An efficient and accurate method for obtaining regional scale rice growth conditions based on WOFOST model and satellite images

2021 ◽  
Author(s):  
Bingyu Zhao ◽  
Meiling Liu ◽  
Jiianjun Wu ◽  
Xiangnan Liu ◽  
Mengxue Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Growth Model ◽  
Growth Parameters ◽  
Remote Sensing Data ◽  
Growth Conditions ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Parallel Method ◽  
Rice Growth

<p>It is very important to obtain regional crop growth conditions efficiently and accurately in the agricultural field. The data assimilation between crop growth model and remote sensing data is a widely used method for obtaining vegetation growth information. This study aims to present a parallel method based on graphic processing unit (GPU) to improve the efficiency of the assimilation between RS data and crop growth model to estimate rice growth parameters. Remote sensing data, Landsat and HJ-1 images were collected and the World Food Studies (WOFOST) crop growth model which has a strong flexibility was employed. To acquire continuous regional crop parameters in temporal-spatial scale, particle swarm optimization (PSO) data assimilation method was used to combine remote sensing images and WOFOST and this process is accompanied by a parallel method based on the Compute Unified Device Architecture (CUDA) platform of NVIDIA GPU. With these methods, we obtained daily rice growth parameters of Zhuzhou City, Hunan, China and compared the efficiency and precision of parallel method and non-parallel method. Results showed that the parallel program has a remarkable speedup (reaching 240 times) compared with the non-parallel program with a similar accuracy. This study indicated that the parallel implementation based on GPU was successful in improving the efficiency of the assimilation between RS data and the WOFOST model and was conducive to obtaining regional crop growth conditions efficiently and accurately.</p>

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

2017 ◽  
Vol 73 (1) ◽  
pp. 2-8 ◽  
Author(s):  
Masayasu MAKI ◽  
Kosuke SEKIGUCHI ◽  
Koki HOMMA ◽  
Yoshihiro HIROOKA ◽  
Kazuo OKI
Keyword(s):  
Remote Sensing ◽  
Growth Model ◽  
Rice Yield ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Sensing Data

scholarly journals Assimilation of Remote Sensing Data into Crop Growth Model for Yield Estimation: A Case Study from India

2021 ◽  
Author(s):  
Murali Krishna Gumma ◽  
M. D. M. Kadiyala ◽  
Pranay Panjala ◽  
Shibendu S. Ray ◽  
Venkata Radha Akuraju ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Growth Model ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Yield Estimation ◽  
Crop Growth Model ◽  
Sensing Data

scholarly journals Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation

2020 ◽  
Author(s):  
Francesco Novelli ◽  
Heide Spiegel ◽  
Taru Sandén ◽  
Francesco Vuolo
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Crop Yield ◽  
Field Data ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Crop Growth Model ◽  
Area Index ◽  
Sentinel 2

<p>The work is based on a previously published study with the aim to further analyse the results obtained. Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields.<br>LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. Better RMSE and RRMSE were obtained in 2017 compared to 2016 (RMSE = 0.44 vs. 0.46) (RRMSE = 17% vs. 19%). In 2016 year, a slightly lower R<sup>2</sup> value was found compared to 2017 (R<sup>2</sup> = 0.72 vs. 0.89). A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The work shows that the assimilation of remote sensing data into the crop growth model can help to overtake some structural problems of the model.  The assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.</p>

scholarly journals MONITORING WHEAT CROP GROWTH PARAMETERS USING TIME SERIES SENTINEL-1 AND SENTINEL-2 DATA FOR AGRICULTURAL APPLICATION IN MONGOLIA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 989-994
Author(s):  
B. Gansukh ◽  
B. Batsaikhan ◽  
A. Dorjsuren ◽  
C. Jamsran ◽  
N. Batsaikhan
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Growth Parameters ◽  
Remote Sensing Data ◽  
Crop Growth ◽  
Wheat Crop ◽  
Sensing Data ◽  
Application Method ◽  
Agricultural Application ◽  
Sentinel 2

Abstract. Wheat is the most important food crop in Mongolia, most of the croplands are utilizing for wheat cultivating area in the central northern region of Mongolia. The Mongolian government has several policies on the agricultural sector with wheat production in the study region has been intensified to meet people’s food demands and economic development. Monitoring wheat-growing areas is thus important to developing strategies for food security in the region. In the present study, we aimed to develop an agricultural application method using remote sensing data. Sentinel-1 SAR and Sentinel-2 MSI analysis of time series data were carried out to monitor the wheat crop growth parameters. Time-series images were acquired during May 2019–September 2019 at different growth stages in Bornuur soum, Tuv province of Mongolia. The wheat crop parameters, i.e. normalized difference vegetation index, vegetation water content, backscatter value of VV, VH channels were estimated using remote sensing data with reference data as cadastre polygons of current cropland area. The results showed that provide timely and valuable information for agricultural production, management and policy-making. The agricultural application method will help to agriculture management and monitoring include crop identification and cropland mapping, crop growth monitoring, inversion of key biophysical, biochemical and environmental parameters, crop damage/disaster monitoring, precision agriculture, etc.

scholarly journals RELATIONSHIPS BETWEEN RICE GROWTH PARAMETERS AND REMOTE SENSING DATA

2010 ◽  
Vol 4 (1) ◽  
Author(s):  
I Wayan Nuarsa ◽  
Fumihiko Nishio
Keyword(s):  
Remote Sensing ◽  
Spectral Characteristic ◽  
Vegetation Index ◽  
Growth Parameters ◽  
Life Stage ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Rice Growth ◽  
Vegetation Water ◽  
The Relationship

Rice is an agriculture plants that has the specific characteristic in the life stage due to the growth stage having different proportion of vegetation, water, and soil. Vegetation index is one of the satellite remote sensing parameter that is widely used to monitor the global vegetation cover. The objective of the study is to know the spectral characteristic of rice plant in the life stage and find the relationship between the rice growth parameters and the remote sensing data by the Landsat ETM data using the correlation and regression analysis. The result of study shows that the spectral characteristic of the rice before one month of age is defferent comparing after one month. All of the examined vegetation index has close linear relationship with rice coverage. Difference Vegetation Index (DVI) is the best vegetation index which estimates rice coverage with equation y = 1.762x + 2.558 and R degree value was 0.946. Rice age has a high quadratic relationship with all of evaluated vegetation index. Transformed Vegetation Index (TVI) is the best vegetation to predict the age of the rice. Formula y = 0.013x - 1.625x + 145.8 is the relationship form between the rice age and the TVI with R = 0.939. Peak of the vegetation index of rice is in the rice age of 2 months. This period is the transition of vegetative and generative stages. Keywords: Vegetation index, Rice growth, Spectral characteristic, Landsat ETM.

Drought loss assessment combining remote sensing and a crop growth model for maize in Yunnan Province, China

2018 ◽  
Vol 40 (5-6) ◽  
pp. 2151-2165 ◽  
Author(s):  
Maofang Gao ◽  
Zhao-Liang Li ◽  
Sanchao Liu ◽  
Ya Gao ◽  
Pei Leng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Growth Model ◽  
Yunnan Province ◽  
Crop Growth ◽  
Loss Assessment ◽  
Crop Growth Model
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