Remote Sensing Vegetation Indices Research Articles

Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

Agricultural vegetation development and harvest date monitoring over large areas requires frequent remote sensing observations. In regions with persistent cloud coverage during the vegetation season this is only feasible with active systems, such as SAR, and is limited for optical data. To date, optical remote sensing vegetation indices are more frequently used to monitor agricultural vegetation status because they are easily processed, and the characteristics are widely known. This study evaluated the correlations of three Sentinel-2 optical indices with Sentinel-1 SAR indices over agricultural areas to gain knowledge about their relationship. We compared Sentinel-2 Normalized Difference Vegetation Index, Normalized Difference Water Index, and Plant Senescence Radiation Index with Sentinel-1 SAR VV and VH backscatter, VH/VV ratio, and Sentinel-1 Radar Vegetation Index. The study was conducted on 22 test sites covering approximately 35,000 ha of four different main European agricultural land use types, namely grassland, maize, spring barley, and winter wheat, in Lower Saxony, Germany, in 2018. We investigated the relationship between Sentinel-1 and Sentinel-2 indices for each land use type considering three phenophases (growing, green, senescence). The strength of the correlations of optical and SAR indices differed among land use type and phenophase. There was no generic correlation between optical and SAR indices in our study. However, when the data were split by land use types and phenophases, the correlations increased remarkably. Overall, the highest correlations were found for the Radar Vegetation Index and VH backscatter. Correlations for grassland were lower than for the other land use types. Adding auxiliary data to a multiple linear regression analysis revealed that, in addition to land use type and phenophase information, the lower quartile and median SAR values per field, and a spatial variable, improved the models. Other auxiliary data retrieved from a digital elevation model, Sentinel-1 orbit direction, soil type information, and other SAR values had minor impacts on the model performance. In conclusion, despite the different nature of the signal generation, there were distinct relationships between optical and SAR indices which were independent of environmental variables but could be stratified by land use type and phenophase. These relationships showed similar patterns across different test sites. However, a regional clustering of landscapes would significantly improve the relationships.

The Greening and Wetting of the Sahel Have Leveled off since about 1999 in Relation to SST

The Sahel, a semi-arid climatic zone with highly seasonal and erratic rainfall, experienced severe droughts in the 1970s and 1980s. Based on remote sensing vegetation indices since early 1980, a clear greening trend is found, which can be attributed to the recovery of contemporaneous precipitation. Here, we present an analysis using long-term leaf area index (LAI), precipitation, and sea surface temperature (SST) records to investigate their trends and relationships. LAI and precipitation show a significant positive trend between 1982 and 2016, at 1.72 × 10 −3 yr −1 (p < 0.01) and 4.63 mm yr−1 (p < 0.01), respectively. However, a piecewise linear regression approach indicates that the trends in both LAI and precipitation are not continuous throughout the 35 year period. In fact, both the greening and wetting of the Sahel have been leveled off (pause of rapid growth) since about 1999. The trends of LAI and precipitation between 1982 and 1999 and 1999–2016 are 4.25 × 10 − 3 yr −1 to − 0.27 × 10 −3 yr −1, and 9.72 mm yr −1 to 2.17 mm yr −1, respectively. These declines in trends are further investigated using an SST index, which is composed of the SSTs of the Mediterranean Sea, the subtropical North Atlantic, and the global tropical oceans. Causality analysis based on information flow theory affirms this precipitation stabilization between 2003 and 2014. Our results highlight that both the greening and the wetting of the Sahel have been leveled off, a feature that was previously hidden in the apparent long-lasting greening and wetting records since the extreme low values in the 1980s.

