Normalized Difference Infrared Index Research Articles

A New Remote Sensing Index for Forest Dryness Monitoring Using Multi-Spectral Satellite Imagery

Canopy water content is a fundamental indicator for assessing the level of plant water stress. The correlation between changes in water content and the spectral reflectance of plant leaves at near-infrared (NIR) and short-wave infrared (SWIR) wavelengths forms the foundation for developing a new remote sensing index, the Infrared Canopy Dryness Index (ICDI), to monitor forest dryness that can be used to predict the consequences of water stress. This study introduces the index, that uses spectral reflectance analysis at near-infrared wavelengths, encapsulated by the Normalized Difference Infrared Index (NDII), in conjunction with specific canopy conditions as depicted by the widely recognized Normalized Difference Vegetation Index (NDVI). Development of the ICDI commenced with the construction of an NDII/NDVI feature space, inspired by a conceptual trapezoid model. This feature space was then parameterized, and the spatial region indicative of water stress conditions, referred to as the dry edge, was identified based on the analysis of 10,000 random observations. The ICDI was produced from the combination of the vertical distance (i.e., under consistent NDVI conditions) from an examined observation to the dry edge. Comparisons between data from drought-affected and non-drought-affected control plots in the Australian Northern Jarrah Forest affirmed that the ICDI effectively depicted forest dryness. Moreover, the index was able to detect incipient water stress several months before damage from an extended drought and heatwave. Using freely available satellite data, the index has potential for broad application in monitoring the onset of forest dryness. This will require validation of the ICDI in diverse forest systems to quantify the efficacy of the index.

Comparison of different spectral indices to differentiate the impact of insect attack on planted forest stands

Eucalyptus plantations are an important contributor to the South African economy and are geographically concentrated in the productive Zululand region. However, this area is becoming increasingly vulnerable to various forest disturbances such as insect attacks and drought. Information of the location, extent and duration of insect infestation is therefore crucial for sustainable timber production and forest management planning, and remote sensing approaches have been successful in the retrieval and analysis of such information along with field surveys. Sappi Forests frequently conduct field surveys to establish the nature of disturbances to improve the performance of trees, as these may be difficult to distinguish from other disturbances such as droughts. Great effort has been made to remotely detect the impact of G. scutellatus using single-date high-resolution commercial satellites, whose application is restricted by high costs. Here, we compare the performance of spectral indices for detecting the impact of G. scutellatus infestation in KwaMbonambi plantations using publicly available Landsat data and also apply anomaly detection framework to further test their performance for detecting the impact of insect damage on trees and other disturbances that occurred on the same compartment over time. Our results showed high performance of short-wave infrared (SWIR) band for detecting the impact of insect damage, followed by the normalized difference infrared index (NDII) and normalized difference vegetation index NDVI, when anomaly detection was applied. Without anomaly detection, the spectral indicators showed changes even to drought impact in 2015, which did not when anomaly detection was used. We believe that indices containing the SWIR would perform better for detecting anomalous changes, and further research is required to explore this. Anomaly detection only showed the impact of insect damage and clearcutting as anomalous changes. Overall, we demonstrated the benefit of identifying vegetation anomalies caused by the impact of insect damage and other disturbances using freely available Landsat time-series data, particularly in data-poor regions that have restricted computational resources.

A Method for Estimating Alfalfa (Medicago sativa L.) Forage Yield Based on Remote Sensing Data

