Ratio Vegetation Index Research Articles

Comparison of Machine and Deep Learning Methods to Estimate Shrub Willow Biomass from UAS Imagery

Shrub willow is considered an important dedicated energy crop in temperate climates for the production of bioenergy, biofuels, and bio-based products. A methodology to rapidly and accurately estimate above-ground biomass (AGB) is essential for understanding potential biomass supply, identifying potential growth limitations, and making management decisions. The main objective of this study was to investigate different statistical, machine learning, and deep learning models to estimate shrub willow AGB at a site in Camillus, NY using multi-spectral unmanned aerial system (UAS) imagery. The efficiency of the convolutional neural network (CNN) deep learning algorithm was compared to the well-known methods including linear regression, decision tree (DT), random forest (RF), and support vector regression (SVR). The RF model estimated the AGB with the root mean square error (RMSE) of 1.73 Mg/ha and R 2 of 0.95, and outperformed other methods. The next most effective method was CNN with the RMSE of 2.69 Mg/ha and R 2 of 0.89. Feature importance analysis indicated that normalized difference vegetation index (NDVI), ratio vegetation index (RVI), and difference vegetation index (DVI) had the greatest contribution to AGB estimation. This study compared shrub willow AGB estimation models using UAS imagery which will streamline bioenergy/biofuel development compared to the existing methods.

Absorption and Aggregation Characteristics and Changes in the Reflectance Spectrum of an Arid Desert Plant under Gold, Copper, Zinc and Nickel Stress

Remote sensing phytochemistry has been proven by many studies to be an effective method for the detection of hidden minerals in vegetation-covered areas. In this study, we determined whether Seriphidium terrae-albae, a small shrub distributed widely in arid deserts, could be an effective sampling medium for application in remote sensing plant geochemistry. The absorption and aggregation characteristics and spectral changes in Seriphidium terrae-albae transplants at varying soil concentrations of copper (Cu), nickel (Ni), gold (Au), and zinc (Zn) were studied via simulation testing using artificial transplant cultivation. The results showed the following. (1) There exists a good logarithmic relationship between Cu and Zn contents in Seriphidium terrae-albae and the corresponding contents in soil, with coefficients of determination (R2) reaching as high as 0.936 for Cu and 0.9568 for Zn, while a linear relationship is observed between Au and Ni contents in the plant and the corresponding contents in soil, with R2 values as high as 0.9524 for Au and 0.9177 for Ni. (2) The accumulation of Cu and Ni in Seriphidium terrae-albae transplants grown in soil with high Cu and Ni contents is higher than in controls grown in normal soil, demonstrating the ability to clearly indicate abnormal Cu and Ni contents. (3) The ratio vegetation index based on the reflectance at 747 and 742 nm can be adopted to estimate Cu content in Seriphidium terrae-albae. These results suggest that Seriphidium terrae-albae exhibits great potential as an effective geochemical remote sensing plant sampling medium for concealed Cu deposits. This study provides proof of concept for the hyperspectral remote sensing technique in the exploration of hidden minerals in arid deserts, a quick measurement means of Cu content anomalies in the Seriphidium terrae-albae plant and a reference for the identification of prospective metallogenic areas, which could not only expand the existing prospecting space but could also improve the prospecting efficiency in arid deserts.

Improving the Selection of Vegetation Index Characteristic Wavelengths by Using the PROSPECT Model for Leaf Water Content Estimation

