Red Edge Position Research Articles

Hyperspectral Imaging Reveals Differential Carotenoid and Chlorophyll Temporal Dynamics and Spatial Patterns in Scots Pine Under Water Stress.

Drought-related die-off events have been observed throughout Europe in Scots pine (Pinus sylvestris L.). Such events are exacerbated by carbon starvation that is, an imbalance of photosynthetic productivity and resource usage. Recent evidence suggests that optically measurable photosynthetic pigments such as chlorophylls and carotenoids respond to water stress (WS). However, there is a lack of measurements using imaging spectroscopy, and the mechanisms linking xanthophyll-related changes in reflectance captured by the photochemical reflectance index (PRI) and chlorophyll changes in red edge position (REP) to WS are not understood. To probe this, we conducted a greenhouse experiment where 3-year-old Pinus sylvestris saplings were subjected to water limitation and followed using hyperspectral imaging (HSI) spectroscopy, water status and photosynthetic measurements. Carotenoids (e.g., xanthophyll cycle) and chlorophylls responded to WS, which was observed using the HSI-derived indices PRI and REP respectively. The spatial-temporal response in these two pigment-reflectance groupings differed. The spatial distribution of PRI represented the light intensity around the time of the measurement, whereas REP reflected the daily averaged light intensity over the experimental course. A further difference was noted upon rewatering, where the carotenoid-related PRI partially recovered but the chlorophyll-related REP did not.

A robust method for mapping soybean by phenological aligning of Sentinel-2 time series

Soybean is an important crop for food and animal feed. Production and area both continue to increase and expand into new areas and countries. Spatially explicit information on soybean cultivation is essential to crop monitoring, production estimation, and national accounting systems. However, its cultivation in diverse climate conditions, landscapes, and agricultural systems poses challenges to accurately map soybean across different regions and years. We propose an innovative soybean mapping method combining phenological alignment with machine learning (named here RF-DTW), which can be applied to diverse geographies and years by aligning phenological shifts and using distinctive features from Sentinel-2 time-series. The method first uses the dynamic time warping (DTW) algorithm to align the growing season between pixels across different sites. Then, based on the harmonized time-series, a set of distinctive features was identified and used to build random forest (RF) models to classify soybean across ten globally distributed sites and multiple years. Results show that the green chlorophyll vegetation index (GCVI), greenness and water content composite index (GWCCI), normalized difference senescent vegetation index (NDSVI), red edge position (REP), and short-wave infrared bands are important inputs for distinguishing soybean from other crops. Spectral-phenological features, particularly the curve slope metrics of GCVI and GWCCI during the peak to late growing season, rank as the most important features for mapping soybean. RF-DTW demonstrates good generalizability across ten study sites, achieving an overall accuracy (OA) of 0.92 and an F1-score of 0.84. F1-scores for eight out of ten sites ranged between 0.82 and 0.98, outperforming a benchmark method, although they were lower (F1-score < 0.60) for the two sites in Sub-Saharan Africa. Additionally, RF-DTW performs robustly when transferred to untrained regions and years, with most cases showing an F1-score higher than 0.70. Our proposed method, as a combination of phenological alignment and machine learning, can be used to map soybean accurately and efficiently across different regions and years, to provide crucial information for understanding the rapid dynamics of soybean cultivation and its global-scale impacts.

Using a Vegetation Index to Monitor the Death Process of Chinese Fir Based on Hyperspectral Data

Chinese fir is one of the most widely distributed and extensively planted timber species in China. Therefore, monitoring pests and diseases in Chinese fir plantations is directly related to national timber forest security and forest ecological security. This study aimed to identify appropriate vegetation indices for the early monitoring of pests and diseases in Chinese fir plantations. For this purpose, the researchers used an imaging spectrometer to capture hyperspectral images of both experimental and control groups. The experimental group consisted of Chinese fir trees with two sections of bark stripped off, while the control group consisted of healthy Chinese fir trees. The study then assessed the sensitivity of 11 vegetation indices to the physiological differences between the two groups using the Mann–Whitney U test. The results showed that both the green-to-red region spectral angle index (GRRSGI) and the red edge position index (REP) were able to monitor the difference as early as 16 days after damage. However, GRRSGI performs best in monitoring early death changes in Chinese fir trees because it is less affected by noise and is more stable. The green–red spectral area index (GRSAI) also had high stability, but the monitoring effect was slightly worse than that of GRRSGI and REP. Compared with other indices, GRRSGI and GRSAI can better exploit the advantages of hyperspectral data.

