Near-infrared Reflectance Of Vegetation Research Articles

Insights into Canopy Escape Ratio from Canopy Structures: Correlations Uncovered through Sentinel-2 and Field Observation

This study explores the quantitative relationship between canopy structure and the canopy escape ratio (fesc), measured as the ratio of near-infrared reflectance of vegetation (NIRv) to the fraction of absorbed photosynthetically active radiation (fAPAR). We analyzed the correlation between fesc and key indicators of canopy structure—specifically, leaf area index (LAI) and clumping index (CI)—utilizing both Sentinel-2 satellite data and in situ observations. Our analysis revealed a moderate correlation between fesc and LAI, evidenced by an R2 value of 0.37 for satellite-derived LAI, which contrasts with the lower correlation (R2 of 0.15) observed with field-measured LAI. Conversely, the relationship between fesc and CI proved to be significantly weaker (R2 < 0.1), indicating minimal interaction between foliage distribution and light escape at the canopy level. This disparity in correlation strength was further evidenced in time series analysis, which showed little phenological variation in fesc compared to LAI. Our findings elucidate the complexities of estimating fesc based on the NIRv to fAPAR ratio and underscore the need for advanced methodologies in future research to enhance the accuracy of canopy escape models.

Overridingly increasing vegetation sensitivity to vapor pressure deficit over the recent two decades in China

Vapor pressure deficit (VPD) shows significant spatial and temporal variability in the context of global climate change, which is important for studying the implications of climate change on the structure and function of ecosystems to analyze the effects of VPD on vegetation dynamics. Spatial patterns of vegetation sensitivity to VPD have been recently investigated, however, the feedback of different vegetation types to VPD may vary depending on physiological characteristics, it is unclear how different types influence the sensitivity to VPD. In this study, the ERA5-Land reanalysis time-series dataset was used to analyze the spatial and temporal trends of VPD under different vegetation types. It was found that VPD showed an increasing trend in vegetated areas over the past 20 years with large spatial heterogeneity, generally increasing with drying conditions. On this basis, the spatial patterns of vegetation sensitivity to VPD and temporal trends in sensitivity were evaluated over the past 20 years in China using the enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) which can describe vegetation dynamics. The results show that the sensitivities under the two indices have high spatial consistency, with northeastern and central China showing positive sensitivities and southern China showing negative sensitivities, respectively. The positive sensitivities are relatively high for Deciduous Broadleaf Forests (DBF), Deciduous Needleleaf Forests (DNF), Grasslands (GL), and Croplands (CL) types, while the negative sensitivities are larger for Shrublands (SL) and Savannas (SA) types. Under different climatic zones, the sensitivity of CL and GL are independent of climatic zones (both showing positive), while the sensitivity of SL is negative in the Humid zone and positive in the Semi-Arid zone. Temporally, the sensitivity showed a slow increasing trend over the last 20 years. In terms of vegetation types, sensitivities of Evergreen Broadleaf Forests (EBF), DBF, GL and CL types showed a significant increasing trend (p < 0.05), except for the SL type, which showed a significant decreasing trend (p < 0.05). The trends of sensitivity are not affected by the differences in vegetation types (all of them show an increasing trend) under arid and semi-arid conditions, while dry sub-humid and humid have a greater impact on sensitivity trends. The finding of an overall increase in sensitivity suggests a mechanism of erratic change in vegetation growth under climate change. Notably, the increased sensitivity of certain vegetation types (especially GL and CL) suggests that these may become progressively vulnerable to increased VPD as global climate change persists, with the risk of moving from facilitation to inhibition of photosynthesis.

Unraveling the spatial-temporal patterns of typhoon impacts on maize during the milk stage in Northeast China in 2020

Typhoons are increasing in inland areas and affecting agriculture under climate change. Assessing the large-scale impact of typhoons on crops is essential for improving agricultural resilience. However, typhoon impacts are typically intricate and uncertain, depending on the different stages of crop growth and the indicators used for assessment. Here, we leveraged satellite data to examine the spatial-temporal patterns of typhoon-induced impacts on maize during the milk stage in Northeast China in 2020. We found that land surface water index (LSWI) showed a significantly higher negative relative change of 161.5% in regions severely affected by typhoons, surpassing that of normalized difference vegetation index (NDVI, 19.7%), near-infrared reflectance of vegetation (NIRV, 31.7%), and enhanced vegetation index (EVI, 25.2%), suggesting the outperformance of LSWI in monitoring the impact of typhoons on maize. Based on LSWI, we revealed the spatial-temporal distribution of typhoon impacts. Specifically, significant impacts on maize growth were observed in central Northeast China, particularly in Heilongjiang (49% of maize fields) and Jilin provinces (53% of maize fields). Furthermore, week-long lagging typhoon impacts on maize growth were generally found but lasted until the end of the maize growing season. We also found the typhoon impacts were partially mitigated by spatial and temporal compensations. Regions with above-average maize growth offset the losses experienced in areas severely affected by typhoons. Additionally, better maize growth in the early stages enhanced its resilience against subsequent typhoon impacts. Despite the mitigating effects on maize production, typhoons may increase the risk of maize mold, which warrants further investigation. Given the projected increase in future climate extremes, this study provides valuable insights into the rapid assessment of typhoon impacts using satellite data and cloud computing and supports decision-making in crop management for sustainable agricultural production.