Equivalent water thickness (EWT) is a major indicator for indirect monitoring of leaf water content in remote sensing. Many vegetation indices (VIs) have been proposed to estimate EWT based on passive or active reflectance spectra. However, the selection of the characteristics wavelengths of VIs is mainly based on statistical analysis for specific vegetation species. In this study, a characteristic wavelength selection algorithm based on the PROSPECT-5 model was proposed to obtain characteristic wavelengths of leaf biochemical parameters (leaf structure parameter (N), chlorophyll a + b content (Cab), carotenoid content (Car), EWT, and dry matter content (LMA)). The effect of combined characteristic wavelengths of EWT and different biochemical parameters on the accuracy of EWT estimation is discussed. Results demonstrate that the characteristic wavelengths of leaf structure parameter N exhibited the greatest influence on EWT estimation. Then, two optimal characteristics wavelengths (1089 and 1398 nm) are selected to build a new ratio VI (nRVI = R1089/R1398) for EWT estimation. Subsequently, the performance of the built nRVI and four optimal published VIs for EWT estimation are discussed by using two simulation datasets and three in situ datasets. Results demonstrated that the built nRVI exhibited better performance (R2 = 0.9284, 0.8938, 0.7766, and RMSE = 0.0013 cm, 0.0022 cm, 0.0030 cm for ANGERS, Leaf Optical Properties Experiment (LOPEX), and JR datasets, respectively.) than that the published VIs for EWT estimation. It is demonstrated that the built nRVI based on the characteristic wavelengths selected using the physical model exhibits desirable universality and stability in EWT estimation.

Exploring the potential of canopy reflectance spectra for estimating organic carbon content of aboveground vegetation in coastal wetlands

ABSTRACT Accurate estimation of organic carbon content or carbon storage of coastal wetland vegetation is essential for understanding the carbon cycle of coastal wetland ecosystems. This study aimed to explore the potential of canopy spectra of coastal wetland vegetation in estimating the organic carbon content of aboveground vegetation (OCCAV). A total of 54 representative vegetation quadrats were selected from coastal wetlands in Jiangsu Province, China, and their canopy reflectance spectra were measured using an in situ field spectrometer. In particular, two advanced spectral algorithms, fractional-order derivative (FOD) and optimal band combination algorithm, were used for spectral pre-treatments. Partial least squares regression (PLSR) and support vector machine (SVM) were employed to establish the OCCAV estimation models. Accuracies of the models constructed by the processed spectral parameters were compared with that of the models corresponding to five traditional pre-treatment methods and 15 common vegetation indices. Results showed that the FOD method captured subtler spectral characteristics than the first and second derivatives. The optimal estimation accuracies of PLSR and SVM models were obtained based on 0.75 and 1.50 order derivative spectra, respectively, and the ratios of performance to interquartile range (RPIQ) of the models were 2.57 and 2.97, respectively. Moreover, the optimal band combination algorithm effectively extracted the sensitive spectral parameters related to OCCAV, and the accuracy of the model established based on the spectral parameters extracted by this algorithm was generally better than that of the model obtained by the common vegetation indices. The optimal estimation result was achieved by the SVM model based on the optimized ratio vegetation index, with an RPIQ of 3.10. In summary, this research provides a theoretical basis for future studies on estimating the organic carbon and carbon storage of wetland vegetation based on large-scale canopy hyperspectral images and also helps to improve the knowledge of the carbon cycle of wetland ecosystems.

Fractional vegetation coverage downscaling inversion method based on Land Remote-Sensing Satellite (System, Landsat-8) and polarization decomposition of Radarsat-2

ABSTRACT Due to multi-source data information fusion, the precision of eco-hydrology models is improving rapidly. In particular, the fractional vegetation coverage (FVC) is of great significance in the remote sensing monitoring of surface parameters. In this study, downscaling inversion was performed using Normalized Difference Vegetation Index (NDVI) and Ratio Vegetation Index (RVI) data from Land Remote-Sensing Satellite (System, Landsat-8) and RVI-Freeman data from Radarsat-2 with polarization decomposition, incorporating the scattering entropy (H) and anisotropy (α). Further, modified vegetation indices (mVIs) and corresponding calculation methods were developed to describe the FVC precisely. Two deep learning (DL) methods were used for mVI optimization. The results showed that the inclusion of H and α greatly facilitated FVC estimation and that the mVIs and DL provided higher accuracies and smaller errors than the previous methods (NDVI or RVI). HαmRVI, one of the mVIs, had the highest accuracy in FVC simulation using a vegetation index, and the particle swarm optimization neural network (PSONN) achieved the best performance. The FVC was then predicted with 8 m resolution using the mVIs and PSONN, demonstrating that the proposed method effectively compensates for the fluctuations in high-FVC valley wetlands caused by high water content, avoids overestimation in grasslands, and provides great detail while retaining the original regional variations.