Identifying the optimal waveband positions for autumn grassland senescence detection using the broadband multispectral remotely sensed dataset

While remote sensing of grass senescence is addressed in the literature, knowledge of optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses is still limited. Notably, detection of senescent grass is important for understanding the available forage in rangeland environments and associated ecological implications. The free provision of remote sensing data from modern broadband multispectral sensors with improved spatial and spectral properties offers prospects for reliable and wall-to-wall monitoring of grassland senescence in rangeland ecosystems. The current study tested the potential of the modern multispectral remote sensing dataset (i.e., Sentinel 2 and Landsat) in mapping the senescent grass, and to identify the optimal waveband positions that are suitable for discriminating between senescent and non-senescent grasses. Locational information for both senescent and non-senescent grasses was acquired on the field and was used to train the classification process. A Random Forest classification approach was employed using the Landsat 8 and Sentinel 2 multispectral datasets to spectrally discern between senescent and non-senescent grasses. Our analysis yielded overall classification accuracies of 0.82 and 0.78 and kappa coefficients of 0.64 and 0.56 for Sentinel 2 and Landsat 8, respectively. Using the stepwise selection approach, the study further identified that the Red Edge Position (REP), and the visible green and red bands of the electromagnetic spectrum were the optimal waveband positions for separating between senescent and non-senescent grasses based on the broadband multispectral remote sensing. This study has demonstrated the value of the broadband multispectral remote sensing data in mapping autumn grassland senescence, and this lays a foundation for effective operational scale monitoring of foraging resources at the landscape scale, particularly during dry periods.

Analysis of Cadmium Contamination in Lettuce (Lactuca sativa L.) Using Visible-Near Infrared Reflectance Spectroscopy

In order to rapidly and accurately monitor cadmium contamination in lettuce and understand the growth conditions of lettuce under cadmium pollution, lettuce is used as the test material. Under different concentrations of cadmium stress and at different growth stages, relative chlorophyll content of lettuce leaves, the cadmium content in the leaves, and the visible-near infrared reflectance spectra are detected and analyzed. An inversion model of the cadmium content and relative chlorophyll content in the lettuce leaves is established. The results indicate that cadmium concentrations of 1 mg/kg and 5 mg/kg promote relative chlorophyll content, while concentrations of 10 mg/kg and 20 mg/kg inhibit relative chlorophyll content. The cadmium content in the leaves increases with increasing cadmium concentrations. Cadmium stress caused a “blue shift” in the red edge position only during the mature period, while the red valley position underwent a “blue shift” during the seedling and growth periods and a “red shift” during the mature period. The green peak position exhibited a “blue shift”. After model validation, it was found that the model constructed using the ratio of red edge area to yellow edge area and the normalized values of red edge area and yellow edge area effectively estimated the cadmium content in lettuce leaves. The model established using the normalized vegetation index of the red edge and the ratio of the peak green value to red shoulder amplitude can effectively estimate the relative chlorophyll content in lettuce leaves. This study demonstrates that the visible-near infrared spectroscopy technique holds great potential for monitoring cadmium contamination and estimating chlorophyll content in lettuce.

