Search

Monitoring initial growth phases is essential for the success of forest plantations. This study introduces a methodology aimed at characterizing the growth of Eucalyptus short rotation plantations in Brazil during their first 2 years, based on Sentinel-2 satellite imagery. The primary goal is to detect potential anomalies at the pixel level, covering an area of 400 m2, and to feed operational decision-making strategies aiming at characterizing, correcting or mitigating the problem. The approach relies on predictive machine learning models that estimate an integrated growth index, the volume that the trees will reach at 2 years of age (V2Y). The model uses various plantation characteristics such as planting density, genotypic characteristics and environmental factors and incorporates vegetation indices derived from Sentinel-2 data acquired during the first 2 years of the plantation. These anticipation models were calibrated on an extensive dataset comprising more than 9000 inventory plots spread over more than ninety thousand hectares. The Green Normalized Difference Vegetation index (GNDVI) was shown to give the best results among several vegetation indices tested. The accuracy of V2Y prediction improved significantly when longer periods of vegetation indices were included. Our results demonstrate that using the GNDVI data from the first year or from the initial 18 months of plantation growth yields accurate predictions of V2Y, with R2 values of 0.71 and 0.74 and RMSE values of 7.86 and 7.46 m3 ha−1, respectively. The anticipation model with GNDVI outperformed simpler models that solely rely on stand characteristics. The novel approach developed in this study offers an operational means to reliably estimate an early-stage growth indicator for Eucalyptus plantations in Brazil. [ABSTRACT FROM AUTHOR] more...

Medium Resolution Spectral Imager-II (MERSI-II) on FengYun 3D (FY-3D) meteorological satellite is a visible and infrared spectral imaging instrument comparable with Moderate Resolution Imaging Spectroradiometer (MODIS), which provides ample opportunities for regional crop mapping. However, current research may have overlooked the potential of combining FY-3D MERSI-II data with deep neural network (DNN) algorithms for winter wheat identification. This research utilizes a DNN method to train a nonlinear model with the synthetic normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) at a 250 m resolution from FY-3D MERSI-II as the eigenvalues, enabling to accurately identify spatial distribution of winter wheat in China's main production regions and filling the gap in the application of FY-3D MERSI-II data. The results indicate that the winter wheat identification with the combination of NDVI and EVI achieved an accuracy of 95.32%, with a Kappa coefficient of 0.87 and a determination coefficient (R2) of 0.73, improving the overall accuracy by 1.27% and 1.07% compared to models trained solely using NDVI or EVI, respectively. In terms of spatial distribution, the overlap rate with the winter wheat dataset of China from MODIS at a 500 m resolution exceeded 95%, with an area bias of 2.67%. This study indicates that the medium resolution FY-3D MERSI-II remote sensing dataset combined with the DNN algorithm can accurately identify crop information over large areas. [ABSTRACT FROM AUTHOR] more...

To improve the accuracy of detecting changes in remote sensing time series, an improved algorithm based on the combination of the antileakage least-squares spectral analysis (ALLSSA) algorithm and detecting breakpoints and estimating segments in trends (DBEST) algorithm is proposed and applied. The method uses the ALLSSA algorithm to decompose the time series and identify the trend components in the time series. Then, the trend segmentation mechanism of the DBEST algorithm is used to detect the changes in the trend component. In this paper, the improved algorithm is evaluated using a simulated time series data set, a time series data set with multiple change points, and data set based on the moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) remote sensing time series. The results demonstrate that the average detection accuracies of the improved algorithm and DBEST algorithm are 98.4% and 85.2%, respectively, for the simulated time series data set. For the time series data set with multiple change points, the average root mean square errors (RMSEs) of the trend data for the improved and DBEST algorithms are 0.0386 and 0.0331, respectively. The mean normalized residual norms (MNRNs) of the improved and DBEST algorithms are 0.0252 and 0.0351, respectively. Finally, the improved algorithm, DBEST algorithm, and breaks for additive season and trend (BFAST) algorithm are applied to MODIS NDVI data, and their performance with remote sensing data is compared. The improved algorithm has higher detection accuracy and a smaller MNRN, indicating that more information is included in the trend and seasonal components. Therefore, the proposed method is useful for analysing trends in remote sensing time series data. [ABSTRACT FROM AUTHOR] more...

As the cloud covers the surface thermal radiation during the transmission process and the remote sensor in the air is difficult to detect, Thermal Infrared (TIR) remote sensing inversion of Land Surface Temperature (LST) must be performed under clear-sky and cloudless conditions. This study has established the functional relationship between LST and the vegetation index of cloudless vegetation pixels around the cloud coverage areas in the North China Plain based on the Normalized Difference Vegetation Index (NDVI) to address the issues of TIR remote sensing inversion for LST affected by cloud coverage. By acquiring NDVI and using short-term, relatively stable characteristics of vegetation, the LST of the cloud cover area is estimated. The findings show a linear negative correlation between LST and NDVI in vegetation pixels, with the vegetation type remaining essentially unchanged over time. When there are 20 pixels in each of the two thermal infrared channels of FY-3 D, the MAE value and RMSE value of the 24th thermal infrared channel are 0.77 and 0.88, respectively, and the MAE value and RMSE value of the 25th thermal infrared channel are 0.64 and 0.80, respectively. When the number of pixels is 200, the 24th thermal infrared channel's MAE value and RMSE values are 0.96 and 0.99, respectively, while the 25th thermal infrared channel's MAE value and RMSE values are 0.90 and 0.95, respectively. In other words, the estimation is more accurate and closer to the true value, and the land surface temperature retrieved by the 25th channel deviates from the true value to a lesser extent. The average absolute error and root mean square error are both less than 1, which may satisfy the accuracy demands of practical applications such as agricultural drought monitoring, ecological evaluation, and crop yield estimation. [ABSTRACT FROM AUTHOR] more...

This research investigated the green gram crop by employing a time series of Sentinel-1A Single Look Complex (SLC) and Sentinel-2 datasets, which corresponded to different phenological growth stages of the crop. Field campaigns were conducted on the acquisition dates of Sentinel-1A datasets for the entire crop cycle to estimate crop height and soil moisture. In line with these observations, temporal variations in 10 salient Synthetic Aperture Radar (SAR) parameters, obtained from Sentinel-1A datasets, are systematically analysed, which elucidated the importance of dual-polarized SAR parameters in the diverse phenological growth stages of the crop. Moreover, the Normalized Difference Vegetation Index (NDVI) values from Sentinel-2 datasets, estimated at various green gram growth stages, are categorized into initial (NDVI $ \le $ ≤ 0.5), intermediate (0.5 $ \lt $ < NDVI $ \lt $ < 0.8), and maximum growth of the crop (NDVI $ \ge $ ≥ 0.8), which are then aligned with corresponding SAR and ground-measured parameters. The inversion model development and sensitivity analysis were carried out to correlate ground-measured parameters with SAR parameters across various growth stages based on the categorization of NDVI values as well as for the entire growing season of the crop. Backscattering intensities are found to have good sensitivity to crop height at its intermediate growth (r $ \ge $ ≥ 0.75). Among vegetation indices, the Polarimetric Radar Vegetation Index (PRVI) showed significant sensitivity to crop height in its intermediate growth stage (r $ \approx $ ≈ 0.77). H-A-Alpha decomposition parameters displayed notable sensitivity to soil moisture in the initial and maximum growth stages of green gram. This study demonstrates the effectiveness of Sentinel-1 dual-polarized SAR parameters in monitoring green gram phenological growth stages. The parameters show substantial sensitivity to both green gram height and soil moisture. [ABSTRACT FROM AUTHOR] more...

This study examines the spectral characteristics of two invasive plant species, Amur honeysuckle (Lonicera maackii) and Callery 'Bradford' pear (Pyrus calleryana Decne), in central Kentucky's recreational areas. Using a combination of spectrometer, satellite (National Agriculture Imagery Program (NAIP)) and unmanned aerial vehicle (UAV) data, this study investigated the correlation between leaf locations and normalized difference vegetation index (NDVI), as well as the impact of terrain features on vegetation indices. Significant variations in NDVI and normalized difference red-edge (NDRE) index values across study areas and leaf positions were observed. Terrain attributes, particularly slope and aspect direction, significantly influenced vegetative indices in one study site. Weak to moderate correlations were found between the spectrometer and UAV-derived NDVI and NDRE values. Additionally, the study highlighted the feasibility of upscaling UAV data for NAIP imagery comparison, reaffirming the superior accuracy of UAV imagery in assessing vegetation health. This integration of remote sensing techniques offers valuable insights into the spectral characteristics of invasive plants, essential for their effective mapping and management. [ABSTRACT FROM AUTHOR] more...

The non-photosynthetic vegetation – soil separation index (NSSI) and normalized difference vegetation index (NDVI) can be used to simultaneously estimate the fractional cover of non-photosynthetic vegetation (fNPV), photosynthetic vegetation (fPV), and bare soil (fBS) in vegetation ecosystems. However, these estimates suffer from problems due to shading by dense vegetation or topography. Based on field-measured data and on hyperspectral data acquired by unmanned aerial vehicles, we analyse how shading affects the morphology of NDVI-NSSI and enhanced vegetation index – NSSI (EVI-NSSI). We also investigate the simultaneous estimation of fNPV, fPV, and fBS. The results show that the NDVI-NSSI normalized difference feature space mitigates the impact of shade. Shade causes the NDVI-NSSI and EVI-NSSI to shift rightward parallel to the BS axis and leftward parallel to the PV axis, respectively. Although such shifts may hinder the determination of NPV, PV, and BS endmembers, they do not affect the spectral separation of NPV. Overall, with the NDVI-NSSI and EVI-NSSI methods, the estimation error for fNPV under shaded conditions is approximately 5% greater than that under illuminated conditions. Shaded green vegetation more strongly affects the estimation error of fPV and fBS in EVI-NSSI than in NDVI-NSSI, whereas fBS and fPV depend more strongly on shade than fNPV. [ABSTRACT FROM AUTHOR] more...