Photoperiod Explains the Asynchronization Between Vegetation Carbon Phenology and Vegetation Greenness Phenology

AbstractVegetation carbon phenology is a key indicator of vegetation actual photosynthesis activity and regulates terrestrial ecosystem carbon balance. A comprehensive understanding of the structural difference of carbon phenology and greenness phenology is still lacking. This study evaluated the structural difference of vegetation greenness phenology and carbon phenology on 95 eddy covariance flux sites (637 site‐years). Vegetation greenness phenology is extracted from remote sensing vegetation indices (i.e., enhanced vegetation index [EVI] and normalized difference vegetation index [NDVI]) of Moderate Resolution Imaging Spectroradiometer satellites; carbon phenology is extracted from daily gross primary production estimates at flux sites. We found that there exists remarkable asynchronization between vegetation greenness phenology and carbon phenology on the 95 flux sites. Generally, the asynchronization is more prevalent in the withering season, with the bias about 18.7 ± 13.0 days for EVI and about 29.8 ± 14.4 days for NDVI. In comparison, in the green‐up season, the bias is only about −1.6 ± 12.9 days for EVI and −6.0 ± 13.8 days for NDVI. We found that photoperiod is the dominant factor controlling the asynchronization and the decrease of photosynthetic capacity in the withering season, other than photosynthetically active radiation or temperature. Our study also reveals that EVI × PC and NDVI × PC are good proxies of vegetation carbon phenology, where PC is the photoperiod constraint and is quantified as . Using PC in the withering season reduces the bias of EVI from 18.7 ± 13.0 days to −2.4 ± 11.2 days and the bias of NDVI from 29.8 ± 14.4 days to −1.1 ± 12.7 days.

Satellite Remote Sensing for the Analysis of the Micia and Germisara Archaeological Sites

The capabilities of satellite remote sensing technologies and their derived data for the analysis of archaeological sites have been demonstrated in a large variety of studies over the last decades. Likewise, the Earth Observation (EO) data contribute to the disaster management process through the provision of updated information for areas under investigation. In addition, long term studies may be performed for the in–depth analysis of the disaster–prone areas using archive satellite imagery and other cartographic materials. Hence, satellite remote sensing represents an essential tool for the study of hazards in cultural heritage sites and landscapes. Depending on the size of the archaeological sites and considering the fact that some parts of the site might be covered, the main concern regards the suitability of satellite data in terms of spatial and spectral resolution. Using a multi–temporal Sentinel–2 dataset between 2016 and 2019, the present study focuses on the hazard risk identification for the Micia and Germisara archaeological sites in Romania as they are endangered by industrialisation and major infrastructure works and soil erosion, respectively. Furthermore, the study includes a performance assessment of remote sensing vegetation indices for the detection of buried structures. The results clearly indicate that Sentinel–2 imagery proved to be fundamental in meeting the objectives of the study, particularly due to the extensive archaeological knowledge that was available for the cultural heritage sites. The main conclusion to be drawn is that satellite–derived products may be enhanced by integrating valuable archaeological context, especially when the resolution of satellite data is not ideally fitting the peculiarities (e.g., in terms of size, underground structures, type of coverage) of the investigated cultural heritage sites.

The comparison between remotely-sensed vegetation indices of Meteosat second generation satellite and temperature-based agrometeorological indices for monitoring of main crops in the northeast of Iran

The main crops of northeast region of Iran are grapes and apples, which in this research we compared the fourteen remotely sensed vegetation index from MSG1(IODC) satellite with several temperature-based agrometeorological indices of which comparative results in this article can be generalized to similar climatic regions. The agrometeorological indices and parameters used in the present article are the heating unit, the photothermal unit, the vapor pressure deficit, the photo-temperature, Nycto-temperature, and the cumulative precipitation. Also, remotely sensed vegetation indices are NDVI, MSAVI, PVI, SAVI, SR, MSR, RDVI, NLI, RVI, GEMI, DVI, MNLI, SR × NDVI, and SR × SAVI. In this article, the values of agrometeorological and remotely sensed vegetation indices in two understudy regions, i.e., Bojnourd and Shirvan were compared, and the correlation coefficient between two mentioned coordinates was calculated. The time series charts of these indices for vineyards and apple orchards have shown an unanticipated oscillation with continuous high and low values, whereas the agrometeorological indices increase, remotely sensed vegetation indices raising. There is no specific relationship between cumulative precipitation and remote sensing indices in apples, as the trees in the study area are irrigated. The maximum R2 coefficient belongs to the NDVI, MSR, SAVI, and MSAVI. In the final, due to the high correlation of the NDVI with the agrometeorological indices, for verification, the NDVI product of the MODIS (MOD13C1) and MET8 (MSGNDVD) is used. Thus, these four indices can be used as parallel or alternative monitoring of temperature-based agrometeorological indices.

