Wide Dynamic Range Vegetation Index Research Articles

Winter wheat yield prediction using integrated Landsat 8 and Sentinel-2 vegetation index time-series data and machine learning algorithms

Timely and accurate forecasting of winter wheat yield is important to crop management, food security, and sustainable agricultural development. Unfortunately, the process of predicting winter wheat yield using satellite time series data often fails to capture complete and critical information about the crop growth process, which can restrict the accuracy of crop yield predictions. To overcome this challenge, it is necessary to increase the frequency of monitoring crop growth dynamics, identify suitable vegetation index (VI), and determine the optimal prediction model for time-series remote sensing data. In this study, we propose proposes a novel method for predicting winter wheat yield using integrated Landsat 8 (L8) and Sentinel-2 (S2) vegetation index time-series data and machine learning algorithms. Firstly, the integrated L8 and S2 dataset was obtained through the steps of cloud masking, re-sampling, re-projection, BRDF correction, and band adjustment. Then, the optimal VI was determined based on the association between the growth characteristics of winter wheat and the time-series characteristic curves of each VI. Subsequently, we employed Bayesian optimized CatBoost (BO-CatBoost) regression model to predict winter wheat yield, and compared this method with three other data-driven methods, including least absolute shrinkage and selection operator (LASSO), support vector regression (SVM), and random forest (RF). Our results showed that the winter wheat yield prediction accuracies reached the best performance using integrated Landsat 8 and Sentinel-2 WDRVI (Wide Dynamic Range Vegetation Index) time-series data and BO-CatBoost model. The R2 values were 0.70, 0.63, and 0.68, and RMSE values were 0.62, 0.73, and 0.62 t/ha for the years 2019 to 2021, respectively. In addition, acceptable accuracy was obtained for yield prediction in 2021 based on the model trained with historical data from 2019 and 2020. Moreover, this study demonstrated the result that winter wheat yield could be predicted about 40 days earlier using the proposed method. Finally, results showed that the harmonized data improved the yield estimation accuracies by a factor of 1.52, 1.29, and 1.13 compared with single S2 dataset for years 2019–2021. Various experiments demonstrated that the proposed method could effectively estimate and predict winter wheat yield data with good accuracy and robustness. This study provides technical support for improving the accuracy of winter wheat yield prediction and has the potential to be extended to yield estimation for other crops.

Quantification of Biophysical Parameters and Economic Yield in Cotton and Rice Using Drone Technology

New agronomic opportunities for more informed agricultural decisions and enhanced crop management have been made possible by drone-based near-ground remote sensing. Obtaining precise non-destructive information regarding crop biophysical characteristics at spatial and temporal scales is now possible. Drone-mounted multispectral and thermal sensors were used to assess crop phenology, condition, and stress by profiling spectral vegetation indices in crop fields. In this study, vegetation indices, viz., Atmospherically Resistant Vegetation Index (ARVI), Modified Chlorophyll Absorption Ratio Index (MCARI), Wide Dynamic Range Vegetation Index (WDRVI), Normalized Red–Green Difference Index (NGRDI), Excess Green Index (ExG), Red–Green Blue Vegetation Index (RGBVI), and Visible Atmospherically Resistant Index (VARI) were generated. Furthermore, Pearson correlation analysis showed a better correlation between WDRVI and VARI with LAI (R = 0.955 and R = 0.982) ground truth data. In contrast, a strong correlation (R = 0.931 and R = 0.844) was recorded with MCARI and NGRDI with SPAD chlorophyll ground truth data. Then, the best-performing indices, WDRVI and MCARI in cotton, and VARI and NGRDI in rice, were further used to generate the yield model. This study for determining LAI and chlorophyll shows that high spatial resolution drone imageries are accurate and fast. As a result, finding out the LAI and chlorophyll and how they affect crop yield at a regional scale is helpful. The widespread use of unmanned aerial vehicles (UAV) and yield prediction were technical components of large-scale precision agriculture.