Alfalfa (Medicago sativa L.) is a widely planted perennial legume forage plant with excellent quality and high yield. In production, it is very important to determine alfalfa growth dynamics and forage yield in a timely and accurate manner. This study focused on inverse algorithms for predicting alfalfa forage yield in large-scale alfalfa production. We carried out forage yield and aboveground biomass (AGB) field surveys at different times in 2022. The correlations among the reflectance of different satellite remote sensing bands, vegetation indices, and alfalfa forage yield/AGB were analyzed, additionally the suitable bands and vegetation indices for alfalfa forage yield inversion algorithms were screened, and the performance of the statistical models and machine learning (ML) algorithms for alfalfa forage yield inversion were comparatively analyzed. The results showed that (1) regarding different harvest times, the alfalfa forage yield inversion model for first-harvest alfalfa had relatively large differences in growth, and the simulation accuracy of the alfalfa forage yield inversion model was higher than that for the other harvest times, with the growth of the second- and third-harvest alfalfa being more homogeneous and the simulation accuracy of the forage yield inversion model being relatively low. (2) In the alfalfa forage yield inversion model based on a single parameter, the moisture-related vegetation indices, such as the global vegetation moisture index (GVMI), normalized difference water index (NDWI) and normalized difference infrared index (NDII), had higher coefficients of correlation with alfalfa forage yield/AGB, and the coefficients of correlation R2 values for the first-harvest alfalfa were greater than 0.50, with the NDWI correlation being the best with an R2 value of 0.60. (3) For the alfalfa forage yield inversion model constructed with vegetation indices and band reflectance as multiparameter variables, the random forest (RF) and support vector machine (SVM) simulation accuracy was higher than that of the alfalfa forage yield inversion model based on a single parameter; the first-harvest alfalfa R2 values based on the multiparameter RF and SVM models were both 0.65, the root mean square errors (RMSEs) were 329.74 g/m2 and 332.32 g/m2, and the biases were −0.47 g/m2 and −2.24 g/m2, respectively. The vegetation indices related to plant water content can be considered using a single parameter inversion model for alfalfa forage yield, the vegetation indices and band reflectance can be considered using a multiparameter inversion model for alfalfa forage yield, and ML algorithms are also an optimal choice. The findings in this study can provide technical support for the effective and strategic production management of large-scale alfalfa.

Influence of snowpack on forest water stress in the Sierra Nevada

Higher global temperatures and intensification of extreme hydrologic events, such as droughts, can lead to premature tree mortality. In a Mediterranean climate like California, the seasonality of precipitation is out of sync with the peak growing season. Seasonal snowpack plays a critical role in reducing this mismatch between the timing of water input to the root zone and the peak forest water use. A loss of snowpack, or snow droughts, during warmer years, increases the asynchrony between water inputs and the peak of forest water use, intensifying water stress and tree mortality. Therefore, we hypothesize that the montane vegetation response to interannual climate variability in a Mediterranean climate is regulated by the snowpack. We tested this hypothesis using the 2012–2015 drought as a natural experiment. Regional Generalized Additive Models (GAMs) were used to infer and quantify the role of snowpack on forest water stress. The models simulate the Normalized Difference Infrared Index (NDII) as a proxy of forest water stress using water deficit (as seasonality index), location, slope, and aspect. The GAMs were trained using 75% of the data between 2001 and 2014. The remaining 25% of the data were used for validation. The model was able to simulate forest water stress for 2015 and 2016 across the northern, central, and southern Sierra Nevada with a range of R2 between 0.80 and 0.84. The simulated spatial patterns in forest water stress were consistent with those captured by the USDA Forest Service Aerial Detection Survey. Our findings suggest that the failure of a reduced snowpack in mitigating water deficit exacerbates forest water stress and tree mortality. Variations in water and surface energy budget across an elevational gradient play a critical role in modulating the vegetation response. These results provide insights into the importance of the Sierra Nevada snowpack under a warming climate. The models can aid forest managers to identify future forest water stress and tree die-off patterns.

Precipitation, Vegetation, and Groundwater Relationships in a Rangeland Ecosystem in the Chihuahuan Desert, Northern Mexico

For this study, conducted in a semiarid (318 mm) rangeland setting in the Chihuahuan Desert region in northern Mexico, we evaluated the seasonal and interannual variability of precipitation, vegetation, and groundwater relations. Between 2012 and 2014, a series of soil and water conservation practices (e.g., land imprinting, contour furrows, and planting of native shrub species) were conducted in several areas within the 2500 ha study site. Since 2014, the site has been gradually instrumented to monitor several hydrologic variables, including rainfall, soil water content, and groundwater. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII) vegetation indices were used to evaluate vegetation conditions between 2007 and 2021, before and after the treatment. Soil water content and groundwater began to be monitored in 2014 and 2016, respectively. Study results show that NDVI and NDII values were higher in the years following the treatment. A negative trend in NDVI values was observed in the years before restoration and reversed in the post-treatment years. The relatively low levels of soil water content obtained every year followed a seasonal response to precipitation inputs characterized by a quick rise and decline at the 0.2 m depth and a more gradual rise and decline for sensors at 0.5 m and 0.8 m depths. A positive trend in groundwater levels has been observed since the onset of monitoring in 2016, with seasonal groundwater levels rising between 0.7 m and 1.3 m for most years, except for 2020, when levels dropped 1 m. The yearly recharge of the aquifer ranged between 102 mm and 197 mm. The conservation practices employed have positively affected the state of the rangeland ecosystem. The upward trends in NDVI, NDII, and groundwater levels observed in the post-treatment years were partly attributed to improved land conditions. The findings of this study contribute to the improved understanding of land use and environmental relations in summer precipitation-dominated rangeland ecosystems.

