Estimation Of Leaf Chlorophyll Content Research Articles

Rice leaf chlorophyll content estimation with different crop coverages based on Sentinel-2

Chlorophyll content is an important index for evaluating the health and productivity of crops, and environmental stress on them. The real-time, rapid, and accurate acquisition of chlorophyll content plays a key role in crop growth monitoring. Remote sensing can quickly obtain chlorophyll content in regional and global scale, but how to eliminate the interference of soil background in estimation is a major challenge. The statistical analysis method based on empirical/semi-empirical model is simpler, faster and easier to implement than that based on radiative transfer mechanism model. The statistical analysis method can eliminate the influence of environmental background by looking for a Vegetation index (VI) that is sensitive to the chlorophyll content but not to soil background to a certain extent. However, the accuracy of this method is low in fields with different degrees of crop coverage. Additionally, the special soil background characteristics of rice fields make the method a doubtful tool to estimate the Chlorophyll content in rice coverage. To improve the accuracy of the rice leaf chlorophyll content (LCC) estimation model, we here propose a new model based on crop coverage. We analyzed remote sensing images of the Sentinel-2 over rice-planting areas of Qian Gorlos County in Jilin Province of China. We divided the study area into three regions based on high-, medium-, and low-rice canopy coverage. Rice LCC in each region was estimated by identifying remote sensing vegetation indices that are sensitive to chlorophyll in different rice canopy coverages. Compared to the estimated results without considering the crop coverage, our model achieves a higher accuracy. In addition, we applied the model in the region of Northeast China in 2023 to verify its strong generalisability and robustness. Our study provides a reference for rapidly and non-destructively obtaining rice LCC based on Sentinel-2 images. However, the applicability of our model to other crops will be verified in the future.

Combining canopy spectral reflectance and RGB images to estimate leaf chlorophyll content and grain yield in rice

Predicting rice grain yield using multi-source remotely sensed data is crucial for improving prediction accuracy, optimizing nitrogen management, and advancing precision agricultural development. However, the feasibility and reliability of using multi-source remotely sensed data to predict the grain yield remain unclear. Therefore, this study aimed to explore the possibility of providing rice leaf chlorophyll content (LCC) estimations and predictions of the grain yield using multi-source remotely sensed data. Two rice field experiments were conducted with various rice cultivars and nitrogen rates, and a field spectrometer and an unmanned aerial vehicle (UAV) equipped with a digital camera were employed to acquire the spectral reflectance and red–green–blue (RGB) images at the tillering, jointing, and full-heading stages. Destructive sampling was then conducted to measure the LCC and grain yield. The LCC was used as a bridge to develop remotely sensed prediction models for the grain yield. First, the linear relationship between grain yield and the LCC was determined at the tillering, jointing, and full-heading stages. A multiple linear regression (MLR) model was then developed to predict grain yield using multi-temporal LCCs at three growth stages. Second, multiple stepwise regression, support vector regression, and back propagation neural network were used to evaluate the estimation performance of spectral reflectance, RGB image data, and their combination for LCC. Third, the most accurate LCC estimation model was selected and coupled with the linear and MLR models of grain yield. The results showed that grain yield was significantly and positively related to the LCC at the tillering, jointing, and full-heading stages, and that the MLR model of grain yield achieved the best estimation accuracy using multi-temporal LCCs. The fusion models established by combining spectral reflectance and RGB image data improved LCC estimation accuracies. Using multi-growth stages, the most accurate predictions of grain yield were obtained from LCC estimation models (R2 = 0.698, RMSE = 0.742 t ha−1, rRMSE = 9.004 %) compared to those using single growth stages. Our study concluded that multi-source remotely sensed data fused from ground-based spectral reference and UAV-based RGB images can better predict and explain the grain yield for both single and multi-growth stages. This study provides a novel method of estimating the crop chlorophyll content and grain yield.

Assessment of remote-sensed vegetation indices for estimating forest chlorophyll concentration

Remote-sensed vegetation indices (VIs) have emerged as essential tools for retrieving forest chlorophyll concentration. Although VIs are widely used, some concerns regarding VIs for estimating chlorophyll remain to be addressed, such as saturation effect, leaf area index (LAI) disturbance, and soil brightness influence. Currently, a systematic study on such performance evaluation of chlorophyll-related VIs considering these issues is still insufficient. This study coupled two radiative transfer models, the PROSPECT model and the LESS model, to simulate Eucalyptus monocultures with different chlorophyll content and systematically evaluated the 18 broad-band VIs’ ability in chlorophyll estimation at different scales. Our results indicate that most VIs designed for chlorophyll estimation were relatively resistant to saturation, except for SIPI and some classical VIs (e.g., NDVI and DVI), which were insensitive to chlorophyll decreases and tended to reach saturation quickly (when leaf chlorophyll content (LCC) exceeded 40 ug/cm2). The relationships between NDVI, SR, DVI, and LCC were easily influenced by soil brightness and LAI. S2REP, MTCI, TGI, TCARI, and EVI were insensitive to soil brightness when estimating LCC. Overall, S2REP was best at quantitatively retrieving chlorophyll and resisting interference from other factors. For practical applications, our study suggests that it is preferable to use S2REP for LCC estimation when the red-edge band is available; otherwise, CVI can be used instead. The judicious utilization of VI can effectively depict chlorophyll levels and improve the understanding of vegetation response to climate change. Our findings provide the necessary information for the selection of specific VIs tailored to specific vegetation parameters.

