Rice yield estimation using remote sensing and simulation model

2002 ◽  
Vol 3 (4) ◽  
pp. 461 ◽  
Author(s):  
Jing-feng HUANG
Keyword(s):  
Remote Sensing ◽  
Simulation Model ◽  
Rice Yield ◽  
Yield Estimation

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

2020 ◽  
Vol 12 (13) ◽  
pp. 2099
Author(s):  
Mongkol Raksapatcharawong ◽  
Watcharee Veerakachen ◽  
Koki Homma ◽  
Masayasu Maki ◽  
Kazuo Oki
Keyword(s):  
Remote Sensing ◽  
Simulation Model ◽  
Rice Yield ◽  
Drought Monitoring ◽  
Weather Data ◽  
Area Index ◽  
Drought Impact ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
Rice Yields

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.

scholarly journals RICE YIELD ESTIMATION THROUGH ASSIMILATING SATELLITE DATA INTO A CROP SIMUMLATION MODEL

2016 ◽  
Vol XLI-B8 ◽  
pp. 993-996
Author(s):  
N. T. Son ◽  
C. F. Chen ◽  
C. R. Chen ◽  
L. Y. Chang ◽  
S. H. Chiang
Keyword(s):  
Data Assimilation ◽  
Simulation Model ◽  
Cost Function ◽  
Crop Yield ◽  
Satellite Data ◽  
Rice Yield ◽  
Yield Estimation ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
The Cost

Rice is globally the most important food crop, feeding approximately half of the world’s population, especially in Asia where around half of the world’s poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT) for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO) algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI) and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount) in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government’s yield statistics indicated the root mean square error (RMSE) of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.

scholarly journals Rice yield estimation in An Giang province, the Vietnamese Mekong Delta using Sentinel-1 radar remote sensing data

2021 ◽  
Vol 652 (1) ◽  
pp. 012001
Author(s):  
P Hoang-Phi ◽  
T Nguyen-Kim ◽  
V Nguyen-Van-Anh ◽  
N Lam-Dao ◽  
T Le-Van ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rice Yield ◽  
Mekong Delta ◽  
Remote Sensing Data ◽  
Yield Estimation ◽  
Radar Remote Sensing ◽  
Sensing Data

scholarly journals UAV Remote Sensing Estimation of Rice Yield Based on Adaptive Spectral Endmembers and Bilinear Mixing Model

2021 ◽  
Vol 13 (11) ◽  
pp. 2190
Author(s):  
Ningge Yuan ◽  
Yan Gong ◽  
Shenghui Fang ◽  
Yating Liu ◽  
Bo Duan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rice Yield ◽  
Estimation Error ◽  
Structural Difference ◽  
Mixing Model ◽  
Accurate Estimation ◽  
Endmember Extraction ◽  
Yield Estimation ◽  
Estimation Model ◽  
Heading Stage

The accurate estimation of rice yield using remote sensing (RS) technology is crucially important for agricultural decision-making. The rice yield estimation model based on the vegetation index (VI) is commonly used when working with RS methods, however, it is affected by irrelevant organs and background especially at heading stage. The spectral mixture analysis (SMA) can quantitatively obtain the abundance information and mitigate the impacts. Furthermore, according to the spectral variability and information complexity caused by the rice cropping system and canopy characteristics of reflection and scattering, in this study, the multi-endmember extraction by the pure pixel index (PPI) and the nonlinear unmixing method based on the bandwise generalized bilinear mixing model (NU-BGBM) were applied for SMA, and the VIE (VIs recalculated from endmember spectra) was integrated with abundance data to establish the yield estimation model at heading stage. In two paddy fields of different cultivation settings, multispectral images were collected by an unmanned aerial vehicle (UAV) at booting and heading stage. The correlation of several widely-used VIs and rice yield was tested and weaker at heading stage. In order to improve the yield estimation accuracy of rice at heading stage, the VIE and foreground abundances from SMA were combined to develop a linear yield estimation model. The results showed that VIE incorporated with abundances exhibited a better estimation ability than VI alone or the product of VI and abundances. In addition, when the structural difference of plants was obvious, the addition of the product of VIF (VIs recalculated from bilinear endmember spectra) and the corresponding bilinear abundances to the original product of VIE and abundances, enhanced model reliability. VIs using the near-infrared bands improved more significantly with the estimation error below 8.1%. This study verified the validation of the targeted SMA strategy while estimating crop yield by remotely sensed VI, especially for objects with obvious different spectra and complex structures.