Within the Tibetan Plateau (TP), grassland degradation is of great concern, and there is a need to better understand the spatial variability and landscape patterns in grassland degradation across the TP. This study explores potential patterns in grassland degradation, estimated using Landsat-derived normalized difference vegetation index (NDVI) growing season change products generated by a prior study. Shuttle Radar Topography Mission (SRTM) digital terrain model (DTM)-derived geomorphons, topographic slope and the topographic position index (TPI) were used to explore grassland degradation and assess whether changes in growing season NDVI are associated with specific grasslands or landforms. An increase in NDVI within the TP was observed especially during the 1990 to 2018 and 2000 to 2018 time periods. Higher growing season median NDVI change values were found for the Southeast Tibet Shrublands and Meadows (SETSM) and TP Alpine Shrublands and Meadows (TPASM) grasslands in comparison to the other grassland types analysed, suggesting a more pronounced greening trend in the eastern portion of the plateau in comparison to the western portion. Small differences in NDVI change were observed for different geomorphon-based landforms, while topographic slope and TPI showed only modest correlations with NDVI change for all time periods and grassland types. More localized patterns of grassland degradation were masked by the widespread greening trends. Further analysis of only pixels that experienced a decreasing NDVI magnitude of less than or equal to −0.1 also generally suggested more change in the SETSM and TPASM. While this study generally supports the use of geomorphons as an analysis and aggregating unit for studying change patterns at the hillslope scale, more research is required to fully grasp the dynamics of landscape change at the hillslope scale in the TP. [ABSTRACT FROM AUTHOR] more...

Landslides are some of the most destructive and recurrent natural hazards worldwide. Landslides are triggered by natural phenomena such as extreme rainfall and earthquakes, causing human and economic losses. A rapid response to landslide events is necessary to assess damage mitigation and save lives and property. This study developed a landslide detection model using differential spectral indices and amplitude ratio changes with a classification and regression tree (CART), aiming to detect landslide scars after the occurrence of these events in Asian regions for testing different environment condition. The multi-temporal SAR and optical stack images were pre-processed to reduce speckle noise, seasonal noise, and atmospheric noise. This study explored change detection approaches with a minimum threshold of amplitude ratio change (Aratio), using Sentinel−1 images and the relative difference in the normalized difference vegetation index (rdNDVI), differential bare soil index (dBSI), and differential brightness index (dBI) was obtained using Sentinel−2 images. The accuracy of the model was examined by F1-scores. The accuracy of the model for landslide detection was considered moderately good to excellent. As a result of the landslide detection model, amplitude ratio change detection improved the model as revealed by the F1-scores. Moreover, this study found that differential spectral indices could be used to classify the types of landslides (deep-seated and shallow landslides) according to the level of surface changes and texture of the collapsed material after landslide events. [ABSTRACT FROM AUTHOR] more...

Crop classification is a crucial task in agricultural remote sensing, with the accuracy of such classification heavily relies on field sampling. Reusing historical samples can minimize the reliance on annual field sampling for crop classification. Although previous research primarily focused on the classification accuracy based on the reused historical samples, the underlying factors that influence the accuracy have not been adequately investigated. In this study, we employed a two-dimensional convolutional neural network (2D-CNN) model for crop classification reusing historical samples and investigated the factors influencing the classification accuracy. First, we calculated a normalized difference vegetation index (NDVI) time series from historical data to characterize crop growth patterns. Secondly, we assessed three different time-series construction methods. Subsequently, we used the 2D-CNN model to automatically extract abstract features of crop growth patterns. Finally, we designed various strategies for reusing historical samples to explore the influencing factors. Experiments were conducted in Kuitun, Xinjiang Uygur Autonomous Region, China, from 2016 to 2020, employing a long time series of Sentinel-2 images as remote sensing data. Our results indicated that optimal 2D-CNN models using an irregular satellite image time series (irSITS) outperformed random forest models in inter-annual classifications (the overall accuracies for 2016–2020 were 0.78, 0.61, 0.89, 0.89, and 0.70, respectively). In addition, we identified that the primary factors affecting classification accuracy were 1) the time-series construction method used; 2) the crop growth patterns; and 3) the sample diversity. By reusing historical samples and considering the factors influencing classification accuracy, this study provides valuable insights into high-quality crop classification mapping under limited field sample conditions. [ABSTRACT FROM AUTHOR] more...

Remotely sensed burn severity modelling is commonly addressed by regressing fire-induced forest biophysical damage measured in the field, using the Composite Burn Index (CBI) method on a pre- to post-fire difference of the normalized burn ratio (∆NBR) representing early fire effects. Although less often addressed, it may also be modelled on a wider post-fire temporal range to assess longer-lasting ecological effects. However, this approach may involve a larger set of potential predictors, posing a high-dimensional statistical issue. This study examines the performance of different spectrotemporal combinations of remotely sensed predictors for long-term burn severity modelling of the two largest wildfires during the central Chilean firestorm in the summer of 2017, in order to assess their predictive contribution and to support their optimal selection. Sentinel-2 images acquired annually in 2016–2020 were used to create different spectrotemporal combinations of predictor sets, encompassing ∆NBR, differenced normalized difference water index (∆NDWI) and differenced normalized difference vegetation index (∆NDVI), as well as differenced bands. Thereafter, they were modelled by CBI data collected in the field, using conventional (least-squares-based) and ridge-regularized linear regressions, and assessed by spatial cross-validation. Among the multiple-variable models, ridge regression outperformed multiple linear regression, the highest accuracies being achieved by the multitemporal sets comprising any of the differenced index types. Among these, ∆NBRs reached the lowest average root mean square error (RMSEavg = 0.591). Results confirm the importance of ∆NBR as burn severity predictor in a long-term assessment context, top-performing for both, single- and multiple-variable models. They also highlight the value of tracking the temporal trajectory of this differenced index when enlarging the number of post-fire image dates involved, and hence, no a-priori knowledge about what predictor to select exists. Remotely sensed burn severity assessment at multiple temporal scales results in statistical challenges due to strongly correlated predictors and high dimensionality. The performance of different spectrotemporal combinations of remotely sensed predictors for long-term burn severity modelling was examined in order to assess their predictive contribution and to support their optimal selection. Predictors, single or pooled into sets, were used to linearly model CBI data, considering ridge regularized regressions to deal with multiple and potentially correlated predictors. Competitive performances were achieved when modelling the predictor set comprising all four pre- to post-fire differences of NBRs by ridge regression, outperforming the remaining multiple-variable models. In the context of remotely sensed burn severity modelling involving high-dimensional feature settings, finding the most suitable predictors is a task that ridge regression may conveniently address. [ABSTRACT FROM AUTHOR] more...

As thick crustal layers form on dryland surfaces, they affect the spectral information that is originally dominated by sand or rock. The spectral characteristics of organic matter replace the mineral elements as prominent features. In this case, the growth patterns and spectral characteristics of biological soil crusts (BSCs) can be observed. Satellite spectral data have been used for BSC spatial information extraction. However, the dynamic changes in BSCs can affect the spectra. Two aspects are involved here: moisture change and BSC growth. When these changes are superimposed with BSC succession, they lead to an increase in spectral complexity. This study explored three BSC types, including algal crust, lichen and moss, and discussed their spectra. By selecting BSC samples at different succession states and by combining coverage and simulated precipitation, the response of the spectra to BSC coverage and the spectral characteristics of BSCs under dry and wet conditions were measured and analysed. In addition, the spectral index variations caused by coverage and moisture of three types of BSCs were discussed, where the spectral indices include the normalized difference vegetation index (NDVI), brightness index (BI), crust index (CI), and biological soil crust index (BSCI). The results showed that the succession, moisture and growth of BSCs were the main factors affecting their spectra. BSC types can be distinguished in a particular climatic context to determine the degree of BSC succession. Precipitation in the monitoring areas needs to be considered to avoid the effects of dry and wet BSC variations on remote sensing monitoring. The coverage of different types of BSCs in mixed pixels can be determined by multiple indices. The results of this study will provide a basis for monitoring BSCs using satellite spectral information to guide regional ecological management. [ABSTRACT FROM AUTHOR] more...

Drought disasters significantly threaten the stability of terrestrial ecosystems in arid and semi-arid regions. Although the impact of drought on vegetation has been extensively researched, there is still a lack of understanding regarding the ecological impacts of drought on the heterogeneous vegetation in northern China's arid and semi-arid regions. To investigate the spatial patterns of vegetation and differences in response to drought climate, the Normalized Difference Vegetation Index (NDVI) and Standardized Precipitation Evapotranspiration Index (SPEI) were used to determine vegetation characteristics and the seasonal sensitivity of vegetation types to drought response, as well as to identify the factors driving the sensitivity differences. In the study area, 83.1% of the vegetation exhibited a significant positive correlation with the drought index. The maximum correlation coefficient between vegetation and drought was 0.62, demonstrating that the majority of vegetation was influenced by drought. It was observed that different vegetation types responded differently to drought at varying spatial and temporal scales. The main drivers of vegetation response to drought in arid and semi-arid zones were quantified by using the random forest algorithm. The results indicated that climate and soil factors are the primary limiting factors. These findings enhance our comprehension of arid and semi-arid areas, the effect of drought on different plant species, and the factors that affect vegetation's response to drought. [ABSTRACT FROM AUTHOR] more...

Sentinel-1 and Sentinel-2 satellites are delivering data at the spatial resolutions and geographies needed for operational crop monitoring. Of particular interest, Sentinel-1 Synthetic Aperture Radar (SAR) data in Single Look Complex (SLC) format can be processed to provide polarimetric parameters sensitive to crop development. This study created a Sentinel-1 based SAR vegetation index for canola, calibrated to Normalized Difference Vegetation Index (NDVI) (SARcal-NDVI) values calculated from Sentinel-2. A Random Forest Regressor (RFR) modelled the SARcal-NDVI, from four selected polarimetric features (degree of linear polarization (DoLP), normalized Shannon entropy (SE), the second eigenvalue of the coherency matrix (l2) and the ellipticity angle ( χ)). The coefficient of determination (R2) between the Sentinel-2 NDVI and the SARcal-NDVI was 0.89. RFR-generated SARcal-NDVI estimates, based on the four SLC generated polarimetric parameters, were then used with a Canopy Structure Dynamics Model (CSDM) and with Growing Degree Days (GDD) to estimate the condition of canola at a daily time step. The SARcal--NDVI time series estimated from the CSDM model was correlated to the ground measured biomass. During the rapid accumulation of biomass from early to mid-season, correlations of the SARcal-NDVI to wet biomass were strong (R2 of 0.88). Correlations were still significant albeit weaker during pod development and throughout the period of canola senescence (R2 of 0.42). As climate variability drives uncertainty in the agricultural sector, sensors like the Sentinels can be leveraged to track changes in crop acreages and crop productivity. The next step in this research is to extend to other economically important crops such as corn, soybeans and wheat and test the ability of a Sentinel-1-based vegetation index to inform crop yield estimates. [ABSTRACT FROM AUTHOR] more...