Pretrained Deep Learning Networks and Multispectral Imagery Enhance Maize LCC, FVC, and Maturity Estimation

Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, and maturity. In contrast to the traditional manual collection of crop trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC and FVC maps for breeding materials, facilitating prompt assessments of maturity information. This study addresses the following research questions: (1) Can image features based on pretrained deep learning networks and ensemble learning enhance the estimation of remote sensing LCC and FVC? (2) Can the proposed adaptive normal maturity detection (ANMD) algorithm effectively monitor maize maturity based on LCC and FVC maps? We conducted the following tasks: (1) Seven phases (tassel initiation to maturity) of maize canopy orthoimages and corresponding ground-truth data for LCC and six phases of FVC using UAVs were collected. (2) Three features, namely vegetation indices (VI), texture features (TF) based on Gray Level Co-occurrence Matrix, and deep features (DF), were evaluated for LCC and FVC estimation. Moreover, the potential of four single-machine learning models and three ensemble models for LCC and FVC estimation was evaluated. (3) The estimated LCC and FVC were combined with the proposed ANMD to monitor maize maturity. The research findings indicate that (1) image features extracted from pretrained deep learning networks more accurately describe crop canopy structure information, effectively eliminating saturation effects and enhancing LCC and FVC estimation accuracy. (2) Ensemble models outperform single-machine learning models in estimating LCC and FVC, providing greater precision. Remarkably, the stacking + DF strategy achieved optimal performance in estimating LCC (coefficient of determination (R2): 0.930; root mean square error (RMSE): 3.974; average absolute error (MAE): 3.096); and FVC (R2: 0.716; RMSE: 0.057; and MAE: 0.044). (3) The proposed ANMD algorithm combined with LCC and FVC maps can be used to effectively monitor maize maturity. Establishing the maturity threshold for LCC based on the wax ripening period (P5) and successfully applying it to the wax ripening-mature period (P5–P7) achieved high monitoring accuracy (overall accuracy (OA): 0.9625–0.9875; user’s accuracy: 0.9583–0.9933; and producer’s accuracy: 0.9634–1). Similarly, utilizing the ANMD algorithm with FVC also attained elevated monitoring accuracy during P5–P7 (OA: 0.9125–0.9750; UA: 0.878–0.9778; and PA: 0.9362–0.9934). This study offers robust insights for future agricultural production and breeding, offering valuable insights for the further exploration of crop monitoring technologies and methodologies.

Improving retrieval of leaf chlorophyll content from Sentinel-2 and Landsat-7/8 imagery by correcting for canopy structural effects

Accurately estimating leaf chlorophyll content (Chl) at large spatial scales is crucial for monitoring agricultural production and plant photosynthesis. Sentinel-2 and Landsat-7/8 offer the potential to assess Chl with high spatial resolutions using various physically-based, empirical, and hybrid methods, but they still present challenges due to the confounding effects of canopy structure and soil background. The near-infrared reflectance of vegetation (NIRV) has been proposed to characterize canopy structural and soil background effects in sun-induced chlorophyll fluorescence. Therefore, in this study, we developed a novel strategy that incorporates NIRV to minimize canopy structural and soil background effects on Chl retrieval, using bidirectional reflectance factor (BRF) from Sentinel-2 and Landsat-7/8 imagery. We compared Chl estimation using empirical random forest (RF), the physically-based PROSAIL-5B inversion, and a hybrid method using RF to train simulated datasets from PROSAIL-5B. We validated Chl retrieval performance with measured datasets from the National Ecological Observatory Network, covering seven vegetation types. We also compared spatiotemporal Chl variations from Sentinel-2, Landsat-7/8, and MODIS. The results showed that NIRV effectively characterized the canopy structural effect on Chl estimation, associated with leaf area index, average leaf angle, and canopy fraction of senescent leaves. Compared to BRF, correcting BRF by NIRV improved Chl retrieval with RMSE values reduced by 12.2%–27.0% for Sentinel-2, 7.4%–16.4% for Landsat-7, and 6.9%–13.7% for Landsat-8, respectively. This also resulted in a significant increase in R2 values for Sentinel-2 (0.13–0.46), Landsat-7 (0.08–0.19), and Landsat-8 (0.12–0.2), respectively. Sentinel-2 outperformed Landsat-7/8 in Chl estimation, due to the contribution of red edge bands and a higher spatial resolution. Furthermore, the hybrid method performed best for Chl estimation from Sentinel-2 and Landsat-7/8 with relative RMSE values of 17.33%, 20.22%, and 19.23%, respectively. Our hybrid method demonstrated lower model uncertainty with smaller variations of RMSE values across varying biochemical and structural traits and solar and viewing angles, and exhibited more consistent spatiotemporal variations of Chl from Sentinel-2 with phenology compared to Landsat-7/8 and the published MODIS Chl data. Our hybrid method demonstrates significant potential in mitigating canopy structural effects, enhancing Chl retrieval, and facilitating the development of Chl data from various satellite imagery sources.

