Stability evaluation of the PROSPECT model for leaf chlorophyll content retrieval

The radiative transfer model,PROSPECT,has been widely applied for retrieving leaf biochemical traits.However,little work has been conducted to evaluate the stability of the PROSPECT model with consideration of multiple factors(i.e.,spectral resolution,signal-to-noise ratio,plant growth stages,and treatments).This study aims to investigate the stability of the PROSPECT model for retrieving leaf chlorophyll(Chl)content(Cab).Leaf hemispherical reflectance and transmittance of oilseed rape were acquired at different spectral resolutions,noise levels,growth stages,and nitrogen treatments.The Chl content was also measured destructively by using a microplate spectrophotometer.The performance of the PROSPECT model was compared with a commonly used random forest(RF)model.The results showed that the prediction accuracy of PROSPECT and RF models for Cab did not produce significant differences under varied spectral resolutions ranging from 1 to 20 nm.The ranges of the relative root mean square errors(rRMSE)of the PROSPECT and RF models were 12%-13%and 11.70%-12.86%,respectively.However,the performance of both models for leaf Chl retrieval was strongly influenced by the noise level with the rRMSE of 13-15.37%and 12.04%-15.80%for PROSPECT and RF,respectively.For different growth stages,the PROSPECT model had similar prediction accuracies(rRMSE=9.26%-12.41%)to the RF model(rRMSE=9.17%-12.70%).Furthermore,the superiority of the PROSPECT model(rRMSE=10.10%-12.82%)over the RF model(rRMSE=11.81%-15.47%)was prominently observed when tested with plants growth at different nitrogen treatment levels.The results demonstrated that the PROSPECT model has a more stable performance than the RF model for all datasets in this study.

Artificial Neural Network to Predict Leaf Population Chlorophyll Content from Cotton Plant Images

Leaf population chlorophyll content in a population of crops, if obtained in a timely manner, served as a key indicator for growth management and diseases diagnosis. In this paper, a three-layer multilayer perceptron （MLP） artificial neural network （ANN） based prediction system was presented for predicting the leaf population chlorophyll content from the cotton plant images. As the training of this prediction system relied heavily on how well those leaf green pixels were separated from background noises in cotton plant images, a global thresholding algorithm and an omnidirectional scan noise filtering coupled with the hue histogram statistic method were designed for leaf green pixel extraction. With the obtained leaf green pixels, the system training was carried out by applying a back propagation algorithm. The proposed system was tested to predict the chlorophyll content from the cotton plant images. The results using the proposed system were in sound agreement with those obtained by the destructive method. The average prediction relative error for the chlorophyll density （μg cm^-2） in the 17 testing images was 8.41%.

Leaf Gas Exchange,Chlorophyll Fluorescence,and Fruit Yield in Hot Pepper (Capsicum anmuum L.) Grown Under Different Shade and Soil Moisture During the Fruit Growth Stage

Relative leaf chlorophyll （Chl.） content, leaf gas exchange, Chl. fluorescence, plant biological biomass, and fruit yield were evaluated in growing hot pepper （Capsicum annuum L.） during the fruit-growing stages in hot summer under three shade levels （un-shade, 30% shade, and 70% shade） and four soil water contents （SWC） of 40-55%, 55-70%, 70-85%, and 85- 100% of field moisture capacity （FMC）. Hot pepper crops were more affected by light irradiance than by soil moisture and by their interaction during the whole observed periods. Hot pepper attained greatest relative leaf Chl. content （expressed as SPAD value） and photosynthetic activity when cultivated with 30% shade, resulting in the highest plant biological biomass and fruit yield. Although 70% shade improved leaf photosynthetic efficiency （expressed as Fv/Fm or Fv＇/Fm＇）, crops obtained the lowest photosynthetic rate, photochemical quenching coefficient （qP）, and non-photochemical quenching coefficient （NPQ）. This showed that light irradiance was insufficiency in S70% （70% shade） treatment. The leaf net photosynthetic rates （PN）, Fc/Fm, and fruit yield increased gradually as SWC levels increased from 40-55% to 70- 85% FMC, but decreased as SWC was higher than 70-85% FMC. The water consumption increased progressively with SWC levels, but water-use efficiency （WUE） was the highest when soil moisture was 55-70% FMC. Interaction of shade and soil moisture had significant effects on PN and FJFm, but not on other parameters. Under drought stress （40-55% and 55-70% FMC）, 30% shade could relieve the droughty damage of crops and improve photosynthetic capacity and WUE, but 70% shade could not, oppositely, aggravate the damage. The positive correlation （r2 =0.72） between leaf PN and fruit yield was existent. This indicated that improvement of leaf photosynthesis would increase potentially marketable yield in hot pepper crops during the full fruit-growing stages. For agricultural purposes, approximately S30% （30% shade） with 70- 85% FMC is suggested to cultivate hot pepper during the fruit growth stage in hot summer months.