The Global Change Observation Mission-Climate (GCOM-C), launched in 2017, has suitable bands matching the photochemical reflectance index (PRI) definition. It also has the bands for the normalized difference vegetation index (NDVI). The PRI has a unique capability to detect plant stress caused by excessive light and drought. However, no moderate-resolution satellites had suitable bands for the PRI, requiring two narrow bands in green light in the definition. In this study, we conducted the early validation study of PRI and NDVI derived from the GCOM-C satellite and demonstrated those accuracies and characteristics in Mongolian grassland. The Mongolian Steppes (dry grasslands) are widely distributed on the plateau and therefore suitable for satellite validation. It is particularly suitable for the PRI validation because Mongolian grasslands have water stress due to the small amount of precipitation in summer. Therefore, we carried out field campaigns at three study sites in Mongolia. In this study, we found the seasonal pattern of PRI suggesting the potential to detect the water stress of vegetation, which is essential information for informed management of the grasslands. However, the correlation between the satellite-derived PRI and the in-situ PRI was negative because of the dependence of GCOM-C PRI on the soil moisture at sparse vegetation. For the accuracy assessment of PRI, which depends on rapidly changing light and soil moisture in a day, more exact synchronization of in-situ and satellite observation is required. On the other hand, we found that the NDVI derived from GCOM-C was highly accurate: The correlation coefficient (R) between the satellite-derived NDVI and the in-situ NDVI was 0.988 (RMSE=0.052). GCOM-C NDVI has enough similarities with MODIS NDVI in terms of accuracy, spatial resolution, and frequency. For example, we demonstrated that GCOM-C NDVI could detect the phenology with the same or better accuracy than MODIS NDVI. We also demonstrated their difference: the soil moisture dependence in sparse vegetation. The less dependency of GCOM-C NDVI on the soil moisture leads to a better classification of vegetation and non-vegetation in the sparse grassland than MODIS NDVI. [ABSTRACT FROM AUTHOR] more...

The spectral variability of tropical forests during the Amazonian dry season is not entirely understood because of the divergent responses in Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation indices (VIs) measured under-increased water deficit and high insolation. Here, we used a dataset composed of 493 cloud-free PlanetScope (PS) images to investigate possible effects of solar illumination on the dry-season variability of spectral and spatial attributes. The attributes were calculated from June to September over dense tropical forests of the Amazon. The dry-season images were obtained at nadir viewing between 2017 and 2019 over 12 selected sites representing different climatic and environmental conditions. To detect dry-season patterns of vegetation brightness with changes in the geometry of image acquisition, we applied principal component analysis (PCA) over the PS surface reflectance. We plotted the average surface reflectance (2017–2019) for each of the four PS bands and inspected the variability of two VIs with distinct levels of anisotropy to bidirectional effects: the Enhanced Vegetation Index (EVI) and the Normalized Difference Vegetation Index (NDVI). We also investigated the signal of textural metrics from Grey Level Co-occurrence Matrix (GLCM) obtained from the near-infrared (NIR) band of PS. Finally, we generated shade fractions from Spectral Mixture Analysis (SMA), correlated the spectral and spatial attributes of vegetation with solar angles, and observed the dry-season variability in reflectance and VIs over pseudo-invariant soil surfaces. The results showed the existence of solar illumination effects on PS image acquisition during the dry season of the Amazon, which affected differently the NDVI and EVI. From the beginning (June) to the end (September) of the dry season, the solar zenith angle (SZA) decreased and the solar azimuth angle (SAA) increased during the period of acquisition of the PS images. The amplitude of SZA between June and September increased towards south of the Amazon, while the amplitude of SAA increased towards north of this region. Changes in vegetation brightness from June to September were captured by PCA over some sites. Because of the overall increase in both red and NIR band reflectance, solar illumination effects were compensated during the NDVI calculation. In contrast, because the EVI is largely driven by changes in NIR reflectance, these effects contributed to increase the EVI signal at the end of the dry season. For most sites, GLCM texture mean increased towards the end of the dry season, while texture variance decreased in the opposite direction. Shade fractions decreased towards September when reduced amounts of canopy shadows were sensed by PS. EVI was more anisotropic than NDVI and presented higher negative correlations with SZA and shade fractions and higher positive correlations with SAA and texture mean. The dry-season increase in EVI with solar illumination effects was also observed over pseudo-invariant soil surfaces. From this unprecedent scale of observations at high spatial and temporal resolutions, we recommend caution when using anisotropic VIs for large-scale phenological studies over the Amazon because biophysical and non-biophysical signals may be coupled together. [ABSTRACT FROM AUTHOR] more...

Fast and accurate tree species mapping using remote sensing technology is invaluable to forest sustainable management. However, due to the limitation of ecosystem complexity and the frequent cloudy weather, it remains challenging to obtain detailed knowledge about the varieties in subtropical areas. In this study, 18 scenes of Gaofen-6 (GF6) and 13 scenes of Sentinel-2 (S2) images were harmonized using linear regression models, and the identification abilities of 23 red-edge vegetation index (REVI) time-series and the normalized difference vegetation index (NDVI) time-series were evaluated using the random forest classifier (RF). The feature reduction methods were employed to boost the effectiveness of the input features, and the optimal red-edge combination consisting of spectral and textural features was built to map the dominant tree species precisely in Southeast China. Our results showed as follows: (1) GF6 and S2 could be harmonized with a mean coefficient of determination (R2) greater than 0.94 and the root means squared error (RMSE) less than 0.02, which was helpful to improve the consistency between multi-source remote sensing data and subsequently derived products. (2) Most of the REVI time-series performed better than NDVI time-series, and the transformed chlorophyll absorption ratio indexes (TCARI2) got the highest overall accuracy of 86.28%, which could be further improved to 88.37% by adding 26 textural features. (3) The phenological characteristics from different seasons were conducive to tree species classification compared to the effect of different sensors, and the contribution of spring images played the most important role in RF, followed by autumn and winter, while the mid-summer images seemed to be negligible in the classifier. Overall, this study proposed an accurate and cost-efficient approach for tree species identification, which demonstrated good potential in providing a profound reference for subtropical forest landscape studying. [ABSTRACT FROM AUTHOR] more...

Agricultural productivity is affected by natural disasters, such as drought and flood, which can be assessed by remote sensing (RS) technology. The main objective of this research is to develop an operational methodology for predicting rice yield that is utilized to assess the impacts of drought and flood disasters on agricultural productivity taking Jiangxi, China as an example. For this purpose, we first calculated the Standardized Precipitation Index (SPI) using monthly rainfall data of 83 stations from 1960 to 2020 to identify the disastrous years and their spatial extent of impact. Then, time-series MODIS data from 2000 to 2020, Landsat images, digital elevation model (DEM) were obtained and utilized for rice plantation mapping using decision-tree algorithm and yield estimation. The identified 3-cropping rice plantations were converted into rice masks for calculating the county-level cumulative NDVI (normalized difference vegetation index) to match the reported county-level annual rice yield for building remote sensing-based rice yield models taking the averages of 2016–2019 as test. By comparing the determination coefficient (R2), root mean square error (RMSE) and prediction accuracy of the models, we found that the best model for rice yield estimation is YD = 4.899 × 10−6×NDVI2 + 2.891 × NDVI + 98511.218 (R2 = 0.898). This model was applied to estimate the county-level annual rice production of 2014 and 2015, and provincial annual rice production of 2010–2019 for validation and prediction. The agreement with the government-reported yield reaches 97–98% at provincial level and the models were hence considered reliable. After verification, the models were applied to other remained years after 2010 for estimating the provincial annual rice production. Then, a new disaster indicator, that is, the disaster composite index (DCI) was developed for assessing the disaster impact on annual rice production in a pilot area around Nanchang City. Results show that developed models are capable of achieving rice yield estimation using both MODIS data for regional and Landsat data for local scale. In comparison with the normal or least affected year, impacts of drought and flood were successfully evaluated using DCI for pilot area and their relationship was created. We hence believe the developed methodology is extendable to South China and other rice plantation area in the world. [ABSTRACT FROM AUTHOR] more...

Argentina is one of the three most crucial soybean-producing countries in the world. Timely and accurate monitoring of the soybean planting area and its changes across the country is extremely important for global food security. This study effectively extracted the spatial-temporal nationwide soybean planting areas in Argentina from 2016 to 2019 based on the Google Earth Engine (GEE) and pixel-based machine learning method random forest. About 8000 soybean sampling points were gathered from the high spatial resolution imagery available on Google Earth. Comprehensive consideration of phenological period, monitoring area and imagery availability, multi-bands of Landsat 8 imagery and Normalized Difference Vegetation Index (NDVI) waverage annual temperatures range as used to map dynamics of nationwide soybean planting areas. The primary soybean planting areas were mapped between 2016 and 2019 and occupied 5.90%-6.76% of Argentina's total land area. Soybean planting areas at the provincial level explained more than 84% of the variability in most years compared with those obtained from official statistics. The spatial distribution of primary soybean planting areas changed from 2016 to 2019 resulting in the shift of area-weighted centroids. Results showed that NDVI and NIR features calculated by the first three months' composites contributed more to the classification. This study provides an effective way to retrieve the soybean planting area accurately and rapidly, especially when lacking chances for field measurement over a large spatial extent. [ABSTRACT FROM AUTHOR] more...