Evaluation of Heat Tolerance in Foliar Tissue ofAcerGenotypes

Abstract Background Extreme summer heat events in which temperatures exceed 40 °C are expected to increase in duration and intensity worldwide. Consequently, selecting heat-tolerant trees for future predicted climatic conditions will be one of the significant challenges for urban landscape managers. Method The effect of heat stress (44 °C for 4 hours) on chlorophyll variable:maximum fluorescence (Fv/Fm) ratios and Soil Plant Analysis Development (SPAD) values as measures of damage to the leaf photosynthetic system and leaf chlorophyll content was quantified in 8Acergenotypes (Acer pseudoplatanus‘Negenia’,A. pseudoplatanus‘Spaethii’,A. platanoides‘Royal Red’,A. platanoides‘Princeton Gold’,A. platanoides‘Emerald Queen’,A. platanoides‘Drummondii’,A. campestre,A. campestre‘Louisa Red Shine’). Results Fv/Fm ratios proved to be a sensitive indicator of heat damage and positively identified marked differences in tolerance to and recovery from heat damage between the 8Acergenotypes. Estimated leaf chlorophyll content (SPAD) was not sufficiently sensitive to distinguish differences in tolerance and recovery between genotypes. Conclusions Chlorophyll fluorescence Fv/Fm ratios, but not estimated leaf chlorophyll content (SPAD), offers a rapid screening technique for assessing the tolerance to and recovery from heat stress in leaf tissue of trees.

Comparison of leaf chlorophyll content retrieval performance of citrus using FOD and CWT methods with field-based full-spectrum hyperspectral reflectance data

Citrus is one of the most economically valuable fruit trees in the world, for which leaf chlorophyll content (LCC) serves as a crucial indicator for evaluating its growth and health status. However, the quantitative estimation of LCC using remote-sensing techniques is still challenging owing to unclear sensitive spectral ranges, baseline drift and overlapping spectrum peaks. To resolve these issues, we clarified the spectral response characteristics of citrus LCC using fractional-order derivatives (FOD) and continuous wavelet transform (CWT) methods to determine its sensitive spectral range with in situ full-spectrum leaf hyperspectral data. We proposed a novel method for estimating the LCC of citrus by combining an ensemble learning regression model based on Hyperopt optimization (H-ELR) with partial least squares regression (PLSR) using the ultra-high dimensional feature variables produced by dual- and tri-band combination strategies. We evaluated the retrieval performance of LCC between the FOD- and CWT-based optimal spectral feature variables. Besides, we further examined the feasibility of improving the estimation accuracy of LCC by the combination of their optimal feature variables. Finally, we evaluated the effect of the spectral curve trend changes and different dimensional spectral features on LCC estimation. The results showed that: (1) The FOD- and CWT-based methods improved the correlation between original spectral reflectance and LCC, with the correlation coefficient increasing by 0.046 and 0.054, respectively. We confirmed that 425–740 nm is the optimal spectral range for LCC estimation. (2) We found that the 0.9 order derivative Tri-band index (TBI2 (R571, R1697, R740)) constructed by combining the sensitive spectral bands of leaf water content achieved a high-precision LCC estimation (R2 = 0.876). In addition, the MERIS terrestrial chlorophyll index (MTCI) constructed based on the scale6 of CWT also gained good retrieval accuracy (R2 = 0.806). (3) We demonstrated that a combination of FOD-based and CWT-based sensitive spectral features improves estimation accuracies (R2 = 0.891) of LCC and revealed that the reflectance peaks and slope peaks at 550 and 750 nm are essential variables for predicting the citrus LCC. (4) The combination of PLSR and H-ELR model provided a good retrieval performance of citrus LCC with the kurtosis (γ = 3.2) and skewness (Sk = 0.066) of the residual prediction values. Our proposed method can provide a scientific basis for estimating LCC and other physiological parameters of citrus and other crop types, which is also important to optimize agricultural management practices and improve crop yield.

Potato Leaf Chlorophyll Content Estimation through Radiative Transfer Modeling and Active Learning

Leaf chlorophyll content (LCC) significantly correlates with crop growth conditions, nitrogen content, yield, etc. It is a crucial indicator for elucidating the senescence process of plants and can reflect their growth and nutrition status. This study was carried out based on a potato nitrogen and potassium fertilizer gradient experiment in the year 2022 at Keshan Farm, Qiqihar Branch of Heilongjiang Agricultural Reclamation Bureau. Leaf hyperspectral and leaf chlorophyll content data were collected at the potato tuber formation, tuber growth, and starch accumulation periods. The PROSPECT-4 radiative transfer model was employed to construct a look-up table (LUT) as a simulated data set. This was accomplished by simulating potato leaves’ spectral reflectance and chlorophyll content. Then, the active learning (AL) technique was used to select the most enlightening training samples from the LUT based on the measured potato data. The Gaussian process regression (GPR) algorithm was finally employed to construct the inversion models for the chlorophyll content of potato leaves for both the whole and single growth periods based on the training samples selected by the AL method and the ground measured data of the potatoes. The R2 values of model validation accuracy for the potato whole plantation period and three single growth periods are 0.742, 0.683, 0.828, and 0.533, respectively with RMSE values of 4.207, 4.364, 2.301, and 3.791 µg/cm2. Compared with the LCC inversion accuracy through LUT with a cost function, the validation accuracies of the GPR_PROSPECT-AL hybrid model were improved by 0.119, 0.200, 0.328, and 0.255, and the RMSE were reduced by 3.763, 2.759, 0.118, and 5.058 µg/cm2, respectively. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato LCC estimation, which provides a valid tool for accurately monitoring crop growth and growth health.

