting using satellite-derived meteorological inputs

Wit, Allard de; Diepen, C.A. van

doi:10.1016/j.jag.2007.10.004

Cited by 72 publications

(25 citation statements)

References 18 publications

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“…Air surface temperature (T a ) with high spatial and temporal resolution plays an important role in various applications, such as crop growth monitoring and simulations [1], hydrological, ecological, and environmental studies [2][3][4], weather forecasting [5,6], and climate change [7,8]. It is used as a key input variable and directly affects the accuracy of these applications.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data

2017

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Abstract:Recently, several methods have been introduced and applied to estimate daily air surface temperature (T a ) using MODIS land surface temperature data (MODIS LST). Among these methods, the most common used method is statistical modeling, and the most applied algorithms are linear/multiple linear regression models (LM). There are only a handful of studies using machine learning algorithm models such as random forest (RF) or cubist regression (CB). In particular, there is no study comparing different combinations of four MODIS LST datasets with or without auxiliary data using different algorithms such as multiple linear regression, random forest, and cubist regression for daily T a-max , T a-min , and T a-mean estimation. Our study examines the mentioned combinations of four MODIS-LST datasets and shows that different combinations and differently applied algorithms produce various T a estimation accuracies. Additional analysis of daily data from three climate stations in the mountain area of North West of Vietnam for the period of five years (2009 to 2013) with four MODIS LST datasets (AQUA daytime, AQUA nighttime, TERRA daytime, and TERRA nighttime) and two additional auxiliary datasets (elevation and Julian day) shows that CB and LM should be applied if MODIS LST data is used solely. If MODIS LST is used together with auxiliary data, especially in mountainous areas, CB or RF is highly recommended. This study proved that the very high accuracy of T a estimation (R 2 > 0.93/0.80/0.89 and RMSE~1.5/2.0/1.6 • C of T a-max , T a-min , and T a-mean , respectively) could be achieved with a simple combination of four LST data, elevation, and Julian day data using a suitable algorithm.

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Section: Introductionmentioning

confidence: 99%

Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data

2017

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“…Air temperature is an important parameter of the climate system and useful for a wide range of agriculture applications, including crop growth simulation [1,2], yield prediction [3,4], estimation of heat accumulation during the growing season [5], assessment of high-temperature damage [6], evaluation of crop freeze injury [7,8], and crop insect development prediction [9]. Currently, near-surface temperature data is collected by meteorological stations, and although such measurements offer the advantage of high accuracy and temporal resolution, their spatial resolution may be low and they may not adequately represent surface temperatures in areas with rugged or heterogeneous surfaces [10].These limitations can bias estimates of the spatial distribution of air temperature, even when researchers use advanced spatial interpolation methods [11].With the development of remote sensing technology, it has become possible to use thermal images from satellites to obtain land surface temperatures (LSTs) over wide areas, and this data can be used to instantaneously estimate spatially contiguous air temperatures [12][13][14][15].…”

Section: Introductionmentioning

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

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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.

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“…EnKF-filter was applied to correct low resolution microwave satellite soil moisture estimates in the WOFOST water balance model. During 1995-2003 de Wit and van Diepen [22] applied WOFOST crop model for the EU regional winter wheat yield forecasting in Spain, Poland and Belgium. In their study, the applicability of MeteoSat-satellite derived weather and agrometeorological variables were tested to replace interpolated data from weather stations (temperature, evapotranspiration and radiation).…”

Section: Introductionmentioning

confidence: 99%

Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing

et al. 2010

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Optical VGI yield estimates were validated with CropWatN crop model yield estimates using SPOT and NOAA data (mean R 2 0.71, RMSE 436 kg/ha) and with composite SAR/ASAR and NDVI models (mean R 2 0.61, RMSE 402 kg/ha) using both reflectance and backscattering data. CropWatN and Composite SAR/ASAR & NDVI model mean yields were 4,754/4,170 kg/ha for wheat, 4,192/3,848 kg/ha for barley and 4,992/2,935 kg/ha for oats.

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Crop growth modelling and crop yield forecasting using satellite-derived meteorological inputs

Cited by 72 publications

References 18 publications

Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data

Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data

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

Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing

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