scholarly journals Rice Yield Estimation Based on Vegetation Index and Florescence Spectral Information from UAV Hyperspectral Remote Sensing

2021 ◽  
Vol 13 (17) ◽  
pp. 3390
Author(s):  
Fumin Wang ◽  
Xiaoping Yao ◽  
Lili Xie ◽  
Jueyi Zheng ◽  
Tianyue Xu
Keyword(s):  
Remote Sensing ◽  
Field Experiments ◽  
Rice Yield ◽  
Vegetation Indices ◽  
Estimation Method ◽  
Estimation Accuracy ◽  
Spectral Information ◽  
Growth Stages ◽  
Yield Estimation ◽  
First Derivative

Rice floret number per unit area as one of the key yield structure parameters is directly related to the final yield of rice. Previous studies paid little attention to the effect of the variations in vegetation indices (VIs) caused by rice flowering on rice yield estimation. Unmanned aerial vehicles (UAV) equipped with hyperspectral cameras can provide high spatial and temporal resolution remote sensing data about the rice canopy, providing possibilities for flowering monitoring. In this study, two consecutive years of rice field experiments were conducted to explore the performance of florescence spectral information in improving the accuracy of VIs-based models for yield estimates. First, the florescence ratio reflectance and florescence difference reflectance, as well as their first derivative reflectance, were defined and then their correlations with rice yield were evaluated. It was found that the florescence spectral information at the seventh day of rice flowering showed the highest correlation with the yield. The sensitive bands to yield were centered at 590 nm, 690 nm and 736 nm–748 nm, 760 nm–768 nm for the first derivative florescence difference reflectance, and 704 nm–760 nm for the first derivative florescence ratio reflectance. The florescence ratio index (FRI) and florescence difference index (FDI) were developed and their abilities to improve the estimation accuracy of models basing on vegetation indices at single-, two- and three-growth stages were tested. With the introduction of florescence spectral information, the single-growth VI-based model produced the most obvious improvement in estimation accuracy, with the coefficient of determination (R2) increasing from 0.748 to 0.799, and the mean absolute percentage error (MAPE) and the root mean squared error (RMSE) decreasing by 11.8% and 10.7%, respectively. Optimized by flowering information, the two-growth stage VIs-based model gave the best performance (R2 = 0.869, MAPE = 3.98%, RMSE = 396.02 kg/ha). These results showed that introducing florescence spectral information at the flowering stage into conventional VIs-based yield estimation models is helpful in improving rice yield estimation accuracy. The usefulness of florescence spectral information for yield estimation provides a new idea for the further development and improvement of the crop yield estimation method.

scholarly journals RICE YIELD ESTIMATION THROUGH ASSIMILATING SATELLITE DATA INTO A CROP SIMUMLATION MODEL

2016 ◽  
Vol XLI-B8 ◽  
pp. 993-996 ◽  
Author(s):  
N. T. Son ◽  
C. F. Chen ◽  
C. R. Chen ◽  
L. Y. Chang ◽  
S. H. Chiang
Keyword(s):  
Data Assimilation ◽  
Simulation Model ◽  
Cost Function ◽  
Crop Yield ◽  
Satellite Data ◽  
Rice Yield ◽  
Yield Estimation ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
The Cost

Rice is globally the most important food crop, feeding approximately half of the world’s population, especially in Asia where around half of the world’s poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT) for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO) algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI) and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount) in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government’s yield statistics indicated the root mean square error (RMSE) of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.

scholarly journals Rice Yield Estimation Using Below Cloud Remote Sensing Images Acquired by Unmanned Airborne Vehicle System

2016 ◽  
Vol 6 (4) ◽  
pp. 516 ◽  
Author(s):  
C.C Teoh ◽  
N Mohd Nadzim ◽  
M.J Mohd Shahmihaizan ◽  
I Mohd Khairil Izani ◽  
K Faizal ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Rice Yield ◽  
Yield Estimation ◽  
Remote Sensing Images ◽  
Vehicle System ◽  
Airborne Vehicle
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