Classification of Coniferous and Broad-Leaf Forests in China Based on High-Resolution Imagery and Local Samples in Google Earth Engine

As one of the world’s major forestry countries, accurate forest-type maps in China are of great importance for the monitoring and management of forestry resources. Classifying and mapping forest types on a large scale across the country is challenging due to the complex composition of forest types, the similarity of spectral features among forest types, and the need to collect and process large amounts of data. In this study, we generated a medium-resolution (30 m) forest classification map of China using multi-source remote sensing images and local samples. A mapping framework based on Google Earth Engine (GEE) was constructed mainly using the spectral, textural, and structural features of Sentinel-1 and Sentinel-2 remote sensing images, while local acquisition data were utilized as the mapping channel for training. The proposed method includes the following steps. First, local data processing is performed to obtain training and validation samples. Second, Sentinel-1 and Sentinel-2 data are processed to improve the classification accuracy by using the enhanced vegetation index (EVI) and the red-edge position index (REPI) computed based on the S2A data. Third, to improve classification efficiency, useless bands are removed and important bands are retained through feature importance analysis. Finally, random forest (RF) is used as a classifier to train the above features, and the classification results are used for mapping and accuracy evaluation. The validation of the samples showed an accuracy of 82.37% and a Kappa value of 0.72. The results showed that the total forest area in China is 21,662,261.17 km2, of which 1,127,294.42 km2 of coniferous forests account for 52% of the total area, 981,690.98 km2 of broad-leaf forests account for 45.3 % of the total area, and 57,275.77 km2 of mixed coniferous and broad-leaf forests account for 2.6% of the total area. Upon further evaluation, we found that textural and structural features play a greater role in classification compared to spectral features. Our study shows that combining multi-source high-resolution remote sensing imagery with locally collected samples can produce forest maps for large areas. Our maps can accurately reflect the distribution of forests in China, which is conducive to forest conservation and development.

Quantifying Aboveground Grass Biomass Using Space-Borne Sensors: A Meta-Analysis and Systematic Review

Recently, the move from cost-tied to open-access data has led to the mushrooming of research in pursuit of algorithms for estimating the aboveground grass biomass (AGGB). Nevertheless, a comprehensive synthesis or direction on the milestones achieved or an overview of how these models perform is lacking. This study synthesises the research from decades of experiments in order to point researchers in the direction of what was achieved, the challenges faced, as well as how the models perform. A pool of findings from 108 remote sensing-based AGGB studies published from 1972 to 2020 show that about 19% of the remote sensing-based algorithms were tested in the savannah grasslands. An uneven annual publication yield was observed with approximately 36% of the research output from Asia, whereas countries in the global south yielded few publications (&lt;10%). Optical sensors, particularly MODIS, remain a major source of satellite data for AGGB studies, whilst studies in the global south rarely use active sensors such as Sentinel-1. Optical data tend to produce low regression accuracies that are highly inconsistent across the studies compared to radar. The vegetation indices, particularly the Normalised Difference Vegetation Index (NDVI), remain as the most frequently used predictor variable. The predictor variables such as the sward height, red edge position and backscatter coefficients produced consistent accuracies. Deciding on the optimal algorithm for estimating the AGGB is daunting due to the lack of overlap in the grassland type, location, sensor types, and predictor variables, signalling the need for standardised remote sensing techniques, including data collection methods to ensure the transferability of remote sensing-based AGGB models across multiple locations.

A dataset of paddy rice planting areas along the Yangtze River (Jiangsu section) in 2020

As a staple food crop in China, paddy rice is one of the most widely grown crops in the country. Thanks to its abundant water resources, the Yangtze River has superiority in paddy rice planting. The prompt and accurate monitoring of paddy rice planting information plays an important role in providing essential technical support to safeguard China's food security. However, there is a scarcity of studies on the extraction of paddy rice planting areas along the Yangtze River using the remote sensing technology. In this paper, guided by paddy rice growth phenology information, we selected the Sentinel-2 satellite imagery that covered the paddy rice growing period (from May to September) along the Yangtze River in 2020. On the basis of high-spatial resolution images combined with field investigation, we obtained the real distribution map of the paddy rice planting area in Menghe Town and selected samples and validation samples from the map. Based on the maximum likelihood, neural network, SVM (Support Vector Machine) and other machine learning methods, we carried out the paddy rice extraction research, compared and analyzed the accuracy of extraction results of NDVI (Normalized Difference Vegetation Index) and REP (Red Edge Position Index) spectral index sets. The results show that the REP index sets of the three time phases in May, August and September are most effectively extracted using the neural network method. The overall accuracy of the results is 92.73%, with a corresponding Kappa coefficient of 0.72. We achieved global applicability by optimizing key parameters such as timing, spectral bands, and classifier methods for paddy rice extraction. Additionally, we conducted post-processing to refine the results. Finally, we obtained a vector dataset of paddy rice growing areas along the Yangtze River (Jiangsu section) in 2020, with an overall classification accuracy of 87.15%. This dataset is expected to provide a reference for further monitoring the paddy rice planting areas and enhancing the accuracy of grain production estimates throughout the Yangtze River region.