Mapping Invasive Aquatic Plants in Sentinel-2 Images Using Convolutional Neural Networks Trained With Spectral Indices

Multispectral images collected by the European Space Agency (ESA)'s Sentinel-2 satellite offer a powerful resource for accurately and efficiently mapping areas affected by the distribution of invasive aquatic plants. In this work, we use different spectral indices to detect invasive aquatic plants in the Guadiana river, Spain. Our methodology uses a convolutional neural network (CNN) as the baseline classifier and trains it using spectral indices calculated using different Sentinel-2 band combinations. Specifically, we consider the following spectral indices: with two bands, we calculate the normalized difference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference infrared index (NDII). With three bands, we calculate the red-green-blue (RGB) composite and the floating algae index (FAI). Finally, we also use four bands to calculate the bare soil index (BSI). In our results, we observed that CNNs can better map invasive aquatic plants in the considered case study when trained intelligently (using spectral indices) as compared to using all spectral bands provided by the Sentinel-2 instrument.

Response of crop water indices to soil wetness and vegetation water content

Hyperspectral sensors offer a unique opportunity to monitor crop stress through the multiple narrow bands from visible to short wave infrared wavelengths. It provides prominent water absorption bands in near and shortwave regions centered at 970 nm, 1200 nm, 1440 nm, 1664 nm, 1950 nm and 2250 nm, which are unavailable with conventional multispectral sensors. It gives bands which otherwise are not available with traditional multispectral sensors. However, the spectral regions of 1350–1480, 1800–2000 and 2350–2500 nm, has strong absorption of light due to water vapour and hence not useful. This study investigates the indices, namely the Water Band Index (WBI), Normalized Difference Water Index (NDWI), Normalized Difference Infrared Index (NDII) and Land Surface Water Index (LSWI), derived from the narrow bands of the AVIRIS-NG sensor for its response to varying degree of soil wetness and vegetation water content. The analyses showed that the WBI and NDWI could discern the saturated soil. At the same time, The NDII and LSWI displayed a better dynamic range because of the stronger absorption due to soil/vegetation liquid water content with reference to the base wavelength of 862 nm, which enables the depiction of the different degrees of soil wetness and vegetation water content. Since the water indices have varied responses to the same soil wetness and vegetation water content, a Water Indices vs Vegetation Index triangle space was used to derive the Water Condition Index (WCI). The analyses of the WCI’s indicate that the values of the water indices are normalized and have similar values under different soil wetness and vegetation water content. However, the WCIWBI had lower values among WCIs, and WCILSWI could demonstrate the variation in the soil wetness and vegetation water content owing to its better dynamic range.

Sentinel-2 Images Based Modeling of Grassland Above-Ground Biomass Using Random Forest Algorithm: A Case Study on the Tibetan Plateau

