Dynamic estimation of rice aboveground biomass based on spectral and spatial information extracted from hyperspectral remote sensing images at different combinations of growth stages

Xu, Tianyue; Wang, Fumin; Shi, Zhou; Xie, Lu; Yao, Xiaoping

doi:10.1016/j.isprsjprs.2023.05.021

Cited by 14 publications

(6 citation statements)

References 57 publications

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“…Considering that the information on multispectral bands is limited and sensitive to the atmosphere and clouds, integrating multi-source remote sensing data is one future study target ( Yu et al., 2023 ; Zhai et al., 2023 ; Liu et al., 2023b ). For instance, hyperspectral remote sensing, synthetic aperture radar (SAR), and thermal infrared remote sensing (TIR) could provide more abundant spectral and temperature information, and help to minimize the influence of weather and environmental factors ( Alebele et al., 2020 ; Liu et al., 2022a ; Xu et al., 2023 ; Zhang et al., 2024 ). Spectral analysis methods such as wavelet analysis could also be employed to provide more integrated and in-depth data ( Wang et al., 2022b ).…”

Section: Discussionmentioning

confidence: 99%

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages

Dai,

Yu,

et al. 2024

Front. Plant Sci.

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IntroductionUnmanned aerial vehicles (UAVs) equipped with visible and multispectral cameras provide reliable and efficient methods for remote crop monitoring and above-ground biomass (AGB) estimation in rice fields. However, existing research predominantly focuses on AGB estimation based on canopy spectral features or by incorporating plant height (PH) as a parameter. Insufficient consideration has been given to the spatial structure and the phenological stages of rice in these studies. In this study, a novel method was introduced by fully considering the three-dimensional growth dynamics of rice, integrating both horizontal (canopy cover, CC) and vertical (PH) aspects of canopy development, and accounting for the growing days of rice.MethodsTo investigate the synergistic effects of combining spectral, spatial and temporal parameters, both small-scale plot experiments and large-scale field testing were conducted in Jiangsu Province, China from 2021 to 2022. Twenty vegetation indices (VIs) were used as spectral features, PH and CC as spatial parameters, and days after transplanting (DAT) as a temporal parameter. AGB estimation models were built with five regression methods (MSR, ENet, PLSR, RF and SVR), using the derived data from six feature combinations (VIs, PH+CC, PH+CC+DAT, VIs+PH +CC, VIs+DAT, VIs+PH+CC+DAT).ResultsThe results showed a strong correlation between extracted and ground-measured PH (R2 = 0.89, RMSE=5.08 cm). Furthermore, VIs, PH and CC exhibit strong correlations with AGB during the mid-tillering to flowering stages. The optimal AGB estimation results during the mid-tillering to flowering stages on plot data were from the PLSR model with VIs and DAT as inputs (R2 = 0.88, RMSE=1111kg/ha, NRMSE=9.76%), and with VIs, PH, CC, and DAT all as inputs (R2 = 0.88, RMSE=1131 kg/ha, NRMSE=9.94%). For the field sampling data, the ENet model combined with different feature inputs had the best estimation results (%error=0.6%–13.5%), demonstrating excellent practical applicability.DiscussionModel evaluation and feature importance ranking demonstrated that augmenting VIs with temporal and spatial parameters significantly enhanced the AGB estimation accuracy. In summary, the fusion of spectral and spatio-temporal features enhanced the actual physical significance of the AGB estimation models and showed great potential for accurate rice AGB estimation during the main phenological stages.

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Section: Discussionmentioning

confidence: 99%

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages

Dai,

Yu,

et al. 2024

Front. Plant Sci.

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“…This study performed a correlation analysis between different orders of derivative spectrum and AGB at different growth stages of rice. Generally, the correlation between the derivative spectrum of various orders and AGB tended to increase and then decrease throughout the growth stage, potentially due to the influence of the rice growth cycle ( Xu et al., 2023 ). From the tillering stage to heading stage, as the chlorophyll content in rice leaves increases and the vegetation canopy structure becomes fully developed, the canopy becomes richer in spectral information, which can more accurately reflect crop characteristics ( Ren et al., 2018 ).…”

Section: Discussionmentioning

confidence: 99%

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm

Nian,

Su,

Yue

et al. 2024

Front. Plant Sci.