Groth’s algorithm to detect the possible presence of landmines using changes in the reflection of plants

Abstract. Post-conflict reconstruction includes the removal of land mines and remnants of war. The CSIR conducted field experiments to determine the impact of TNT in the soil on plants as a possible means to detect landmines. All the leaf clip readings were done using a spectrometer to determine reflection and absorption of light at one micrometre intervals between 350 to 2500 micrometre. Laboratory analysis such as UPLC qTOF MS indicated that the TNT have an influence on the plants. Several indices such as Modified Red Edge Normalized Difference Vegetation Index (mNDVI705), Red Edge Position (REP) and Moisture Stress Index (MSI) did not show any significant differences between control plants and experimental plants with different TNT concentrations. Groth’s pattern matching algorithm designed to match several photographs of the same part of the universe. A set of triangles using dominant stars are created for each photograph and matched using an error band. If the selected triangles from the two photographs fit within the error band, then they are from the same section of the universe. Bands for the Pléiades instruments were simulated using the data from the spectrometer for each plant. The reflectance value of the band and the normalised midpoint wavelength of each Pléiades band were used to construct the triangles. The control plant triangle is then matched with the experimental plant and if the triangles do not match, then the effect of TNT on the plant is significant. The initial results with the control plants and experimental plants are positive.

Foliar Spectral Signature Analysis of Three Pepper (Piper nigrum L.) Cultivars in Malaysia

Pepper (Piper nigrum L.) is an important cash crop for smallholders in the state of Sarawak, Malaysia. At present, there are more than eight pepper cultivars being planted in the state since the 1950s. In this study, the unique spectral signatures of three high yielding pepper cultivars in Malaysia were examined using a ground-based spectral sensor to detect varietal differences. Concurrent foliar spectral signatures, chlorophyll concentration, and Normalized Difference Vegetation Index (NDVI) of cultivar Kuching, Semongok Aman and Semongok Emas were recorded under normal field conditions. Foliar chlorophyll concentration and NDVI results showed that all the matured pepper vines under this assessment were in healthy condition. From a rough observation, except for a slight increase in the visible (VIS) region of the spectrum (500 to 699 nm) for cultivar Semongok Aman and Semongok Emas, spectral reflectance of all three pepper cultivars were similar in the near-infrared region (700 to 1000 nm). The results for First Derivative Curves analysis indicated that the 684.5 to 756.5 nm region showed the highest separation followed by region with the second lowest separation (504.5 to 540.5 nm). The highest value was obtained in the Red Edge Position (REP) for cultivar Kuching, Semongok Aman and Semongok Emas which were 709, 706 and 710, respectively. The highest value obtained in the VIS region for cultivar Kuching, Semongok Aman and Semongok Emas were 523, 524 and 527, respectively. These fundamental findings suggested that it is possible to discriminate pepper cultivars through field spectroscopy and first derivative analysis. In addition, this information may be used for further studies related to precision agriculture particularly in integrated nutrient and disease management.