Consistency analysis of GF-1 and GF-6 satellite wide field view multi-spectral band reflectance

Chinese GF-1 and GF-6 satellites are currently operational for routine Earth observation, which can acquire multi-spectral data with moderate spatial resolution, high temporal resolution and wide coverage. There are slight differences between the wide field view (WFV) sensors onboard both satellites. Therefore, consistency analysis is very necessary for the networking of GF-1 and GF-6 WFV data to enhance the observation frequency. In this study, spectral band reflectance and vegetation indices in three regions dominated by urban, cropland and grassland, and forest were used to analyze the consistency of GF-1 and GF-6 WFV data. Consistency analysis results showed that the average coefficients of determination (R2) between GF-1 and GF-6 WFV data were 0.86, 0.88, 0.88 and 0.93 for blue, green, red and NIR bands, as well as 0.86, 0.82 and 0.6 for normalized difference vegetation index (NDVI), ratio vegetation index (RVI) and soil adjusted vegetation index (SAVI), respectively. Therefore, the subtle difference and high correlation of spectral band reflectance and vegetation indices indicated that the GF-1 and GF-6 WFV data had a degree of good consistency and had the potential of networking to monitor the rapid change of the Earth surface.

Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging

BackgroundPyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported.ResultsIn this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%.ConclusionsThis study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

Corn Nitrogen Status Diagnosis with an Innovative Multi-Parameter Crop Circle Phenom Sensing System

Accurate and non-destructive in-season crop nitrogen (N) status diagnosis is important for the success of precision N management (PNM). Several active canopy sensors (ACS) with two or three spectral wavebands have been used for this purpose. The Crop Circle Phenom sensor is a new integrated multi-parameter proximal ACS system for in-field plant phenomics with the capability to measure reflectance, structural, and climatic attributes. The objective of this study was to evaluate this multi-parameter Crop Circle Phenom sensing system for in-season diagnosis of corn (Zea mays L.) N status across different soil drainage and tillage systems under variable N supply conditions. The four plant metrics used to approximate in-season N status consist of aboveground biomass (AGB), plant N concentration (PNC), plant N uptake (PNU), and N nutrition index (NNI). A field experiment was conducted in Wells, Minnesota during the 2018 and the 2019 growing seasons with a split-split plot design replicated four times with soil drainage (drained and undrained) as main block, tillage (conventional, no-till, and strip-till) as split plot, and pre-plant N (PPN) rate (0 to 225 in 45 kg ha−1 increment) as the split-split plot. Crop Circle Phenom measurements alongside destructive whole plant samples were collected at V8 +/−1 growth stage. Proximal sensor metrics were used to construct regression models to estimate N status indicators using simple regression (SR) and eXtreme Gradient Boosting (XGB) models. The sensor derived indices tested included normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), estimated canopy chlorophyll content (eCCC), estimated leaf area index (eLAI), ratio vegetation index (RVI), canopy chlorophyll content index (CCCI), fractional photosynthetically active radiation (fPAR), and canopy and air temperature difference (ΔTemp). Management practices such as drainage, tillage, and PPN rate were also included to determine the potential improvement in corn N status diagnosis. Three of the four replicated drained and undrained blocks were randomly selected as training data, and the remaining drained and undrained blocks were used as testing data. The results indicated that SR modeling using NDVI would be sufficient for estimating AGB compared to more complex machine learning methods. Conversely, PNC, PNU, and NNI all benefitted from XGB modeling based on multiple inputs. Among different approaches of XGB modeling, combining management information and Crop Circle Phenom measurements together increased model performance for predicting each of the four plant N metrics compared with solely using sensing data. The PPN rate was the most important management metric for all models compared to drainage and tillage information. Combining Crop Circle Phenom sensor parameters and management information is a promising strategy for in-season diagnosis of corn N status. More studies are needed to further evaluate this new integrated sensing system under diverse on-farm conditions and to test other machine learning models.