The impact of groundwater depth on the spatial variance of vegetation index in the Ordos Plateau, China: A semivariogram analysis

As important sensitive feedback of ecosystems, spatial distribution and patterns of vegetation can remarkably reflect eco-environmental conditions in arid and semiarid areas, where groundwater plays a significant role. The impact of groundwater depth (GD) on the spatial variance in remote-sensing vegetation index is highlighted in this study over the Ordos Plateau, China. The 250 m resolution mean annual Enhanced Vegetation Index (EVI) data are analyzed and compared to the same resolution data of altitude, air temperature, aridity index and GD. A linear regression analysis indicates a significant weak correlation between the EVI and the climatic and topographic controls at the regional scale. A semivariogram analysis is introduced to quantify the GD-dependent variance in the residual EVI (WEVI) filtered from this linear regression. The pixel-to-pixel dependency of WEVI on GD is nonlinear and significant only when GD is smaller than 10 m. The semivariance of WEVI increases with the lag distance and slightly fluctuates around the sill when the distance is sufficiently larger than 5 km. The sill of WEVI variance in the situation of GD ≤ 5 m is significantly higher than that in deeper groundwater situations. The semivariogram range of WEVI in the middle zone of the study area is lowest due to the lowest semivariogram range of GD. The nugget effect in the WEVI variance is weak but positively related to the nugget of GD variance when groundwater is shallow. High sensitivity of the vegetation cover to the shallow groundwater should be considered in the regulation of groundwater resources for ecological benefits.

Plant Traits Help Explain the Tight Relationship between Vegetation Indices and Gross Primary Production

Remotely-sensed Vegetation Indices (VIs) are often tightly correlated with terrestrial ecosystem CO2 uptake (Gross Primary Production or GPP). These correlations have been exploited to infer GPP at local to global scales and over half-hour to decadal periods, though the underlying mechanisms remain incompletely understood. We used satellite remote sensing and eddy covariance observations at 10 sites across a California climate gradient to explore the relationships between GPP, the Enhanced Vegetation Index (EVI), the Normalized Difference Vegetation Index (NDVI), and the Near InfraRed Vegetation (NIRv) index. EVI and NIRv were linearly correlated with GPP across both space and time, whereas the relationship between NDVI and GPP was less general. We explored these interactions using radiative transfer and GPP models forced with in-situ plant trait and soil reflectance observations. GPP ultimately reflects the product of Leaf Area Index (LAI) and leaf level CO2 uptake (Aleaf); a VI that is sensitive mainly to LAI will lack generality across ecosystems that differ in Aleaf. EVI and NIRv showed a strong, multiplicative sensitivity to LAI and Leaf Mass per Area (LMA). LMA was correlated with Aleaf, and EVI and NIRv consequently mimic GPP’s multiplicative sensitivity to LAI and Aleaf, as mediated by LMA. NDVI was most sensitive to LAI, and was relatively insensitive to leaf properties over realistic conditions; NDVI lacked EVI and NIRv’s sensitivity to both LAI and Aleaf. These findings carry implications for understanding the limitations of current VIs for predicting GPP, and also for devising strategies to improve predictions of GPP.