Cotton yield prediction using drone derived LAI and chlorophyll content

The unmanned aerial vehicles (UAV) have become a better solution for agricultural growers due to advanced features such as minimal maintenance costs, quick set-up time, low acquisition costs, and live data capturing. Near-ground remote sensing (drone) has opened up new agronomic opportunities for better crop management. This study predicted the seed cotton yield for a cotton field area located at Tamil Nadu Agricultural University, Coimbatore. Pearson correlation analysis and regression analysis were done for ground truth data and vegetation indices for validation and accuracy and also to find the best-performing indices. It was concluded that the Wide Dynamic Range Vegetation Index (WDRVI) showed a better correlation coefficient (R=0.959) with LAI ground truth data (R2=0.919). In contrast, the Modified Chlorophyll Absorption Ratio Index (MCARI) showed a better correlation coefficient (R=0.919) with SPAD chlorophyll ground truth data (R2=0.845). Then the best performing indices WDRVI and MCARI were further used for generating the yield model. High spatial resolution drone imageries for determining LAI and chlorophyll are reliable and rapid, as per the study. It helps to determine the LAI and chlorophyll at a spatial scale and their influence on yield production. This yield prediction was technical support for the widespread adoption and application of unmanned aerial vehicle (UAV) remote sensing in large-scale precision agriculture.

APPLICATION OF RANDOM FOREST IN IDENTIFYING WINTER WHEAT USING LANDSAT8 IMAGERY

Mastering accurate spatial planting and distribution status of the crops is significantly important for the nation to guide the agricultural production and formulate agricultural policies from a macro perspective. In this paper, the Landsat-8 OLI satellite images were taken as the data sources. And as for the nine crop types within the study area, such as the wheat, rice, and other crops, three classification methods of the random forest classification (RFC), the support vector machine (SVM), and the maximum likelihood classification (MLC) were applied in extracting the planting area of winter wheat in Wushi County of Xinjiang Uygur Autonomous Region. It can be seen from the results that, general classification accuracy of MLC, SVM, and RFC are respectively 80.58%, 87.95%, and 95.96%, while their Kappa coefficients are respectively 0.61, 0.76, and 0.86. The RFC method shows higher classification accuracy that those of MLC and SVM methods. The principal component analysis (PCA) was carried out on the original 7-band image to extract the first 4 principal components and calculate the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), wide dynamic range vegetation index (WDRVI), and normalized difference water index (NDWI). Meanwhile, the 6 additional auxiliary feature bands were superimposed on the original 7-band images to carry out reclassification, through which, the general accuracy of MLC increased by 3 percent while its Kappa coefficient increased by 0.06; the SVM general accuracy increased by 3.02 percent while its Kappa coefficient increased by 0.13; and the general accuracy of the RFC increased by 0.85 percent while its Kappa coefficient increased by 0.02. This indicates that, the adding of auxiliary information can improve the crop classification and identification ability and accuracy. Based on the comprehensive evaluation, the classification method of random forest is proved to have better performance in winter wheat identification.

Hyperspectral image-based vegetation index (HSVI): A new vegetation index for urban ecological research

Accurately monitoring the quantity and quality of urban vegetation contributes to regional greening efforts and improves the understanding of vegetation's impact on the environment. However, factors such as building shadows and synthetic materials can greatly obstruct vegetation estimates. Additionally, vegetation indices (VIs) saturate quickly in high biomass conditions, complicating vegetation quality assessments. To address these issues, we propose a new vegetation index, namely the hyperspectral image-based vegetation index (HSVI). HSVI is built in three steps: band selection, saturable band reconstruction, and index structure redefinition. Firstly, we select four representative bands and construct an enhanced vegetation index (EVI) to eliminate the interference of complex urban surface factors. Secondly, we reconstruct the easily saturable band (760 nm) through an exponential function to form an optimized enhanced vegetation index (OEVI). Finally, the index structure is redefined by adding the denominator of the sum of the red edge (689 nm) and green (520 nm) bands to OEVI to further enhance the spectral information of vegetation and eliminate the saturation problem typical of VIs. Three datasets with different geomorphological features (Shanghai Theatre Academy, Dazhu Mountain, and the University of Houston) are used to compare the performance of HSVI with six VIs widely adopted in urban ecological research, i.e., the difference vegetation index (DVI), the normalized difference vegetation index (NDVI), the simple ratio (SR), the optimized soil-adjusted vegetation index (OSVAI), the modified transformed vegetation index (MTVI2) and the wide-dynamic-range vegetation index (WDRVI). The results show that the vegetation extraction accuracy of HSVI is more than 90%, which is significantly higher than those of the other VIs. Additionally, HSVI also solves the VIs saturation issue. Therefore, HSVI shows high potential usefulness for urban ecological research applications.

