Estimation of rice yield from a C-band radar remote sensing image by integrating a physical scattering model and an optimization algorithm

Zhang, Yuan; Yan, Wenjia; Yang, Bin; Yang, Tianpeng; Liu, Xiaohui

doi:10.1007/s11119-019-09664-8

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…As a result, using SAR imaging to monitor rice in foggy areas is highly significant. Multi-time-phase SAR imagery, which combines several time-phase SAR images, can accurately depict the changes in rice during the growth cycle and offers more benefits than single-time-phase radar (Neetu & Ray, 2020;Mosleh et al, 2015;Zhang et al, 2020). The most diverse time phases are selected to identify rice from other ground objects based on a comparative analysis of the differences in backscattering strength between rice during the growing period and other ground objects.…”

Section: Introductionmentioning

confidence: 99%

Mapping Sowing Dates of Paddy in Different Agro-Climatic Regions of Punjab using SAR Satellite Data

Kumar,

Jolly,

Singh

2024

eatp

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Rice is a major staple crop in the world, and therefore, accurate acreage assessment of crop area is essential. Traditional methods of crop acreage estimation often rely on ground surveys and manual data collection, which are labor intensive, timeconsuming, and may lack spatial coverage. In recent years, the availability of satellite remote sensing data has revolutionized the monitoring of agricultural systems by providing frequent, spatially extensive, and cost-effective information. This study presents a remote sensing approach for assessing crop area and identifying sowing dates using satellite data. We used the Sentinel-1 synthetic aperture radar (SAR) data to estimate the acreage of rice crop and sowing/transplanting of kharif rice during the years 2019 & 2020. Results showed that the rice area and sowing dates were validated using ground truth, and an overall accuracy of 87 to 92% was achieved for both years. Satellite based assessment of crop area and sowing date identification provides reliable information with high spatial and temporal resolution, enabling timely decisionmaking for farmers, policymakers, and agricultural stakeholders. Furthermore, the scalability and cost-effectiveness of satellite remote sensing make it a valuable tool for large-scale agricultural monitoring and management. In conclusion, this study highlights the potential of Sentinel-1 SAR satellite data for early-stage assessment of rice crop areas, offering a practical and efficient solution for monitoring agricultural dynamics at regional and global scales. Continued advancements in satellite technology and data processing techniques promise further improvements in the accuracy and applicability of remote sensing-based approaches for agricultural monitoring and management.

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Section: Introductionmentioning

confidence: 99%

Mapping Sowing Dates of Paddy in Different Agro-Climatic Regions of Punjab using SAR Satellite Data

Kumar,

Jolly,

Singh

2024

eatp

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“…[20][21][22][23][24][25][26][27][28] The yield prediction method based on semi-empirical models inverts the biological parameters of rice and creates regression models with yield. [29][30][31][32][33] Crop growth simulation models and semi-empirical models require a certain number of parameters. 19 Compared with the previous two methods, the empirical model-based yield estimation method is simple in principle and eliminates the need for crop growth and environmental parameters, resulting in its widespread use.…”

Section: Introductionmentioning

confidence: 99%

“… – 28 The yield prediction method based on semi-empirical models inverts the biological parameters of rice and creates regression models with yield 29 – 33 Crop growth simulation models and semi-empirical models require a certain number of parameters 19 …”

Section: Introductionmentioning

confidence: 99%

Rice yield prediction using radar vegetation indices from Sentinel-1 data and multiscale one-dimensional convolutional long- and short-term memory network model