Evaluation of the Vegetation-Index-Based Dimidiate Pixel Model for Fractional Vegetation Cover Estimation

Remote sensing estimation based on the dimidiate pixel model (DPM) using vegetation indices (VIs) is a common approach for mapping fractional vegetation cover (FVC). The major drawback of DPM is that it does not consider real endmember conditions and multiple scattering between soil and vegetation. An analysis of FVC uncertainties caused by these model deficiencies is still lacking. Here, we first calculated the FVC theoretical uncertainty caused by reflectance uncertainties based on the law of prapagation of uncertainty (LPU). Then, we tested the performance of DPM using six VIs over 3-D forest scenes. We simulated both Aqua-MODIS and Landsat-OLI surface reflectance (SR) at their corresponding spatial resolutions and spectral response functions (SRFs) using a well-validated 3-D radiative transfer (RT) model which helps to separate the model and input uncertainties. We found that ratio vegetation index (RVI)- and enhanced vegetation index (EVI)-based models were most affected by sensors, followed by the normalized difference vegetation index (NDVI)-, enhanced vegetation index 2 (EVI2)-, renormalized difference vegetation index (RDVI)-, and difference vegetation index (DVI)-based models. Without considering SR uncertainties, the DVI-based model performed best (FVC absolute difference < 0.1); however, the commonly used NDVI model reached a maximum difference of 0.35. At the same time, input uncertainty increased the uncertainty of FVC retrieval. We noticed that the increase of solar zenith angle (SZA) resulted in a clear increase of retrieved FVC under the uniform distribution, which can be explained by the increased shadow proportion. Besides, model accuracy was dominated by the purity of soil (vegetation) endmember in low (high) vegetation cover area. This study provides a reference for the selection of the optimal VI for FVC retrieval based on the DPM.

Water Benefit-Based Ecological Index for Urban Ecological Environment Quality Assessments

Urbanization and climate change cause the urban ecological environment to become increasingly dependent on water. However, open water areas and green spaces in cities are constantly decreasing, making water resources increasingly scarce. There is an urgent need for a method that aligns with the current urban status and can quickly assess the urban ecological–environmental quality (UEEQ). Traditional UEEQ methods have abandoned the water factor, neglecting the influence of water on the ecological environment. In modern cities, water, which guarantees the operation and maintenance of the urban ecological environment, must be considered in the UEEQ system. Therefore, we propose a water benefit-based ecological index (WBEI). In the formulation of the WBEI, we integrate a water ecofactor, the thermal environment, and the land cover type to represent the surface ecological environment. We first construct a surface potential water abundance index (SPWI) to describe the spatial distribution of water. The combination of the SPWI and the normalized difference latent heat index allows the WBEI to better evaluate the UEEQ around water areas. Then, we choose the land-surface temperature to represent the thermal environment. To represent the land cover type, the ratio vegetation index and the normalized difference soil index are adopted in the WBEI. Finally, we use an entropy-based fusion method to fuse these indicators and obtain the WBEI values. The performance of the WBEI is tested using eight datasets with a variety of environmental characteristics. The results show that 75% of the WBEI results are consistent with the EI values. The correlation coefficient between the WBEI and EI is 0.8883, which is significantly better than those of the other methods. The research shows that the UEEQ of the Qingdao West-Coast Economic New Zone is declining continuously at a rate of 3.7% per year. From 2013 to 2017, the percentage of areas with good environments decreased by 21.46%, and the percentage of areas with poor environments increased by 12.76%. The UEEQ inside the city deteriorated radially outward along the main traffic route, the UEEQ in the suburbs did not change significantly, and the UEEQ in the water areas deteriorated significantly. These relevant research results can provide quantitative information for the green sustainable development of cities.