Estimation of nitrogen status and yield of rice crop using unmanned aerial vehicle equipped with multispectral camera

Nitrogen is one of the essential nutrients required for crop growth, and hence should be applied efficiently for attaining optimum yield. To fulfil nitrogen need, absorbed nitrogen in the plant is required to be estimated. Various methods are available to estimate crop nitrogen such as tissue analysis using the methods of Kjeldahl and Dumas, which are accurate, but time-consuming and destructive. Satellite imagery provides a more extensive field view. However, they are limited to their spatial and temporal resolution. Unmanned aerial vehicle (UAV) is emerging as a promising tool that can provide the status of crop nitrogen rapidly with high spatial and temporal resolution. The objective of the study was to evaluate UAV-based imageries to show nitrogen status and predict rice yield at different growth stages. The experiments were conducted using two rice cultivars, six nitrogen applications, two water management practices, and with three replications. Soil plant analysis development (SPAD) meter readings were collected at various growth stages. First, aerial imageries of experimental site were collected using an octocopter UAV equipped with a multispectral sensor that provides reflectance values in four different bands (red, green, red edge, and near-infrared) along with SPAD values for respective flight. Second, aerial images were processed in pix4D software, to identify the most appropriate vegetation index that shows nitrogen status variation in the field and to predict yield using different vegetation indices. Nine vegetation indices were considered: ratio vegetation index, normalized difference vegetation index, normalized green red difference index, red edge difference vegetation index, green ratio vegetation index, green normalized difference vegetation index (GNDVI), wide dynamic range vegetation index, transformed normalized difference vegetation index (TNDVI), and normalized difference red edge. After that, a linear regression model was developed between the representative index and SPAD values. Finally, linear regression models developed by using VI and SPAD values were evaluated and results revealed that GNDVI-based model simulates SPAD values with R2 of 0.49, 0.49, and 0.74 at panicle, milky, and booting stages, respectively. It is also found that TNDVI-based linear regression model predicts yield with R2 of 0.71 at milky stage.

UAV- and Random-Forest-AdaBoost (RFA)-Based Estimation of Rice Plant Traits

Rapid, accurate and inexpensive methods are required to analyze plant traits throughout all crop growth stages for plant phenotyping. Few studies have comprehensively evaluated plant traits from multispectral cameras onboard UAV platforms. Additionally, machine learning algorithms tend to over- or underfit data and limited attention has been paid to optimizing their performance through an ensemble learning approach. This study aims to (1) comprehensively evaluate twelve rice plant traits estimated from aerial unmanned vehicle (UAV)-based multispectral images and (2) introduce Random Forest AdaBoost (RFA) algorithms as an optimization approach for estimating plant traits. The approach was tested based on a farmer’s field in Terengganu, Malaysia, for the off-season from February to June 2018, involving five rice cultivars and three nitrogen (N) rates. Four bands, thirteen indices and Random Forest-AdaBoost (RFA) regression models were evaluated against the twelve plant traits according to the growth stages. Among the plant traits, plant height, green leaf and storage organ biomass, and foliar nitrogen (N) content were estimated well, with a coefficient of determination (R2) above 0.80. In comparing the bands and indices, red, Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Red-Edge Wide Dynamic Range Vegetation Index (REWDRVI) and Red-Edge Soil Adjusted Vegetation Index (RESAVI) were remarkable in estimating all plant traits at tillering, booting and milking stages with R2 values ranging from 0.80–0.99 and root mean square error (RMSE) values ranging from 0.04–0.22. Milking was found to be the best growth stage to conduct estimations of plant traits. In summary, our findings demonstrate that an ensemble learning approach can improve the accuracy as well as reduce under/overfitting in plant phenotyping algorithms.

Sensor system for acquisition of vegetation indexes

ABSTRACT Vegetation indexes are important indicators of the health and yield of agricultural crops. Among the sensors used to evaluate vegetation indexes, proximal sensors can be used for real-time decision-making. Thus, the objective of this study was to develop a proximal sensor system based on phototransistors to acquire and store the following vegetation indexes: normalized difference vegetation index, simple ratio, wide dynamic range vegetation index, soil-adjusted vegetation index, and optimized soil-adjusted vegetation index. The sensor system was developed using an analog circuit to acquire reflectance data from red and near-infrared bands. The sensor system was calibrated according to the results of a spectroradiometer, using Zoysia japonica grass as the target. An algorithm that calculates and stores vegetation indexes in a file was developed. The Pearson correlation between the vegetation indexes obtained with the sensor system and the spectroradiometer was evaluated. The vegetation indexes presented a Pearson correlation higher than 0.92 to the estimated values by the spectroradiometer. Under the evaluation conditions, the proposed sensor system could be used to determine all vegetation indexes evaluated.

