Prediction of crop biophysical variables with panel data techniques and radar remote sensing imagery

Blas, Clara Simón de; Valcarce-Diñeiro, Rubén; Sipols, Ana E.; Martín, Nilda Sánchez; Arias-Pérez, Benjamín; Santos-Martín, M. Teresa

doi:10.1016/j.biosystemseng.2021.02.014

Cited by 4 publications

(1 citation statement)

References 53 publications

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“…The spatial panel model [37] can use contemporaneous values of the dependent variable or disturbance terms at adjacent locations as explanatory variables to assess the spatial correlation [38] and capture unobservable heterogeneity between study agents and over time because this heterogeneity cannot be detected either with time series studies or with cross-sections [39]. Compared to existing research that mainly utilizes linear regression to analyze the correlation between multiple influencing factors and snow cover parameters, there is a lack of consideration for spatial autocorrelation, and no research has incorporated neighborhood effects into the measurement of influencing factors.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Spatial, Proximity, and Multiscale Effects of Influencing Factors in the Snowmelt Process in the Manas River Basin Using a Novel Zonal Spatial Panel Model

Li,

Liu,

Xiao

et al. 2023

Remote Sensing

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It is essential to investigate the influences of environmental elements on snow cover to understand the mechanism of the snowmelt process. These elements, as influencing factors, have spatial heterogeneity, which results in significant differences and uncertainties in the extent and range of their effects at different scales. However, little research has been conducted on the spatial interaction and mechanisms of these factors at multiple scales. This study selected the Manas River basin in the Tianshan Mountains as the study area. The study period is 2015–2020. The snow cover status index is calculated based on available Landsat8-OLS/TIRS data; influencing factors are collected from multiple datasets. Their relationships are explored using a novel zonal spatial panel model, fully considering the spatial, proximity, and scale effects. The findings are as follows: (1) There is a robust spatial interaction and proximity effect between snowmelt and various factors, and such effects display distinct spatial heterogeneity. The elevation (ELE), slope (SLP), land surface temperature (LST), and normalized digital vegetation index (NDVI) showed significant overall dominant effects on the snow melting process. The influencing factors with apparent proximity effects are LST, ELE, SLP, NDVI, and air temperature (TEMP), and their influence ranges are different. (2) The relative importance and significance rank of dominant influencing factors vary under different partition schemes and scales. As the scale decreases, the significance of terrain- and vegetation-related factors increases, whereas the significance of temperature- and elevation-related factors decreases, and the number of dominant factors also decreases. (3) The influencing factors represent distinct characteristics among each zone at the optimally partitioned scale we defined. The overall influencing pattern demonstrates a characteristic of being globally dictated by elevation and temperature, with local terrain factors, vegetation, and wind speed modifying this pattern. Our study provides practical data support and a theoretical basis for deepening our understanding of the influence mechanism of the snow melting process.

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Section: Introductionmentioning

confidence: 99%

Investigating the Spatial, Proximity, and Multiscale Effects of Influencing Factors in the Snowmelt Process in the Manas River Basin Using a Novel Zonal Spatial Panel Model

Li,

Liu,

Xiao

et al. 2023

Remote Sensing

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Crop-climate link in the southeastern USA: A case study on oats and sorghum

Sharma

Dhillon

Kumar

et al. 2023

Journal of Agriculture and Food Research

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Machine learning-based early prediction of rice-growing fields using multi-temporal Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral data

Son,

Chen,

Lin

et al. 2024

J. Appl. Rem. Sens.

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Rice is the most important food crop in Taiwan. Early information on rice-growing conditions is thus vital for estimating rice production to guarantee national food security and grain exports. The rice-harvested area is conventionally inspected twice a year by costly interpretation of aerial photographs and intensive labor-field surveys. However, such methods of rice monitoring are inadequate for providing the government with timely information on rice-cultivated conditions. This study aims to use time series of Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral data to develop a machine-learning approach for the early prediction of rice-growing fields in Taiwan. An object-based random forest (OBRF) was developed to process remotely sensed data for rice-cropping seasons from 2018 to 2021. The prediction results compared with the reference data showed that rice-growing fields could be accurately predicted before harvest, about three months for the first crop and two months for the second crop. The F-score and Kappa coefficient values achieved for the first crop were 0.87 and 0.85, and those for the second crop were 0.72 and 0.71, respectively. These findings were reaffirmed by close agreement between the official statistics and the rice area estimated from the satellite data, with the correlation coefficient of determination (R 2 ) value greater than 0.96. A large portion of the first crop's rice areas was abandoned or converted to upland crop cultivation in the second crop, which was confirmed by a visual interpretation of Landsat images and official statistics. Ultimately, this study proved the efficacy of using Sentinel-1/2 images and OBRF for the early prediction of rice-cultivated fields in Taiwan. Quantitative and geographical information produced from such methods was essential for the early estimation of rice production to nationally address food security concerns.

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Prediction of crop biophysical variables with panel data techniques and radar remote sensing imagery

Cited by 4 publications

References 53 publications

Investigating the Spatial, Proximity, and Multiscale Effects of Influencing Factors in the Snowmelt Process in the Manas River Basin Using a Novel Zonal Spatial Panel Model

Investigating the Spatial, Proximity, and Multiscale Effects of Influencing Factors in the Snowmelt Process in the Manas River Basin Using a Novel Zonal Spatial Panel Model

Crop-climate link in the southeastern USA: A case study on oats and sorghum

Machine learning-based early prediction of rice-growing fields using multi-temporal Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral data

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