Rice yield estimation using synthetic aperture radar (SAR) and the ORYZA crop growth model: development and application of the system in South and South-east Asian countries

Setiyono, T. D.; Quicho, Emma D.; Holecz, F.; Khan, Nasreen Islam; Romuga, G.; Maunahan, A.; Garcia, C.; Rala, Arnel; Raviz, J.; Collivignarelli, Francesco; Gatti, Luca; Barbieri, Massimo; Phuong, Do Minh; Minh, Võ Quang; Vo, Quoc Tuan; Intrman, A.; Rakwatin, Preesan; Sothy, Men; Touch, Veasna; Pazhanivelan, S.; Mabalay, Mary Rose

doi:10.1080/01431161.2018.1547457

Cited by 50 publications

(36 citation statements)

References 40 publications

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“…SAR data processing for both rice crop area estimation, yield forecasting and flood inundation mapping have been done using MapScape-RICE software developed by SARMAP (Nelson et al, 2014). For yield forecasting, RICE-YES, developed by IRRI, provides interface between MapScape-RICE and ORYZA crop growth model (Setiyono et al, 2018). ORYZA crop growth model is the driver of weather and processed based yield estimation (Bouman et al, 2001).…”

Section: Methodolgymentioning

confidence: 99%

“…Pre-harvest yield forecast was generated using ORYZA crop growth model and RICE-YES software, along with local weather, soil, varietal, and crop management information in Srikakulam (Setiyono et al, 2017;2018). During the 'Titli' cyclone event, rice crop stages in Srikakulam district ranges from flowering to maturity with some fields ready to be harvested in 2 week time.…”

Section: Yield Forecast Estimationmentioning

confidence: 99%

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Quantitative Assessment of Rice Crop Damage Post Titli Cyclone in Srikakulam, Andhra Pradesh Using Geo-Spatial Techniques

Ponnurangam¹,

Setiyono

Maunahan

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Mapping the extent of damage due to natural calamities remains one of the thrust areas in monitoring resource inventory through geo-spatial techniques. The effect of the cyclone 'Titli' and heavy rains during first fortnight of October 2018 in Srikakulam district, Andhra Pradesh State has been demonstrated using geo-spatial technology in terms of flood inundated rice area and corresponding yield and production loss. The pre-and post-cyclone (5 and 13 October 2018) flood inundation maps were generated using Sentinel-1A and TerraSAR-X Synthetic Aperture Radar (SAR) data respectively. The pre-cyclone rice area estimates were derived from multi-temporal Sentinel-1A SAR data, while yield forecast is based on the combination of satellite observations and yield simulation using ORYZA crop growth model. An intensive ground truth data collection had been carried out for the validation of satellitederived rice area estimation of pre-cyclone event. An accuracy assessment has been carried out for district, mandal and village level. An overall accuracy of 96% with kappa coefficient 0.92 has been achieved. With the help map flood inundation and rice area maps, mandal-wise flood affected rice area and corresponding yield loss have been estimated. The post-cyclone ground truth data had been collected for quantitative assessment of crop damaged area. An overall accuracy of the flood affected rice map was 85% with kappa coefficient 0.70. It was estimated that rice crop damage assessment with SAR data indicated 53312 ha out of 205174 ha were affected and corresponding estimated yield as well as production are 0.8 t/ha and 189160 t respectively.

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Section: Methodolgymentioning

confidence: 99%

Section: Yield Forecast Estimationmentioning

confidence: 99%

Quantitative Assessment of Rice Crop Damage Post Titli Cyclone in Srikakulam, Andhra Pradesh Using Geo-Spatial Techniques

Ponnurangam¹,

Setiyono

Maunahan

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

Self Cite

View full text Add to dashboard Cite

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“…As a consequence, sensor systems with a low spatial resolution but a high temporal resolution such as AVHRR, MODIS, and SPOT are used predominantly. The most popular crop to be forecast is wheat [84][85][86][87][88][89][90][91][92][93][94][95][96][97][98], followed by corn [93,94,[99][100][101][102][103][104][105][106], sugarcane [107][108][109][110], and rice [111,112]. Furthermore, forecasts have been made for wine [113] and potato [114] yields, as well as multiple crop types within a single study [91,93,94,99,100,115].…”