Retrieving decametric-resolution leaf chlorophyll content from GF-6 WFV by assessing the applicability of red-edge vegetation indices

Accurate and timely retrieval of leaf chlorophyll content (LCC) at fine spatial scales is crucial for monitoring plants' photosynthetic capacity and nutritional status. The wide field-of-view camera (WFV) onboard Gaofen-6 (GF-6) satellite has two red-edge bands at 16-meter spatial resolution, providing an opportunity to derive decametric-resolution LCC. However, the potential of GF-6 WFV data in LCC estimation has yet to be studied. Vegetation indices (VIs) are widely used in LCC estimation due to their simplicity and efficiency, and the accuracy of LCC inversion can be improved with red-edge VIs. This study focused on exploring the retrieval of LCC with GF-6 WFV using the VI-based method by assessing the applicability of nine red-edge VIs derived from GF-6 WFV. Our results revealed that (1) the newly developed canopy structure-insensitive CSI (chlorophyll sensitive index) achieved comparable accuracy to the physically-based method and reduced the limitation of the VI-based method being species- and time-specific. Validation results showed that the root-mean-square error (RMSE) of the estimated LCC from the CSI-based model was 7.91–11.38 μg/cm2 for four plant functional types (similar accuracy to Croft et al. (2020) based on the lookup table method with RMSE of 9.25–13.18 μg/cm2). The accuracy of the CSI-based model remained stable across species and growth conditions, indicating its feasibility for large-scale applications. (2) GF-6 WFV provided accurate LCC estimates and well-captured the spatial and seasonal variations in LCC. GF-6 LCC maps showed comparable accuracy (R2 = 0.53, RMSE = 9.78 μg/cm2) and similar spatiotemporal distribution to the 30-m Sentinel-2 LCC products. Additionally, GF-6 LCC maps outperformed Sentinel-2 LCC products regarding observation frequency and spatial continuity. Our study highlights the potential of GF-6 WFV in retrieving decametric-resolution LCC and provides further support for applications of GF-6 WFV data in precision agriculture and forestry.

Solar-induced chlorophyll fluorescence extraction based on heterogeneous light distribution for improving in-situ chlorophyll content estimation

Accurate estimation of leaf chlorophyll content (LCC) in field for wheat crops is important to provide guidance for precision management. Solar-induced chlorophyll fluorescence (SIF) extraction based on hyperspectral imaging is an effective tool for LCC estimation, which is because SIF is a signal directly and intrinsically related to photosynthetic activity of crops. However, soil background and structural difference always lead to mixing of soil, shadowed and sunlit canopy components, and heterogeneous light distribution among canopy, which increase the complexity of SIF extraction and reduce the LCC estimation accuracy. The purpose of this study is to propose an accurate SIF extraction method by eliminating above challenges to improve the LCC estimation accuracy. Hyperspectral image and LCC measurements were conducted at the jointing and heading stages of wheat crops. Firstly, to eliminate the effect of soil background, two image segmentation methods, excess green index (ExG) and optimized soil-adjusted vegetation index (OSAVI), are compared to extract the crop region. Secondly, to reduce the canopy structure effect, light distribution condition is evaluated by the brightness, which is extracted from intensity component of hue-saturation-intensity color space. Then K-means clustering based on the brightness distribution is utilized to divide the segmented crop region further into shadowed and sunlit regions. Finally, SIF was extracted based on the Fraunhofer line discriminator method at 687 and 761 nm, and was modified by normalization of brightness to reduce the influence of heterogeneous light distribution. The performance of random forest regression models, which are built by extracting SIF based on raw image, segmented crop by ExG segmentation, segmented crop by OSAVI segmentation, and shadowed and sunlit regions, respectively, is compared. Results show the estimation accuracy of the models increases when image segmentation is performed (determination coefficient of prediction set (RP2) = 0.37 for ExG segmentation; RP2 = 0.47 for OSAVI segmentation) compared with raw image (RP2 = 0.30). Moreover, shadowed region clustering based on brightness was optimal region for SIF extraction (RP2 = 0.54) compared with the sunlit region (RP2 = 0.27). The modified SIF at shadowed regions, normalized by the brightness of crops to reduce the effect of heterogeneous light distribution, yields the highest relationship with LCC (RP2 = 0.79), which outperforms SIF normalized by the absorbed photosynthetically active radiation (APAR). The reason is that brightness is more related to LAI, a parameter representing canopy structure, than APAR. It suggests that OSAVI segmentation for soil background effect elimination and SIF extraction considering canopy structure and heterogeneous light distribution facilitate the improvement of LCC estimation accuracy for high-throughput phenotyping in field.