Temporal evolution of measured and simulated infiltration following wildfire in the Colorado Front Range, USA: Shifting thresholds of runoff generation and hydrologic hazards

Destructive flash floods and debris flows are a common menace following wildfire. The restoration of protection provided by forests from post-fire floods and debris flows depends on the recovery of infiltration and attendant reduction of infiltration-excess surface runoff generation. This work examines seven years of post-fire infiltration measurements and temporal relations fit to soil-hydraulic properties from the Colorado Front Range, USA, to assess infiltration recovery with increasing time since fire. Point-scale Green-Ampt simulations of infiltration across a full spectrum of rainfall events are used to evaluate infiltration changes and shifts in surface runoff generation thresholds with post-fire temporal recovery. Measured and simulated infiltration generally recovered monotonically with increasing time since fire. This indicates a reduced vulnerability to infiltration-excess runoff generation as time elapses, with the greatest risk in the first two years after the fire. The threshold for infiltration-excess runoff advances with increasing time to rainfall events with higher intensity and greater return intervals; by the third year after wildfire, only extreme events (30–100 year recurrence) generate surface runoff and by the fifth and seventh year even extreme rainfall events typically fail to generate surface runoff. Remotely-sensed vegetation indices indicate linked, or at least contemporaneous, recovery of understory vegetation and field-saturated hydraulic conductivity at this field site, suggesting coincident recovery of multiple hillslope properties impacting surface runoff generation. This work indicates the importance of coupled assessments of hillslope property recovery and stochasticity of high-intensity rainfall for flash flood and debris flow hazards arising from infiltration-excess runoff generation; these hazards likely shift to subsurface mechanisms with increasing time since fire. Post-fire hazard assessments using static hillslope properties could fail to predict flash floods and debris flows associated with infrequent extreme rainfall events that strike during the post-fire recovery period when hillslope properties are partially recovered.

Predicting grain protein content of field-grown winter wheat with satellite images and partial least square algorithm.

Remote sensing has been used as an important means of modern crop production monitoring, especially for wheat quality prediction in the middle and late growth period. In order to further improve the accuracy of estimating grain protein content (GPC) through remote sensing, this study analyzed the quantitative relationship between 14 remote sensing variables obtained from images of environment and disaster monitoring and forecasting small satellite constellation system equipped with wide-band CCD sensors (abbreviated as HJ-CCD) and field-grown winter wheat GPC. The 14 remote sensing variables were normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), optimized soil-adjusted vegetation index (OSAVI), nitrogen reflectance index (NRI), green normalized difference vegetation index (GNDVI), structure intensive pigment index (SIPI), plant senescence reflectance index (PSRI), enhanced vegetation index (EVI), difference vegetation index (DVI), ratio vegetation index (RVI), Rblue (reflectance at blue band), Rgreen (reflectance at green band), Rred (reflectance at red band) and Rnir (reflectance at near infrared band). The partial least square (PLS) algorithm was used to construct and validate the multivariate remote sensing model of predicting wheat GPC. The research showed a close relationship between wheat GPC and 12 remote sensing variables other than Rblue and Rgreen of the spectral reflectance bands. Among them, except PSRI and Rblue, Rgreen and Rred, other remote sensing vegetation indexes had significant multiple correlations. The optimal principal components of PLS model used to predict wheat GPC were: NDVI, SIPI, PSRI and EVI. All these were sensitive variables to predict wheat GPC. Through modeling set and verification set evaluation, GPC prediction models' coefficients of determination (R2) were 0.84 and 0.8, respectively. The root mean square errors (RMSE) were 0.43% and 0.54%, respectively. It indicated that the PLS algorithm model predicted wheat GPC better than models for linear regression (LR) and principal components analysis (PCA) algorithms. The PLS algorithm model’s prediction accuracies were above 90%. The improvement was by more than 20% than the model for LR algorithm and more than 15% higher than the model for PCA algorithm. The results could provide an effective way to improve the accuracy of remotely predicting winter wheat GPC through satellite images, and was conducive to large-area application and promotion.