Sun,

Zhang,

et al. 2024

J. Appl. Rem. Sens.

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Reliable rice yield information is critical for global food security. Optical vegetation indices (OVIs) are important parameters for rice yield estimation using remote sensing. Studies have shown that radar vegetation indices (RVIs) are correlated with OVIs. However, research on the implementation of RVIs in rice yield prediction is still in its early stages. In addition, existing deep learning yield prediction models ignore the contribution of temporal features at each time step to the predicted yield and lack the extraction of higher-level features. To address the above issues, this study proposed a rice yield prediction workflow using RVIs and a multiscale one-dimensional convolutional long-and short-term memory network (MultiscaleConv1d-LSTM, MC-LSTM). Sentinel-1 vertical emission and horizontal reception of polarization vertical emission and vertical reception of polarization data and county-level rice yield statistics covering Guangdong Province, China, from 2017 to 2021 were used. The experimental results show that the performance of the RVIs is comparable to that of the OVIs. The proposed MC-LSTM model can effectively improve the accuracy of rice yield prediction. For early rice yield prediction based on RVIs, the optimal accuracy of MC-LSTM [coefficient of determination R 2 of 0.67, unbiased root mean square error (ubRMSE) of 217.77 kg∕ha] was significantly better than that of the LSTM model (R 2 of 0.61, ubRMSE of 229.52 kg∕ha). For late rice yield prediction based on RVIs, the optimal accuracy of MC-LSTM (R 2 of 0.61 and ubRMSE of 456.54 kg∕ha) was significantly better than that of the LSTM model (R 2 of 0.55 and ubRMSE of 486.76 kg∕ha). The above results show that the proposed method has excellent application prospects in crop yield prediction. This work can provide a new feasible scheme for syntheticaperture radar data to serve agricultural monitoring and improve the efficiency of rice yield monitoring in a large area.

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“…Based on a single TerraSAR image, explored the effects of water-cloud model with different layers on rice yield estimation, indicating that singlelayer water-cloud model is better than a double-layer watercloud model in grain number estimation. In later developments, rice yield estimation based on remote sensing images also began to be combined to computer science, including physical scattering model, optimization algorithm, and gradient regression (Zhang et al, 2020;Arumugam et al, 2021). The results are better than those of the original models.…”

Section: Introductionmentioning

confidence: 99%

Field-scale rice yield estimation based on UAV-based MiniSAR data with Ku band and modified water-cloud model of panicle layer at panicle stage

Wang

Wang²,

Wang³

et al. 2022

Front. Plant Sci.

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Scientific and accurate estimation of rice yield is of great significance to food security protection and agricultural economic development. Due to the weak penetration of high frequency microwave band, most of the backscattering comes from the rice canopy, and the backscattering coefficient is highly correlated with panicle weight, which provides a basis for inversion of wet biomass of rice ear. To solve the problem of rice yield estimation at the field scale, based on the traditional water cloud model, a modified water-cloud model based on panicle layer and the radar data with Ku band was constructed to estimate rice yield at panicle stage. The wet weight of rice ear scattering model and grain number per rice ear scattering model were constructed at field scale for rice yield estimation. In this paper, the functional area of grain production in Xiashe Village, Xin'an Town, Deqing County, Zhejiang Province, China was taken as the study area. For the first time, the MiniSAR radar system carried by DJI M600 UAV was used in September 2019 to obtain the SAR data with Ku band under polarization HH of the study area as the data source. Then the rice yield was estimated by using the newly constructed modified water-cloud model based on panicle layer. The field investigation was carried out simultaneously for verification. The study results show: the accuracies of the inversion results of wet weight of rice ear scattering model and grain number per rice ear scattering model in parcel B were 95.03% and 94.15%; and the accuracies of wet weight of rice ear scattering model and grain number per rice ear scattering model in parcel C+D+E were over 91.8%. In addition, different growth stages had effects on yield estimation accuracy. For rice at fully mature, the yield estimation accuracies of wet weight of ear and grain number per ear were basically similar, both exceeding 94%. For rice at grouting stage, the yield estimation accuracy of wet weight of ear was 92.7%, better than that of grain number per ear. It was proved that it can effectively estimate rice yield using the modified water-cloud model based on panicle layer constructed in this paper at panicle stage at field scale.

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Estimation of rice yield from a C-band radar remote sensing image by integrating a physical scattering model and an optimization algorithm

Cited by 7 publications

References 37 publications

Mapping Sowing Dates of Paddy in Different Agro-Climatic Regions of Punjab using SAR Satellite Data

Mapping Sowing Dates of Paddy in Different Agro-Climatic Regions of Punjab using SAR Satellite Data

Rice yield prediction using radar vegetation indices from Sentinel-1 data and multiscale one-dimensional convolutional long- and short-term memory network model

Field-scale rice yield estimation based on UAV-based MiniSAR data with Ku band and modified water-cloud model of panicle layer at panicle stage

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