Spatial Prediction and Mapping of Soil Water Content by TPE-GBDT Model in Chinese Coastal Delta Farmland with Sentinel-2 Remote Sensing Data

Soil water content is an important indicator used to maintain the ecological balance of farmland. The efficient spatial prediction of soil water content is crucial for ensuring crop growth and food production. To this end, 104 farmland soil samples were collected in the Yellow River Delta (YRD) in China, and the soil water content was determined using the drying method. A gradient boosting decision tree (GBDT) model based on a tree-structured Parzen estimator (TPE) hyperparametric optimization was developed, and then the soil water content was predicted and mapped based on the soil texture and vegetation index from Sentinel-2 remote sensing images. The results of statistical analysis showed that the soil water content had a high coefficient of variation (55.30%), a non-normal distribution, and complex spatial variability. Compared with other models, the TPE-GBDT model had the highest prediction accuracy (RMSE = 6.02% and R2 = 0.71), and its mapping results showed that the areas with high soil water content were distributed on both sides of the river and near the estuary. Furthermore, the results of Shapley additive explanation (SHAP) analysis showed that the soil texture (PC2 and PC5), modified normalized difference vegetation index (MNDVI), and Sentinel-2 red edge position (S2REP) index provided important contributions to the spatial prediction of soil water content. We found that the hydraulic physical properties of soil texture and the vegetation characteristics (such as vegetation coverage, root action, and transpiration) are the key factors affecting the spatial migration and heterogeneity of the soil water content in the study area. The above results show that the TPE algorithm can quickly capture the hyperparameters that are most suitable for the GBDT model, so that the GBDT model can ensure prediction accuracy, reduce the loss function with less training data, and accurately learn of the nonlinear relationship between soil water content and environmental factors. This paper proposes a machine learning method for hyperparameter optimization that shows considerable potential to predict the spatial heterogeneity of soil water content, which can effectively support regional farmland soil and water conservation and high-quality agricultural development.

Links between light availability and spectral properties of forest floor in European forests

Remote sensing using spectral data has been commonly applied to retrieve tree layer properties while the monitoring of forest floor remains a less studied topic. We investigated the links between light availability at forest floor, and forest floor's spectral reflectance properties (350–2500 nm) and fractional cover across boreal and temperate Europe. We hypothesized that tree canopy structure (and thus, light availability at forest floor) is linked not only to the vegetation composition of forest floor, as has been shown previously, but also to forest floor's spectral reflectance properties, and that these relationships differ between forest biomes. Data were collected in situ from a total of 67 forest stands in southern boreal, hemiboreal, temperate floodplain, and temperate mountain sites. The variation of light availability at forest floor was linked to both the forest floor's composition and spectral reflectance properties. Each study site exhibited site-specific spectral features and a different mean reflectance spectrum. Openness in tree canopies was related to an increase in the fractional cover of vascular plants and to a decrease of plant litter, consequently enhancing the forest floors’ spectral absorptance features in the red and shortwave-infrared wavelengths, as well as reflectance in the near-infrared region. Also, the variations of normalized difference index values and red edge positions as functions light availability at forest floor and forest floor's composition were different for each site. Our results suggest that incorporating biome-specific relationships between tree canopy structure and forest floor reflectance properties would improve interpretation of optical remote sensing data. The measurement data are openly available.

Exploring the Potential of UAV-Based Hyperspectral Imagery on Pine Wilt Disease Detection: Influence of Spatio-Temporal Scales

Pine wilt disease (PWD), caused by pine wood nematode (PWN, Bursaphelenchus xylophilus), poses a serious threat to the coniferous forests in China. This study used unmanned aerial vehicle (UAV)-based hyperspectral imaging conducted at different altitudes to investigate the impact of spatio-temporal scales on PWD detection in an monoculture Masson pine plantation. The influence of spatio-temporal scales on hyperspectral responses of pine trees infected with PWD and detection accuracies were evaluated by Jeffries–Matusita (J-M) distances and the random forest (RF) algorithm. The optimal vegetation indices (VIs) and spatial resolutions were identified by comparing feature importance and model accuracy. The main results showed that the VIs and J-M distances were greatly affected by spatio-temporal scales. In the early, mid-, and late infection stages, the RF-based PWD detection model had accuracies ranging between 72.05 and 79.48%, 83.71 and 89.59%, and 96.81 and 99.28%, peaking at the 10 cm, 8 cm, and 4 cm spatial resolutions, respectively. The green normalized difference vegetation index (GNDVI) and red edge position (REP) were the optimal VIs in early and mid-infection stages, respectively. This study can be important to improve the efficiency of PWD detection and reducing the loss of forests resources.

