Regional water-saving potential calculation method for paddy rice based on remote sensing

Wei, Jun; Cui, Yuanlai; Zhou, Sihang; Luo, Yufeng

doi:10.1016/j.agwat.2022.107610

Cited by 8 publications

(3 citation statements)

References 42 publications

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“…The original system is being replaced by pressure pipes, with meters being installed in the plots. In a recent study by Wei [74], a regional water-saving potential calculation method for paddy rice based on remote sensing (RWSP-RS) was proposed.…”

Section: Water Productivity and Crop Yieldmentioning

confidence: 99%

A Remote-Sensing-Assisted Estimation of Water Use in Rice Paddy Fields: A Study on Lis Valley, Portugal

2023

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Rice culture is one of the most important crops in the world, being the most consumed cereal grain (755 million tons in 2020). Since rice is usually produced under flooding conditions and water performs several essential functions for the crop, estimating its water needs is essential. Remote sensing techniques have shown effectiveness in estimating and monitoring the water use in crop fields. An estimation from satellite data is a challenge, but could be very useful, in order to spatialize local estimates and operationalize production models. This study intended to derive an approach to estimate the actual crop evapotranspiration (ETa) in rice paddies from a temporal series of satellite images. The experimental data were obtained in the Lis Valley Irrigation District (central coast of Portugal), during the 2019 to 2021 rice growing seasons. The average seasonal ETa (FAO56) resulted 586 ± 23 mm and the water productivity (WP) was 0.47 ± 0.03 kg m−3. Good correlations were found between the crop coefficients (Kc) proposed by FAO and the NDVI evolution in the control rice fields, with R2 ranging between 0.71 and 0.82 for stages II+III (development + middle) and between 0.76 and 0.82 for stage IV (late). The results from the derived RS-assisted method were compared to the ETa values obtained from the surface energy balance model METRIC, showing an average estimation error of ±0.8 mm d−1, with a negligible bias. The findings in this work are promising and show the potential of the RS-assisted method for monitoring ETa and water productivity, capturing the local and seasonal variability in rice growing, and then predicting the rice yield, being a useful and free tool available to farmers.

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Section: Water Productivity and Crop Yieldmentioning

confidence: 99%

A Remote-Sensing-Assisted Estimation of Water Use in Rice Paddy Fields: A Study on Lis Valley, Portugal

2023

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“…Consequently, it is crucial to develop timely and effective mapping methods for paddy fields that can provide accurate spatial information. This spatial data is essential for effective agricultural [16] and water resources management [17,18].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Rice Field Mapping in the Northern Region of China: An Asynchronous Flooding Signal and Object-Based Method

Li,

Zhou,

Liu

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Accurate delineation of paddy fields holds importance in ensuring food security, efficient water resource management, and precise evaluation of greenhouse gas emissions. Here we propose an innovative approach, the Asynchronous Flooding and Object-Based (AF-OB) model, aimed at optimizing phenologybased paddy field mapping. The AF-OB model capitalizes on the asynchronous flooding phenomenon observed between paddy fields and non-paddy fields, along with the seasonal variations in the Normalized Difference Vegetation Index. The Simple Non-Iterative Clustering (SNIC) algorithm is integrated to mitigate the common issue of the "pretzel effect" encountered in paddy field mapping. Evaluation through independent samples yields compelling results, with the paddy field map generated by the AF-OB method achieving an Overall Accuracy (OA) of 94.28%. The paddy fields extracted using the AF-OB method exhibit alignment with statistical data, surpassing comparable algorithms relying on alternative land use products in terms of visual quality. Furthermore, the AF-OB model exhibits stability across time, space, and sensors, thus enhancing its applicability and robustness. The outputs of the AF-OB method offer reference data for informed agricultural production planning and the effective management of water resources.

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“…Hyperspectral imaging (HSI) technology has advanced significantly in the last two decades, providing high-quality images with contiguous and narrow spectral bands in the visible-near infrared (VNIR-0.4 to 1.0 µm), shortwave infrared (SWIR-1.0 to 2.5 µm), and thermal infrared (TIR-3 to 10 µm) regions. Recently, the HSI technology is being widely applied in the precise characterization of different surface materials, including rocks, soils, vegetation, and water [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. Rocks are mixtures of minerals, which, in turn, are naturally occurring solid crystalline compounds, mainly silicates, with fixed chemical composition and crystal structures.…”

Section: Introductionmentioning

confidence: 99%

Modelling Spectral Unmixing of Geological Mixtures: An Experimental Study Using Rock Samples

Sahoo

Kalimuthu

et al. 2023

Remote Sensing

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Spectral unmixing of geological mixtures, such as rocks, is a challenging inversion problem because of nonlinear interactions of light with the intimately mixed minerals at a microscopic scale. The fine-scale mixing of minerals in rocks limits the sensor’s ability to identify pure mineral endmembers and spectrally resolve these constituents within a given spatial resolution. In this study, we attempt to model the spectral unmixing of two rocks, namely, serpentinite and granite, by acquiring their hyperspectral images in a controlled environment, having uniform illumination, using a laboratory-based imaging spectroradiometer. The endmember spectra of each rock were identified by comparing a limited set of pure hyperspectral image pixels with the constituent minerals of the rocks based on their diagnostic spectral features. A series of spectral unmixing paradigms for explaining geological mixtures, including those ranging from simple physics-based light interaction models (linear, bilinear, and polynomial models) to classification-based models (support vector machines (SVMs) and half Siamese network (HSN)), were tested to estimate the fractional abundances of the endmembers at each pixel position of the image. The analysis of the results of the spectral unmixing algorithms using the ground truth abundance maps and actual mineralogical composition of the rock samples (estimated using X-ray diffraction (XRD) analysis) indicate a better performance of the pure pixel-guided HSN model in comparison to the linear, bilinear, polynomial, and SVM-based unmixing approaches. The HSN-based approach yielded reduced errors of abundance estimation, image reconstruction, and mineralogical composition for serpentinite and granite. With its ability to train using limited pure pixels, the half-Siamese network model has a scope for spectrally unmixing rock samples of varying mineralogical composition and grain sizes. Hence, HSN-based approaches effectively address the modelling of nonlinear mixing in geological mixtures.

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Regional water-saving potential calculation method for paddy rice based on remote sensing

Cited by 8 publications

References 42 publications

A Remote-Sensing-Assisted Estimation of Water Use in Rice Paddy Fields: A Study on Lis Valley, Portugal

A Remote-Sensing-Assisted Estimation of Water Use in Rice Paddy Fields: A Study on Lis Valley, Portugal

Optimizing Rice Field Mapping in the Northern Region of China: An Asynchronous Flooding Signal and Object-Based Method

Modelling Spectral Unmixing of Geological Mixtures: An Experimental Study Using Rock Samples

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