Relevance of near-surface soil moisture vs. terrestrial water storage for global vegetation functioning

Abstract. Soil water availability is an essential prerequisite for vegetation functioning. Vegetation takes up water from varying soil depths depending on the characteristics of its rooting system and soil moisture availability across depth. The depth of vegetation water uptake is largely unknown across large spatial scales as a consequence of sparse ground measurements. At the same time, emerging satellite-derived observations of vegetation functioning, surface soil moisture, and terrestrial water storage present an opportunity to assess the depth of vegetation water uptake globally. In this study, we characterize vegetation functioning through the near-infrared reflectance of vegetation (NIRv) and compare its relation to (i) near-surface soil moisture from the ESA's Climate Change Initiative (CCI) and (ii) total water storage from the Gravity Recovery and Climate Experiment (GRACE) mission at a monthly timescale during the growing season. The relationships are quantified through partial correlations to mitigate the influence of confounding factors such as energy- and other water-related variables. We find that vegetation functioning is generally more strongly related to near-surface soil moisture, particularly in semi-arid regions and areas with low tree cover. In contrast, in regions with high tree cover and in arid regions, the correlation with terrestrial water storage is comparable to or even higher than that of near-surface soil moisture, indicating that trees can and do make use of their deeper rooting systems to access deeper soil moisture, similar to vegetation in arid regions. At the same time, we note that this comparison is hampered by different noise levels in these satellite data streams. In line with this, an attribution analysis that examines the relative importance of soil water storage for vegetation reveals that they are controlled by (i) water availability influenced by the climate and (ii) vegetation type reflecting adaptation of the ecosystems to local water resources. Next to variations in space, the vegetation water uptake depth also varies in time. During dry periods, the relative importance of terrestrial water storage increases, highlighting the relevance of deeper water resources during rain-scarce periods. Overall, the synergistic exploitation of state-of-the-art satellite data products to disentangle the relevance of near-surface vs. terrestrial water storage for vegetation functioning can inform the representation of vegetation–water interactions in land surface models to support more accurate climate change projections.

Improving retrieval of leaf chlorophyll content from Sentinel-2 and Landsat-7/8 imagery by correcting for canopy structural effects

Accurately estimating leaf chlorophyll content (Chl) at large spatial scales is crucial for monitoring agricultural production and plant photosynthesis. Sentinel-2 and Landsat-7/8 offer the potential to assess Chl with high spatial resolutions using various physically-based, empirical, and hybrid methods, but they still present challenges due to the confounding effects of canopy structure and soil background. The near-infrared reflectance of vegetation (NIRV) has been proposed to characterize canopy structural and soil background effects in sun-induced chlorophyll fluorescence. Therefore, in this study, we developed a novel strategy that incorporates NIRV to minimize canopy structural and soil background effects on Chl retrieval, using bidirectional reflectance factor (BRF) from Sentinel-2 and Landsat-7/8 imagery. We compared Chl estimation using empirical random forest (RF), the physically-based PROSAIL-5B inversion, and a hybrid method using RF to train simulated datasets from PROSAIL-5B. We validated Chl retrieval performance with measured datasets from the National Ecological Observatory Network, covering seven vegetation types. We also compared spatiotemporal Chl variations from Sentinel-2, Landsat-7/8, and MODIS. The results showed that NIRV effectively characterized the canopy structural effect on Chl estimation, associated with leaf area index, average leaf angle, and canopy fraction of senescent leaves. Compared to BRF, correcting BRF by NIRV improved Chl retrieval with RMSE values reduced by 12.2%–27.0% for Sentinel-2, 7.4%–16.4% for Landsat-7, and 6.9%–13.7% for Landsat-8, respectively. This also resulted in a significant increase in R2 values for Sentinel-2 (0.13–0.46), Landsat-7 (0.08–0.19), and Landsat-8 (0.12–0.2), respectively. Sentinel-2 outperformed Landsat-7/8 in Chl estimation, due to the contribution of red edge bands and a higher spatial resolution. Furthermore, the hybrid method performed best for Chl estimation from Sentinel-2 and Landsat-7/8 with relative RMSE values of 17.33%, 20.22%, and 19.23%, respectively. Our hybrid method demonstrated lower model uncertainty with smaller variations of RMSE values across varying biochemical and structural traits and solar and viewing angles, and exhibited more consistent spatiotemporal variations of Chl from Sentinel-2 with phenology compared to Landsat-7/8 and the published MODIS Chl data. Our hybrid method demonstrates significant potential in mitigating canopy structural effects, enhancing Chl retrieval, and facilitating the development of Chl data from various satellite imagery sources.