Accurate information on grassland above-ground biomass (AGB) is critical to better understanding the carbon cycle and conserve grassland resources. As a climate-sensitive key ecological function area, it is important to accurately estimate the grassland AGB of the Tibetan Plateau. Sentinel-2 (S2) images have advantages in reducing mixed pixels and the scale effect for remote sensing, while the data volume is correspondingly larger. In order to improve the estimation accuracy while reducing the data volume required for AGB estimation and improving the computational efficiency, this study used the Recursive Feature Elimination (RFE) algorithm to find the optimal feature set and compared the performance of the Cubist, Gradient Boosting Regression Tree (GBRT), random forest (RF) and eXtreme Gradient Boosting (XGBoost) algorithms for estimating AGB. In this study, ten S2 bands, ten S2-derived vegetation indexes, 218 pieces of AGB field survey data, four types of meteorological data and three types of topographic data were used as the alternative input features for the AGB estimation model. The impurity and permutation importance were used as the feature importance calculation method input to the RFE, and the Cubist, GBRT, RF and XGBoost algorithms were used to construct the AGB estimation models. The results showed that the RF algorithm based on the monthly average temperature (T), elevation, Normalized Difference Phenology Index (NDPI), Normalized Difference Infrared Index (NDII) and Palmer Drought Severity Index (PDSI) performed best (R2 = 0.8838, RMSE = 35.05 g/m2, LCCC = 2.44, RPPD = 0.91). The above findings suggest that the RF model based on the features related to temperature, altitude, humidity and leaf water content is beneficial to estimate the grassland AGB on the Tibetan Plateau.

A comparison of radar and optical remote sensing to detect cyclone-induced canopy disturbance in two subtropical forest landscapes

Optical remote sensing is a tool frequently used to assess cyclone-induced forest disturbances. However, the frequent cloud cover limits the availability of optical data in cyclone basins. On the other hand, radar remote sensing is not affected by cloud cover and has been used to detect windthrows. Yet, the potential of radar sensing in monitoring cyclone damages of varying magnitudes across forest landscapes remains unclear. Here, we compared radar remote sensing to optical remote sensing of four cyclone disturbances in the Fushan Experimental Forest of northern Taiwan and the El Yunque National Forest in Puerto Rico using Landsat 8 and C-band Sentinel-1 satellite data. We analyzed the change in two optical vegetation indices, EVI (Enhanced Vegetation Index) and NDII (Normalized Difference Infrared Index), and three radar-based metrics, co- and cross-polarized backscatters (VV, VH) and their ratio (Canopy Development Index, CDI) after cyclone disturbances and during approximately the same periods of non-cyclone years. We assessed the improved temporal resolution permitted by Sentinel-1 constellation on the detection of forest canopy disturbance. Bootstrapped comparisons indicated that both optical and radar indices detected canopy change, but their correlations were not significant. Improved temporal resolution of CDI allowed to distinguish cyclone-induced canopy change from the phenological variation and even change by nearby cyclones. Although this, VV and VH backscatters responded more closely to cyclone disturbances than their ratio. Our results demonstrate that the C-band backscatter intensities can track cyclone-induced change of forest canopies, and provide an assessment of C-band capabilities to monitor cyclone disturbances.

Unsupervised self-training method based on deep learning for soil moisture estimation using synergy of sentinel-1 and sentinel-2 images

ABSTRACT Here, we present a novel unsupervised self-training method (USTM) for SM estimation. First, a ML model is trained using the labeled and unlabeled data. Then, the pseudo-labeled data are generated employing the second model by adding a proxy labeled data. Eventually, SM is estimated applying the third model by pseudo-labeled data generated by the second model and unlabeled data. The final SM estimation result is obtained by training the third model. Subsequently, in-situ measurements are performed to validate our method. The final model is an unsupervised learning model. Experiments were carried out at two different sites located in southern Tunisia using Sentinel-1A and Sentinel-2A data. The input data include the backscatter coefficient in two-mode polarization ( and ), derived from Sentinel-1A, as well as the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Infrared Index (NDII) for Sentinel-2A and in-situ data. The USTM method based on (Random Forest (RF)- Convolutional neural network (CNN)-CNN) combination allowed obtaining the best performance and precision rate, compared to other combinations (Artificial Neural Network (ANN)-CNN-CNN) and (eXtreme Gradient Boosting (XGBoost)-CNN-CNN).