A new vegetation index combination for leaf carotenoid-tochlorophyll ratio: minimizing the effect of their correlation

The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to stress.However,the correlation between Car and Chl,and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio.This study aims to investigate combinations of vegetation indices(VIs)to minimize the influence of Car-Chl correlation,thus being more sensitive to the variability in the ratio across vegetation species and sites.VIs sensitive to Car and Chl variability were combined into four candidates of combinations,using a simulated dataset from the PROSPECT model.The VI combinations were then tested using six simulated datasets with different Car-Chl correlations,and evaluated against four independent datasets.The ratio of the carotenoid triangle ratio index(CTRI)with the red-edge chlorophyll index(CIred-edge)was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability.Compared with published VIs and two machine learning algorithms,CTRI/CIred-edge also showed the optimal performance in the fourfield datasets.This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio,applicable for assessing vegetation physiology,phenology,and response to environmental stress.

Identifying glyphosate-tolerant maize by soaking seeds in glyphosate solution

The identification of glyphosate-tolerant maize genotypes by field spraying with glyphosate is time-consuming, costly and requires treatment of a large area. We report a potentially better technique of seed-soaking to identify glyphosate-tolerant maize genotypes. The effects of soaking maize seeds in glyphosate solution under controlled conditions were studied on seed germination rate, seedling morphological indices, seedling growth and leaf chlorophyll content. These responses were compared among a glyphosate-tolerant transgenic maize cultivar CC-2, glyphosate-susceptible inbred line Zheng 58（the recurrent parent of CC-2） and hybrid cultivar Zhengdan 1002. The results showed that the germination rate, seedling morphological indices and leaf chlorophyll content of glyphosate-tolerant CC-2 seeds did not change significantly among five different concentrations of glyphosate treatment（0 to 2%）. In contrast, germination rates, seedling morphological indices and leaf chlorophyll contents of Zheng 58 and Zhengdan 1002 seeds were significantly negatively affected by exposure to increasing concentrations of glyphosate. The glyphosate-tolerant inbred line CC-2 displayed a strong tolerance to glyphosate after soaking in 0.1 to 2.0% glyphosate solutions, while both the inbred line Zheng 58 and hybrid Zhengdan 1002 were susceptible to glyphosate. The accuracy of the glyphosate-soaking method for screening glyphosate-tolerant maize was confirmed using a field spraying trial.

Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel-2 data of a mixed mountain forest

Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies.Here,we compared a radiative transfer model(RTM)inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content(LCC),canopy chlorophyll content(CCC),and leaf area index(LAI),in a mixed temperate forest.The accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park,Germany,was evaluated using in situ measurements collected contemporaneously.The RTM inversion using merit function resulted in estimations of LCC(R^(2)=0.26,RMSE=3.9µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.33 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.73 m^(2)/m^(2)),comparable to the estimations based on the machine learning method Random forest regression of LCC(R^(2)=0.34,RMSE=4.06µg/cm^(2)),CCC(R^(2)=0.65,RMSE=0.34 g/m^(2)),and LAI(R^(2)=0.47,RMSE=0.75 m^(2)/m^(2)).Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit function.The performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data.