Cotton hail disaster classification based on drone multispectral images at the flowering and boll stage

Traditional artificial statistical methods of hailstorm damage assessment are often inefficient, subjective, time-consuming, and inaccurate. The use of drones for high-resolution remote sensing imaging enables large-scale, rapid assessment of a target area. The purpose of this study was to use multispectral imaging techniques to promptly and accurately assess the damage degree of hail in crops. In this experiment, cotton was used as the research target, and the experiment was carried out after a hail disaster that occurred during the flowering and boll stage of cotton development. A hail vegetation index (HGVI) was constructed and compared with the classification results of other vegetation indices on the hail disaster degree. It was concluded that: (i) both the hail vegetation index HGVI and the previous vegetation index, ratio vegetation index, are suitable tools of classification for grading cotton hail disasters, with both indices having a Kappa coefficient over 0.85 and a median Kappa coefficient greater than 0.9; (ii) the method used in this study is suitable for simple classification based on multispectral images; (iii) this study explains the possibility of classifying hail damage degrees based on multispectral images. Subsequent research can further diagnose and classify hail disasters during different periods and improve the accuracy and completeness of classification.

Using Machine Learning Algorithms to Estimate Soil Organic Carbon Variability with Environmental Variables and Soil Nutrient Indicators in an Alluvial Soil

Soil organic carbon (SOC) is an important indicator of soil quality and directly determines soil fertility. Hence, understanding its spatial distribution and controlling factors is necessary for efficient and sustainable soil nutrient management. In this study, machine learning algorithms including artificial neural network (ANN), support vector machine (SVM), cubist regression, random forests (RF), and multiple linear regression (MLR) were chosen for advancing the prediction of SOC. A total of sixty (n = 60) soil samples were collected within the research area at 30 cm soil depth and measured for SOC content using the Walkley–Black method. From these samples, 80% were used for model training and 21 auxiliary data were included as predictors. The predictors include effective cation exchange capacity (ECEC), base saturation (BS), calcium to magnesium ratio (Ca_Mg), potassium to magnesium ratio (K_Mg), potassium to calcium ratio (K_Ca), elevation, plan curvature, total catchment area, channel network base level, topographic wetness index, clay index, iron index, normalized difference build-up index (NDBI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI) and land surface temperature (LST). Mean absolute error (MAE), root-mean-square error (RMSE) and R2 were used to determine the model performance. The result showed the mean SOC to be 1.62% with a coefficient of variation (CV) of 47%. The best performing model was RF (R2 = 0.68) followed by the cubist model (R2 = 0.51), SVM (R2 = 0.36), ANN (R2 = 0.36) and MLR (R2 = 0.17). The soil nutrient indicators, topographic wetness index and total catchment area were considered an indicator for spatial prediction of SOC in flat homogenous topography. Future studies should include other auxiliary predictors (e.g., soil physical and chemical properties, and lithological data) as well as cover a broader range of soil types to improve model performance.

Estimation of Peanut Leaf Area Index from Unmanned Aerial Vehicle Multispectral Images.

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.

Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale

The aims of this study were to: (i) evaluate the relationships between vegetation indices (VIs) derived from Sentinel-2 imagery and grain yield (GY) and the number of spikes per square meter (SN) of winter wheat and triticale; (ii) determine the dates and plant growth stages when the above relationships were the strongest at individual field scale, thus allowing for accurate yield prediction. Observations of GY and SN were performed at harvest on six fields (three locations in two seasons: 2017 and 2018) in three regions of Poland, i.e., northeastern (A—Brożówka), central (B—Zdziechów) and southeastern Poland (C—Kryłów). Vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), modified SAVI (mSAVI), modified SAVI 2 (mSAVI2), Infrared Percentage Vegetation Index (IPVI), Global Environmental Monitoring Index (GEMI), and Ratio Vegetation Index (RVI)) calculated for sampling points from mid-March until mid-July, covering within-field soil and topographical variability, were included in the analysis. Depending on the location, the highest correlation coefficients (of about 0.6–0.9) for most of VIs with GY and SN were obtained about 4–6 weeks before harvest (from the beginning of shooting to milk maturity). Therefore, satellite-derived VIs are useful for the prediction of within-field cereal GY as well as SN variability. Information on GY, predicted together with the results for soil nutrient availability, is the basis for the formulation of variable fertilize rates in precision agriculture. All examined VIs were similarly correlated with GY and SN via the commonly used NDVI. The increase in NDVI by 0.1 unit was related to an average increase in GY by about 2 t ha−1.