Intraseasonal interactive effects of successive typhoons characterize canopy damage of forests in Taiwan: A remote sensing-based assessment

Wind gusts and rainfall from tropical cyclones can heavily damage forest canopies, leading to abrupt changes in forest structure and tree demography. Although many studies have shown that successive tropical cyclones can interact with each other through residual effects, the role of past disturbances is unclear because they may lead to damage amplification of the second cyclone because of weakened forest structure, or damage reduction of the second cyclone because of previous damage to susceptible trees. We investigated the interaction between consecutive cyclones between 2001 and 2017 for five well-conserved forests in Taiwan, which experiences an average of 1.75 typhoons annually. Using MODIS imagery, we computed the typhoon-induced change of a canopy vegetation index, the Normalized Difference Infrared Index (NDII). The effects of successive typhoons were assessed separately for typhoons occurring within a single year (annual analysis) and within two consecutive years (biennial analysis). We used mixed effect models of reductions in NDII, a measurement of canopy damage, in relation to target and past typhoon characteristics and damage magnitude. NDII reduction induced by preceding typhoons was slightly more important and statistically significant in explaining the variation in NDII reduction associated with the target typhoon in the annual than in the biennial analysis, where the effect was non-significant. Canopy damage did not always decrease across typhoons occurring within the same season, however, for most successive typhoons in the biennial analysis, the second cyclone caused equal or less canopy damage (16 out of 21 typhoon pairs). These results support the idea that residual interactive effects of previous typhoons decrease quickly over time and rarely last for several typhoon seasons for Taiwanese forests, contributing to their high resistance to frequent typhoon disturbance.

Evaluation of Vegetation Indexes and Green-Up Date Extraction Methods on the Tibetan Plateau

The vegetation green-up date (GUD) of the Tibetan Plateau (TP) is highly sensitive to climate change. Accurate estimation of GUD is essential for understanding the dynamics and stability of terrestrial ecosystems and their interactions with climate. The GUD is usually determined from a time-series of vegetation indices (VIs). The adoption of different VIs and GUD extraction methods can lead to different GUDs. However, our knowledge of the uncertainty in these GUDs on TP is still limited. In this study, we evaluated the performance of different VIs and GUD extraction methods on TP from 2003 to 2020. The GUDs were determined from six Moderate Resolution Imaging Spectroradiometer (MODIS) derived VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), normalized difference infrared index (NDII), phenology index (PI), normalized difference phenology index (NDPI), and normalized difference greenness index (NDGI). Four extraction methods (βmax, CCRmax, G20, and RCmax) were applied individually to each VI to determine GUD. The GUDs obtained from all VIs showed similar patterns of early green-up in the eastern and late green-up in the western plateau, and similar trend of GUD advancement in the eastern and postponement in the western plateau. The accuracy of the derived GUDs was evaluated by comparison with ground-observed GUDs from 19 agrometeorological stations. Our results show that two snow-free VIs, NDGI and NDPI, had better performance in GUD extraction than the snow-calibrated conventional VIs, NDVI and EVI. Among all the VIs, NDGI gave the highest GUD accuracy when combined with the four extraction methods. Based on NDGI, the GUD extracted by the CCRmax method was found to have the highest consistency (r = 0.62, p < 0.01, RMSE = 11 days, bias = −3.84 days) with ground observations. The NDGI also showed the highest accuracy for preseason snow-covered site-years (r = 0.71, p < 0.01, RMSE = 10.69 days, bias = −4.05 days), indicating its optimal resistance to snow cover influence. In comparison, NDII and PI hardly captured GUD. NDII was seriously affected by preseason snow cover, as indicated by the negative correlation coefficient (r = −0.34, p < 0.1), high RMSE and bias (RMSE = 50.23 days, bias = −24.25 days).

Spatiotemporal estimation of gross primary production for terrestrial wetlands using satellite and field data

The main goal of this study was to determine the GPP at the Biebrza Wetlands for sedges, reeds and grasses habitats, applying satellite data. The ground measurements of Net Ecosystem Exchange (NEE) and respiration (RESP) were conducted at 26 sites at the peatland using chamber method during the vegetation growth from April to October 2015–2020. Applying round-the-clock registrations of Photosynthetically Active Radiation (PAR) and one ground measurement of NEE and RESP the simulation of the daily course of GPP was performed. GPP daily mean values were the object of estimation applying the models based on the satellite data derived from Sentinel 2, Sentinel 3 and MODIS. The main factors of photosynthetic carbon uptake – greenness and moisture were described by satellite vegetation indices Normalized Difference Vegetation Index (NDVI), Normalized Difference Infrared Index (NDII), Accumulated Photosynthetically Active Radiation (APAR), evapotranspiration, latent heat and the differences of surface temperature (Ts) and air temperature (Ta). The models presented as a result of this research can be used applying solely the satellite data. The results of the analysis revealed that NDVI index is the driving factor for the assessment of GPP independently on the type of satellite data (r = 0.64) Up to 40% of GPP variance is explained by the variable connected with the vegetation greenness. The contribution of vegetation moisture indices is about two times smaller. The best fit has been achieved for the Sentinel-3 models with inclusion of the environmental parameters, as NDVI, temperature difference (Ts–Ta), latent heat. The differences of uptake the CO2 between the habitats depending on their phases of phenological development have been presented.