Accurate estimations of soil cover parameters (fractional coverage of crop residue [fcr], crop [fc], and bare soil [fbs]) in croplands can assist in decision-making for agricultural management organisations and are important for understanding the interrelations between global changes and terrestrial ecosystems. In recent decades, optical remote sensing has been widely used for mapping fc, fcr, and fbs. The crop residue and soil spectra decrease rapidly with increasing cropland moisture primarily because of water absorption in the near-infrared and shortwave infrared bands. This leads to a decrease in the denominator of current normalized crop residue spectral indices (shortwave infrared [SWIR] band 1 + band 2), thereby causing them to increase with increasing cropland moisture. This study revealed that the slopes of the soil and crop residue spectra between SWIR1 and SWIR2 were less affected by cropland moisture changes. Based on this feature, we propose a shortwave infrared angle (SWIRA) index and jointly use it with normalized difference vegetation index (NDVI) to estimate and produce multi-temporal maps of fc, fcr, and fbs using broadband Sentinel-2 MSI images. The proposed SWIRA is the slope of the spectrum between SWIR1 and SWIR2. This study (i) uses laboratory-based spectral reflectance to evaluate the performance of the SWIRA and (ii) further evaluates the performance of the SWIRA–NDVI method using multi-temporal Sentinel-2 MSI images. Our results indicate that (i) cropland moisture has a considerably smaller effect on SWIRA than it does on current broadband normalized crop residue spectral indices, and (ii) maps of the estimated fc, fcr, and fbs values can be used to improve crop growth and decision-making, for example, by providing harvest maps and soil tillage intensity maps. [ABSTRACT FROM AUTHOR] more...

Accurate, timely, and non-destructive early crop yield prediction at the field scale is essential in addressing changing crop production challenges and mitigating impacts of climate variability. Unmanned aerial vehicles (UAVs) are increasingly popular in recent years for agricultural remote sensing applications such as crop yield forecasting and precision agriculture (PA). The objective of this study was to evaluate the performance of a low-cost UAV-based remote sensing technology for Bambara groundnut yield prediction. A multirotor UAV equipped with a near-infrared sensitive consumer-grade digital camera was used to collect image data during the 2018 growing season (April to August). Flight missions were carried out six times during critical phenological stages of the life-cycle of the monitored crop. Yield was recorded at harvest. Four vegetation indices (VIs) namely normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), green normalized difference vegetation index (GNDVI), and simple ratio (SR) generated from the Red-Green-Near Infrared bands were calculated using the georeferenced orthomosaic UAV images. Pearson's product-moment correlation coefficient (r) and Bland–Altman testing showed a significant agreement between remotely and proximally sensed VIs. Significant and positive correlations were found between the four VIs and yield, with the strongest relationship observed between SR and yield at podfilling stage (r = 0.81, P < 0.01). Multi-temporal accumulative VIs improved yield prediction significantly with the best index being ∑SR and the best interval being from podfilling to maturity (r = 0.88, P < 0.01). The accumulated ∑SR from podfilling to maturity resulted in higher prediction accuracy with a coefficient of determination (R2) of 0.71, root mean square error (RMSE) of 0.20 and mean absolute percentage error (MAPE) of 14.2% than SR spectral index at a single stage (R2 = 0.68, RMSE = 0.24, MAPE = 15.1%). Finally, a yield map was generated using the model developed, to better understand the within-field spatial variations of yield for future site-specific or variable-rate application operations. [ABSTRACT FROM AUTHOR] more...

Brazil is one of the largest producers of maize worldwide. However, this production is threatened due to low soil fertility, especially low levels of potassium (K+). K+ is one of the most important nutrients in plant metabolism, acting on enzymatic activation and also on photosynthetic processes. The identification of its deficiency by using traditional methods is difficult with regard to timely restoration of the nutrient to adequate levels. Therefore, the use of low-cost modified cameras attached to Unmanned Aerial Vehicles (UAVs) are important tools for agricultural monitoring. Nevertheless, there are no reports of studies with the purpose of evaluating the monitoring of K+ deficiency in maize crops using multispectral images captured from UAVs. Therefore, this study aimed at exploring the possibility of identifying K+ deficiency and quantifying the nutrient leaf content by using a Vegetation Index (VI). The experiment was carried out at the National Soybean Research Centre (Embrapa Soja, a branch of the Brazilian Agricultural Research Corporation). The experimental plots were constantly managed in order to obtain different conditions of K+ availability to plants, achieving levels that ranged from severe deficiency to an adequate nutrient level. The following treatments were established: severe potassium deficiency (SPD), moderate potassium deficiency (MPD) and adequate supply of potassium (ASP). The evaluations were performed in the Brazilian maize crop referred to as 'safrinha, at the V7, V12 and R3 developmental stages, with image capture covering the visible and near-infrared region, using two Fujifilm IS PRO digital cameras attached to an UAV. In these development stages, leaves were collected to determine tissue K+ concentration. The images were radiometrically corrected with the support of calibration targets and reference values, using an Fieldspec 3 Jr. spectroradiometer. The VIs comprised the ratio among the red, green and infrared spectral bands, that is, green normalized difference vegetation index (GNDVI), normalized difference vegetation index (NDVI), ratio between infrared and green (GRVI), ratio between green and infrared (GNIR), ratio between red and infrared (RNIR) and ratio between infrared and red (RVI). Regarding all the treatments assessed, the results showed that foliar K+ was statistically different. The VIs were efficient only in differentiating SPD and ASP treatments at all development stages evaluated. However, none were statistically significant for MPD. The linear regressions showed a high coefficient of determination (R2) and low root mean square error (RMSE) value; the best prediction of K+ concentration obtained was at V12 for regressions with these VIs: GRVI (R2 = 0.79, RMSE 4.50 g kg−1) and RVI (R2 = 0.71, RMSE 4.39 g kg−1). The grain yield values showed that SPD caused an average reduction of 5,645.90 kg ha−1 in relation to the ASP. Considering MPD, the grain yield was 1,242.00 kg ha−1 lower in comparison with ASP. In conclusion, estimating foliar K+ content and identifying its deficiency in maize crops based on the VIs of multispectral images from cameras attached to UAVs is possible, which ensures agility to these evaluations in a non-destructive manner, improving efficiency of K+ fertilization and providing farmers with a new tool. [ABSTRACT FROM AUTHOR] more...

An approach to monitor vegetation without the influence of cloud cover and employing Synthetic Aperture Radar (SAR) data was developed by the DPSVI (Dual Polarization SAR Vegetation Index), based on Sentinel-1 mission data. However, DPSVI performance in areas of dense vegetation and variation in vegetation classes is unknown. Thus, this paper aimed to investigate the performance of DPSVI in the monitoring of vegetation in the Brazilian Atlantic Forest biome, as well as to propose modifications to improve its capacity to monitor the vegetation and investigate seasonal and spatial influences on the performance of the proposed index (DPSVIm). Three approaches were adopted: 1) use Sentinel-1 and Landsat 8 scenes obtained from four hydrological years (2015–2016 to 2018–2019), to compute monthly DPSVI, DPSVIm, NDVI (Normalized Difference Vegetation Index), and EVI (Enhanced Vegetation Index) indices, to investigate seasonal influences; 2) perform topographic correction in Landsat 8 and Sentinel-1 data to verify the influence of relief on DPSVIm performance; and 3) confronting DPSVIm with an attribute of Atlantic Forest fragments: the above ground biomass (AGB). The scenes were processed on the Google Earth Engine platform. The DPSVIm was able to better distinguish the vegetation, compared to the DPSVI, mainly in forest planting areas and Atlantic Forest, which evidences the proposed improvements. The rainfall regime made the DPSVIm performance seasonal in comparison with the NDVI and EVI (Normalized Difference Vegetation Index and Enhanced Vegetation Index), presenting better agreements in the dry season and worse in the rainy season. There was a greater agreement between DPSVIm and EVI in the results to monitor dense vegetation since EVI does not saturate as NDVI does. Finally, the DPSVIm has a good fit to AGB, being able to distinguish different levels of AGB from 50 to 200 Mg ha, approximately. The study allowed improving the DPSVI making the DPSVIm adequate to quantify vegetation in the study area, even considering the effects of topography and rainfall regime. The latter environmental factor should be addressed in future studies, searching to mitigate this source of temporal uncertainty in DPSVIm. [ABSTRACT FROM AUTHOR] more...

The agricultural land use combined with agronomic management practices shall be structured on sustainable practices, guaranteeing both the maximization of productivity and environment preservation. NDVI (Normalized Difference Vegetation Index) time-series has been recognized as a useful methodology to monitor crop development and its spatial distribution. However, there is always a trade-off between spatial and temporal resolutions in satellite data. Hence, high spatial and temporal resolutions from Planet CubeSat represent a possibility to overcome this trade-off. This paper investigated the potential of using high spatial resolution daily NDVI-time-series from Planet CubeSat images for crop monitoring. One hundred nineteen images from 2017, at 3 m ground sampling distance, over cotton, spring corn and winter wheat fields, were acquired and converted into NDVI. The harmonic analysis of time series (HANTS) algorithm was applied to obtain a smoothed cloud and gap-free daily time-series. The 3 m daily time-series were resized to daily 9 and 30 m resolution; and resampled to temporal resolutions at 4, 8 and 16 days intervals to assess the impact of spatial and temporal resolution on NDVI time-series. NDVI time-series were evaluated by their minimum, maximum, average and coefficient of variation across the year. Principal component analysis (PCA) and the stepwise procedure were applied to assess optimum features (days across the year) to assist the NDVI-time-series interpretation. PCA and stepwise highlighted the best time across the year for NDVI-time-series interpretation. As the spatial resolution decreases, the range of NDVI and its standard deviation within field also decreases, leading to loss of within field spectral variability. At daily temporal resolution, slight differences in crop development can be detected in a very short time interval, but as the temporal resolution decreases the changes in crop development are detected at larger rates. The high temporal and spatial resolutions from Planet CubeSat images demonstrated great potential to monitor agricultural systems and can subsidize, on forthcoming research, the local and regional monitoring of agricultural areas and contribute to better management regarding strategic planning of governmental and corporate decision making over technical issues. [ABSTRACT FROM AUTHOR] more...