Use of an Active Canopy Sensor Mounted on an Unmanned Aerial Vehicle to Monitor the Growth and Nitrogen Status of Winter Wheat

Using remote sensing to rapidly acquire large-area crop growth information (e.g., shoot biomass, nitrogen status) is an urgent demand for modern crop production; unmanned aerial vehicle (UAV) acts as an effective monitoring platform. In order to improve the practicability and efficiency of UAV based monitoring technique, four field experiments involving different nitrogen (N) rates (0–360 kg N ha−1) and seven winter wheat (Triticum aestivum L.) varieties were conducted at different eco-sites (Sihong, Rugao, and Xinghua) during 2015–2019. A multispectral active canopy sensor (RapidSCAN CS-45; Holland Scientific Inc., Lincoln, NE, USA) mounted on a multirotor UAV platform was used to collect the canopy spectral reflectance data of winter wheat at key growth stages, three growth parameters (leaf area index (LAI), leaf dry matter (LDM), plant dry matter (PDM)) and three N indicators (leaf N accumulation (LNA), plant N accumulation (PNA) and N nutrition index (NNI)) were measured synchronously. The quantitative linear relationships between spectral data and six growth indices were systematically analyzed. For monitoring growth and N nutrition status at Feekes stages 6.0–10.0, 10.3–11.1 or entire growth stages, red edge ratio vegetation index (RERVI), red edge chlorophyll index (CIRE) and difference vegetation index (DVI) performed the best among the red edge band-based and red-based vegetation indices, respectively. Across all growth stages, DVI was highly correlated with LAI (R2 = 0.78), LDM (R2 = 0.61), PDM (R2 = 0.63), LNA (R2 = 0.65) and PNA (R2 = 0.73), whereas the relationships between RERVI (R2 = 0.62), CIRE (R2 = 0.62) and NNI had high coefficients of determination. The developed models performed better in monitoring growth indices and N status at Feekes stages 10.3–11.1 than Feekes stages 6.0–10.0. To sum it up, the UAV-mounted active sensor system is able to rapidly monitor the growth and N nutrition status of winter wheat and can be deployed for UAV-based remote-sensing of crops.

Predicting grain yield and protein content using canopy reflectance in maize grown under different water and nitrogen levels