Daily monitoring of Effective Green Area Index and Vegetation Chlorophyll Content from continuous acquisitions of a multi-band spectrometer over winter wheat

Green area index (GAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) are key variables that are closely related to crop growth. Concurrent and continuous monitoring of GAI, LCC and CCC is critical to keep consistency among variables and make decisions for field precision managements. Previous studies have developed several instruments and algorithms to monitor continuous GAI, while the autonomous monitoring of three variables simultaneously has been lacking. This study presents a novel algorithm to retrieve daily GAI, LCC and CCC from continuous directional observations acquired by a fixed and economic affordable multi-band spectrometer (6 bands covering red, red-edge and near infrared domains) and a photosynthetically active radiation (PAR) sensor in the field. It is composed of three main steps, corresponding to three crucial questions when retrieving variables under natural environments using multi-band spectrometer installed on a near-surface platform: diffuse fraction in each spectral band, radiometric calibration and diurnal sun variation of daily acquisitions. First, we estimated diffuse fraction in each spectral band from the relationship with PAR diffuse fraction based on simulations of the 6S atmospheric radiative transfer model. Second, we computed the relative value of each band to the reference of mean of measurements on all six bands from near-surface measurements, in place of absolute radiometric calibration to limit the influence of changing illumination conditions. In the third step, we combined PROSAIL canopy radiative transfer model and kernel-driven models to retrieved GAI, LCC and CCC from artificial neural network using above spectral diffuse fraction and diurnal multi-angle relative observations. The algorithm was evaluated over 43 IoTA (Internet of things for Agriculture) systems that were installed in 29 wheat fields in France from March to May 2019. Results showed that our method provides good estimates of GAI with root mean square error (RMSE) of 0.54, relative RMSE (RRMSE) of 26.95%, R2 of 0.86, LCC (RMSE = 12.06 μg/cm2, RRMSE = 33.34%, R2 = 0.52) and CCC (RMSE = 0.23 g/m2, RRMSE = 24.58%, R2 = 0.93). This study shows great potentials for concurrent estimates of GAI, LCC and CCC from continuous ground measurements. It will be useful over other vegetations or other near-surface platforms for simultaneous estimations of biophysical variables.

Estimation of above ground biomass, biophysical and quality parameters of spinach (Spinacia Oleracea L.) using Sentinel-2 to support the supply chain

The estimation of biophysical and biochemical parameters using Sentinel-2 satellite imagery provides crucial information to support agronomic management and logistics for the processing industry, yet this approach has been poorly explored for vegetable crops, such as spinach (Spinacia oleracea L.). For this reason, Sentinel-2 satellite images were used to estimate biophysical and biochemical parameters of open field spinach crops (Above Ground Biomass (AGB), Canopy Nitrogen Content (CNC), Leaf Area Index (LAI), Canopy Chlorophyll Content (CCC), Leaf Dry Matter Content (LDMC), Leaf Nitrogen Content (LNC), Leaf Nitrate Content (LNO3−C) and Leaf Chlorophyll Content (LCC)). Spinach samples were collected in northern Italy in two different growing seasons (2020 – 2021) to measure biophysical and biochemical parameters which were linearly regressed via linear regression k-fold to five vegetation indices retrieved from Sentinel-2 images (MCARI, NDRE, NDVI, NDWI and SR). The AGB estimation models were also validated at field scale using historical data, totally independent from those used for the model calibration, collected by the processing industry between 2018 and 2021. Overall, canopy-level parameters (AGB, LAI, CNC, CCC) were estimated more accurately than leaf-level parameters (LDMC, LNC, LNO3−C, LCC). The highest accuracy was observed for the estimation of the canopy-scale parameter AGB (nRMSE = 9.48 %, R2 = 0.87) using MCARI, while the lowest accuracy was observed for the estimation of the leaf-scale parameter LCC (nRMSE = 29.24 %, R2 = 0.01) using NDWI. At field level, the validation of the AGB estimation models showed the highest performance using SR (nRMSE = 19.69 %, R2 = 0.21). This work demonstrates that using Sentinel-2 satellite images, it is feasible to estimate biophysical and biochemical parameters useful for monitoring the health of spinach crops for both agronomic management and the industrial supply chain.

Incremental learning for crop growth parameters estimation and nitrogen diagnosis from hyperspectral data