Analysis of Influencing Factors on Winter Wheat Yield Estimations Based on a Multisource Remote Sensing Data Fusion

HighlightsThe potential of fusing GF-1 WFV and MODIS data by the ESTARFM algorithm was demonstrated.A better time window selection method for estimating yields was provided.A better vegetation index suitable for yield estimation based on spatiotemporally fused data was identified.The effect of the spatial resolution of remote sensing data on yield estimations was visualized.Abstract. The accurate estimation of crop yields is very important for crop management and food security. Although many methods have been developed based on single remote sensing data sources, advances are still needed to exploit multisource remote sensing data with higher spatial and temporal resolution. More suitable time window selection methods and vegetation indexes, both of which are critical for yield estimations, have not been fully considered. In this article, the Chinese GaoFen-1 Wide Field View (GF-1 WFV) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data were fused by the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to generate time-series data with a high spatial resolution. Then, two time window selection methods involving distinguishing or not distinguishing the growth stages during the monitoring period, and three vegetation indexes, the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2) and wide dynamic range vegetation index (WDRVI), were intercompared. Furthermore, the yield estimations obtained from two different spatial resolutions of fused data and MODIS data were analyzed. The results indicate that taking the growth stage as the time window unit division basis can allow a better estimation of winter wheat yield; and that WDRVI is more suitable for yield estimations than NDVI or EVI2. This study demonstrates that the spatial resolution has a great influence on yield estimations; further, this study identifies a better time window selection method and vegetation index for improving the accuracy of yield estimations based on a multisource remote sensing data fusion. Keywords: Remote sensing, Spatiotemporal data fusion, Winter wheat, Yield estimation.

HIGH-RESOLUTION IMAGERY DATA TO ASSESS THE SPATIAL VARIABILITY OF SUGARCANE FIELDS/ DADOS DE IMAGENS DE ALTA RESOLUÇÃO PARA AVALIAÇÃO DA VARIABILIDADE ESPACIAL DE TALHÕES DE CANA-DE-AÇÚCAR

The vegetation index (VI) generated from orbital images are essential tools to identify the spatial variability of the crops. The objective of this study was to evaluate the spatial variability of sugarcane fields using imagery data. Also, to explore the ideal period to correlate the Normalized Difference Vegetation Index (NDVI), Normalized Difference Red-Edge Index (NDRE) and Wide Dynamic Range Vegetation Index (WDRVI) with sugarcane yield. Four fields were selected in the state of Sao Paulo (56.37 ha) during the 2017/2018 and 2018/2019 growing seasons, as well as five fields in the state of Goias (86.86 ha) during the 2019/2020 growing season. The VIs were calculated using orbital images from Sentinel-2 (spatial resolution of 10 m). The yield data were generated by a commercial sensor-system installed on the harvesters with a resolution of 0.20 Hz. Yield data were filtered and interpolated using the same resolution of the orbital images. Pearson's correlation was calculated between the yield and the VIs for each orbital image. The considered VIs were able to identify the spatial variability of sugarcane fields with coefficients of correlation of 0.95 and 0.96. The sugarcane stalks growth was the best period to correlate the VIs and the yield maps among the analyzed fields.

Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles

Corn yields vary spatially and temporally in the plots as a result of weather, altitude, variety, plant density, available water, nutrients, and planting date; these are the main factors that influence crop yield. In this study, different multispectral and red-green-blue (RGB) vegetation indices were analyzed, as well as the digitally estimated canopy cover and plant density, in order to estimate corn grain yield using a neural network model. The relative importance of the predictor variables was also analyzed. An experiment was established with five levels of nitrogen fertilization (140, 200, 260, 320, and 380 kg/ha) and four replicates, in a completely randomized block design, resulting in 20 experimental polygons. Crop information was captured using two sensors (Parrot Sequoia_4.9, and DJI FC6310_8.8) mounted on an unmanned aerial vehicle (UAV) for two flight dates at 47 and 79 days after sowing (DAS). The correlation coefficient between the plant density, obtained through the digital count of corn plants, and the corn grain yield was 0.94; this variable was the one with the highest relative importance in the yield estimation according to Garson’s algorithm. The canopy cover, digitally estimated, showed a correlation coefficient of 0.77 with respect to the corn grain yield, while the relative importance of this variable in the yield estimation was 0.080 and 0.093 for 47 and 79 DAS, respectively. The wide dynamic range vegetation index (WDRVI), plant density, and canopy cover showed the highest correlation coefficient and the smallest errors (R = 0.99, mean absolute error (MAE) = 0.028 t ha−1, root mean square error (RMSE) = 0.125 t ha−1) in the corn grain yield estimation at 47 DAS, with the WDRVI index and the density being the variables with the highest relative importance for this crop development date. For the 79 DAS flight, the combination of the normalized difference vegetation index (NDVI), normalized difference red edge (NDRE), WDRVI, excess green (EXG), triangular greenness index (TGI), and visible atmospherically resistant index (VARI), as well as plant density and canopy cover, generated the highest correlation coefficient and the smallest errors (R = 0.97, MAE = 0.249 t ha−1, RMSE = 0.425 t ha−1) in the corn grain yield estimation, where the density and the NDVI were the variables with the highest relative importance, with values of 0.295 and 0.184, respectively. However, the WDRVI, plant density, and canopy cover estimated the corn grain yield with acceptable precision (R = 0.96, MAE = 0.209 t ha−1, RMSE = 0.449 t ha−1). The generated neural network models provided a high correlation coefficient between the estimated and the observed corn grain yield, and also showed acceptable errors in the yield estimation. The spectral information registered through remote sensors mounted on unmanned aerial vehicles and its processing in vegetation indices, canopy cover, and plant density allowed the characterization and estimation of corn grain yield. Such information is very useful for decision-making and agricultural activities planning.

The effect of using different vegetation indices for mangrove leaf area index modelling

Spectral vegetation index (SVI) is a mathematical combination of image spectral bands, especially ranges from visible to near infrared portion of the light spectrum. The purpose of SVI is to emphasize the vegetation content information from an image and doesn’t directly correlate with any physical or bio-physical characteristics of vegetation. One of the important biophysical parameters of vegetation that can be derived from SVI is Leaf Area Index (LAI). LAI can be defined as one half the total green leaf area per unit horizontal ground surface area and considered as an indicator to determine the level of mangrove health. Various SVIs have been developed and different SVI affects the accuracy of the LAI model. This study aimed to (1) compare and contrast the performance of several SVIs applied to WorldView-2 (WV-2) image to estimate the LAI, and (2) find the most accurate model for estimating LAI of mangrove forest in Perancak Estuary, Bali. The SVI used are Simple Ratio (SR), Normalized Differenced Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Green Atmospherically Resistant Vegetation Index (GARI), dan Wide Dynamic Range Vegetation Index (WDRVI). The LAI models developed were based on the semi-empirical relationships between SVIs and field LAI measured from hemispherical photograph. The corresponding values of both parameters were correlated to find the regression function for the modelling. The results show that the best accuracy was obtained from NDVI which has an R2 value of 0.83 and an estimation accuracy of 89.10 %.

Monitoring of sugar beet growth indicators using wide-dynamic-range vegetation index (WDRVI) derived from UAV multispectral images

The normalized vegetation index (NDVI) is widely used to monitor the spatial, temporal, physiological, and biophysical characteristics of vegetation. However, if the ground biomass is high, the NDVI becomes rapidly saturated. In the leaf area index (LAI) range of 2 to 6, reflectance in the near-infrared band is significantly higher than that in the red band. When reflectance in the near-infrared band exceeds 40%, the contribution of that reflectance to the NDVI is low. Here, we applied a weight coefficient, α (range 0.05 to 0.5), to the near-infrared band, thus developing a wide-dynamic-range vegetation index (WDRVI). We calculated (α * NIR-RED)/(α * NIR + RED) values; these emphasized the Fresh Weight of Beet LAI, the Fresh Weight of Leaves (FWL), and the Fresh Weight of Roots (FWR). We sought a vegetation index that optimally monitored early-stage sugar beet growth. The WDRVI sensitivities using various α coefficients were higher than those of the NDVI in the LAI range of 2 to 6. The determination coefficients (r2 values) of the sugar beet LAI, FWL, and FWT models established using the WDRVI1 were 0.957, 0.950, and 0.963, respectively. Using the WDRVI1 index model to estimate the accuracy of beet growth indicators can improve 1.05% to 5.07%. Use of the WDRVI reduces beet growth indicator saturation when the ground biomass is high, enhancing the accuracy of growth monitoring. This is useful as the sugar beet has huge ground biomass.

Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data

Leaf area index (LAI) is a key variable for characterizing crop growth conditions and estimating crop productivity. Despite continuing efforts to develop LAI estimation algorithms, LAI datasets still need improvement at spatial and temporal resolutions to meet the requirements of agricultural applications. Advancements in data fusion technique and the emergence of new satellite data provide opportunities for LAI data at higher resolutions in both space and time. In this study, we derived new LAI estimations by leveraging novel satellite remote sensing datasets, STAIR fusion (MODIS-Landsat fusion) and Planet Labs' CubeSat data (through a reprocessed pipeline) for a typical agricultural landscape in the U.S. Corn Belt. The STAIR fused data and our reprocessed CubeSat data have both fine spatial resolutions (30 m and 3.125 m, respectively) and high frequencies (daily for both). To reliably estimate LAI from these advanced satellite datasets, we used two methods: inversion of a radiative transfer model (RTM), and empirical relationship with vegetation index (VI) calibrated from field measured LAI. Compared to the ground-truth LAI collected at 36 sites across the study region, reliable approximations were achieved by both LAI estimations based on PROSAIL RTM (STAIR: R2 = 0.69 and root mean squared error (RMSE) = 1.12 (m2 m−2), CubeSat: R2 = 0.76 and RMSE = 1.09 (m2 m−2)), and LAI estimations based on Green Wide Dynamic Range Vegetation Index (GrWDRVI) (STAIR: R2 = 0.75, RMSE = 1.10 (m2 m−2), CubeSat: R2 = 0.76, RMSE = 1.08 (m2 m−2), where validation ground-truth is independent from calibration data). Newly estimated high-resolution LAI data were aggregated at 500 m resolution and compared with MODIS and VIIRS LAI products, revealing substantial uncertainties and biases in these two products. We also demonstrated phenology stage estimation at fine spatial resolutions based on our high-frequency LAI data. The proposed LAI estimation methods at both high spatial resolution and temporal frequency can be applied to the entire U.S. Corn Belt and provide significant advancement to crop monitoring and precision agriculture.

Estimating Grassland Carbon Stocks in Hulunber China, Using Landsat8 Oli Imagery and Regression Kriging.

Accurately estimating grassland carbon stocks is important in assessing grassland productivity and the global carbon balance. This study used the regression kriging (RK) method to estimate grassland carbon stocks in Northeast China based on Landsat8 operational land imager (OLI) images and five remote sensing variables. The normalized difference vegetation index (NDVI), the wide dynamic range vegetation index (WDRVI), the chlorophyll index (CI), Band6 and Band7 were used to build the RK models separately and to explore their capabilities for modeling spatial distributions of grassland carbon stocks. To explore the different model performances for typical grassland and meadow grassland, the models were validated separately using the typical steppe, meadow steppe or all-steppe ground measurements based on leave-one-out crossvalidation (LOOCV). When the results were validated against typical steppe samples, the Band6 model showed the best performance (coefficient of determination (R2) = 0.46, mean average error (MAE) = 8.47%, and root mean square error (RMSE) = 10.34 gC/m2) via the linear regression (LR) method, while for the RK method, the NDVI model showed the best performance (R2 = 0.63, MAE = 7.04 gC/m2, and RMSE = 8.51 gC/m2), which were much higher than the values of the best LR model. When the results were validated against the meadow steppe samples, the CI model achieved the best estimation accuracy, and the accuracy of the RK method (R2 = 0.72, MAE = 8.09 gC/m2, and RMSE = 9.89 gC/m2) was higher than that of the LR method (R2 = 0.70, MAE = 8.99 gC/m2, and RMSE = 10.69 gC/m2). Upon combining the results of the most accurate models of the typical steppe and meadow steppe, the RK method reaches the highest model accuracy of R2 = 0.69, MAE = 7.40 gC/m2, and RMSE = 9.01 gC/m2, while the LR method reaches the highest model accuracy of R2 = 0.53, MAE = 9.20 gC/m2, and RMSE = 11.10 gC/m2. The results showed an improved performance of the RK method compared to the LR method, and the improvement in the accuracy of the model is mainly attributed to the enhancement of the estimation accuracy of the typical steppe. In the study region, the carbon stocks showed an increasing trend from west to east, the total amount of grassland carbon stock was 79.77 × 104 Mg C, and the mean carbon stock density was 47.44 gC/m2. The density decreased in the order of temperate meadow steppe, lowland meadow steppe, temperate typical steppe, and sandy steppe. The methodology proposed in this study is particularly beneficial for carbon stock estimates at the regional scale, especially for countries such as China with many grassland types.