Multidimensional Response Evaluation of Remote-Sensing Vegetation Change to Drought Stress in the Three-River Headwaters, China

The impact of drought on ecosystems has become increasingly prominent. This study used the MODIS remote-sensing vegetation index and the standardized precipitation index to quantify vegetation coverage status and meteorological drought level. Using the copula method, the joint distribution of the drought index and related vegetation cover variables were simulated for the first time. The conditional distribution of vegetation biomass was deduced, and the multidimensional response between the vegetation biomass and drought was explored to understand the possible vegetation loss under different drought severity conditions. The three-river headwaters (TRH) region is the source place of the Yangtze River, Yellow River, and Lancang River, where the ecosystem is characterized by the innate vulnerability. Using this region as a case study, the results show that the spatiotemporal evolution of drought and vegetation in the TRH region has apparent regional heterogeneity. The vegetation cover in the eastern region is significantly better than that in the northwestern region, while the vegetation growth trend in the northwestern region is stronger than that in the southeastern region. It is feasible to build a vegetation-drought multidimensional response model based on the copula method. In 92.5% of the TRH region, vegetation cover was significantly affected by the severity of the drought. The impact on the growth of vegetation caused by persistent drought events was higher than that of short-term but high-intensity drought. The vegetation in the TRH area has a certain degree of drought resistance. This study provides outstanding theoretical support and reference for the protection of the TRH basin ecosystem.

Spatiotemporal dynamics of the vegetation in Ningxia, China using MODIS imagery

The vegetation in the Ningxia Hui Autonomous Region (henceforth, Ningxia) of north-western China plays an important role in guarding regional ecological safety and sustainable development. However, it is unclear how climate affects vegetation growth in terms of seasonality and various vegetation types in Ningxia. Based on remote sensing vegetation index from 2001 to 2016, climatic parameters, and the Chinese vegetation type data, this article examines the spatiotemporal effects of climate parameters on vegetation. The relative importance to variability in the normalized difference vegetation index (NDVI) for different seasons and various vegetated types is also determined. The results demonstrate that the vegetation increased from 2001 to 2016 in Ningxia. The rate of NDVI increase was fastest in summer and slowest in spring. Areas with significant increases in vegetation occurred primarily in the southern mountain, Liupan Mountain, and central arid areas. Degraded vegetation occurred in the Yellow River irrigation area with intense human activity influence. The vegetation in most areas of Ningxia will continue to increase in the future. The sensitivity of vegetation to temperature, precipitation, sunshine duration, and wind velocity showed significantly seasonal variability. Sunshine duration and wind velocity were important climatic factors affecting vegetation growth in Ningxia. However, the impact of summer precipitation variation on summer NDVI (SMN) demonstrated a time lag effect. The impact of climate variation on vegetation was distinct among various vegetation types. Moreover, the spatial pattern of vegetation in Ningxia was also impacted by human activities.

Challenges for remote sensing of the Sustainable Development Goal SDG 15.3.1 productivity indicator

Progress towards combatting land degradation as intended by Sustainable Development Goal 15.3.1 will be monitored using three sub-indicators, of which productivity of vegetation is one. This indicator is to be measured using trends in a remotely-sensed vegetation index. The use of vegetation indices is well-established and remotely-sensed data are readily available. However, their uses for monitoring production that is relevant to sustainable livelihoods have received little attention. This review identifies four areas in the currently-proposed monitoring methodology that are in need of further development. The first is the derivation of primary production from vegetation indices, which requires attention to physiological processes such as light-use efficiency and plant respiration. The second concerns the subsequent steps, in which ecological processes transform the net production into production of goods and services, such as crop products. The third is the need for explicit baselines or reference conditions that specify the productivity in the absence of anthropogenic degradation. The fourth, and most difficult, is to distinguish anthropogenic causes of degradation from potentially similar effects of natural environmental processes. Some of these issues are difficult to tackle with remote sensing alone, although several improvements are available, and others are in development. However, the current use of vegetation indices alone to remotely-sense degradation of ecosystem services does not provide an adequate SDG 15.3.1 productivity indicator.