Nitrogen is an essential nutrient in crop growth cycle and directly affects the photosynthesis of crops. The leaf chlorophyll content (LCC) and leaf area index (LAI) are important for characterizing photosynthetic capacity of crops and are critical indicators for diagnosing nitrogen status of crops. Hyperspectral remote sensing technology provided a means to achieve crop LCC and LAI estimation. However, the redundancy of spectral data and canopy structure effect can cause poor robustness of the estimating models, further hindering the development and application of estimating models across different crop species. In this study, a method based on incremental learning was proposed for the simultaneous estimation of LCC and LAI, and for nitrogen diagnosis across crops. First, for the spectral dataset generated by the PROSAIL model, a deep neural network was used to construct LCC and LAI estimation models (called DNNCA model). Secondly, for the hyperspectral data collected from field crops (soybean, canola and wheat), incremental learning using regularization (LwF algorithm) was used to update the DNNCA model parameters. Finally, the dilution curve model based on the LCC-LAI anisotropic growth relationship was developed to assess crop nitrogen status. The results showed that: (1) The constrained bi-objective optimizated DNNCA model can consider the interactive effect of LAI and LCC on spectral reflectance, and achieved reliable estimation on PROSAIL simulation data set (LAI:R2 = 0.82, RMSE = 0.77 m2/m2; LCC:R2 = 0.91, RMSE = 6.4 ug/cm2). (2) By incremental learning, DNNCA model has continuous learning capability and stable estimation on cross-crop (canola, soybean and wheat) field-measured data (LAI:R2 = 0.64–0.82, RMSE = 0.58–1.02 m2/m2; LCC:R2 = 0.56–0.82, RMSE = 3.9–10.5 ug/cm2). (3) The relationship between the NNILCC and the NNILNC was significant. The NNILCC derived from the anisotropy relationship between crop LAI and LCC was an effective tool for crop nitrogen status diagnosis. (4) The process of crop LAI, LCC and NNILCC reflected the effect of water and nitrogen supply on crop growth. The appropriate water-nitrogen treatments contributed to LAI increase and LCC accumulation. The study demonstrated that hyperspectral remote sensing technology combined with incremental learning is an effective method for cross-crop growth monitoring and nitrogen diagnosis. These results provide a reference and basis for filed water-nitrogen supply and management.

Utilizing Hyperspectral Reflectance and Machine Learning Algorithms for Non-Destructive Estimation of Chlorophyll Content in Citrus Leaves

To address the demands of precision agriculture and the measurement of plant photosynthetic response and nitrogen status, it is necessary to employ advanced methods for estimating chlorophyll content quickly and non-destructively at a large scale. Therefore, we explored the utilization of both linear regression and machine learning methodology to improve the prediction of leaf chlorophyll content (LCC) in citrus trees through the analysis of hyperspectral reflectance data in a field experiment. And the relationship between phenology and LCC estimation was also tested in this study. The LCC of citrus tree leaves at five growth seasons (May, June, August, October, and December) were measured alongside measurements of leaf hyperspectral reflectance. The measured LCC data and spectral parameters were used for evaluating LCC using univariate linear regression (ULR), multivariate linear regression (MLR), random forest regression (RFR), K-nearest neighbor regression (KNNR), and support vector regression (SVR). The results revealed the following: the MLR and machine learning models (RFR, KNNR, SVR), in both October and December, performed well in LCC estimation with a coefficient of determination (R2) greater than 0.70. In August, the ULR model performed the best, achieving an R2 of 0.69 and root mean square error (RMSE) of 8.92. However, the RFR model demonstrated the highest predictive power for estimating LCC in May, June, October, and December. Furthermore, the prediction accuracy was the best with the RFR model with parameters VOG2 and Carte4 in October, achieving an R2 of 0.83 and RMSE of 6.67. Our findings revealed that using just a few spectral parameters can efficiently estimate LCC in citrus trees, showing substantial promise for implementation in large-scale orchards.

Prior knowledge and active learning enable hybrid method for estimating leaf chlorophyll content from multi-scale canopy reflectance

Real-time and accurate assessment of leaf chlorophyll content (Cab) will be significant for monitoring plant physiological status. Development of hybrid methods advances the assessment of Cab with the advantages of both physical mechanism and calculation capability, while the estimation accuracies were constrained by the lacking of prior knowledge and representative real samples. This study explored the use of prior knowledge and active learning to enable the development of efficient hybrid methods for assessing Cab from multi-scale canopy reflectance. The hybrid method was established by using the simulated dataset from the PROSAIL model to train the Gaussian process regression (GPR) model. We used two measured crop datasets and three publicly-available grassland, herbaceous, and tree species datasets collected from near-ground, unmanned aerial vehicle, and airborne platforms to examine the model performances. Prior knowledge for model parameters was obtained from ground measurements, and an active learning method, namely improved Greedy sampling (iGS), was employed to select new representative samples. The results showed that performance of the hybrid method in Cab estimations varied with leaf structure parameter, carotenoid content, anthocyanin content, carbon-based constituents, average leaf angle, leaf area index, and hot-spot size parameter. When prior knowledge for the ranges of model parameters was available, the hybrid method better estimated Cab with the root mean square error (RMSE) reduced by 7.59%–65.96%. Compared with the random selection, iGS was able to select representative samples to update the estimation models with only 10% of the measured samples, and a higher ratio (e.g., 20%) may be needed for small datasets. The hybrid method coupled with prior knowledge and active learning obtained satisfied estimations of Cab across different datasets with the relative RMSE of 12.30%–27.24%. The proposed method will contribute to improving the applicability and robustness of hybrid modeling for multi-scale monitoring of leaf biochemical traits.