Expanding ensembles of species present-day and future climatic suitability to consider the limitations of species occurrence data

Abstract Outputs of species distribution models (SDMs) are widely used as indicators of climate conditions favorable for species occurrence. When using these outputs to inform planning and decision making, it is essential that the uncertainties associated with the projections of present-day and future climatic suitability are carefully considered. Climate change assessments routinely consider the uncertainty introduced into SDM outputs by differences in future climate projections, and other uncertainty sources, such as the choice of the threshold to convert simulated probabilities to binary climatically suitable areas, are also oftentimes considered. However, the uncertainty introduced by the limitations of the species occurrence data used in the SDM calibration is rarely evaluated. These limitations, which include location error, sampling bias, and species misidentification, may reduce the utility of SDM outputs in conservation research and practice. Using understory bamboo species in southwest China as examples, here we demonstrate that species occurrences obtained using remote sensing offer an additional dataset for calibrating SDMs that, in conjunction with conventional observations and employing an ensemble approach of outputs from multiple models, provide an estimate of the uncertainty introduced by the species occurrence data. A biweekly time series of the satellite-based Wide Dynamic Range Vegetation Index (WDRI) was employed to estimate bamboo occurrence based on phenological signatures of the bamboo species and their overstory canopies. Using Maxent, a popular modeling framework, present-day and projected future climatic suitability were assessed separately for conventional species presence observations from the Fourth National Giant Panda Survey and for the remotely-sensed presence estimates. The ensemble of model outputs suggests that the uncertainty introduced by the species occurrence data, along with the interaction with other sources of uncertainty, may be as substantial as the uncertainty introduced by the use of different climate scenarios or by the threshold used to estimate binary climatically suitable areas. Ignoring the uncertainty introduced by the limitations of the species occurrences may compromise the interpretation of SDM outputs and reduce their usefulness for conservation planning. Remote sensing is a largely untapped resource for assessing uncertainty in SDM simulations.

Red-Edge Band Vegetation Indices for Leaf Area Index Estimation From Sentinel-2/MSI Imagery

The estimation of leaf area index (LAI) from optical remotely sensed data based on vegetation indices (VIs) is a quick and practical approach to acquire LAI over vast areas. Reflectance in the red-edge bands is sensitive to vegetation status, and its information is thought to be useful in agricultural applications. Based on three red-edge band observations (represented as RE1, RE2, and RE3 for bands 5–7) from the Multispectral Instrument (MSI) onboard the Sentinel-2 satellite, this article aims to investigate the feasibility and performance of using red-edge bands for LAI estimates with the VI method and ground-measured LAI data sets. Sensitivity analysis from PROSAIL simulations revealed that RE1 is mainly affected by the influence of the leaf chlorophyll content, and this uncertainty should not be ignored during LAI estimation. For the normalized difference vegetation index (NDVI), modified simple ratio (MSR), chlorophyll index (CI), and wide dynamic range vegetation index (WDRVI), the optimal combination of Sentinel-2 bands for LAI estimation was RE2 and RE3, with a minimum root-mean-square error (RMSE) of 0.75. Four 3-band red-edge VIs were proposed to exploit the full content of the red-edge bands of Sentinel-2, and their performance in LAI estimation improved slightly. However, both 2-band red-edge VIs and 3-band red-edge VIs remained slightly saturated at high LAI levels; therefore, a segmental estimation with a threshold was suggested for large LAIs. The results indicate that the optimal 2-band red-edge VIs and proposed 3-band red-edge VIs are effective tools for crop LAI estimation in multiple-growth stages with Sentinel-2 MSI images.