Two dimensional searching paths exhibit fractal distribution that change with food availability (Normalized Difference Infrared Index, NDII)

Monitoring grazing activity and grasslands productivity has a great interest for the understanding of the feedback processes that regulate dynamic and conservation of grasslands. In this study we examined the spatiotemporal patterns of free foraging sheep and cattle (no shepherd) to understand how the two species adapt to the changes in food availability and quality in alpine and subalpine pastures during the summer. We used two vegetation indices derived from Sentinel 2 imagery (S2A) to characterize grassland: Normalized Difference Vegetation Index (NDVI) and Normalized Difference Infrared Index (NDII). We hypothesized that sheep and cattle would show different spatiotemporal displacement patterns, which would also change over time with phenology of pasture. We tested this using movement data of tagged sheep and cattle with satellite GPS collars. Animals are yearly free ranging in the studied alpine and subalpine pastures since June to end of September, when they are moved to the shelters. According with optimal searching strategy we predicted that displacement distribution would approach to a random searching path when the forage is more nutritive and abundant, i.e., when livestock have just arrived to their summer pastures. As the summer advances, animals would change their searching paths to an auto-correlated random walk, because pastures lose nutritional quality and consequently food availability is smaller, also due to animal consumption in the grasslands. Our results confirmed that hypothesis and showed that average NDII, a proxy of both pasture quality and time of green-up in the summer pastures, influenced the spatiotemporal patterns during grazing activity of free ranging livestock. We provided the first demonstration of two dimension searching path exhibiting a fractal distribution that changes with the availability of food and the intrinsic characteristic of the forager species, i.e., diet selection. The use of temporal sequences of foraging animals can be useful as indicator of food availability for free ranging animals that can be included in the data recording of GPS monitored livestock.

Detection of grassland mowing frequency using time series of vegetation indices from Sentinel-2 imagery

ABSTRACT Management intensity deeply influences meadow structure and functioning, therefore affecting grassland ecosystem services. Conservation and management measures, including European Common Agricultural Policy subsidies, should therefore be based on updated and publicly available data about management intensity. The mowing frequency is a crucial trait to describe meadows management intensity, but the potential of using vegetation indices from Sentinel-2 imagery for its retrieval has not been fully exploited. In this work we developed on the Google Earth Engine platform a four-phases algorithm to identify mowing frequency, including i) vegetation index time-series computing, ii) smoothing and resampling, iii) mowing detection, and iv) majority analysis. Mowing frequency during 2020 of 240 ha of grassland fields in the Italian Alps was used for algorithm optimization and evaluation. Six vegetation indexes (EVI, GVMI, MTCI, NDII, NDVI, RENDVI783.740) were tested as input to the proposed algorithm. The Normalized Difference Infrared Index (NDII) showed the best performance, resulting in mean absolute error of 0.07 and 93% overall accuracy on average at the four sites used for optimization, at pixel resolution. A slightly lower accuracy (mean absolute error = 0.10, overall accuracy = 90%) was obtained aggregating the maps to management parcels. The algorithm showed a good generalization ability, with a similar performance between global and local optimization and an average mean absolute error of 0.12 and an overall accuracy of 89% on average on the sites not used for parameters optimization. The lowest accuracies occurred in intensively managed grasslands surveyed by one satellite orbit only. This study demonstrates the suitability of the proposed algorithm to monitor very fragmented grasslands in complex mountain ecosystems. Google Earth Engine was used to develop the model and will enable researchers, agencies and practitioners to easily and quickly apply the code to map grassland mowing frequency for extensive grasslands protection and conservation, for mowing event verification, or for forage system characterization.

