Development of a rice simulation model for remote-sensing (SIMRIW-RS)

Homma, Koki; Maki, Masayasu; Hirooka, Yoshihiro

doi:10.2480/agrmet.d-14-00022

Cited by 17 publications

(11 citation statements)

References 31 publications

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“…SIMRIW and its component modules have been widely utilized in diverse areas of rice production science and technologies. Those include the phenology prediction for the decision support for crop managements, 121–129) the crop monitoring and prediction in combination with remote sensing technology, 130–133) as well as the impact assessments of global climate change on rice. 22,44,45,47,48,50,134,135) Also, the components of SIMRIW are adopted in the later models for various purposes.…”

Section: Dynamic Process Models For Predicting Environmental Effects mentioning

confidence: 99%

Global warming and rice production in Asia: Modeling, impact prediction and adaptation

Horie

2019

Proc. Jpn. Acad., Ser. B

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Since the projection of global warming emerged in 1980s with the potential of laying enormous impacts on agriculture and food security of the world, we have conducted experimental and modeling studies for clarifying its effects on rice production in Asia and for developing adaptive rice production technologies. On the basis of measurement of rice responses to climate and carbon dioxide concentration ([CO2]), the dynamic process model named SIMRIW was developed to predict global warming effects on irrigated rice. The model predicted differential regional effects of the projected global warming by doubling [CO2] on the rice yield over Asia, and indicated that high tolerance to heat-induced spikelet sterility and high yield potential under elevated [CO2] are the two important characteristics required for rice genotypes adaptive to global warming environment. Further, genetic traits associated with these characteristics and their genetic resources for breeding adaptive genotypes were identified from diverse rice germplasms. This article reviews our initiative studies in the light of the recent studies, and points out further research that is needed for better understanding and overcoming of this unprecedentedly large problem.

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Section: Dynamic Process Models For Predicting Environmental Effects mentioning

confidence: 99%

Global warming and rice production in Asia: Modeling, impact prediction and adaptation

Horie

2019

Proc. Jpn. Acad., Ser. B

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“…Users have to collect parameters dependent on different climate conditions and rice varieties among ecological zones [28]. In Japan, Homma et al developed a simulation model combined with remote-sensing, to evaluate rice production, including the water budget, nitrogen uptake, phenological development, leaf area index (LAI) growth, dry matter production, and yield formation [29]. Both the ORYZA and SIMRIW models are based on weather conditions, and simulate the growth and yield of rice.…”

Section: Introductionmentioning

confidence: 99%

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Shiu

Chuang

2019

Remote Sensing

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Precisely estimating the yield of paddy rice is crucial for national food security and development evaluation. Rice yield estimation based on satellite imagery is usually performed with global regression models; however, estimation errors may occur because the spatial variation is not considered. Therefore, this study proposed an approach estimating paddy rice yield based on global and local regression models. In our study area, the overall per-field data might not available because it took lots of time and manpower as well as resources. Therefore, we gathered and accumulated 26 to 63 ground survey sample fields, accounting for about 0.05% of the total cultivated areas, as the training samples for our regression models. To demonstrate whether the spatial autocorrelation or spatial heterogeneity exists and dominates the estimation, global models including the ordinary least squares (OLS), support vector regression (SVR), and the local model geographically weighted regression (GWR) were used to build the yield estimation models. We obtained the representative independent variables, including 4 original bands, 11 vegetation indices, and 32 texture indices, from SPOT-7 multispectral satellite imagery. To determine the optimal variable combination, feature selection based on the Pearson correlation was used for all of the regression models. The case study in Central Taiwan rendered that the error rate was between 0.06% and 13.22%. Through feature selection, the GWR model’s performance was more relatively stable than the OLS model and nonlinear SVR model for yield estimation. Where the GWR model considers the spatial autocorrelation and spatial heterogeneity of the relationships between the yield and the independent variables, the OLS and nonlinear SVR models lack this feature; this led to the rice yield estimation of GWR in this study be more stable than those of the other two models.

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“…SIMRIW-RS [15] is a crop simulation model for rice that requires only three sets of inputs. First, the daily weather dataset, which includes rainfall, average temperature, day length, and average solar radiation.…”

Section: Simriw-rs and Recalibration Methodsmentioning

confidence: 99%

Satellite-Based Drought Impact Assessment on Rice Yield in Thailand with SIMRIW−RS

et al. 2020

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Advances in remote sensing technologies have enabled effective drought monitoring globally, even in data-limited areas. However, the negative impact of drought on crop yields still necessitates stakeholders to make informed decisions according to its severity. This research proposes an algorithm to combine a drought monitoring model, based on rainfall, land surface temperature (LST), and normalized difference vegetation index/leaf area index (NDVI/LAI) satellite products, with a crop simulation model to assess drought impact on rice yields in Thailand. Typical crop simulation models can provide yield information, but the requirement for a complicated set of inputs prohibits their potential due to insufficient data. This work utilizes a rice crop simulation model called the Simulation Model for Use with Remote Sensing (SIMRIW–RS), whose inputs can mostly be satisfied by such satellite products. Based on experimental data collected during the 2018/19 crop seasons, this approach can successfully provide a drought monitoring function as well as effectively estimate the rice yield with mean absolute percentage error (MAPE) around 5%. In addition, we show that SIMRIW–RS can reasonably predict the rice yield when historical weather data is available. In effect, this research contributes a methodology to assess the drought impact on rice yields on a farm to regional scale, relevant to crop insurance and adaptation schemes to mitigate climate change.

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Development of a rice simulation model for remote-sensing (SIMRIW-RS)

Cited by 17 publications

References 31 publications

Global warming and rice production in Asia: Modeling, impact prediction and adaptation

Global warming and rice production in Asia: Modeling, impact prediction and adaptation

Yield Estimation of Paddy Rice Based on Satellite Imagery: Comparison of Global and Local Regression Models

Satellite-Based Drought Impact Assessment on Rice Yield in Thailand with SIMRIW−RS

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