Response of Vegetation Productivity to Greening and Drought in the Loess Plateau Based on VIs and SIF

In the context of global warming, the frequent occurrence of drought has become one of the main reasons affecting the loss of gross primary productivity (GPP) of terrestrial ecosystems. Under the influence of human activities, the vegetation greening trend of the Loess Plateau increased significantly. Therefore, it is of great significance to study the response of GPP to drought in the Loess Plateau under the greening trend. Here, we comprehensively assessed the ability of vegetation indices (VIs) and solar-induced chlorophyll fluorescence (SIF) to capture GPP changes at different seasonal scales and during drought. Specifically, we utilized three vegetation indices: normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRV), and kernel NDVI index (kNDVI), and determined the drought period of the Loess Plateau in 2001 based on the standardized precipitation evapotranspiration index (SPEI) and the standardized soil moisture index (SSMI). Moreover, the anomalies of VIs and SIF during the drought period and the relationship with GPP anomalies were compared. The results showed that both SIF and VIs were able to capture changes during the drought period as well as in normal years. Overall, SIF captured drought changes better due to water and heat stress as well as GPP changes compared to VIs. Across different time scales, SIF showed the strongest relationship with GPP (meanR2 = 0.85), followed by NIRV (meanR2 = 0.84), NDVI (meanR2 = 0.76), and kNDVI (meanR2 = 0.74), suggesting that SIF is more sensitive to physiological changes in vegetation. Notably, kNDVI performed best in sparse vegetation (meanR2 = 0.85). In capture during drought, NIRV and kNDVI performed better in less productive land classes; SIF showed superior capture as land use class productivity increased. In addition, GPP anomalies correlated better with kNDVI anomalies (meanR2 = 0.50) than with other index anomalies. In the future, efforts to integrate the respective strengths of SIF, NIRV, and kNDVI will improve our understanding of GPP changes.

Warm Early Summer Compensated Reduction in Photosynthesis Caused by 2022 Late Summer Extreme Drought Over the Tibetan Plateau

AbstractThe Tibetan Plateau (TP) is ecologically delicate, its vegetation highly sensitive to climate variations. In 2022, China endured an unprecedented compound drought‐heatwave, escalating TP's August temperature to a record high since 2000. This study examined this event's impact on TP's photosynthesis using multi‐source satellite‐based products. We demonstrated persistent high temperatures from April to August 2022, accompanied by shifting soil water (SW). Initially moist from April to June, the soil turned notably dry, hitting its lowest point in August. Consequently, early summer witnessed a substantial enhancement in gross primary productivity (GPP) and near‐infrared reflectance of vegetation (NIRv) predominantly due to high temperatures, except for reduction in the eastern and southeastern TP. June recorded the largest positive GPP anomaly since 2000. As summer progressed, severe drought, not high temperatures, dominated, causing obvious negative anomalies in GPP and NIRv, especially in August, concentrated in the central and southwestern TP. Separating the growing season into May–June–July (MJJ) and August–September (AS) revealed a clear contrast. Enhancement in photosynthesis during MJJ compensated the decline in AS, making average positive GPP and weak negative NIRv anomalies in the entire growing season. Specifically, MJJ saw GPP magnitudes of 5.724 TgC mo−1 in FluxSat, 9.491 TgC mo−1 in GPP v2.2 and OCO‐2‐based Solar‐induced chlorophyll fluorescence (GOSIF), and 0.004 in NIRv. Conversely, AS experienced a decline: −4.802 TgC mo−1 in FluxSat, −2.837 TgC mo−1 in GOSIF GPP, and −0.006 in NIRv. This delicate balance underscores the intricate interplay between early growth conditions, subsequent environmental stressors, and vegetation's adaptive responses.