Predicting grain yield and protein content of maize using spectral reflectance data is very important for improved agricultural production. In this study, we predicted the grain yield and protein content of maize grown under different irrigation and nitrogen levels in 2018 and 2019 based on canopy spectral reflectance measurements at the V6 (sixth leaf), VT (tassel), and R2 (blister) stages. We developed a predictive approach, namely, spectral reflectance–physiological parameters–productivity, to predict grain yield and protein content on maize crop. First, the quantitative relationships between grain yield and protein content and physiological parameters (canopy chlorophyll content [CCC], leaf carbon accumulation [LCA], leaf nitrogen content [LNC], and leaf nitrogen accumulation [LNA]) were analysed. Then, vegetation indices (VIs) and wavelet features based on spectral reflectance were used to establish estimation models for physiological parameters. The physiological parameters were used as a bridge to connect the spectral reflectance data with grain yield and protein content. The purpose was to establish spectral inversion models to indirectly estimate grain yield and protein content. Results showed that grain yield had a significant linear relationship with CCC and LCA. In addition a grain protein content and LNC and LNA were also significantly related under different water, and nitrogen availability. The physiological parameter models with ratio vegetation indices (RVI), biorthogonal 3.3 (bior3.3), and reverse biorthogonal 1.5 (rbio1.5) were reliable in terms of predictability and applicability. Independent data verification suggested that grain yield was predicted using RVI769,758 (R2 = 0.773, RMSE = 2.509) in water availability, rbio1.5781,29 (R2 = 0.744, RMSE = 2.850) in nitrogen availability, and rbio1.5772,11 (R2 = 0.506, RMSE = 2.297) in water–nitrogen availability. In addition, the grain protein content was predicted using RVI793,757 (R2 = 0.704, RMSE = 0.744) in water availability, bior3.3743,19 (R2 = 0.717, RMSE = 0.957) in nitrogen availability, and RVI492,418 (R2 = 0.715, RMSE = 1.224) in water–nitrogen availability. Therefore, maize grain yield and protein content can be accurately predicted using our modelling approach.

Monitoring leaf nitrogen concentration and nitrogen accumulation of double cropping rice based on crop growth monitoring and diagnosis apparatus

To verify the accuracy and adaptability of crop growth monitoring and diagnosis apparatus (CGMD) in monitoring nitrogen nutrition index of double cropping rice, we established a monitoring model of leaf nitrogen concentration (LNC) and leaf nitrogen accumulation (LNA) for double cropping rice based on CGMD. Eight early and late rice cultivars were selected and four nitrogen application rates were set up. The differential vegetation index (DVI), normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) were collected using CGMD. Meanwhile, ASD FH2 high spectrometer was used to collect canopy spectral reflectance and calculated DVI, NDVI, and RVI. To verify the accuracy of CGMD, we compared the canopy vegetation indices change characteristics collected by CGMD and ASD FH2. The CGMD-based monitoring models of LNC and LNA were established, which was tested with independent field data. The results showed that LNC, LNA, DVI, NDVI and RVI of early and late rice increased with increasing nitrogen application rate, and increased first and then decreased with the advance of growth progress. The determination coefficient (R2) of fitting for DVI, NDVI and RVI from CGMD and ASD FH2 were 0.9350, 0.9436 and 0.9433, respectively. This result indicated that the measurement accuracy of CGMD was high, and that the CGMD could be used to replace ASD FH2 to measure canopy vegetation indices of early and late rice. Compared with the three canopy vegetation indices based on CGMD, the correlation between NDVICGMD and LNC and that between RVICGMD and LNA was the highest. The exponential model based on NDVICGMD could be used to accurate estimate LNC with the R2 in the range of 0.8581-0.9318, and the root mean square error (RMSE), relation root mean square error (RRMSE) and correlation coefficient (r) of model validation in the range of 0.1%-0.2%, 4.0%-8.5%, and 0.9041-0.9854, respectively. The power function model based on RVICGMD could be used to estimate LNA with the R2 in the range of 0.8684-0.9577, and the RMSE, RRMSE and r of model validation in the range of 0.37-0.89 g·m-2, 6.7%-20.4% and 0.9191-0.9851, respectively. Compared with the chemical testing method, using the CGMD could conveniently and accurately measure LNC and LNA of early and late rice, which had a potential to be widely applied for high yield and high efficiency cultivation and precise management of nitrogen fertilizer in double cropping rice production.