Divergent vegetation responses to extreme spring and summer droughts in Southwestern China

In the winter and spring of 2009–2010 and summer of 2011, Southwestern China was hit by two severe drought events that are unprecedented over the past three decades with substantial economic damage. The understanding of how vegetation growth responds to extreme drought events in different seasons is inadequate, and underlying mechanisms are unclear. Here, we investigated the impacts of the 2009–2010 winter-spring drought and 2011 summer drought on vegetation growth and productivity in Southwestern China, using multiple remote sensing vegetation indexes including Normalized Difference Vegetation Index, Enhanced Vegetation Index, gross primary productivity/net primary productivity and vegetation optical depth. Our analyses reveal that severe soil moisture deficit in 2009–2010 winter-spring drought resulted in reduction in evapotranspiration, Enhanced Vegetation Index (0.047) and gross primary productivity (1.833 P cg) /net primary productivity in 2010 spring, worse decreased Normalized Difference Vegetation Index (0.135) and decreased vegetation optical depth in summer. By contrast, the 2011 summer drought led to enhanced vegetation greenness and higher summer gross primary productivity (1.272 P cg) /net primary productivity (0.546) compared with normal years (2007–2008), due to increased solar radiation over forests region. Our study highlights the divergent responses of vegetation to extreme drought events occurred in different seasons.

A robust method for reconstructing global MODIS EVI time series on the Google Earth Engine

Obtaining high quality remote sensing vegetation index that is not noticeably affected by abiotic factors is critical for agricultural, ecological, climate and hydrological studies. Here we developed a computationally efficient and well-performed denoising method for reconstructing remote sensing vegetation indices, namely wWHd (weighted Whittaker with dynamic parameter λ in spatial). The single parameter λ is automatically estimated for every pixel by a multiple linear regression. Weights updating and the inherit nature of Whittaker make Whittaker robust for contaminations. We applied the wWHd on the Google Earth Engine (GEE) platform for reconstructing 500 m resolution enhanced vegetation index (EVI) time series from moderate-resolution imaging spectroradiometer (MODIS) at global scale and for the period of 2000–2017. To demonstrate its robustness, wWHd was compared with four well-known denoising methods, i.e. Fourier-based approach (Fourier), Savitzky-Golay filter (SG), Asymmetric Gaussian (AG) and double logistic (DL) at 16,000 randomly selected sites. All approaches were evaluated using two indices at each site: (1) root mean square error (RMSE) between observed best quality EVI and gap-filled EVI series, and (2) roughness of gap-filled EVI series. Results show that wWHd has an RMSE (indicating fidelity) of ∼0.032, which is similar to Fourier and SG at ∼90% sampled sites, but outperforms (∼0.02 less in the RMSE) AG and DL at ∼45% and ∼25% sampled sites. Among the four, wWHd has the lowest (best) roughness of ∼0.003. These performances demonstrate that wWHd balances fidelity and roughness well. Another advantage is that the wWHd is computationally more efficient than others, and is currently the only one denoising method deployed on the GEE. Our results suggest that it is promising to use the proposed wWHd method for processing remote sensing vegetation indices with high spatiotemporal resolution and the reconstructed EVI product should be widely used by global community.

Assessing the impact of dams on riparian and deltaic vegetation using remotely-sensed vegetation indices and Random Forests modelling

Abstract Riparian and deltaic areas exhibit a high biodiversity and offer a number of ecosystem services but are often degraded by human activities. Dams, for example, alter the hydrologic and sediment regimes of rivers and can negatively affect riparian areas and deltas. In order to sustainably manage these ecosystems, it is, therefore, essential to assess and monitor the impacts of dams. To this end, site-assessments and in-situ measurements have commonly been used in the past, but these can be laborious, resource demanding and time consuming. Here, we investigated the impact of three dams on the riparian forest of the Nestos River Delta in Greece by employing multi-temporal satellite data. We assessed the evolution in the values of eight vegetation indices over 27 years, derived from 14 dates of Landsat data. We also employed a modelling approach, using a machine learning Random Forests model, to investigate potential linkages between the observed changes in the indices and a host of climatic and terrestrial predictor variables. Our results show that low density vegetation (0–25%) is more affected by the construction of the dams due to its proximity to anthropogenic influences and the effects of hydrologic regime alteration. In contrast, higher density vegetation cover (50–75%) appears to be largely unaffected, or even improving, due to its proximity to the river, while vegetation with intermediate coverage (25–49%) exhibits no clear trend in the Landsat-derived indices. The Random Forests model found that the most important parameters for the riparian vegetation (based on the Mean Decrease Gini and the Mean Decrease Accuracy) were the distance to the dams, the sea and the river. Our results suggest that management plans of riparian and deltaic areas need to incorporate and take into consideration new innovative management practices and monitoring studies that employ multi-temporal satellite data archives.

Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data

Remotely sensed vegetation indices (RSVIs) can be used to efficiently estimate terrestrial primary productivity across space and time. Terrestrial productivity, however, has many facets (e.g., spatial and temporal variability, including seasonality, interannual variability, and trends), and different vegetation indices may not be equally good at predicting them. Their accuracy in monitoring productivity has been mostly tested in single-ecosystem studies, but their performance in different ecosystems distributed over large areas still needs to be fully explored. To fill this gap, we identified the facets of terrestrial gross primary production (GPP) that could be monitored using RSVIs. We compared the temporal and spatial patterns of four vegetation indices (NDVI, EVI, NIRV, and CCI), derived from the MODIS MAIAC data set and of GPP derived from data from 58 eddy-flux towers in eight ecosystems with different plant functional types (evergreen needle-leaved forest, evergreen broad-leaved forest, deciduous broad-leaved forest, mixed forest, open shrubland, grassland, cropland, and wetland) distributed throughout Europe, covering Mediterranean, temperate, and boreal regions. The RSVIs monitored temporal variability well in most of the ecosystem types, with grasslands and evergreen broad-leaved forests most strongly and weakly correlated with weekly and monthly RSVI data, respectively. The performance of the RSVIs monitoring temporal variability decreased sharply, however, when the seasonal component of the time series was removed, suggesting that the seasonal cycles of both the GPP and RSVI time series were the dominant drivers of their relationships. Removing winter values from the analyses did not affect the results. NDVI and CCI identified the spatial variability of average annual GPP, and all RSVIs identified GPP seasonality well. The RSVI estimates, however, could not estimate the interannual variability of GPP across sites or monitor the trends of GPP. Overall, our results indicate that RSVIs are suitable to track different facets of GPP variability at the local scale, therefore they are reliable sources of GPP monitoring at larger geographical scales.

Estimating grassland aboveground biomass on the Tibetan Plateau using a random forest algorithm

Effective and accurate monitoring of grassland aboveground biomass (AGB) is necessary for improving our understanding of regional carbon cycle and pastoral agricultural management. In this study, we developed a suitable AGB estimation model for the Tibetan alpine grasslands based on the random forest algorithm, using 256 AGB observation data, remote sensing vegetation indices, meteorological data, and topographical data. We estimated the grassland AGB on the Tibetan Plateau during 2000–2014, analyzed its spatiotemporal changes, and further explored the response of AGB to the variation in climatic factors. The results indicated that (1) the RF model performed well in the AGB estimation, which can explain 86% of the variation of the observation data. (2) The grassland AGB decreased from the southeast to the northwest in this region, with an average value of 77.12 gm−2. (3) In the whole study area, the grassland AGB showed significantly positive correlation with temperature and precipitation. The correlation between grassland AGB and MAP was 0.54 (P < 0.05), much higher than that of MAT (R = 0.38, P < 0.05). (4) The inter-annual variation of AGB on the Tibetan Plateau was significantly and positively correlated with temperature (R2 = 0.45, P < 0.05). This study demonstrated that RF model can help improve our understanding of the spatiotemporal dynamics of the grassland AGB and the effects of climate variation.