Rapid early-season maize mapping without crop labels

Maize (Zea mays), the second most-produced crop worldwide, serves as the cornerstone for global food security and human livelihood. Early-season maize mapping benefits maize production forecasting and other pre-harvest decision-making applications. However, most existing early-season mapping efforts rely heavily on either the current-year or historical crop labels to train classifiers, limiting the potential applications to new regions lacking crop labels. To explore the possibility of maize mapping only using satellite data in the early season, we proposed a Multi-temporal Gaussian Mixture Model (MGMM) to map maize planting areas without any crop labels. A chlorophyll content relevant proxy, named the Red-edge position (REP), was selected as model input, based on the truth that summer maize tends to show a higher chlorophyll content than other summer crops (e.g., soybean, cotton, peanut, sunflowers, etc.) in the peak season. The novel early-season mapping framework using the REP-based MGMM (MGMM-REP) was applied in four diverse areas (Iowa and Georgia in the US, Heilongjiang province (HLJ) in China, and Grand-Est in France). The MGMM-REP could generate maize maps more than two months before harvest with reasonable accuracy (F1 ≥ 77%) using all the available Sentinel-2 (S2) images and the Google Earth Engine platform (GEE). Our early-season maps agreed well with the existing crop maps and official statistics. The correlation coefficient (R) of the maize acreage between our early-season maps and statistics was higher than 0.94. The high inter-class difference of REP between maize and other summer crops could increase the F1 score by 2–47% compared to the other commonly used Vegetation indices (VIs). Since MGMM-REP does not rely on crop labels, it had the potential to be transferred to label-scarce maize-cropped regions and contribute to the international commodity trade and food security forecast.

High-throughput phenotyping salt tolerance in JUNCAOs by combining prompt chlorophyll a fluorescence with hyperspectral spectroscopy

The planting of salt-tolerant plants is regarded as the one of important measurements to improve the saline–alkali lands. The outstanding biological properties of JUNCAOs have made them candidates to improve and utilize saline–alkali lands. At present, little attention has been paid to developing a non-destructive and high throughput approach to evaluate the salt tolerance of JUNCAO. To close the gaps, three typical JUNCAOs (A.donax. No.1, A.donax. No.5 and A.donax. No.10) were evaluated by combining prompt chlorophyll a fluorescence (ChlF) with hyperspectral spectroscopy (HS). The results showed that salt stress reduced relative stem growth, water content, and total chlorophyll content but enhanced the malondialdehyde (MDA) content. It caused a significant change in chlorophyll a fluorescence kinetics with an appearance of L-, K- and J-band, implying damaging energetic connectivity between PSII units, uncoupling of the oxygen evolving complex (OEC) and inhibition of the QA−reoxidation. The negative impact of salt stress on JUNCAOs increased with the increasing level of salt concentration. Effect on spectral reflectance in the in the visible region with shifts on red edge position (REP) and blue edge position (BEP) to shorter wavelength was also found in salt stress plants. Combining principal component analysis (PCA) with the membership function method based on spectral indices and JIP-test parameters could well screen JUNCAOs salt tolerant ability with the highest for A.donax. NO.10 but lowest for A.donax. NO.1, which was the same as that of using conventional approach. The results demonstrate that prompt ChlF coupling with HS could provide potentials for non-invasively and high-throughput phenotyping salt tolerance in JUNCAOs.

