Research on the Inversion of Key Growth Parameters of Rice Based on Multisource Remote Sensing Data and Deep Learning DOI Creative Commons
Jian Li, Jian Lü,

Hongkun Fu

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(12), P. 2326 - 2326

Published: Dec. 19, 2024

This study accurately inverts key growth parameters of rice, including Leaf Area Index (LAI), chlorophyll content (SPAD) value, and height, by integrating multisource remote sensing data (including MODIS ERA5 imagery) deep learning models. Dehui City in Jilin Province, China, was selected as the case area, where multidimensional vegetation indices, ecological function parameters, environmental variables were collected, covering seven stages rice. Data analysis parameter prediction conducted using a variety machine models Partial Least Squares (PLSs), Support Vector Machine (SVM), Random Forest (RF), Long Short-Term Memory Networks (LSTM), among which LSTM model demonstrated superior performance, particularly at multiple critical time points. The results show that performed best inverting three with LAI inversion accuracy on 21 August reaching coefficient determination (R2) 0.72, root mean square error (RMSE) 0.34, absolute (MAE) 0.27. SPAD same date achieved an R2 0.69, RMSE 1.45, MAE 1.16. height 25 July reached 0.74, 2.30, 2.08. not only verifies effectiveness combining advanced algorithms but also provides scientific basis for precision management decision-making rice cultivation.

Language: Английский

Advancing early-stage plant phosphorus assessment for winter rye via hyperspectral data: A model-based approach harnessing feedforward neural networks DOI Creative Commons
Ewa Panek, Karol Paradowski, Beata Rutkowska

et al.

European Journal of Agronomy, Journal Year: 2025, Volume and Issue: 169, P. 127667 - 127667

Published: May 9, 2025

Language: Английский

Citations

0
In situ measurement techniques in remote sensing research over grasslands DOI Creative Commons
Magdalena Łągiewska, Radosław Gurdak, Dariusz Ziółkowski

et al.

Miscellanea Geographica, Journal Year: 2025, Volume and Issue: unknown

Published: May 17, 2025

Abstract Remote satellite observations have played a crucial role in monitoring vegetation since the 1970s, starting with development of Normalized Difference Vegetation Index (NDVI) by Rouse (1974) and Tucker (1979). Despite advances technology, validation situ measurements, which are often locally sparse, remains essential. Areas such as grasslands wetlands, vital for CO 2 balance water quality, require special attention. Within GrasSAT project, measurements were conducted Poland Norway, included LAI, soil moisture, biomass. This article focuses exclusively on studies carried out Poland, presents results related to models operating under Polish environmental conditions, highlighting importance local factors context comparing ground data. Different sampling methods, linear transect quadrat considered. The research aimed understand improve consistency between data, is accurate models.

Language: Английский

Citations

0
Research on the Inversion of Key Growth Parameters of Rice Based on Multisource Remote Sensing Data and Deep Learning DOI Creative Commons
Jian Li, Jian Lü,

Hongkun Fu

et al.

Agriculture, Journal Year: 2024, Volume and Issue: 14(12), P. 2326 - 2326

Published: Dec. 19, 2024

This study accurately inverts key growth parameters of rice, including Leaf Area Index (LAI), chlorophyll content (SPAD) value, and height, by integrating multisource remote sensing data (including MODIS ERA5 imagery) deep learning models. Dehui City in Jilin Province, China, was selected as the case area, where multidimensional vegetation indices, ecological function parameters, environmental variables were collected, covering seven stages rice. Data analysis parameter prediction conducted using a variety machine models Partial Least Squares (PLSs), Support Vector Machine (SVM), Random Forest (RF), Long Short-Term Memory Networks (LSTM), among which LSTM model demonstrated superior performance, particularly at multiple critical time points. The results show that performed best inverting three with LAI inversion accuracy on 21 August reaching coefficient determination (R2) 0.72, root mean square error (RMSE) 0.34, absolute (MAE) 0.27. SPAD same date achieved an R2 0.69, RMSE 1.45, MAE 1.16. height 25 July reached 0.74, 2.30, 2.08. not only verifies effectiveness combining advanced algorithms but also provides scientific basis for precision management decision-making rice cultivation.

Language: Английский

Citations

0