Vegetation Index‐Based Models Without Meteorological Constraints Underestimate the Impact of Drought on Gross Primary Productivity

AbstractRecently developed solar‐induced chlorophyll fluorescence‐related vegetation indices (e.g., near infrared reflectance of vegetation (NIRv) and kernel normalized difference vegetation index (kNDVI)) have been reported to be appropriate proxies for vegetation photosynthesis. These vegetation indices can be used to estimate gross primary productivity (GPP) without considering meteorological constraints. However, it is not clear whether such a statement holds true under various environmental conditions. In this study, we explored whether these vegetation indices require meteorological constraints to better characterize GPP under extreme drought conditions using three extreme drought cases in Europe in 2003, 2010, and 2018. According to the long‐term series of observations, vegetation indices (NIRv and kNDVI) alone explained 60% and 57%, respectively, of the weekly GPP variation across the 66 flux sites. The explained variation increased to 69% and 64%, respectively, for the models that take into account radiative effects (NIRv and kNDVI multiplied by radiation). However, without considering meteorological constraints, these vegetation index‐based estimations severely underestimated negative GPP anomalies under drought stress, especially in models that incorporate radiative effects. After incorporating vapor pressure deficit (VPD)‐based meteorological constraints, the GPP estimations exhibited more pronounced negative anomalies during drought periods while maintaining model accuracy (at 70% and 65%, respectively). In addition, the GPP models based on site observations were applied at the regional scale (Europe). Our results indicated that the models without meteorological constraints again underestimated the impact of drought on GPP. This study emphasizes the importance of meteorological constraints in the estimation of GPP, especially under extreme drought conditions.

Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau

The accuracy of vegetation indices (VIs) in estimating forest stand age is significantly inadequate due to insufficient consideration of the differences in the physiological functions of forest ecosystems, which limits the accuracy of carbon sink simulation. In this study, remote sensing inversion and mapping of forest stand age were carried out on the Loess Plateau under consideration of the remote sensing mechanism of VIs and the physiological function and canopy structure of the forest using multiple linear regression (MLR) and random forest (RF) models. The main conclusions are as follows: (1) The canopy reflectance of different forest stands has a significant change pattern, and the older the forest stands, the lower the NIR reflectance. The relationship between forest stands and red edge is the most significant, and r is 0.53, and the relationship between Simple Ratio Index (SR), near-infrared reflectance of vegetation (NIRv), normalized difference vegetation index (NDVI), Global Vegetation Index and forest stands is more nonlinear than linear. (2) Principal component analysis (PCA) of canopy spectral information shows that SR, NDVI and red edge (B5) could explain 98% of all spectral information. SR, NDVI and red edge (B5) were used to construct a multiple linear regression model and random forest (RF) algorithm model, and RF has high estimation accuracy (R2 = 0.63). (3) The accuracy of the model was evaluated using reference data, and it was found that the accuracy of the RF model (R2 = 0.63) was higher than that of the linear regression model (R2 = 0.61), but both models underestimated the forest stand age when the forest stand age was greater than 50a, which may be caused by the saturation of the reflectance of the old forest canopy. The RF model was used to generate the dataset of forest stand information in the Loess Plateau, and it was found that the forest is dominated by young forests (&lt;20a), accounting for 38.26% of the forest area, and the average age of forests in the Loess Plateau is 56.1a. This study not only improves the method of forest stand age estimation, but also provides data support for vegetation construction in the Loess Plateau.

Stress-induced changes in photosynthesis and proximal fluorescence emission of turfgrass

Remote measurements of solar-induced fluorescence (SIF) are now widely used to model gross primary productivity (GPP). However, the ability of SIF to track GPP in different environments, conditions, and at different scales remains uncertain. We designed an experiment to measure SIF and photosynthesis while inducing plant stress in replicated turfgrass. Immediately following application of abscisic acid (ABA), treated grasses experienced a 75% decrease in photosynthesis and an 18% decline in SIFyield, with evidence of alterations in energy partitioning. Withholding water resulted in slower photosynthetic inhibition of lower magnitude, with full recovery upon rewatering. In both treatments, reductions in SIF co-occurred with reductions in canopy greenness. However, we did not observe a relationship between the SIF and near-infrared reflectance of vegetation (NIRv) responses to our treatments in turfgrass. The response differences between treatments highlight the nuances of using SIF as a proxy for GPP.