Mapping Soybean Maturity and Biochemical Traits Using UAV-Based Hyperspectral Images

Soybeans are rich in high-quality protein and raw materials for producing hundreds of chemical products. Consequently, soybean cultivation has gained widespread prevalence across diverse geographic regions. Soybean breeding necessitates the development of early-, standard-, and late-maturing cultivars to accommodate cultivation at various latitudes, thereby optimizing the utilization of solar radiation. In the practical process of determining the maturity of soybean breeding materials within the breeding field, the ripeness is assessed based on three critical criteria: pod moisture content, leaf color, and the degree of leaf shedding. These parameters reflect the crown structure, physicochemical parameters, and reproductive organ changes in soybeans during the maturation process. Therefore, methods for analyzing soybean maturity at the breeding plot scale should match the standards of agricultural experts to the maximum possible extent. This study presents a hyperspectral remote sensing approach for monitoring soybean maturity. We collected five periods of unmanned aerial vehicle (UAV)-based soybean canopy hyperspectral digital orthophoto maps (DOMs) and ground-level measurements of leaf chlorophyll content (LCC), flavonoids (Flav), and the nitrogen balance index (NBI) from a breeding farm. This study explores the following aspects: (1) the correlations between soybean LCC, NBI, Flav, and maturity; (2) the estimation of soybean LCC, NBI, and Flav using Gaussian process regression (GPR), partial least squares regression (PLSR), and random forest (RF) regression techniques; and (3) the application of threshold-based methods in conjunction with normalized difference vegetation index (NDVI)+LCC and NDVI+NBI for soybean maturity monitoring. The results of this study indicate the following: (1) Soybean LCC, NBI, and Flav are associated with maturity. LCC increases during the beginning bloom period (P1) to the beginning seed period (P3) and sharply decreases during the beginning maturity period (P4) stage. Flav continues to increase from P1 to P4. NBI remains relatively consistent from P1 to P3 and then drops rapidly during the P4 stage. (2) The GPR, PLSR, and RF methodologies yield comparable accuracy in estimating soybean LCC (coefficient of determination (R2): 0.737–0.832, root mean square error (RMSE): 3.35–4.202 Dualex readings), Flav (R2: 0.321–0.461, RMSE: 0.13–0.145 Dualex readings), and NBI (R2: 0.758–0.797, RMSE: 2.922–3.229 Dualex readings). (3) The combination of the threshold method with NDVI < 0.55 and NBI < 8.2 achieves the highest classification accuracy (accuracy = 0.934). Further experiments should explore the relationships between crop NDVI, the Chlorophyll Index, LCC, Flav, and NBI and crop maturity for different crops and ecological areas.

Hyperspectral Estimation of Chlorophyll Content in Apple Tree Leaf Based on Feature Band Selection and the CatBoost Model

Leaf chlorophyll content (LCC) is a crucial indicator of nutrition in apple trees and can be applied to assess their growth status. Hyperspectral data can provide an important means for detecting the LCC in apple trees. In this study, hyperspectral data and the measured LCC were obtained. The original spectrum (OR) was pretreated using some spectral transformations. Feature bands were selected based on the competitive adaptive reweighted sampling (CARS) algorithm, random frog (RF) algorithm, elastic net (EN) algorithm, and the EN-RF and EN-CARS algorithms. Partial least squares regression (PLSR), random forest regression (RFR), and the CatBoost algorithm were used before and after grid search parameter optimization to estimate the LCC. The results revealed the following: (1) The spectrum after second derivative (SD) transformation had the highest correlation with LCC (–0.929); moreover, the SD-based model produced the highest accuracy, making SD an effective spectrum pretreatment method for apple tree LCC estimation. (2) Compared with the single band selection algorithm, the EN-RF algorithm had a better dimension reduction effect, and the modeling accuracy was generally higher. (3) CatBoost after grid search optimization had the best estimation effect, and the validation set of the SD-EN-CARS-CatBoost model after parameter optimization had the highest estimation accuracy, with the determination coefficient (R2), root mean square error (RMSE), and relative prediction deviation (RPD) reaching 0.923, 2.472, and 3.64, respectively. As such, the optimized SD-EN-CARS-CatBoost model, with its high accuracy and reliability, can be used to monitor the growth of apple trees, support the intelligent management of apple orchards, and facilitate the economic development of the fruit industry.

Hyperspectral-to-image transform and CNN transfer learning enhancing soybean LCC estimation

Leaf chlorophyll content (LCC) is a distinct indicator of crop health status used to estimate nutritional stress, diseases, and pests. Thus, accurate LCC information can assist in the monitoring of crop growth. The combined use of hyperspectral and deep learning techniques (e.g., convolutional neural network [CNN] and transfer learning [TL]) can improve the performance of crop LCC estimation. We propose a hyperspectral-to-image transform (HIT) technique for converting canopy hyperspectral reflectance into 2D images. We designed a CNN architecture called LCCNet that fuses the deep and shallow features of CNNs to improve soybean LCC estimation. This study evaluated the combined use of hyperspectral remote sensing (RS), HIT, CNN, and TL techniques to estimate soybean LCC for multiple growth stages. The LCCNet was pre-trained based on a simulated dataset (n = 114,048) from the PROSAIL radiative transfer model (RTM) and used as prior knowledge for this work. The soybean canopy hyperspectral RS dataset (n = 910) was obtained using a FieldSpec 3 spectrometer. The knowledge gained while learning to estimate LCC from PROSAIL RTM was applied when estimating field soybean LCC (Dualex readings). TL was used to enhance the soybean estimation model, called the Soybean-LCCNet (RTM + HIT + CNN + TL) model. We tested the LCC (Dualex readings) estimation performance using (a) HIT + CNN, (b) LCCNet (RTM + HIT + CNN), (c) Soybean-LCCNet (RTM + HIT + CNN + TL), and (d) widely used LCC spectral features + partial least squares regression (PLSR). Four methods were ranked based on their LCC estimation performance: Soybean-LCCNet (R2 = 0.78, RMSE = 4.13 (Dualex readings)) > HIT + CNN (R2 = 0.75, RMSE = 4.41 (Dualex readings)) > PLSR-based method (R2 = 0.61, RMSE = 5.39 (Dualex readings)) > LCCNet (R2 = 0.53, RMSE = 7.11 (Dualex readings)). The main conclusions of this work are as follows: (1) HIT + CNN can provide a more robust LCC estimation performance than the widely used LCC SIs; (2) Fusing the deep and shallow features of CNNs can improve the performance of RS soybean LCC (Dualex readings) estimation; and (3) Soybean-LCCNet can reuse the CNN layer information of a pre-trained LCCNet based on a PROSAIL RTM dataset and improve the soybean LCC estimation performance.