Summer Maize Growth Estimation Based on Near-Surface Multi-Source Data

Rapid and accurate crop chlorophyll content estimation and the leaf area index (LAI) are both crucial for guiding field management and improving crop yields. This paper proposes an accurate monitoring method for LAI and soil plant analytical development (SPAD) values (which are closely related to leaf chlorophyll content; we use the SPAD instead of chlorophyll relative content) based on the fusion of ground–air multi-source data. Firstly, in 2020 and 2021, we collected unmanned aerial vehicle (UAV) multispectral data, ground hyperspectral data, UAV visible-light data, and environmental cumulative temperature data for multiple growth stages of summer maize, respectively. Secondly, the effective plant height (canopy height model (CHM)), effective accumulation temperature (growing degree days (GDD)), canopy vegetation index (mainly spectral vegetation index) and canopy hyperspectral features of maize were extracted, and sensitive features were screened by correlation analysis. Then, based on single-source and multi-source data, multiple linear regression (MLR), partial least-squares regression (PLSR) and random forest (RF) regression were used to construct LAI and SPAD inversion models. Finally, the distribution of LAI and SPAD prescription plots was generated and the trend for the two was analyzed. The results were as follows: (1) The correlations between the position of the hyperspectral red edge and the first-order differential value in the red edge with LAI and SPAD were all greater than 0.5. The correlation between the vegetation index, including a red and near-infrared band, with LAI and SPAD was above 0.75. The correlation between crop height and effective accumulated temperature with LAI and SPAD was above 0.7. (2) The inversion models based on multi-source data were more effective than the models made with single-source data. The RF model with multi-source data fusion achieved the highest accuracy of all models. In the testing set, the LAI and SPAD models’ R2 was 0.9315 and 0.7767; the RMSE was 0.4895 and 2.8387. (3) The absolute error between the extraction result of each model prescription map and the measured value was small. The error between the predicted value and the measured value of the LAI prescription map generated by the RF model was less than 0.4895. The difference between the predicted value and the measured value of the SPAD prescription map was less than 2.8387. The LAI and SPAD of summer maize first increased and then decreased with the advancement of the growth period, which was in line with the actual growth conditions. The research results indicate that the proposed method could effectively monitor maize growth parameters and provide a scientific basis for summer maize field management.

Gas exchange for the plants on the example of coastal sedge and comparison with the materials of spectro-gasometric ground-based measurements from the UAV and the Sentinel-2 satellite

Spectro-gasometric ground-based measurements were carried out during 2020-2021. It was determined that five vegetation indices - REP (Red Edge Position), Green NRDI (Normalized Difference Vegetation Index), Green MOD (Green Model) and Red MOD (Red edge Model) are more responsive to the presence of СО2 concentration depending on leaf photosynthesis and leaf respiration of the coastal sedge (Carex riparia) with high correlation under Pearson from 0.60 to 0.72. Certain vegetation indices capture changes in СО2 concentration and can be recommended for use in carbon flux models for vegetation canopy. Data from DJI P4 Multispectral UAV, Parrot Bebop Pro Thermal and Sentinel-2 satellite compared to ground measurements on May 25, 2021.

Seasonal variations of canopy spectra and their indications to carbon fluxes in a temperate forest in northeast China

The seasonal variations of forest canopy spectral characteristics are critical to improving the utilization of remote sensing methodology to quantify forest physiology, especially forest carbon sink. However, the seasonal variations of forest canopy spectra are poorly understood. Combined field survey and EO-1 Hyperion imageries, we extracted the spectral curves of seven forest types of Changbai Mountain in China in seven periods. We also calculated various remote sensing indexes and analyzed their seasonal change of spectral characteristics among different forest types. Optimal indexes were selected to indicate the seasonal variation of forest carbon fluxes. Our results showed that there were differences in spectral curves among forest types. The reflectance of coniferous forests was lower than that of broad-leaved forests in growing season. Changbai Scotch pine forest owned the lowest spectral reflectance, whereas the reflectance of Mongolian oak forest was the highest, especially in the near-infrared region. The red edge slope (RES) of broad-leaved forest was higher than coniferous forest in spring and summer. The RES of broad-leaved and coniferous forests was similar in autumn. The red edge position of various forest types showed slight shift in different seasons. Four typical forest types showed different spectral characteristics with seasonal changes. The seasonal variation of coniferous forest spectral curves was not obvious. The seasonal variation of broad-leaved forest spectra was the largest. Most of the spectral indexes can indicate the seasonal variation characteristics of each forest type. Enhanced vegetation index (EVI) is better than normalized difference vegetation index (NDVI) to indicate the forest phenology. Seasonal curves of spectral indexes were different in all forest types. Spectral indexes of coniferous forests were most stable throughout the year. The curves of each index in broad-leaved forests showed significant difference in autumn, which may be influenced by the understory vegetation after their defoliation. For broad-leaved Korean (BK) pine forest, the scaled value of photochemical reflectance index (SPRI)*EVI owned the highest correlation with gross primary productivity (R = 0.99 and P < 0.01) and net ecosystem exchange (R = − 0.77 and P < 0.05), respectively. SPRI*NDVI showed the highest correlation with ecosystem respiration (R = 0.96 and P < 0.01). The seasonal variation of carbon fluxes of different forest types retrieved from the optimal remote sensing index were consistent, but their peaks occurred at different times.