Normalized Difference Vegetation Index (NDVI) time series data are used by agricultural agencies for many essential operational crop monitoring programmes. But optical sensors often miss key growth stages due to cloud cover interference, impacting the performance of operational activities. Although the synergistic use of optical and Synthetic Aperture Radar (SAR) imagery can provide time series data, any SAR-optical integration necessitates building a relationship between these two data sources. The objective of this study was to use a semi-empirical Canopy Structure Dynamics Model (CSDM), Growing Degree Days (GDD), and SAR parameters calibrated to optical NDVI to derive daily estimates of canola crop condition over an entire growing season. RADARSAT-2 Fine Quad-pol and RapidEye images were collected over three years for a study site in western Canada. Object-based image analysis was applied to study the relationship between the optical and SAR time series data. Significant correlations were documented between a number of SAR parameters and optical NDVI, specifically a ratio of backscatter intensities (HH-HV)/(HH+HV), a ratio of volume to surface scattering extracted from the Freeman Durden decomposition, and Entropy from the Cloude-Pottier decomposition. Correlations (r-values) between these SAR parameters and optical NDVI ranged from 0.63 to 0.84 for the three years of data. Based on this analysis, a simple statistical model was used to relate SAR parameters to optical NDVI, creating a SAR-calibrated NDVI (SARcal-NDVI). A CSDM was fit to the SARcal-NDVI for each canola field, constructing a temporal vegetation index curve which captured canopy development from emergence to senescence. Coefficients of determination (R2) were 0.87 0.86 and 0.82 for entropy, the volume-surface scattering ratio, and the ratio of backscatter intensities (HH-HV)/(HH+HV), respectively, demonstrating a good model fit. The CSDM describes well the temporal evolution of SARcal-NDVI. Using the CSDM, SARcal-NDVI and GDD, the canola condition can be estimated for any given day in the growing season. In fact when the CSDM was used to estimate SARcal-NDVI for the exact days of RapidEye acquisitions, correlations with optically derived NDVI were high. The strongest correlations with RapidEye NDVI were reported for the volume-surface scattering ratio (R2 of 0.69 and RMSE of 0.15). The SARcal-NDVI estimated from the CSDM was also physically meaningful. Field-based biomass was significantly correlated (R2 of 0.79) with the SARcal-NDVI calculated using the volume-surface scattering ratio. Although further research is needed to extend this method to other crops, these results demonstrate that SAR data can be used to estimate vegetation conditions and when coupled with a CSDM, integrated into current monitoring operations based on optical NDVI. As a next step, the research team will be assessing SARcal-NDVI in a national operational programme which reports on crop yields using modelling with optical-based NDVI. [ABSTRACT FROM AUTHOR] more...

The longest Normalized Difference Vegetation Index (NDVI) time series produced from the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) has ended in 2017. At some point in the near future, all AVHRR sensors will be retired. To maintain continuity and consistency of this global data set, it is imperative to extend NDVI from other sensors, especially the operational Visible Infrared Imaging Radiometer Suite (VIIRS), which planned to maintain continuity at least through 2038. NDVI could be de-composited into two components: (1) the multi-year climatology and (2) the vegetation condition index (VCI). The former contains climate information and a majority of sensor noise, and the latter contains weather information and residual sensor noise. With the assumption that VCI from different sensors are similar, we re-composited the cross-sensor/cross-production NDVI with original VCI and the cross-sensor/cross-production climatology, and compared various cross-converted datasets with the three base NDVI datasets: two NDVI productions derived from AVHRR observation and another from VIIRS observation. As a result, the re-composited NDVI agrees well with the target base NDVI spatially and temporally, with an accuracy of 0.02 NDVI unit at a global scale. The comparison with several regression approaches distinguish the superiority of the new re-compositing approach. [ABSTRACT FROM AUTHOR] more...

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. [ABSTRACT FROM AUTHOR] more...

The potato (Solanum tuberosum L.) is one of the most important food crops and is consumed by more than a billion people worldwide. Despite the fact that crop growth models are widely used to optimize crop management and agricultural decision-making, they fail to provide spatial information, whereas satellite-based methods do, and in a cost-effective manner. In order to improve satellite-based crop yield prediction models, we developed a methodology based on Sentinel-2 (S2) data using Machine Learning (ML) techniques. In addition, we included and compared the commonly used Normalized Difference Vegetation Index (NDVI) to a newly developed spectral indicator, the Potato Productivity Index (PPI). Our results showed the capacity of our ML models to predict potato yield with great accuracy (coefficient of determination (R2) = 0.77 and Root Mean Square Error (RMSE) = 15.42%), using S2 bands 2, 3, 4, 5, 8A, and 9, as well as the PPI index. This index aims to provide information concerning plant photosynthetic activity as well as its water stress. The Random Forest (RF) model using the S2 bands and the PPI index as predictors obtained better predictive results (R2 = 0.77, RMSE = 15.42%) for potato crops than using S2 bands and NDVI (R2 = 0.66, RMSE = 16.88%). This study demonstrates the suitability of our models to predict potato yields in the region studied and improves on a previous approach presented by the same authors in terms of model interpretability and inter-annual variability. [ABSTRACT FROM AUTHOR] more...

Herein, a random-forest-based Kriging downscaling method (RFRKriging) was tested to increase the spatial resolution of Landsat 8 thermal-infrared remote sensing (TIRS) brightness temperature (Tb) over the central areas of Guangzhou, China. The 240-m Landsat 8 Tb was upscaled from the resampled 30-m Landsat 8 Tb by using the spatial averaging method. Tb was downscaled based on its relationship to spectral reflectance in visible/near-infrared and shortwave infrared bands, spectral indices, including normalized difference vegetation index (NDVI), normalized difference building index (NDBI), and modified normalized difference water index (MNDWI), digital elevation model (DEM), longitude, latitude, impervious surface, vegetation, and soil fractions. The linear regression Kriging (LRKriging) and NDVI-based thermal sharpening algorithm (TsHARP) regression Kriging (TsHARPKriging) were also implemented. Relative to the reference data from the resampled Landsat 8 Tb, the RFRKriging produced satisfactory downscaling results with respect to the maximum coefficient of determination (R2), the minimum root-mean-square error (RMSE), and the minimum ratio between RMSE and the correspondent Landsat 8 Tb standard deviation (RSD). RFRKriging captured Tb differences over roads, forests, water bodies, and buildings and enhanced the spatial details of downscaled Tb in all urban areas. Furthermore, integrating the multi-type predictor variables from 10-m Sentinel-2A and 30-m Landsat 8 image sets, RFRKriging was further applied to downscale 30-m Landsat 8 Tb to 10 m. Then, it was upscaled to 30 m and assessed with the original 30-m Landsat 8 Tb. RFRKriging was an effective and practical method for increasing the spatial resolution, with a low RSD of 0.254, a low RMSE of 0.525 K and a high R2 of 0.935. The RSD of less than 0.5 indicates that RFRKriging could achieve an acceptable downscaling accuracy. Furthermore, RFRKriging enhances the spatial details of a 10-m Tb in complex urban environments while maintaining the intrinsic information and spatial patterns of a downscaled Tb. Due to the unavailability of the 30-m and 10-m Tb truth, this study may only provide a quantitative assessment from some theoretical experiments. Also, a comparison of the RFRKriging downscaled Tb with the resampled Landsat 8 Tb was performed. The results suggest that the proposed RFRKriging downscaling method shows better downscaling performance with more spatial details than the cubic convolution method applied by the National Aeronautics and Space Administration (NASA) Centre. [ABSTRACT FROM AUTHOR] more...

Retaining a residue cover (RC) over the soil is the key to ensure its health. Since tropical agriculture systems, mostly in sugarcane, are characterized by intensive management, the discrimination of residue types becomes difficult and aims to be detected. Our goal was to identify features regarding sugarcane residues using hyperspectral and simulated Sentinel-2 dataset. Therefore, we collected green leafs (GL), straw (ST) and burn material (BM), added to two classes of contrasting soils, Ferralsols (S1) and Arenosols (S2). We developed control samples (GL, ST, BM, and soil) and also treatments with a combination of soil and RC with different dosages. We used the FieldSpec (350 to 2500 nm) to obtain the spectral data to each sample. The spectral data were subsequently used for the simulation of Sentinel-2 bands. GL presented features related to water absorption (1400 and 1900 nm) and pigments in the visible region. ST does not present features relating to pigments, neither water absorption. Due to the increase of BM, we observed a decreasing in the intensity of reflectance. Regardless the type of RC, soil features (kaolinite and mineralogy) were lost when more than 70% coverage was reached. The soil background affects the reflectance in all treatments, brighter soils influenced the spectral in the vis region. Sentinel-2 presented a strong positive correlation in the RedEdge (band 5) to assess green vegetation, in the Short-wave infrared (SWIR) to non-photosynthetically active material and in the blue band to BM. The Cellulose Absorption Index (CAI) combined with Normalized Difference Vegetation Index (NDVI) and reflectance in the red could assist in distinction of the tree RC in mixed systems. We provided an interesting spectral analysis that could assist in futures studies to improve indices on the task for soil management. [ABSTRACT FROM AUTHOR] more...

Hyperspectral reflectance is becoming an increasingly popular source of information for plants' physio-chemical status. In this study, net photosynthetic rate (A) and the activity of four antioxidant enzymes of Phaseolus vulgaris L., superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX) and guaiacol peroxidase (GPX), were compared with hyperspectral data using regression methods (principal component regression (PCR) and partial least squares regression (PLSR)) and vegetation indices (normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), water index (WI) and the normalized reflectance (R) ratio R710/R750). The experiment was conducted on potted plants of two common bean genotypes ('Blue lake' and 'Terli'), receiving four treatments (control, drought, salinity and drought + salinity), in an unheated greenhouse. The results suggest that both types of abiotic stress have a significant impact on the photosynthetic operation and the function of the antioxidant mechanism. The two regression methods performed well in predicting photosynthetic rate (A), while a strong correlation between A with the two vegetation indices was also revealed. Antioxidant enzymes appeared to be correlated with the WI (GPX and SOD enzymes) and the normalized reflectance (R) ratio R710/R750 (APX and SOD enzymes). Hyperspectral data seem to be particularly useful for fast and non-destructive assessment method for photosynthesis and enzymes activity of common bean plant. [ABSTRACT FROM AUTHOR] more...

