Abstract
Waterlogging is becoming an obvious constraint on food production due to the frequent occurrence of extremely high-level rainfall events. Leaf water content (LWC) is an important waterlogging indicator, and hyperspectral remote sensing provides a non-destructive, real-time and reliable method to determine LWC. Thus, based on a pot experiment, winter wheat was subjected to different gradients of waterlogging stress at the jointing stage. Leaf hyperspectral data and LWC were collected every 7 days after waterlogging treatment until the winter wheat was mature. Combined with methods such as vegetation index construction, correlation analysis, regression analysis, BP neural network (BPNN), etc., we found that the effect of waterlogging stress on LWC had the characteristics of hysteresis and all waterlogging stress led to the decrease of LWC. LWC decreased faster under severe stress than under slight stress, but the effect of long-term slight stress was greater than that of short-term severe stress. The sensitive spectral bands of LWC were located in the visible (VIS, 400–780 nm) and short-wave infrared (SWIR, 1 400–2 500 nm) regions. The BPNN Model with the original spectrum at 648 nm, the first derivative spectrum at 500 nm, the red edge position (λr), the new vegetation index RVI (437, 466), NDVI (437, 466) and NDVI’ (747, 1 956) as independent variables was the best model for inverting the LWC of waterlogging in winter wheat (modeling set: R2=0.889, RMSE=0.138; validation set: R2=0.891, RMSE=0.518). These results have important theoretical significance and practical application value for the precise control of waterlogging stress.
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Claims 9
Claim · model-written summary
Waterlogging stress led to a decrease of leaf water content (LWC) in winter wheat in a pot experiment.
- Taxon
- winter wheat
Triticum aestivume1 - Organ / stage
- jointing stagee1
- Treatment
- waterlogging stresse2
- Outcome
- LWCe2
- Direction
- decreasee2
- Setting
- pot experimente1
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“based on a pot experiment, winter wheat was subjected to different gradients of waterlogging stress at the jointing stage”
e2“all waterlogging stress led to the decrease of LWC”
Claim · model-written summary
The effect of waterlogging stress on LWC of winter wheat had the characteristics of hysteresis.
- Taxon
- winter wheatTriticum aestivume1
- Treatment
- waterlogging stresse3
- Outcome
- LWCe3
- Qualifier
- characteristics of hysteresise3
Not reportedComparatorDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“based on a pot experiment, winter wheat was subjected to different gradients of waterlogging stress at the jointing stage”
e3“the effect of waterlogging stress on LWC had the characteristics of hysteresis”
Claim · model-written summary
Severe waterlogging stress decreased LWC of winter wheat faster than slight stress.
- Taxon
- winter wheatTriticum aestivume1
- Treatment
- severe stresse4
- Outcome
- LWCe4
- Direction
- decreased fastere4
- Comparator
- slight stresse4
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“based on a pot experiment, winter wheat was subjected to different gradients of waterlogging stress at the jointing stage”
e4“LWC decreased faster under severe stress than under slight stress”
Claim · model-written summary
Long-term slight waterlogging stress had a greater effect on LWC of winter wheat than short-term severe stress.
- Taxon
- winter wheatTriticum aestivume1
- Treatment
- long-term slight stresse5
- Outcome
- effecte5
- Direction
- greatere5
- Comparator
- short-term severe stresse5
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“based on a pot experiment, winter wheat was subjected to different gradients of waterlogging stress at the jointing stage”
e5“the effect of long-term slight stress was greater than that of short-term severe stress”
Claim · model-written summary
The sensitive spectral bands of LWC in winter wheat lie in the visible and short-wave infrared regions.
- Taxon
- winter wheatTriticum aestivume1
- Outcome
- sensitive spectral bands of LWCe6
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e1“based on a pot experiment, winter wheat was subjected to different gradients of waterlogging stress at the jointing stage”
e6“The sensitive spectral bands of LWC were located in the visible (VIS, 400–780 nm) and short-wave infrared (SWIR, 1 400–2 500 nm) regions”
Claim · model-written summary
The BPNN model with the listed spectral variables was the best model for inverting LWC of waterlogging in winter wheat, with modeling-set R2 of 0.889.
- Taxon
- winter wheatTriticum aestivume7
- Outcome
- inverting the LWCe7
- Value
- R2=0.889e7
- Qualifier
- modeling sete7
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e7“was the best model for inverting the LWC of waterlogging in winter wheat (modeling set: R2=0.889, RMSE=0.138; validation set: R2=0.891, RMSE=0.518)”
Claim · model-written summary
The BPNN model had a modeling-set RMSE of 0.138 for LWC of winter wheat.
- Taxon
- winter wheatTriticum aestivume7
- Outcome
- RMSEe7
- Value
- RMSE=0.138e7
- Qualifier
- modeling sete7
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e7“was the best model for inverting the LWC of waterlogging in winter wheat (modeling set: R2=0.889, RMSE=0.138; validation set: R2=0.891, RMSE=0.518)”
Claim · model-written summary
The BPNN model had a validation-set R2 of 0.891 for LWC of winter wheat.
- Taxon
- winter wheatTriticum aestivume7
- Outcome
- R2e7
- Value
- R2=0.891e7
- Qualifier
- validation sete7
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e7“was the best model for inverting the LWC of waterlogging in winter wheat (modeling set: R2=0.889, RMSE=0.138; validation set: R2=0.891, RMSE=0.518)”
Claim · model-written summary
The BPNN model had a validation-set RMSE of 0.518 for LWC of winter wheat.
- Taxon
- winter wheatTriticum aestivume7
- Outcome
- RMSEe7
- Value
- RMSE=0.518e7
- Qualifier
- validation sete7
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e7“was the best model for inverting the LWC of waterlogging in winter wheat (modeling set: R2=0.889, RMSE=0.138; validation set: R2=0.891, RMSE=0.518)”
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