Identification and characteristic analysis of urban vegetation spectra under different dust deposition.

Urban atmospheric environmental problems have raised increasing attention in recent years. To confirm the impact of plant dust deposition capacity on urban atmosphere and spectral characteristics, this study carried out experiments in Xuhui District and Minhang District of Shanghai, and 4 common greening species were selected as research objects. In order to explore the changes in vegetation spectral characteristics, ASD FieldSpec 3 Spectrometer and 1/10000 electronic balance were used to measure the spectral data and dust data of samples. The results show as follows: (1) 380-680 nm and 750-1350 nm are the best spectral wavelengths to analyze the influence of dust deposition on spectrum. (2) The canopy reflectance spectra of tree species decrease with the increase of dust deposition, especially in the wavelength range of 750-1350 nm. (3) The first derivative and the second derivative are beneficial to observe the spectral changes and judge the position of the red edge. The red edge position of some tree species is easy to move under the interference of dust deposition. (4) Among the four tree species, the spectrum of Osmanthus fragrans is relatively undisturbed by dust deposition, and Osmanthus fragrans is a great tree species for urban greening. The research made a foundation for the future use of spectral information to estimate vegetation dust.

Ground Hyper-Spectral Remote-Sensing Monitoring of Wheat Water Stress during Different Growing Stages

Monitoring agricultural drought via ground hyper-spectral remote sensing has always been a hot topic in the fields of agriculture and meteorology. In this study, a greenhouse experiment was conducted on wheat subjected to water stress during its different growth stages, namely tillering, jointing, and milk maturity. An instrument (HOBO ware PRO) used to continuously measure soil moisture was employed to measure the soil water content (SWC). An analytical spectral device (ASD) was utilized to obtain the spectral curve of wheat subject to different water treatment methods. The canopy temperature was obtained using thermal infrared sensors (METER SI-400). The relationships between the SWC, wheat drought stage, canopy temperature, and spectral response characteristics were elucidated. The results showed that the significant differences in spectral characteristics were due to water stress during the different growth stages of wheat. Red-edge parameters of red-valley position (RVP) and red-edge position (REP) both changed by 21 nm for the tillering-stage drought and the jointing-stage drought; however, the RVP and REP values for the milk maturity stage drought and the treatment under no water stress changed by 2 nm. Further, it was proved that the red-edge blue-shift phenomenon was affected not only by the different wheat growth processes, but also by the water stress at different growth stages. Red-edge reflectance clearly reflects wheat water stress at different growth stages. From SWC and canopy temperature analysis results, SWC and canopy temperature had a significant difference between wheat drought at different growth stages, and the canopy temperature at the jointing stage drought had the strongest change. The water index (WI) based on eleven vegetation water indexes exhibited a good performance for distinguishing wheat water stress at different growth stages. In conclusion, ground-based hyperspectral remote sensing can provide a large amount of high temporal and spectral resolution data on vegetation and its surrounding environment, making it an important technical tool for wheat drought monitoring, which has a great significance on the monitoring and early warning of wheat drought, reducing drought-related yield losses, and ensuring food security.