The potentiality and specificity of using multitemporal stacks of thermal infrared (TIR) satellite scenes for modelling changes in tundra land cover and predicting geohazards in areas under development are studied using the example of two areas in northern West Siberia. The study aims at developing and testing a method for relative radiometric normalization of Land Remote-Sensing Satellite (Landsat) TIR images applied to estimate changes in land surface temperature (LST) in the tundra with regard to the high-latitude climate conditions. The suggested normalization method (RNUC) differs from other similar approaches in the use of unsupervised classification to determine pseudo-invariant features where master and slave images are spectrally identical. The error in LST contrasts is minimized iteratively by choosing the optimal number of land-cover classes and linear regression coefficients by cross-validation. The normalization procedure ensures error reduction by a factor of two to four, which is evidence of its high performance. The results of processing and analysis of 19 pairs of Landsat thermal images at the sites of the Marre-Sale and Urengoy oil and gas-condensate fields using the RNUC method showed that the relative error of LST contrast normalization could be in the range of approximately 3% to 10%. Based on the time series of normalized values of LST and the normalized difference vegetation index (NDVI) of sites with pyrogenic damage in the area of the Urengoy oil and gas-condensate field, it is shown that the process of stabilization of the tundra cover is characterized by a negative correlation of these parameters and lasts 25 to 28 years. A positive correlation between LST and NDVI characterizes a different type of landscape transformation; in particular, it may be an increase in the thickness of secondary vegetation cover or a change in the hydrological conditions. [ABSTRACT FROM AUTHOR] more...

Foliar Nitrogen (N) and Carbon (C) are two vital leaf biochemical components that can indicate forest health. In this study, the spatial distribution of foliar N and C was mapped using Sentinel-2 data in a tropical moist deciduous sal (Shorea robusta) forest of northwest Himalayan foothills of India. Empirical relationships were established between satellite data-derived spectral indices, band reflectance and ground measured foliar N and C using machine learning algorithm (MLA). Performance of MLAs viz. Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM) were assessed. Using all the independent spectral variables, RF performed better than ANN and SVM in explaining the variation in foliar N and C. RF was further used to optimize the independent variables for identifying the best predictor variables. It was found that foliar N is strongly related to shortwave infrared-1 (SWIR-1) band and Normalized Difference Red Edge Index (NDRE). Foliar C was found to be strongly related to SWIR-1 band and spectral vegetation indices: Green Normalized Difference Vegetation Index (GNDVI), Green Red Vegetation Index (GRVI), Green Chlorophyll Index (GCI), and Ratio of Modified Chlorophyll Absorption in Reflectance Index and Optimized Soil Adjusted Vegetation Index (MCARI/OSAVI). Using these best predictor variables, the spatial variability of foliar N and C was mapped using RF algorithm. On validation of the predicted N and C with ground measured foliar N and C, R2 of 0.85 (RMSE = 0.04%) for N and R2 of 0.86 (RMSE = 0.26%) for C were observed. It can be concluded that MLAs have great potential to map the spatial variability of foliar N and C in the tropical forests using the broad bands of Sentinel-2 imagery. [ABSTRACT FROM AUTHOR] more...

Maps of burned area derived from Land Remote-Sensing Satellite (LANDSAT) system imagery may be more reliable than fire perimeter records based on ground observation or manual cartographic delineation. This study evaluates accuracy of LANDSAT-derived burned area maps associated with 19 fires (65 to 86,776 ha) within shrublands of southern California in the period 1996–2018. High spatial resolution aerial images collected soon after these 19 fires were used to verify burned fractions within LANDSAT ground resolution elements. Validated burned fractions were used to optimize classification thresholds applied to burn severity metrics based on LANDSAT Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Normalized Difference Vegetation Index (NDVI). The burn severity metrics included post-fire spectral index data (e.g., NBR), pre-fire to post-fire differences (e.g., dNBR), and relative differences (e.g., RdNBR). Optimized classifications of dNBR had the greatest overall accuracy (94%) compared to dNBR2 (93%) and dNDVI (92%). Classifications based on standard difference metrics yielded burn area maps that were 1% more accurate than those based on relative-difference metrics for each spectral index. Classification products based solely on post-fire imagery were 85% to 90% accurate, depending on the spectral index utilized. In comparison, perimeter data from the California Fire and Resource Assessment Programme (FRAP) and Monitoring Trends in Burn Severity (MTBS) substantially overestimated burned extent (by about 44%). The MTBS severity maps excluding the 'unburned/low severity' class slightly overestimated burned extent (11%). Commission error in the FRAP data set was attributed to low cartographic detail and inclusion of internal unburned patches. Site-specific differences in unburned soil and vegetation fractions (within partly-burned areas) correlated strongly with overestimation of burned area in the optimized LANDSAT-derived maps; areal overestimates of 10%–15% resulted from data grain size rather than map commission error. The main innovation presented in this study is an empirical method to predict dNBR thresholds based on local (pre-fire) NDVI mean and variance, which produced burned area maps of 90% median accuracy. This LANDSAT-based method could support an efficient reconstruction of fire history in recent decades, which would be more comprehensive and accurate than available fire records for southern California. [ABSTRACT FROM AUTHOR] more...

An accurate estimation of soybean yield while the plants are still in the field is highly necessary for industry applications and decision-making policies related to planning. Remote sensing is a powerful tool, due to its spatio-temporal coverage, for developing empirical models to predict and evaluate crop yields at regional and national scales. In Argentina, soybean (Glycine max (L.) Merr.) is the most important crop, particularly in Córdoba province the 89% of the sown area and 88% of the production is concentrated in eleven departments. The objectives of this work were to evaluate the performance of three vegetation indices: Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Differential Water Index (NDWI), from Moderate Resolution Imaging Spectroradiometer (MODIS), to explain the anomalies in the soybean yield at department-level in Córdoba, and to develop regressions models for estimate this variable using anomalies of these indices and average crop yield, considering time series of historical records and different sources of data. The results showed that the anomalies of the three vegetation indices fit, with very good precision, the anomalies of soybean yield (Pearson correlation coefficient values from 0.71 to 0.85). The evolution of the NDVI anomalies of mid-season crop development stage, for all periods considered, showed a similar pattern to yield anomalies, particularly differentiating years where droughts or highest soybean yields occurred, independently of data sources used. The regression models estimated soybean yield with NDVI anomalies, obtained prior to harvest, with % RMSE values between 8% and 17%. These simple and versatile models show that using free MODIS data, we can produce reasonable real-time estimates of soybean yield at department-level without previous crop classification. [ABSTRACT FROM AUTHOR] more...

The suitable thermal environment is a prerequisite for biological survival. In order to evaluate the change of land surface temperature (LST) and its effect on organisms, in this study, the Land satellite (Landsat) series data was used to invert the LST from 1988 to 2018 in Tianjin. The main surface features were extracted from the remotely sensed images using normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI) and normalized difference impervious surface index (NDISI). A multi-scale temperature grading method based on biological heat reaction was proposed to classify the LST. In terms of the suitability of the organism to temperature, the land surface temperature suitability (LSTS) model was established based on the niche suitability analysis method, and the suitability of organisms to temperature was evaluated based on the model. In the aspect of land area change, both the land surface temperature area (LSTA) model and the average relative contribution (ARC) were proposed to comprehensively evaluate the impact of the area change of surface features on LST. The results indicated that the temperature suitability of most organisms had decreased. The change of LST was related to the comprehensive change of surface features. In conclusion, the rise of LST had adverse effect on most organisms in Tianjin. Limiting the occupation of water area and cultivated land may inhibit the continuous rise in LST. Besides, the evaluation method novelty proposed in this study could provide new ideas to study the development trend of spatial objects in their environment thus would be applied widely. [ABSTRACT FROM AUTHOR] more...

In this study, we investigated the potential of using decision tree Machine Learning (ML) algorithm and profiles of vegetation and moisture indices extracted from Land Remote-Sensing Satellite (System, Landsat) time series to identify the ages of rubber plantations in Thalang district, Phuket province, southern Thailand. The secondary Land Use and Land Cover (LULC) data and historical imagery from Google EarthTM were used to distinguish plantation boundary and the establishment year of rubber plantation (T0). The inter-annual profiles of spectral indices for each rubber plantation were obtained from 129 Landsat images (summer period from October 1991 to April 2018). The predictors were generated from summary distribution values of spectral indices during summer, including their difference and ratio at two years before to six years T0 for Recursive Partitioning (RP) supervised classification algorithm. Modelling dataset from 'known T0' plantations was divided into the training and testing datasets with a 60:40 ratio. The training model was 30 times repeated training while cross-validation assessment was tested to optimize an appropriated hyperparameter based on F1 score. Then, the best performance training model was applied to both modelling and predicting ('unknown T0' plantations) datasets. The predicted T0 for each plantation was selected based on results aggregation of 100 times repeated prediction. The results show that the RP model with a complexity parameter as 0.01 and the predictors generated only from Normalized Difference Vegetation Index (NDVI) profile acceptable achieves an accuracy of 84.4% and 84.7% for lowland and highland modelling datasets, respectively. The root means squared error (RMSE) of predicted establishment year were 0.83 years at plantation scale. [ABSTRACT FROM AUTHOR] more...