Combining Chlorophyll Fluorescence and Vegetation Reflectance Indices to Estimate Non-Photochemical Quenching (NPQ) of Rice at the Leaf Scale

Non-photochemical quenching (NPQ) is an indicator of crop stress. Until now, only a limited number of studies have focused on how to estimate NPQ using remote sensing technology. The main challenge is the complicated regulatory mechanism of NPQ. NPQ can be divided into energy-dependent (qE) and non-energy-dependent (non-qE) quenching. The contribution of these two components varies with environmental factors, such as light intensity and stress level due to the different response mechanisms. This study aims to explore the feasibility of estimating NPQ using photosynthesis-related vegetation parameters available from remote sensing by considering the two components of NPQ. We concurrently measured passive vegetation reflectance spectra by spectrometer, as well as active fluorescence parameters by pulse-amplitude modulated (PAM) of rice (Oryza sativa) leaves. Subsequently, we explored the ability of the selected vegetation parameters (including the photochemical reflectance index (PRI), inverted red-edge chlorophyll index (IRECI), near-infrared reflectance of vegetation (NIRv), and fluorescence quantum yield (ΦF)) to estimate NPQ. Based on different combinations of these remote sensing parameters, empirical models were established to estimate NPQ using the linear regression method. Experimental analysis shows that the contribution of qE and non-qE components varied under different illumination conditions. Under high illumination, the NPQ was attributed primarily to the qE component, while under low illumination, it was equally attributed to the qE and non-qE components. Among all tested parameters, ΦF was sensitive to the qE component variation, while IRECI and NIRv were sensitive to the non-qE component variation. Under high illumination, integrating ΦF in the regression model captured NPQ variations well (R2 &gt; 0.74). Under low illumination, ΦF, IRECI, and NIRv explained 24%, 62%, and 65% of the variation in NPQ, respectively, while coupling IRECI or NIRv with ΦF considerably improved the accuracy of NPQ estimation (R2 &gt; 0.9). For all the samples under both low and high illumination, the combination of ΦF with at least one of the other parameters (including IRECI, NIRv and PAR) offers a more versatile and reliable approach to estimating NPQ than using any single parameter alone. The findings of this study contribute to the further development of remote sensing methods for NPQ estimation at the canopy scale in the future.

Improving extraction phenology accuracy using SIF coupled with the vegetation index and mapping the spatiotemporal pattern of bamboo forest phenology

Monitoring plant phenology is vital to maintaining the global carbon balance and management under climate change. Bamboo forest is an essential forest type in subtropical China with a strong carbon sequestration capacity. In recent years, vegetation indices (VIs), which characterize canopy structural parameters, and solar-induced chlorophyll fluorescence (SIF), indicating the photosynthetic activity of vegetation, have provided new perspectives on plant phenology at regional and global scales. However, the best data sources and methods for extracting the phenology of bamboo forests remain to be explored. In this study, new vegetation indices were innovatively constructed by normalizing the VIs (enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2), and near-infrared reflectance of vegetation (NIRv)) based on Moderate Resolution Imaging Spectroradiometer (MODIS) products and SIF products (GOSIF) based on OCO-2 satellites and then taking the mean values of the normalized VIs (EVI, EVI2 and NIRv) and SIF. We called the new indices SVs (SIF and VIs combined indices, including Se (SIF and EVI combined index), Se2 (SIF and EVI2 combined index), or Sn (SIF and NIRv combined index)). Two time series reconstruction methods (asymmetric Gaussian (AG) function fitting and double logistic (DL) function) and two extractive phenology parameter methods (dynamic threshold method (DT) and comparative threshold method (CT)) were employed to extract phenological information. The advantages of SVs for extracting bamboo forest phenology (BFP) were verified by comparing the extraction performance of VIs, SIF, and SVs on the SOS and EOS of bamboo forests. Thus, the best way to extract BFP was explored, and the spatial distribution and spatial-temporal variation characteristics of BFP in China from 2011 to 2020 were analyzed. The results are described as follows: (1) SVs are better able to extract BFP parameters compared with VIs and SIF, especially in bamboo forest-specific off-years and on-years; (2) SIF has better accuracy than VIs in extracting BFP, where both SOS and EOS values obtained from VIs are overestimated, and SIF can reflect BFP information earlier; and (3) the best data sources for extracting SOS and EOS in bamboo forests are Sn and Se, respectively, and the optimal methods are AG_CT and DL_DT, respectively. Compared with SIF, the R2 values of Sn and Se extracted SOS and EOS are improved by 40.7% and 7.7%, and the RMSE values are reduced by 24.7% and 0.7%, respectively; and (4) the SOS for bamboo forests in China from 2011 to 2020 was mainly concentrated in 80–100 days, with an overall advancing trend; the EOS was mainly concentrated in 300–320 days, with an overall delay. The results show that the SVs obtained by coupling VIs and SIF can better track the BFP information, providing a practical reference for macroscopic monitoring of BFP based on medium-resolution time series data.

