Estimation of rice yield by SIMRIW-RS, a model that integrates remote sensing data into a crop growth model

Maki, Masayasu; Kosuke, Sekiguchi; Homma, Koki; Hirooka, Yoshihiro; Oki, K.

doi:10.2480/agrmet.d-14-00023

Cited by 35 publications

(22 citation statements)

References 12 publications

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“…However, obtaining suitable optical satellite imagery is difficult because of the weather effects in cloudy areas [16]. To overcome the limitations of optical imagery, some studies used synthetic aperture radar (SAR) imagery to map the spatial distribution [17,18], crop height [19,20], and yield estimation of paddy rice [21][22][23]. Similar to the studies using optical imagery only, the integration of optical and SAR imagery for yield estimation also involved regression models [15,24] and artificial neural networks [25].…”

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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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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“…RS data contain the growing condition of the crop at the observation time only, making it difficult to estimate crop yield directly [72]. In addition, although RS-based empirical forecasting models are relatively simple to build, these models cannot take into account temporal changes in crop yields without long-term field experiments [7].…”

Section: Discussionmentioning

confidence: 99%

Forecasting Transplanted Rice Yield at the Farm Scale Using Moderate-Resolution Satellite Imagery and the AquaCrop Model: A Case Study of a Rice Seed Production Community in Thailand

Prathumchai

Nagai

Tripathi

et al. 2018

IJGI

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Thailand has recently introduced agricultural policies to promote large-scale rice farming through supporting and integrating small-scale farmers. However, achieving these policies requires agricultural tools that can assist farmers in rice farming planning and management. Crop models, along with remote sensing technologies, can be useful for farmers and field managers in this regard. In this study, we used the AquaCrop model along with moderate-resolution satellite images (30 m) to simulate the rice yield for small-scale farmers. We conducted field surveys on rice characteristics in order to calibrate the crop model parameters. Data on rice crop, leaf area index (LAI), canopy cover (CC) and agricultural practices were used to calibrate the model. In addition, the optimal rice constant value for conversion of CC was investigated. HJ-1A/B satellite images were used to calculate the CC value, which was then used to simulate yield. The validated results were applied to 126 sample pixels within transplanted rice fields, which were extracted from satellite imagery of activated rice plots using equivalent transplanting methods to the study area. The rice yield simulated using the AquaCrop model and assimilated with the results of HJ-1A/B, produced a satisfactory outcome when implemented into the validated rice plots, with RMSE = 0.18 t ha −1 and R 2 = 0.88. These results suggest that integration of moderate-resolution satellite imagery and this crop model are useful tools for assisting rice farmers and field managers in their planning and management.

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“…Many studies have successfully demonstrated that SAR data can be used to monitor crop parameters. In a recent study conducted by Maki et al [99] they used LAI derived from SAR images of COSMO-Sky-Med satellite and integrate it in a crop model (SIMRIW) to successfully estimate rice yield at regional scale.…”

Section: Estimation Of Crop Parameters From Remote Sensingmentioning

confidence: 99%

Contribution of Remote Sensing on Crop Models: A Review

et al. 2018

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Abstract:Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products.

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Estimation of rice yield by SIMRIW-RS, a model that integrates remote sensing data into a crop growth model

Cited by 35 publications

References 12 publications

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

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

Forecasting Transplanted Rice Yield at the Farm Scale Using Moderate-Resolution Satellite Imagery and the AquaCrop Model: A Case Study of a Rice Seed Production Community in Thailand

Contribution of Remote Sensing on Crop Models: A Review

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