Solar-Induced chlorophyll Fluorescence (SIF) is associated with vegetation canopy photosynthesis and is potentially used to retrieve Gross Primary Productivity (GPP). However, the coarse resolutions of the currently available SIF satellite data limit their applications. To expand the applicability of the SIF dataset, a framework was developed to disaggregate the Global Ozone Monitoring Experiment-2 (GOME-2) SIF dataset, which was based on statistical relationships between SIF and remotely sensed measurements of the Normalized Difference Vegetation Index (NDVI), the fraction of absorbed photosynthetically active radiation (fPAR), the soil moisture index and Land Surface Temperature (LST). The statistical relationships were established within a zone of n × n pixels (n∈[1, 25]) with a moving window technique. The regression function established within n × n pixels with the smallest Root Mean Square Error (RMSE) and highest coefficient of determination (R2) was selected for downscaling regression. Compared with the fixed window technique (n = 5) and theglobal regression model, the moving window technique presented low residuals and high R2 values. Validated with flux-tower eddy covariance measurements, the GPP retrieved within the downscaled SIF data shows the potential to improve vegetation GPP prediction, and the downscaled SIF could trace the seasonal phenology of evergreen forests. [ABSTRACT FROM AUTHOR] more...

China has experienced rapid urbanization over the past decades, which has changed the physical environment of its urban areas. Based on Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature observations from the Google Earth Engine, this study focuses on the difference in daytime and night-time temperature between the city centres and the annual expansion areas of China's 34 municipalities, defined as the surface urban heat difference (SUHD), from 2002 to 2013 considering both summer and winter. Our result showed that the land surface temperature in the urban expansion areas in nearly all cities was lower than those in the city centres in three out of the four periods except for winter days. For temporal characteristics, the largest SUHD occurred in the winter night-time, followed by the summer night-time, summer daytime and winter daytime. Then we revealed spatial characteristics of SUHD on city and urban expansion region level. Cities were grouped into two major clusters based on the average temperature difference between the city centres and the urban expansion areas, exhibiting significant spatial heterogeneity. SUHD of moister cities mostly ranged from 0°C to 2°C in four different times while that of dryer cities distributed from −2°C to 4°C. Generally, the SUHD of cities with moist climates was stronger in the day but weaker at night and decreased more rapidly after land cover was urbanized. When comparing the SUHD in different expansion years, our results showed that areas that had developed earlier had a weaker SUHD. Normalized Difference Vegetation Index (NDVI) difference between urban centre and urban expansion regions and moisture were significant indicators effecting the SUHD. [ABSTRACT FROM AUTHOR] more...

We consider the relationship between leaf area index (LAI) and normalized difference vegetation index (NDVI) for green-leaf vegetation from a subarctic study site, specifically to test whether relationships optimized for lower-latitude vegetation can be assumed to hold at higher latitudes. We focus attention particularly on dwarf-shrub vegetation, which has received little previous investigation. We have collected hyperspectral measurements of the optical properties (reflectance and absorptance) of single leaves from dwarf shrub and tree species common to northern European Russia, and have developed a simple physical model of the properties of assemblages ('leaf stacks') of these leaves. The model is shown to provide a satisfactory explanation of the effect of varying the number of leaves in a stack on its NDVI, and can be easily adapted to make simple measurements using relatively inexpensive equipment. Our results show that the LAI-NDVI relationship for a vegetation canopy will saturate (approach within 10% of its limiting value) when the LAI reaches a value of around 2 to 3. Values this low are not uncommon in subarctic vegetation. It is also shown that dwarf shrub vegetation may show lower NDVI than trees for the same LAI. [ABSTRACT FROM AUTHOR] more...

Snow cover has a major influence on the global energy balance through the reflection of shortwave solar radiation as well as influencing ecological processes and human activity. Numerous studies have found that snow cover extent (SCE) is decreasing in the Northern Hemisphere and this decline appears to be influencing temperatures and might be a major factor in the polar amplification. This research used satellite-derived Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover data (MOD10C2), Land Surface Temperature (LST) data (MOD11C3), and Normalized Difference Vegetation Index (NDVI) data (MOD13C2) to detect changes in SCE and its potential relationship to changes in land surface temperature and vegetation growth from 2000 to 2017 over northeastern North America. There is a lack of detailed research concerning these variables for northeastern North America. The data were composited into seasonal and annual (snow-year: September–June) groupings. Two different change analyses were undertaken: 1) significant change using the Mann–Kendall statistical analysis and 2) univariate differencing using three different time periods (3 years, 5 years, 8 years). A regression and correlation analysis was undertaken between SCE and LST and NDVI to determine the relationship between changing SCE and changes in LST and NDVI. Based on the Mann–Kendall statistical change analysis (p-value = 0.05) for the 16-day data (32-day data), the area of declining SCE was more than 12 times the area of increasing SCE (more than 5 times for 32-day data) with declines occurring in all seasons, most notably in fall, June and the entire snow-year. Based on the univariate differencing analysis, SCE declined more than increased 96% of the time. Based on the regression/correlation analysis, SCE explains variability in LST (NDVI) for the snow-year: 43% (51%), spring: 31% (22%), June 34% (no significant relationship), fall: 40% (no significant relationship), and winter with no significant relationship (30%). It was determined that there is a weak to moderate inverse relationship between SCE and LST and a similar, but less prominent relationship between SCE and NDVI. A multiple regression/correlation with SCE and LST (independent) and NDVI (dependent), LST was a better predictor of NDVI than SCE. This relationship indicates that there is a potential positive feedback mechanism warming the region and increasing the region's NDVI. [ABSTRACT FROM AUTHOR] more...

Recently, availability of valuable high spatial and temporal resolution optical satellite imagery such as Sentinel-2 has provided a great opportunity for a wide variety of crop-related studies including crop phenology detection, crop biophysical and biochemical parameter estimation, and crop-type mapping. Most current crop mapping methods focus on supervised classification, demanding a large number of field sampling points to feed the classifier. In this paper, we propose an automatic method for silage maize mapping based on Sentinel-2 time-series data in three agricultural areas in Iran (namely Marvdasht, Abyek, and Mashhad) and an individual area in USA (Tulare County, California). We considered Marvdasht as the training site and other three sites as the test sites. After the preprocessing step, temporal profiles of the Normalized Difference Vegetation Index (NDVI) were computed from Sentinel-2 time-series images in 2017 and 2018. Then, the Savitzky–Golay filter was applied to the NDVI profile for noise reduction. Linear interpolation was also used for the construction of continuous NDVI time-series during the maize growing season. Then, phenological parameters were extracted in Marvdasht, for maize and other crops. After comprehensive analyses, the ratio of the slope from the peak of the greenness to the harvest (SPGH) to the length of the growing season (LOS) was suggested as an appropriate variable for automatic maize detection. Evaluation of this variable in the study sites confirmed its feasibility for automatic maize mapping, with the kappa coefficient values of 0.89, 0.8, 0.9, and 0.8 in Abyek, Marvdasht, Mashhad, and Tulare in 2017, respectively. The performance of the suggested method for the second year (2018) is close to those of 2017 which means that maize detection algorithm results are consistent and robust across years. Moreover, to ascertain high potential of the proposed variable, the results were compared with those of the maximum likelihood (ML) and support vector machine (SVM) classifiers in the test sites. The performance of SVM (with a large number of training samples) was analogous to that of the suggested automatic method in terms of accuracy; however, inferior performance of ML was evident. The results of this research demonstrated the great potential of our proposed phenological method in automatic silage maize mapping. [ABSTRACT FROM AUTHOR] more...

Rice is the most important food crop in Taiwan, directly feeding more than 23 million people in the country. Information on rice production is thus crucial for crop management and food policymaking. This study aims to develop a machine learning (ML) approach for predicting rice crop yields in Taiwan using time-series Moderate Resolution Imaging Spectroradiometer (MODIS) data. We processed the data for the period from 2000 to 2018, following three main steps: (1) data pre-processing to generate smooth time-series Normalized Difference Vegetation Index (NDVI) data, (2) establishment of models for yield predictions using the heading date (HD) NDVI value, and the accumulated NDVI value of the dates from heading to maturity (DHM). The data from 2000 to 2017 were used for building predictive models using the random forests (RF) and support vector machines (SVM), leaving the 2018 data for model assessment, and (3) evaluation of model performance. The results compared with the government's yield statistics indicated good predictions, with the root mean square error (RMSE) and mean absolute error (MAE) values between 7.1% and 11.8%, and Willmott's index of agreement (d) values between 0.81 and 0.84 for the first crop, and 5.6% and 11.3% and d values between 0.91 and 0.95 for the second crop, respectively. A slight underestimation of yield predictions was observed for both crops, with the relative error (RE) values of −6.5% to −8.2% and −3.8% to −6% for the first and second crops, respectively. The results of regression analysis also confirmed a close agreement between these two datasets, with the correlation coefficient (r) higher than 0.84 (p-value <0.05), in both cases. Although some factors, including mixed-pixel issues, boundary effects, and cloud cover potentially affected the modelling results, our study demonstrated the effectiveness of ML methods for regional rice yield predictions from MODIS NDVI data in Taiwan. [ABSTRACT FROM AUTHOR] more...

In this study, we analyzed time series of a vegetation index to identify land-cover/land-use changes in HHK mountain regions administered by Pakistan. Monthly MODIS (Moderate Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) series were decomposed to retrieve changes in long-term mean, peaking magnitude and seasonal characteristics. Resulting linear trend patterns were used as a baseline to map syndromes over the study area. To address non-linear changes, inter-annual variation (IAV) and short-term variation (STV) was also computed. Distributed lag-models (DLM) were used to determine significant correlation between GIMMS (Global Inventory Monitoring and Mapping) NDVI (Normalized Difference Vegetation Index) and rainfall as an additional syndrome to highlight climate sensitive regions. Our results indicate that land use is mainly controlled by two factors: elevation and river network. Based on that, there is a particular spatial distribution of agricultural intensification because of deforestation, forest degradation from unsustainable use, forest regeneration and human settlements near rivers. Most of the trends observed showed a persistent greening pattern compared to small groups of pixels with negative trends. Outcomes of DLM do not provide plausible links between rain and forest biomass. It does however suggest a positive response of crops to rainfall events in the arid zones of the study area. High short-term fluctuation in EVI residuals occurred in areas that experienced considerable land modification and where land-use patterns are not stable. IAV was high in regions around rivers and water works and its impact on forest dynamics could not be substantiated. Land change patterns described here can be used by decision makers for forest restoration programmes. [ABSTRACT FROM AUTHOR] more...