Solar-induced chlorophyll fluorescence captures photosynthetic phenology better than traditional vegetation indices

Accurate characterization of plant phenology is of great importance for monitoring global carbon, water, and energy cycling. Remotely sensed satellite observations have been widely used to estimate land surface phenology across multiple spatial scales in the last three decades. Recent development on satellite solar-induced chlorophyll fluorescence (SIF) observations have opened an opportunity to monitor the seasonality of plant growth from the perspective of photosynthesis phenology. The SIF observations from the TROPOspheric Monitoring Instrument (TROPOMI) with high spatial resolution (up to 7 km × 3.5 km pixels) and near-daily global coverage provide unprecedented opportunity to observe photosynthetic and land surface phenology from space. However, the performance of TROPOMI SIF-derived phenology has not been systematically evaluated. In this study, we used flux tower gross primary productivity (GPP) and PhenoCam green chromatic coordinate (Gcc) data as the benchmark to verify phenology metrics derived from satellite observations. The phenology metrics including the start (SOS), end (EOS), length of growing season (LOS), and the peak of growing season (POS) were estimated from TROPOMI SIF, normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), near‐infrared reflectance of vegetation (NIRv), and Global Vegetation Phenology product (MCD12Q2), and the latter four were obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) across six vegetation types over North America during the period of 2018–2020. We found that the overall agreements of SIF (R2 ranging from 0.30 to 0.63) against GPP-estimated phenology were stronger than NDVI (0.17–0.41), EVI (0.19–0.39), NIRv (0.23–0.62), and MCD12Q2 (0.19–0.48) derived phenological events. In reference to GPP and Gcc-estimated phenology, SIF also generally has the least error and bias compared to other satellite remote sensing-derived phenology metrics. No significant differences were found between SIF and GPP-derived phenology (P>0.05, two-tailed t-test). In addition, we found that the spatial distribution of SIF-derived phenology reflected the expected latitudinal patterns in phenology dates. SOS, EOS, LOS, and POS observed by MCD12Q2 appeared to be earlier, later, longer, and earlier than TROPOMI SIF-derived phenology, respectively. SIF-based phenological transition dates more closely tracked GPP-based phenology dates, indicating TROPOMI SIF could be a great measure to track photosynthesis seasonality and land surface phenology.

Resilience of riparian vegetation productivity to early 21st century drought in northern California, USA

AbstractDrought and intensive land use can interact as stressors on riparian vegetation, especially along rivers flowing through seasonally dry landscapes. Knowledge of past riparian vegetation response to drought and land use change can provide land managers with a better understanding of changes induced by upstream management actions, climate change, and chronic stressors. To investigate the response of riparian vegetation productivity to drought and land use, we developed a 21‐year time series (2000–2020) of growing season vegetation dynamics using near‐infrared reflectance of vegetation (NIRV) derived from satellite data across 30 watershed subbasins that drain into the San Francisco Bay Delta in central California, USA. We observed a strong response of riparian vegetation to drought, but rapid recovery and very few long‐term declines in productivity. At a local level, vegetation communities' response to drought and post‐drought productivity dynamics were highly variable across biophysical settings and land use gradients. Most of the riparian areas with long‐term declines in NIRV were located in the lower elevation Coast Range on the western side of the study area where there is little to no water engineering or agricultural irrigation runoff to subsidize riparian vegetation. Riparian areas with the greatest long‐term increase were along rivers draining the higher elevation Sierra Nevada range to the east. Our results suggest that river systems with a high proportion of water originating as snowmelt may be more buffered against long‐term drought‐driven declines in productivity than those dependent exclusively on winter rainfall. The long‐term increase in NIRV in the vast majority of riparian areas within our study area may also have been driven in part by increasing atmospheric CO2 concentrations, which have been shown to increase plant water use efficiency.

Comparison and evaluation of evapotranspiration estimations by different approaches in the Yellow River Basin

Abstract Accurately quantifying large-scale evapotranspiration (ET) is of significant scientific importance. In this study, the Shuttleworth–Wallace (SW) model incorporates gross primary productivity (GPP) values from the near-infrared reflectance of vegetation (NIRv) GPP product (NIRvGPP). Monthly ET estimations for the Yellow River Basin (YRB) were obtained using the SW models with GPP derived from the GLASS GPP product (GLASSGPP) and NIRvGPP, as well as GLASS ET and CR ET products (SW_GLASSGPP ET, SW_NIRvGPP ET, GLASS ET, and CR ET). The study analyzes the annual spatio-temporal patterns of these products, evaluates their accuracy and uncertainty, and examines the applicability of a fused ET estimation. The results revealed: (1) differences exist in ET estimations among GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET; (2) monthly GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET align well with the monthly ETWB, with SW_GLASSGPP ET and SW_NIRvGPP ET outperforming GLASS ET and CR ET; (3) average uncertainties of GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET display different spatial variation patterns; and (4) compared to GLASS ET, CR ET, SW_GLASSGPP ET, and SW_NIRvGPP ET, the fused ET estimation achieves the best performance within the YRB.