Section: Research Topicsmentioning

confidence: 99%

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Koehler

Kuenzer

2020

Remote Sensing

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Reliable forecasts on the impacts of global change on the land surface are vital to inform the actions of policy and decision makers to mitigate consequences and secure livelihoods. Geospatial Earth Observation (EO) data from remote sensing satellites has been collected continuously for 40 years and has the potential to facilitate the spatio-temporal forecasting of land surface dynamics. In this review we compiled 143 papers on EO-based forecasting of all aspects of the land surface published in 16 high-ranking remote sensing journals within the past decade. We analyzed the literature regarding research focus, the spatial scope of the study, the forecasting method applied, as well as the temporal and technical properties of the input data. We categorized the identified forecasting methods according to their temporal forecasting mechanism and the type of input data. Time-lagged regressions which are predominantly used for crop yield forecasting and approaches based on Markov Chains for future land use and land cover simulation are the most established methods. The use of external climate projections allows the forecasting of numerical land surface parameters up to one hundred years into the future, while auto-regressive time series modeling can account for intra-annual variances. Machine learning methods have been increasingly used in all categories and multivariate modeling that integrates multiple data sources appears to be more popular than univariate auto-regressive modeling despite the availability of continuously expanding time series data. Regardless of the method, reliable EO-based forecasting requires high-level remote sensing data products and the resulting computational demand appears to be the main reason that most forecasts are conducted only on a local scale. In the upcoming years, however, we expect this to change with further advances in the field of machine learning, the publication of new global datasets, and the further establishment of cloud computing for data processing.

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“…It is reported that there are approximately 32 types of CGM combining multiple data sources and methods to monitor the potato yield under conditions of water, nitrogen fertilizer and CO 2 atmospheric levels [8]. However, the di culty of obtaining large amounts of input data is one of the major limitations of the widespread employment of models due to their own complexity [9][10][11]. Furthermore, eld investigation, another traditional method, is a destructive estimation way.…”

Section: Introductionmentioning

confidence: 99%

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

Luo

et al. 2020

Preprint

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Background The accurate estimation of potato yield at regional scale is crucial for food security, precision agriculture and agricultural sustainable development. Methods In this study, we developed a new method using multi-period relative vegetation indices (rVIs) and relative LAI (rLAI) data to improve the accuracy of potato yield estimation based on weighted growth stage. Two experiments of field and greenhouse (water and nitrogen fertilizer experiments) in 2018 were performed to obtain the spectra and LAI data of the whole growth stage of potato. Then the weighted growth stage was determined by three weighting methods (improved analytic hierarchy process method, IAHP; entropy weight method, EW; and optimal combination weighting method, OCW) and Slogistic model. A comparison of the estimation performance of rVI-based and rLAI-based models with single stage and weighted stage was completed. Results The results showed that among the six test rVIs (rNDVI, rCIred edge, rCIgreen, rEVI2, rNDRE and rMTCI), rCIred edge was the optimal index of the single-stage estimation models with the correlation with potato yield. The most suitable single stage for potato yield estimation was the tuber expansion stage. For weighted growth stage models, the OCW-LAI model was determined as the best one to accurately predict the potato yield with adjusted R2 value of 0.8333, and the estimation error about 8%. Conclusion This study emphasizes the importance of inconsistent contributions of multi-period or different types of data to the results when they are used together, and the weights need to be considered.

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Rice yield estimation using synthetic aperture radar (SAR) and the ORYZA crop growth model: development and application of the system in South and South-east Asian countries

Cited by 50 publications

References 40 publications

Quantitative Assessment of Rice Crop Damage Post Titli Cyclone in Srikakulam, Andhra Pradesh Using Geo-Spatial Techniques

Quantitative Assessment of Rice Crop Damage Post Titli Cyclone in Srikakulam, Andhra Pradesh Using Geo-Spatial Techniques

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

Nondestructive estimation of potato yield using relative variables derived from multi-period LAI and hyperspectral data based on weighted growth stage

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