An understanding of drought and land cover interaction plays a crucial role in vegetation vulnerability studies and land use planning. However, there is paucity of information on drought, land cover and land use interaction in southern Africa. We analysed the drought impact on land cover using Globcover land cover data and Vegetation Condition Index (VCI) for the 2015 to 2016 season. The 2015 to 2016 season was chosen because it was the worst drought in southern Africa since the 1980s. We developed a novel land cover 'social pixels' or 'village pixels' which represents rural communities. The Kruskal–Wallis test was used to evaluate whether there is a significant difference in drought impact among the land cover classes. The response of each land cover to drought impact was calculated by correlating Standardized Precipitation Evapotranspiration Index (SPEI) and Normalized Difference Vegetation Index (NDVI). Our results reveal that the evergreen forests and the flooded vegetation were the most severely affected by the 2015–2016 drought. However, the lowest VCI values were recorded within the village pixels land cover, indicating the vulnerability of rural communities to drought impacts. The vegetation response to drought impact ranged from 2 months (crops) to 8 months (flooded vegetation). With regards to drought recurrence (1998 to 2018), the crop and grassland land cover recorded the highest drought frequency whilst the forest had the least drought frequency. [ABSTRACT FROM AUTHOR] more...

Plant functional types (PFTs) have been widely used to represent the vegetation characteristics and their interlinkage with the surrounding environment in various earth system models. The present study aims to generate a PFT map for the Northwest Himalayan (NWH) foothills of India using seasonality parameters, topographic conditions, and climatic information from various satellite data and products using Random Forest (RF) algorithm in Google Earth Engine (GEE) platform. The seasonality information was extracted by carrying out a harmonic analysis of Normalized Difference Vegetation Index (NDVI) time-series (2008 to 2018) from Moderate Resolution Imaging Spectroradiometer (MODIS) Terra surface reflectance 8 day 500 m data (MOD09A1). For topographic information, Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) derived aspect and Multi-Scale Topographic Position Index (MTPI) were used, whereas, for climatic variables, WorldClim V2 Bioclimatic (Bioclim) variables were used. RF, a machine learning classifier, was used to generate a PFT map using these datasets. The overall accuracy of the resulting PFT map was found to be 83.33% with a Kappa coefficient of 0.71. The present study provides an effective approach for PFT classification using different well-established, freely available satellite data and products in the GEE platform. This approach can also be implemented in different ecological settings by using various meaningful variables at varying resolutions. [ABSTRACT FROM AUTHOR] more...

The Bayesian Maximum Entropy (BME) algorithm that can model larger-scale spatial heterogeneity and integrate multiple types of data is a better spatial estimation algorithm in avoiding the circular problem and improving the estimation accuracy of parameters than original empirical geostatistic and spatial statistic methods. To obtain higher-resolution and higher-accuracy soil moisture (SM) data, this study used the BME algorithm to integrate the multiple environmental factor variables related to SM as auxiliary data (11 data sets in total) such as the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and Albedo products with 1 kilometre (km) grid resolution from the Moderate-resolution Imaging Spectroradiometer (MODIS), the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) satellite data, and the Slope, Aspect, Plan curvature, Profile curvature, Surface roughness, Wetness index, Relief amplitude products generated from ASTER GDEM data. First, the FengYun 3-B satellite (FY3-B) SM product with 25 km grid resolution was downscaled to 1 km grid resolution using the visible, infrared (IR), and microwave fusion method. The downscaled FY3-B SM data and auxiliary data were used to generate the weighted probability soft data (SD) under four cases, case 1: 500 sample points of SD, case 2: 450 sample points of SD, case 3: 400 sample points of SD, and case 4: 350 sample points of SD. Particularly, we used two methods, the multivariable correlation analysis method and principal component analysis (PCA) method to get the weight values of environmental factor variables, i.e. NDVI, LST, Albedo, the Digital Elevation Model (DEM), Slope, Aspect, Plan curvature, Profile curvature, Surface roughness, Wetness index, and Relief amplitude with SM. Then, the in-situ SM measurements as hard data (HD) were used to calibrate the weighted probability SD in the procedure of BME SM estimation. The SM under four cases were estimated from the weighted probability SD and HD using BME algorithm. Finally, comparisons of the estimated SM using BME algorithm with in-situ SM measurements at maize study area were carried out in this study. Our results indicated that the accuracy of the estimated SM using BME algorithm, the root-mean-square error (RMSE) = 0.049 cm3 cm−3, the correlation coefficient (r) = 0.639, the unbiased RMSE (RMSEu) = 0.047 cm3 cm−3, and Bias = 0.002 cm3 cm−3, under case 1 based on PCA method was obviously better than the downscaled FY3-B SM (RMSE = 0.079 cm3 cm−3, r = 0.096, RMSEu = 0.069 cm3 cm−3, and Bias = 0.039 cm3 cm−3) that was generated by the visible, IR, and microwave fusion method. We concluded that integrating auxiliary data into SM estimation using BME algorithm could further improve the downscaled FY3-B SM that was generated by visible, IR, and microwave fusion method. [ABSTRACT FROM AUTHOR] more...

Accurate grassland aboveground biomass (AGB) estimation is crucial for effective grassland utilization. However, most current satellites cannot provide data with high spatial and temporal resolutions simultaneously. Spatiotemporal fusion models can combine the resolution advantages of different remote sensing data and support high-precision vegetation monitoring. In order to obtain accurate grassland AGB maps with high resolution in the Three-River Headwaters Region (TRHR) of China, we developed an estimation method based on the synthetic 30 m growing season averaged normalized difference vegetation index (GS-NDVI), which was fused from 30 m Landsat 8 Operational Land Imager (OLI) and 250 m Moderate-resolution Imaging Spectroradiometer (MODIS) NDVI data. To choose the optimal fusion model, we investigated the performances of three spatiotemporal fusion models for NDVI fusion, the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), and the rule-based piecewise regression tree model (RPRTM). The three models all produced reasonable NDVI predictions, with the coefficient of determination (R2) ranging from 0.58 to 0.86. RPRTM had the highest efficiency and was more suitable for large-scale spatiotemporal data fusion. Compared with the models generated from 250 m MODIS GS-NDVI, the AGB estimation models based on 30 m synthetic GS-NDVI were more accurate, demonstrating the effectiveness of our methods. The resulting AGB map of 30 m resolution provides spatially detailed AGB information that will be useful for regional ecosystem studies and local land management decisions. [ABSTRACT FROM AUTHOR] more...

Spectroscopy plays a key role in Earth observations, especially for studies involving vegetation function and structure. These measurements are critical in the context of carbon cycle monitoring from leaf to global scales. Reflectance-based vegetation indices (RIs) have been used extensively in remote sensing studies from the unpiloted aerial vehicle, aerial, and space-based platforms to model quantities related to productivity, such as gross primary production (GPP), while more recently chlorophyll fluorescence (CF) measurements are increasingly exploited to track GPP. CF and RI measurements vary in magnitude, depend on different portions of the spectrum, and are derived from unique equations; thus, instrument uncertainty manifests distinctly for these measurements. Although this is well known, it is often unexamined in experiments and analyses. We use a portable spectroradiometer to make measurements of reflectance-based vegetation indices (RIs) and chlorophyll fluorescence (CF) in order to characterize how measurements of RIs and CF compare to one another. In particular, we examine fluorescence (F) and fluorescence yield (FYield) under a light-emitting diode grow light (LED), solar-induced fluorescence (SIF), solar-induced fluorescence yield (SIFYield), absorbed photosynthetically active radiation (APAR), and reflectance-based vegetation indices (the normalized difference vegetation index (NDVI), the chlorophyll/carotenoid index (CCI), and the photochemical reflectance index (PRI)) and include maximized propagated uncertainty of the spectroradiometer for each measurement. We show that RIs have a significantly lower propagated error relative to the mean (0.01% to 0.28%) than CF measurements (0.01% to 1.28%) and that while fine resolution spectrometer CF measurements are outside the noise of the instrument and have potential to provide relative measurements of productivity, show why this instrument having fine spectral resolution and sampling is more effective for measurements of APAR and RIs. We also demonstrate that F and FYield measurements have low propagated uncertainty and propose that future studies of plant function using this spectrometer/LED technique and the full range of spectra be undertaken. Finally, measurements of SIF, F, and APAR can provide estimates of SIFYieldand FYield in the same order of magnitude, but further examination is required to determine how these measurements compare under a range of illumination and environmental conditions and how they might compare to PRI. [ABSTRACT FROM AUTHOR] more...

Conventional meteorological data and remotely sensed Normalized Difference Vegetation Index (NDVI) images can be proficiently combined to predict actual evapotranspiration (ETA) on different spatial and temporal scales. Up to now, however, the operational application of this approach in heterogeneous Mediterranean regions has found difficulty due to the insufficient spatial resolution of satellite sensors having high acquisition frequency (i.e. 250 m of Terra/Aqua Moderate Resolution Imaging Spectroradiometer, MODIS). The current study investigates the advantages brought for this objective by the recent availability of NDVI data taken from the Sentinel-2 MultiSpectral Instrument (MSI), which has a spatial resolution of 10 m. The investigation has been performed in two Mediterranean areas characterized by different spatio-temporal variability of vegetation cover. The first is a mountain coniferous forest, where such variability is low, while the second is a relatively small (around 10 ha) irrigated tomato field surrounded by other annual crops showing diversified growing cycles. An ETA estimation method based on NDVI data is applied in the two study areas and its performances are evaluated against ground references obtained through the elaboration of site measurements (i.e. meteorological, soil water content, and crop coefficient observations). The advantage of using MSI over MODIS NDVI images is marginal in the first case study, while is evident in the second. More particularly, such advantage is outstanding when the remote sensing method is applied in an operational mode, i.e. without using the information on the water supplied to the tomato crop by irrigation. This confirms that the utility of higher spatial resolution data is dependent not only on the fragmentation of the observed landscapes but also on the synchronicity of major vegetation growing cycles, which is influenced by both environmental and human-induced factors. [ABSTRACT FROM AUTHOR] more...