Establishment and application of the modified Chen NDVI model integrated with ground object classification

Heterogeneous land surface causes the scale effect of remotely sensed land surface parameters. Addressing on quantitatively describing the influence of different ground objects on scale effect of the common surface parameter normalized difference vegetation index (NDVI), the paper proposed an improved NDVI scale transformation model. The model integrated accurate classification information from medium- or high- spatial resolution remote sensing images to improve the traditional Chen NDVI scale conversion model, and showed its superiority for NDVI scale effect description. Xiamen was taken as the experimental area for the study and the conclusions could be obtained from the experimental results. Compared with the traditional Chen NDVI model with rough information, the improved Chen NDVI model incorporating fine ground information provides a finer and more quantitative description of the influence of different land types on the NDVI scale effect. Furthermore, it is found that the presence of water is the key factor underlying the NDVI scale effect. The conclusions of this study have important implications for the scale effect research of other NDVI-like surface parameters such as ratio vegetation index (RVI), normalized difference built-up index (NDBI), normalized burn ratio (NBR).

Nondestructive estimation of bok choy nitrogen status with an active canopy sensor in comparison to a chlorophyll meter

Precise estimation of vegetable nitrogen (N) status is critical in optimizing N fertilization management. However, nondestructive and accurate N diagnostic methods for vegetables are relatively scarce. In our two-year field experiment, we evaluated whether an active canopy sensor (GreenSeeker) could be used to nondestructively predict N status of bok choy (Brassica rapa subsp. chinensis) compared with a chlorophyll meter. Results showed that the normalized difference vegetation index (NDVI) and ratio vegetation index (RVI) generated by the active canopy sensor were well correlated with the aboveground biomass (AGB) (r = 0.698–0.967), plant N uptake (PNU) (r = 0.642–0.951), and root to shoot ratio (RTS) (r = −0.426 to −0.845). Compared with the chlorophyll meter, the active canopy sensor displayed much higher accuracy (5.0%–177.4% higher) in predicting AGB and PNU and equal or slightly worse (0.54–1.82 times that of the chlorophyll meter) for RTS. The sensor-based NDVI model performed equally well in estimating AGB (R2 = 0.63) and PNU (R2 = 0.61), but the meter-based model predicted RTS better (R2 = 0.50). Inclusion of the days after transplanting (DAT) significantly improved the accuracy of sensor-based AGB (19.0%–56.7% higher) and PNU (24.6%–84.6% higher) estimation models. These findings suggest that the active canopy sensor has a great potential for nondestructively estimating N status of bok choy accurately and thus for better N recommendations, especially with inclusion of DAT, and could be applied to more vegetables with some verification.

Sentinel-2 Imagery Processing for Tree Logging Observations on the Białowieża Forest World Heritage Site

Deforestation is currently among the most critical ecological issues, which need to be addressed urgently. Hence, identification of effective environmental monitoring methods is of top priority, especially in locations where no precise ground-based data are available. Constant development of remote sensing technology provides an increasing number of tools needed for that purpose, based on extraction of information about Earth’s surface. One of the most advanced Earth Observation (EO) programs is Copernicus, established by European Space Agency (ESA). It incorporates a constellation of Sentinel satellites continuously delivering imagery, which can serve as input data for further environmental analyses. They can be performed in the Sentinel Application Platform (SNAP), the software also developed by ESA. The Sentinel-2 (S-2) mission was designed specifically for Earth’s surface observation. It acquires high-resolution data within visible and infrared range of electromagnetic spectrum (EMS), which has found applications in forest cover monitoring. In this paper, S-2 imagery was processed in SNAP software to determine its potential for deforestation observation on the example of 2017 tree logging in Białowieża Forest. For this purpose, images from October 2016 and 2018, covering the area of interest, were downloaded from the Copernicus Open Hub Platform. They then underwent pre-processing, involving atmospheric correction, resampling, and subset operations. As a part of environmental analysis, a set of chosen radiometric and biophysical indices was computed to preliminarily determine their usefulness for deforestation mapping. Index values were extracted from tree logging areas using pinpoints and region of interest (ROI) mask. The most effective indicators were the MERIS Terrestrial Chlorophyll Index (MTCI) and the Brightness Index (BI). The Normalized Difference Vegetation Index (NDVI), as well as the Ratio Vegetation Index (RVI), also displayed promising results. The results were visualized in Quantum GIS (QGIS) software, provided by the Open Source Geospatial Foundation (OSGeo).