Winter wheat yield prediction in the conterminous United States using solar-induced chlorophyll fluorescence data and XGBoost and random forest algorithm

Predicting crop yield before harvest and understanding the factors determining yield at a regional scale is vital for global food security, supply chain management in agribusiness, crop and insurance pricing and optimising crop production. Often satellite remote sensing data, environmental data or their combinations are used to model crop yield at a regional scale. However, their contribution, including that of recently developed remote sensing data like solar-induced chlorophyll fluorescence (SIF) and near-infrared reflectance of vegetation (NIRv), are not explored sufficiently. This study aims to assess the contribution of weather, soil and remote sensing data to estimate wheat yield prediction at a regional scale. For this, we employed four types of remote sensing data, thirteen climatic variables, four soil variables, and nationwide yield data of 14 years combined with statistical learning methods to predict winter wheat yield in the Conterminous United States (CONUS) and access the role of predicting variables. Machine-learning algorithms were used to build yield prediction models in different experimental settings, and predictive performance was evaluated. Further, the relative importance of predictor variables for the models was assessed to gain insight into the model's behaviour. NIRv and SIF data are found to be promising for crop yield prediction. The model with only NIRv data explained up to 64% of the variability in yield, and adding SIF data improved it to 69%. We also found that vegetation indices, SIF, climate and soil data all contribute unique and overlapping information to crop yield prediction. The study also identified important variables and the time of the growing period when these variables have higher explanatory power for winter wheat yield prediction. This study enhanced our knowledge of yield-predicting variables, which will contribute to optimising the yield and developing better yield prediction models.

Tracking autumn photosynthetic phenology on Tibetan plateau grassland with the green–red vegetation index

Accurate monitoring autumn photosynthetic phenology is essential for understanding carbon cycles. The broadband green-red vegetation index (GRVI) derived from broadband red and green reflectance has been increasingly used in this field. However, the performance of GRVI in large areas remains unclear. We evaluated the performance of the normalized vegetation index (NDVI), normalized difference greenness index (NDGI), the near-infrared reflectance of vegetation (NIRv), solar-induced chlorophyll fluorescence (SIF) and GRVI in tracking autumn photosynthetic phenology of alpine grassland at flux sites and the entire Tibetan Plateau (TP). The results revealed that GRVI (R2 = 0.42, RMSE = 7.68 d and Bias = 4.39 d) performed comparable with SIF (R2 = 0.45, RMSE = 5.79 d and Bias = 0.71 d) in extracting the end of the photosynthetically active season (EOS) with eddy covariance flux measurements as reference. On contrary, a systematically later EOS was estimated by NDVI (Bias = 20.35 d), NDGI (Bias = 13.62 d) and NIRv (Bias = 6.56 d). The application example on the TP collaborated these findings. The divergent performances between indicators were rooted in the photosynthesis downregulation in autumn which is jointly controlled by canopy structure and physiology. Due to the fact that NDVI, NDGI and NIRv primarily depict vegetation structure, while the use of SIF, which represents vegetation photosynthetic physiology, is influenced by the limited spatial resolution and temporal coverage. Our study highlights the unique advantage of GRVI over other existing satellite indicators for estimating autumn photosynthetic phenology with high resolution and long-time span. We suggest revisiting the dynamics of autumn photosynthetic phenology using GRVI, which has significant implications on carbon uptake studies.

Effects of eastern vs. central Pacific El Niño on Northern Hemisphere photosynthetic seasonality