Surface modelling of forest aboveground biomass based on remote sensing and forest inventory data

An accurate estimation of forest aboveground biomass (AGB) is important for carbon accounting. In this study, six methods, including partial least squares regression, regression kriging, k-nearest neighbour, support vector machines, random forest and high accuracy surface modelling (HASM), were used to simulate forest AGB. Forest AGB was mapped by combining Geoscience Laser Altimeter System data, optical imagery and field inventory data. The Normalized Difference Vegetation Index (NDVI) and Wide Dynamic Range Vegetation Index (WDRVI0.2) of September and October, which had a stronger correlation with forest AGB than that of the peak growing season, were selected as predictor variables, along with tree cover percentage and three GLAS-derived parameters. The results of the different methods were evaluated. The HASM model had the best modelling accuracy (small MAE, RMSE, NRMSE, RMSV and NMSE and large R 2). A forest AGB map of the study area was generated using the optimal model.

Estudio de bofedales en los Andes ecuatorianos a través de la comparación de imágenes Landsat-8 y Sentinel-2

<p>The objective of the present study was to compare the Landsat-8 and Sentinel-2 images to calculate the wetland´s extension, distribution and degree of conservation, in Reserva de Producción de Fauna Chinborazo (RPFCH) protected area located in the Andean region of Ecuador. This process was developed with in situ work in 16 wetlands, distributed in different conservation levels. The Landsat-8 and Sentinel-2 images were processed through a radiometric calibration (restoration of lost lines or píxels and correction of the stripe of the image) and an atmospheric correction (conversion of the digital levels to radiance values), to later calculate the Vegetation spectral indexes: NDVI, SAVI (L = 0.5) where L is a constant of the soil brightness component, EVI2 (improved vegetation index 2), NDWI (standard difference water index), WDRI (wide dynamic range vegetation index) and the Red Edge model that only this one has in Sentinel-2 in this study. Making a classification of the Bofedal ecosystem in satellite images by applying Random Forest, the most important variables with Landsat-8 were EVI2 (37.72%) and SAVI with L = 0.5 (30.97%), while with Sentinel-2 the most important variables correspond to the Red Edge (38.54%) and WDRI (27.06%). With the indices calculated, two categories of analysis were determined: a) wetland integrated by the levels: intervened [1], moderately conserved [2] and conserved [3] and b) other than wetland [4] integrated by areas that do not correspond to this ecosystem. Landsat-8 shows that the percentage of correct classifications of píxels belonging to the wetland category corresponds to: [1] 72.76%, [2] 58.38%, [3] 68.42%, while for the category other [4] were correct 95.15%. With Sentinel-2, the percentage of correct classifications corresponds to [1] 95.00%, [2] 82.60%, [3] 96.25%, while for the category other [4] the correct answers were 98.13%. In this way with Landsat-8 the wetland corresponds to 21.708,54 ha (41.21%), while with Sentinel-2 the wetland represents a total of 20,518 ha (38.95%), of the 52,560 ha that belong to the RPFCH, concluding that Sentinel-2, due to its better spatial resolution, and the incorporation of its new bands in Red Edge, obtains better results in image classification.</p>

Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting

Leaf area per unit surface (LAI—leaf area index) is a valuable parameter to assess vine vigour in several applications, including direct mapping of vegetative–reproductive balance (VRB). Normalized difference vegetation index (NDVI) has been successfully used to assess the spatial variability of estimated LAI. However, sometimes NDVI is unsuitable due to its lack of sensitivity at high LAI values. Moreover, the presence of hail protection with Grenbiule netting also affects incident light and reflection, and consequently spectral response. This study analyses the effect of protective netting in the LAI–NDVI relationship and, using NDVI as a reference index, compares several indices in terms of accuracy and sensitivity using linear and logarithmic models. Among the indices compared, results show NDVI to be the most accurate, and ratio vegetation index (RVI) to be the most sensitive. The wide dynamic range vegetation index (WDRVI) presented a good balance between accuracy and sensitivity. Soil-adjusted vegetation index 2 (SAVI2) appears to be the best estimator of LAI with linear models. Logarithmic models provided higher determination coefficients, but this has little influence over the normal range of LAI values. A similar NDVI–LAI relationship holds for protected and unprotected canopies in initial vegetation stages, but different functions are preferable once the canopy is fully developed, in particular, if tipping is performed.