Combining Environmental Monitoring and Remote Sensing Technologies to Evaluate Cropping System Nitrogen Dynamics at the Field-Scale

Nitrogen (N) losses from cropping systems in the U.S. Midwest represent a major environmental and economic concern, negatively impacting water and air quality. While considerable research has investigated processes and controls of N losses in this region, significant knowledge gaps still exist, particularly related to the temporal and spatial variability of crop N uptake and environmental losses at the field-scale. The objectives of this manuscript study were (i) to describe the unique application of environmental monitoring and remote sensing technologies to quantify and evaluate relationships between artificial subsurface drainage nitrate (NO3-N) losses, soil nitrous oxide (N2O) emissions, soil N concentrations, corn (Zea mays L.) yield, and remote sensing vegetation indices, and (ii) to discuss the benefits and limitations of using recent developments in technology to monitor cropping system N dynamics at field-scale. Preliminary results showed important insights regarding temporal (when N losses primarily occurred) and spatial (measurement footprint) considerations when trying to link N2O and NO3-N leaching losses within a single study to assess relationship between crop productivity and environmental N losses. Remote sensing vegetation indices were significantly correlated with N2O emissions, indicating that new technologies (e.g. unmanned aerial vehicle platform) could represent an integrative tool for linking sustainability outcomes with improved agronomic efficiencies, with lower vegetation index values associated with poor crop performance and higher N2O emissions. However, the potential for unmanned aerial vehicle to evaluate water quality appears much more limited because NO3-N losses happened prior to early-season crop growth and image collection. Building on this work, we encourage future research to test the usefulness of remote sensing technologies for monitoring environmental quality, with the goal of providing timely and accurate information to enhance the efficiency and sustainability of food production.

Quantifying Grazing Intensity Using Remote Sensing in Alpine Meadows on Qinghai-Tibetan Plateau

Remote sensing data have been widely used in the study of large-scale vegetation activities, which have important significance in estimating grassland yields, determining grassland carrying capacity, and strengthening the scientific management of grasslands. Remote sensing data are also used for estimating grazing intensity. Unfortunately, the spatial distribution of grazing-induced degradation remains undocumented by field observation, and most previous studies on grazing intensity have been qualitative. In our study, we tried to quantify grazing intensity using remote sensing techniques. To achieve this goal, we conducted field experiments at Gansu Province, China, which included a meadow steppe and a semi-arid region. The correlation between a vegetation index and grazing intensity was simulated, and the results demonstrated that there was a significant negative correlation between NDVI and relative grazing intensity (p &lt; 0.05). The relative grazing intensity increased with a decrease in NDVI, and when the relative grazing intensity reached a certain level, the response of NDVI to relative grazing intensity was no longer sensitive. This study shows that the NDVI model can illustrate the feasibility of using a vegetation index to monitor the grazing intensity of livestock in free-grazing mode. Notably, it is feasible to use the remote sensing vegetation index to obtain the thresholds of livestock grazing intensity.

Relationships between urban development level and urban vegetation states: A global perspective

Urban development exerts both benign and malignant impacts on urban vegetation. Mixed results have been reported regarding the responses of urban vegetation to urbanization. However, these results are limited by both sample size and location. It is essential to conduct global-scale research in urban areas with various development levels in order to acquire a more comprehensive understanding of the relationship between urban development and vegetation states. Thus, we firstly created an urban development score to quantify the development intensity of 68,651 urban areas across 14 biomes through using nighttime light and population density data. We then classified each urban area into a relative development level according to the score and biome. Based on the urban development scores and levels, we determined the relationship between urban development and vegetation states, as represented by the remote-sensed vegetation index. Findings suggested that four distinct types of relationships exist across biomes, namely positive, convex (positive then negative), non-significant, and negative. The positive corrections were commonly found between urban development and vegetation states especially in less-productive biomes such as deserts and open shrublands. However, the positive correlations diminished and eventually turned negative as urban areas became more developed and/or were located in more productive biomes. Turning points of the relationship between urban development and vegetation states were detected in four biomes that have moderate productivity. Completely negative relationships were only found in highly productive tropical and temperate biomes. The results of this study call for a closer examination of the altered urban vegetation states as the result of human engineering.