The impact of variable illumination on vegetation indices and evaluation of illumination correction methods on chlorophyll content estimation using UAV imagery

BackgroundThe advancements in unmanned aerial vehicle (UAV) technology have recently emerged as an effective, cost-efficient, and versatile solution for monitoring crop growth with high spatial and temporal precision. This monitoring is usually achieved through the computation of vegetation indices (VIs) from agricultural lands. The VIs are based on the incoming radiance to the camera, which is affected when there is a change in the scene illumination. Such a change will cause a change in the VIs and subsequent measures, e.g., the VI-based chlorophyll-content estimation. In an ideal situation, the results from VIs should be free from the impact of scene illumination and should reflect the true state of the crop’s condition. In this paper, we evaluate the performance of various VIs computed on images taken under sunny, overcast and partially cloudy days. To improve the invariance to the scene illumination, we furthermore evaluated the use of the empirical line method (ELM), which calibrates the drone images using reference panels, and the multi-scale Retinex algorithm, which performs an online calibration based on color constancy. For the assessment, we used the VIs to predict leaf chlorophyll content, which we then compared to field measurements.ResultsThe results show that the ELM worked well when the imaging conditions during the flight were stable but its performance degraded under variable illumination on a partially cloudy day. For leaf chlorophyll content estimation, The r^2 of the multivariant linear model built by VIs were 0.6 and 0.56 for sunny and overcast illumination conditions, respectively. The performance of the ELM-corrected model maintained stability and increased repeatability compared to non-corrected data. The Retinex algorithm effectively dealt with the variable illumination, outperforming the other methods in the estimation of chlorophyll content. The r^2 of the multivariable linear model based on illumination-corrected consistent VIs was 0.61 under the variable illumination condition.ConclusionsOur work indicated the significance of illumination correction in improving the performance of VIs and VI-based estimation of chlorophyll content, particularly in the presence of fluctuating illumination conditions.

UAV-Based Remote Sensing for Soybean FVC, LCC, and Maturity Monitoring

Timely and accurate monitoring of fractional vegetation cover (FVC), leaf chlorophyll content (LCC), and maturity of breeding material are essential for breeding companies. This study aimed to estimate LCC and FVC on the basis of remote sensing and to monitor maturity on the basis of LCC and FVC distribution. We collected UAV-RGB images at key growth stages of soybean, namely, the podding (P1), early bulge (P2), peak bulge (P3), and maturity (P4) stages. Firstly, based on the above multi-period data, four regression techniques, namely, partial least squares regression (PLSR), multiple stepwise regression (MSR), random forest regression (RF), and Gaussian process regression (GPR), were used to estimate the LCC and FVC, respectively, and plot the images in combination with vegetation index (VI). Secondly, the LCC images of P3 (non-maturity) were used to detect LCC and FVC anomalies in soybean materials. The method was used to obtain the threshold values for soybean maturity monitoring. Additionally, the mature and immature regions of soybean were monitored at P4 (mature stage) by using the thresholds of P3-LCC. The LCC and FVC anomaly detection method for soybean material presents the image pixels as a histogram and gradually removes the anomalous values from the tails until the distribution approaches a normal distribution. Finally, the P4 mature region (obtained from the previous step) is extracted, and soybean harvest monitoring is carried out in this region using the LCC and FVC anomaly detection method for soybean material based on the P4-FVC image. Among the four regression models, GPR performed best at estimating LCC (R2: 0.84, RMSE: 3.99) and FVC (R2: 0.96, RMSE: 0.08). This process provides a reference for the FVC and LCC estimation of soybean at multiple growth stages; the P3-LCC images in combination with the LCC and FVC anomaly detection methods for soybean material were able to effectively monitor soybean maturation regions (overall accuracy of 0.988, mature accuracy of 0.951, immature accuracy of 0.987). In addition, the LCC thresholds obtained by P3 were also applied to P4 for soybean maturity monitoring (overall accuracy of 0.984, mature accuracy of 0.995, immature accuracy of 0.955); the LCC and FVC anomaly detection method for soybean material enabled accurate monitoring of soybean harvesting areas (overall accuracy of 0.981, mature accuracy of 0.987, harvested accuracy of 0.972). This study provides a new approach and technique for monitoring soybean maturity in breeding fields.