The El Niño–Southern Oscillation (ENSO) affects many climatic controls on vegetation activity, driving interannual variation in timing (phenology) and magnitude of terrestrial carbon uptake. However, the climatic effects of ENSO can differ for sea surface temperature (SST) anomalies primarily centered in the eastern vs. central Pacific (EP and CP, respectively). Here, we examine the extent to which EP and CP SST anomalies affect Northern Hemisphere photosynthetic seasonality and whether their effects differ. Using two decades of satellite near-infrared reflectance of vegetation (NIRv) and FLUXNET2015 gross primary production, we estimated annual 0.05° start and end of growing season timing corresponding to the onset and offset of photosynthetic activity, as well as peak NIRv magnitude as a proxy for peak growing season productivity. We find that correlations between Northern Hemisphere photosynthetic timing/magnitude and ENSO differ for EP- and CP-centered SST anomalies, though in many regions the effects and differences between them are quite small. Warmer SSTs generally led to an earlier start of the photosynthetic season, especially in North America and parts of Eurasia. However, the magnitude (and even direction) of the relationships between start of season and SST differed for CP- and EP-dominated events. Correlations of both peak NIRv magnitude and end of season timing with ENSO tended to be smaller in magnitude and more regionally idiosyncratic, though with strong evidence of different effects of CP and EP SSTs. In southern North America, CP SSTs (but not EP SSTs) were positively associated with peak NIRv, while in boreal regions of North America and Eurasia, CP SSTs were negatively associated with peak NIRv (despite small positive associations with EP SSTs). Differences between the effects of EP and CP SST anomalies suggest that short-term vegetation forecasts based on aggregate ENSO indices could be improved by separately considering the EP and CP components.

Correction of illumination effects on seasonal divergent NIRv photosynthetic phenology

Accurate estimation of photosynthetic phenology is of great importance for understanding the response of terrestrial biosphere to climate change. The near-infrared reflectance of vegetation (NIRv) has been increasingly used to estimate photosynthetic phenology. However, topography significantly affects illumination conditions and induces uncertainty in the retrieval of NIRv photosynthetic phenology over mountainous areas. We evaluated the illumination effects on three modalities of NIRv: (i) the original NIRv, (ii) the product of NIRv and solar incident radiation (NIRvP), and (iii) the topographically corrected NIRv (TCNIRv). We assessed the impact of sun geometry on the phenological metrics for the timing of the start and end of the season (i.e., SOS and EOS) derived from satellite NIRv time series as compared with those derived from in-situ gross primary production (GPP) measured at the Brasschaat (BE-Bra) and Lägeren (CH-Lae) flux towers located, respectively, over a flat and a mountainous forest area in Europe. We observed a seasonally divergent performance of NIRv in photosynthetic phenology extraction: NIRv-derived SOS had a good consistency with GPP-derived estimates (109/114d for NIRv vs 108/115d for GPP at BE-Bra/CH-Lae), whilst it showed a positive time lag for the EOS (291/292d for NIRv vs 264/268d for GPP at BE-Bra/CH-Lae). The radiation constraint in NIRvP formulation corrected the bias in NIRv-based EOS estimates. Path length correction (PLC) also alleviates the illumination effects on the original NIRv, making TCNIRv comparable to NIRvP in estimating SOS and EOS. We conclude that illumination effects must be corrected from NIRv for the extraction of photosynthetic phenology, especially for the autumn phenology. Our study has significant implications for understanding the responses of phenology to climate change and the climate-carbon feedbacks, particularly over complex topography mountainous areas.

Large discrepancies among remote sensing indices for characterizing vegetation growth dynamics in Nepal

Mountain ecosystems provide multiple ecosystem services and are “natural laboratories” to understand ecosystem responses to global change. Because of the inaccessibility and the high cost of field surveys, remote sensing indices are the major and sometimes the only measures to monitor the vegetation growth dynamics in mountains. However, there are large discrepancies in those indices that should be quantified in mountainous regions. This case study in Nepal, a highly mountainous region, explores the consistency and inconsistency of six widely used remote sensing indices in monitoring vegetation growth from 2000 to 2020. The study considers three greenness indices of normalized difference vegetation indices (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRv), one cover index of leaf area index (LAI), and two productivity indices of gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF). We find high spatial consistency in the multiyear means (r = 0.79∼1, N = 4300, p < 0.01), especially in the highlands and between EVI and NIRv, and a logarithmic relationship between greenness indices or GOSIF and LAI or GPP. In contrast, the long-term trends differ substantially by index and space. Only 7% of the lands show synchronized significant increase though all the indices show a widespread increasing tendency (77∼87% of the lands). The prevalent non-significant changes of all the indices primarily contribute to the trend uncertainties, especially in the highlands. The inconsistencies between greenness and productivity indices and in them further exaggerate the uncertainties. Our results emphasize the large discrepancies of remote sensing indices in quantifying mountain vegetation growth dynamics. Larger inconsistency is expected if we consider disparities among the quality-control schemes, study seasons, remote sensing models, satellite platforms, and sensors. Reinforced remote sensing data, model improvements and/or new indices are needed for an accurate quantification of the vegetation growth dynamics in mountain regions.