Dynamic Characteristics of Canopy and Vegetation Water Content during an Entire Maize Growing Season in Relation to Spectral-Based Indices

A variety of spectral vegetation indices (SVIs) have been constructed to monitor crop water stress. However, their abilities to reflect dynamic canopy water content (CWC) and vegetation water content (VWC) during the growing season have not been concurrently examined, and the underlying mechanisms remain unclear, especially in relation to soil drying. In this study, a field experiment was conducted and designed with various irrigation regimes applied during two consecutive growing seasons of maize. The results showed that CWC, VWC, and the SVIs exhibited obvious trends of first increasing and then decreasing within a growing season. In addition, VWC was allometrically related to CWC across the two growing seasons. A linear relationship between the five SVIs and CWC occurred within a certain CWC range (0.01–0.41 kg m−2), while the relationship between these SVIs and VWC was nonlinear. Furthermore, the five SVIs indicated critical values for VWC, and these values were 1.12 and 1.15 kg m−2 for the water index (WI) and normalized difference water index (NDWI), respectively; however, the normalized difference infrared index (NDII), normalized difference vegetation index (NDVI), and optimal soil-adjusted vegetation index (OSAVI) had the same critical value of 0.55 kg m−2. Therefore, in comparison to the NDII, NDVI, and OSAVI, the WI and NDWI better reflected the crop water content based on their sensitives to CWC and VWC. Moreover, CWC was the most important direct biotic driver of the dynamics of SVIs, while leaf area index (LAI) was the most important indirect biotic driver. VWC was a critical indirect regulator of WI, NDWI, NDII, and OSAVI dynamics, whereas vegetation dry mass (VDM) was the critical indirect regulator of NDVI dynamics. These findings may provide additional information for estimating agricultural drought and insights on the impact mechanism of soil water deficits on SVIs.

Detecting White Pine Needle Damage through Satellite Remote Sensing

Eastern white pines (Pinus strobus L.) of New England forests have been recently impacted by a fungal disease known as White Pine Needle Damage (WPND), causing widespread needle damage. To complement current WPND monitoring methods based on field and aerial detection surveys, we evaluated the potential of satellite remote sensing technology to detect WPND outbreaks. Using Sentinel-2 spectral vegetation indices (SVIs), we directly visualized change overlapping WPND outbreaks and ran Random Forest machine learning classifiers for feature selection and WPND detection and severity classification. Direct visualization of WPND associated change was most effective through the Normalized Difference Infrared Index (NDII), which captured decreases in vegetation health conditions coinciding with peak WPND symptoms. We obtained good accuracies in binary (WPND vs. Non-WPND) detection (70%) and two-class severity modeling of WPND (75%). The highest accuracies were achieved using imagery from early to late summer. The most selected SVIs for modeling were the Carotenoid Reflectance Index1 (CRI1), the Sentinel-2 Red-Edge Position (S2REP), and the Normalized Difference Vegetation Index (NDVI). Our results suggest detecting severe WPND through fine resolution remote sensing is feasible. However, more work is needed to determine the effects of spatial, spectral, and temporal resolution of remote sensing data for detecting WPND severity levels.

Comparison of Long-Term Changes in Non-Linear Aggregated Drought Index Calibrated by MERRA–2 and NDII Soil Moisture Proxies

This study aimed at evaluating Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA–2) and Normalized Difference Infrared Index (NDII) soil moisture proxies in calibrating a comprehensive Non-linear Aggregated Drought Index (NADI). Soil moisture plays a critical role in temperature variability and controlling the partitioning of water into evaporative fluxes as well as ensuring effective plant growth. Long-term variability and change in climatic variables such as precipitation, temperatures, and the possible acceleration of the water cycle increase the uncertainty in soil moisture variability. Streamflow, temperature, rainfall, reservoir storage, MERRA–2, and NDII soil moisture proxies’ data from 1986 to 2016 were used to formulate the NADI. The trend analysis was performed using the Mann Kendall, SQ-MK was used to determine the point of trend direction change while Theil-Sen trend estimator method was used to determine the magnitude of the detected trend. The seasonal correlation between the NADI-NDII and NADI-MERRA–2 was higher in spring and autumn with an R2 of 0.9 and 0.86, respectively. A positive trend was observed over the 30 years period of study, NADI-NDII trend magnitude was found to be 0.02 units per year while that of NADI-MERRA–2 was 0.01 units. Wavelet analysis showed an in-phase relationship with negligible lagging between the NDII and MERRA–2 calibrated NADI. Although a robust comparison is recommended between soil moisture proxies and observed soil moisture, the soil moisture proxies in this study were found to be useful in monitoring long-term changes in soil moisture.

Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel

The overarching aim of this research was to develop a method for deriving crop maps from a time series of Sentinel-2 images between 2017 and 2018 to address global challenges in agriculture and food security. This study is the first step towards improving crop mapping based on phenological features retrieved from an object-based time series on a national scale. Five main crops in Israel were classified: wheat, barley, cotton, carrot, and chickpea. To optimize the object-based classification process, different characteristics and inputs of the mean shift segmentation algorithm were tested, including vegetation indices, three-band combinations, and high/low emphasis on the spatial and spectral characteristics. Four known vegetation indices (VIs)-based time series were tested. Additionally, we compared two widely used machine learning methods for crop classification, support vector machine (SVM) and random forest (RF), in addition to a newer classifier, extreme gradient boosting (XGBoost). Lastly, we examined two accuracy measures—overall accuracy (OA) and area under the curve (AUC)—in order to optimally estimate the accuracy in the case of imbalanced class representation. Mean shift best performed when emphasizing both the spectral and spatial characteristics while using the green, red, and near-infrared (NIR) bands as input. Both accuracy measures showed that RF and XGBoost classified different types of crops with significantly greater success than achieved by SVM. Nevertheless, AUC was better able to represent the significant differences between the classification algorithms than OA was. None of the VIs showed a significantly higher contribution to the classification. However, normalized difference infrared index (NDII) with XGBoost classifier showed the highest AUC results (88%). This study demonstrates that the short-wave infrared (SWIR) band with XGBoost improves crop type classification results. Furthermore, the study emphasizes the importance of addressing imbalanced classification datasets by using a proper accuracy measure. Since object-based classification and phenological features derived from a VI-based time series are widely used to produce crop maps, the current study is also relevant for operational agricultural management and informatics at large scales.

Cold Wave-Induced Reductions in NDII and ChlRE for North-Western Pacific Mangroves Varies with Latitude and Climate History

Mangrove forests growing at the poleward edges of their geographic distribution are occasionally subject to freezing (<0 °C) and cold wave (>0 °C) events. Cold wave effects on mangrove trees are well documented and adaptation to cold stress has been reported for local mangrove populations in the North Atlantic. However, there is less understanding of effects of cold waves on mangroves in the northern Pacific, especially at the regional scale. Moreover, it is unclear if cold tolerant mangrove species of North Asia display variation in resistance to cold temperatures across their geographic distribution. Using a cold wave event that occurred in January 2021, we evaluated the effects of low temperatures on vegetation index (VI) change (relative to a recent five-year baseline) for mangrove forests dominated by Kandelia obovata (Rhizophoraceae) and Avicennia marina (Acanthaceaee) at the northern edge of their geographical range. We used two VIs derived from Sentinel-2 imagery as indicators for canopy health: the normalized difference infrared index (NDII) and the chlorophyll red-edge index (ChlRE), which reflect forest canopy water content and chlorophyll concentration, respectively. We isolated the cold wave effects on the forest canopy from phenology (i.e., cold wave induced deviation from a five-year baseline) and used multiple linear regression to identify significant climatic predictors for the response of mangrove forest canopy VI change to low temperatures. For areas where the cold wave resulted in temperatures <10 °C, immediate decreases in both VIs were observed, and the VI difference relative to the baseline was generally greater at 30-days after the cold wave than when temperatures initially recovered to baseline values, showing a slight delay in VI response to cold wave-induced canopy damage. Furthermore, the two VIs did not respond consistently suggesting that cold-temperature induced changes in mangrove canopy chlorophyll and water content are affected independently or subject to differing physiological controls. Our results confirm that local baseline (i.e., recent past) climate predicts canopy resistance to cold wave damage across K. obovata stands in the northern Pacific, and in congruence with findings from New World mangroves, they imply geographic variation in mangrove leaf physiological resistance to cold for Northern Pacific mangroves.