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Aboveground biomass (AGB) is regarded as a critical variable in monitoring crop growth and yield. The use of hyperspectral remote sensing has emerged as a viable method for the rapid and precise monitoring of AGB. Due to the extensive dimensionality and volume of hyperspectral data, it is crucial to effectively reduce data dimensionality and select sensitive spectral features to enhance the accuracy of rice AGB estimation models. At present, derivative transform and feature selection algorithms have become important means to solve this problem. However, few studies have systematically evaluated the impact of derivative spectrum combined with feature selection algorithm on rice AGB estimation. To this end, at the Xiaogang Village (Chuzhou City, China) Experimental Base in 2020, this study used an ASD FieldSpec handheld 2 ground spectrometer (Analytical Spectroscopy Devices, Boulder, Colorado, USA) to obtain canopy spectral data at the critical growth stage (tillering, jointing, booting, heading, and maturity stages) of rice, and evaluated the performance of the recursive feature elimination (RFE) and Boruta feature selection algorithm through partial least squares regression (PLSR), principal component regression (PCR), support vector machine (SVM) and ridge regression (RR). Moreover, we analyzed the importance of the optimal derivative spectrum. The findings indicate that (1) as the growth stage progresses, the correlation between rice canopy spectrum and AGB shows a trend from high to low, among which the first derivative spectrum (FD) has the strongest correlation with AGB. (2) The number of feature bands selected by the Boruta algorithm is 19~35, which has a good dimensionality reduction effect. (3) The combination of FD-Boruta-PCR (FB-PCR) demonstrated the best performance in estimating rice AGB, with an increase in R² of approximately 10% ~ 20% and a decrease in RMSE of approximately 0.08% ~ 14%. (4) The best estimation stage is the booting stage, with R2 values between 0.60 and 0.74 and RMSE values between 1288.23 and 1554.82 kg/hm2. This study confirms the accuracy of hyperspectral remote sensing in estimating vegetation biomass and further explores the theoretical foundation and future direction for monitoring rice growth dynamics.

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“…It serves as the only supply of water for the Kassala groundwater aquifer [48]. Gash is also the source of the water that formed the delta (150,000 Hectare) and the Kallahoat Agricultural Scheme (KAS) located in western Kassala, which contains the most agriculturally productive area for food security [49] and is the basis for the land use (LU) and socioeconomic activities in the Kassala area. The people who live along both sides of the River Gash's banks frequently believe it to be a source of horror.…”

Section: The Importance Of the Gash River On Land Use Domainmentioning

confidence: 99%

Impact of Structural and Non-Structural Measures on the Risk of Flash Floods in Arid and Semi-Arid Regions: A Case Study of the Gash River, Kassala, Eastern Sudan

Shuka,

Wang,

Abubakar

et al. 2024

Sustainability

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Sediment precipitation in riverbeds influences the effectiveness of structural and non-structural measures for flash flood mitigation and increases the potential for flooding. This study aimed to disclose the effectiveness of the implemented measures for flood risk mitigation in Kassala town, eastern Sudan. We employed remote sensing (RS) and GIS techniques to determine the change in the Gash River riverbed, the morphology, and the leveling of both the eastern and western sides of the river. Flood model simulation and a 3D path profile were generated using the digital elevation model (DEM) with a data resolution of 12.5 m from the ALOS BILSAR satellite. The main purpose of this study is to extract the layer of elevation of the riverbed on both the western and eastern banks and to determine the variations and their relationship to flood occurrence and mitigation. The construction of dikes and spurs near Kassala town has led to sediment precipitation, causing the riverbed to rise. The results show that it is now 1.5 m above the eastern Kassala town level, with a steep slope of 2 m/km, and the cross-section area at Kassala bridge has shrunk, which indicates that the bridge body will partially impede the river’s high discharge and increase the potential for flood risk in the study area. The eastern part of Kassala town has a higher likelihood of flooding than the western side. This study suggests redesigning structural measures like widening the Gash River, extending Kassala bridge for normal water flow, strengthening early warning systems, and implementing soil conservation activities for normal water flow.

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Dynamic estimation of rice aboveground biomass based on spectral and spatial information extracted from hyperspectral remote sensing images at different combinations of growth stages

Cited by 14 publications

References 57 publications

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages

Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm

Impact of Structural and Non-Structural Measures on the Risk of Flash Floods in Arid and Semi-Arid Regions: A Case Study of the Gash River, Kassala, Eastern Sudan

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