Patterns Of Leaf Area Index Research Articles

Monitoring cropland LAI using Gaussian Process Regression and sentinel – 2 surface reflectance data in Google Earth Engine

ABSTRACT Assessing Leaf Area Index (LAI) is one of the key indicators to study and understand cropland’s productivity. To this end, Sentinel-2 (S2) ideally serves to monitor LAI given its high spatial and temporal resolution. Thanks to the cloud computing prowess of the revolutionary Google Earth Engine (GEE), assessing the temporal changes in LAI over a large area can be achieved quickly, making crop health monitoring in near real-time plausible. To achieve this, this work has integrated the Gaussian Process Regression (GPR) algorithm and the PROSAIL model into GEE to map LAI values for the crops being cultivated accurately. The Automated Radiative Transfer Mode Operator (ARTMO) software generated PROSAIL simulation spectra. The GPR model was trained using the PROSAIL simulation spectra and optimized through the Euclidean distance-Based Diversity (EBD) Active Learning (AL) method. The final GPR model was validated against in-situ data collected on 23rd February, 2023 from the research farms of the Indian Agricultural Research Institute (IARI), New Delhi, India. Through ARTMO, the validated GPR model produced an estimated LAI map over croplands managed by IARI, with an accuracy of 0.688 R2 value, and these LAI values and a precision of 0.96 R2 were observed between the LAI values observed in the ARTMO LAI map and the GEE-generated map. This model was then run into GEE to study the temporal changes in LAI values for Mustard, Chickpea, and Wheat, three prominent crops for the Rabi season. These temporal LAI patterns can assist in understanding the cropland’s phenological changes. Thanks to GEE, crop phenology monitoring can be efficiently realized using temporal LAI estimates.

Canopy light transmittance-based crop growth monitoring and leaf area index estimation in a garlic field

ABSTRACT A smart agricultural system is necessary for monitoring crop growth and stress conditions using near-ground-based remote sensing techniques. Crop growth can be estimated using several standard crop growth parameters. However, obtaining timed sequential data for observing leaf area index (LAI) is challenging, and normalized difference vegetation index (NDVI) estimation in crop fields requires the installation of sensors on frames structure that are taller than the crop. Canopy light transmittance (CLT) indicates the degree of decrease in the amount of light passing through some material. It was conventionally used to understand canopy radiative transfer. This study examined the viability of CLT as a novel crop parameter for monitoring crop growth conditions. The CLT, LAI, and NDVI of a garlic field were recorded for five years. The correlation between daily CLT and LAI was higher than that between NDVI and LAI. Thus, CLT has the potential to sequentially estimate crop LAI values, particularly for capturing the temporal patterns of LAI. In addition, like NDVI, CLT showed sensitivity in representing the canopy structure and the amount of biomass because CLT is conceptually related to the sky gap fraction. Thus, CLT has the potential to serve as a novel growth parameter for continuous crop growth monitoring.

The role of LAI and leaf chlorophyll on NDVI estimated by UAV in grapevine canopies

Despite the wide use of the Normalized Difference Vegetation Index (NDVI) in precision viticulture, there are no studies aimed at discriminating the contribution of plant biomass from that of leaf chlorophyll on canopy NDVI. Leaf area index (LAI) and leaf chlorophyll (Chl) were concomitantly monitored by ground measurements and projected canopy area (PCA), canopy volume (CV) and NDVI by high resolution UAV multispectral images in fully productive “Sangiovese” grapevines either grown in containers or in the field and subjected to different irrigation regimes over two consecutive years. NDVI values calculated from only vine canopy pixels (NDVIUAV) and NDVI obtained from mixed ground-canopy pixels (simulated Satellite NDVI, NDVISAT) were both evaluated as potential predictor of LAI and leaf Chl concentration.The seasonal patterns of LAI and leaf Chl concentration were affected by irrigation, showing differences depending on field vs container-grown conditions. In situations where a decoupling between LAI and leaf Chl occurred, NDVIUAV and NDVISAT showed different responses: NDVIUAV patterns strictly followed the leaf Chl ones, whereas NDVISAT was more affected by LAI. The coefficient of determination between NDVIUAV and leaf Chl ranged between 0.51 and 0.78, that between NDVIUAV and leaf Chl from 0.01 to 0.76, depending on the irrigation-growing conditions combination. NDVISAT was a better predictor of LAI (R2=0.69) than NDVIUAV (R2=0.42). In field-grown vines the relationships between NDVI (both UAV and SAT) and LAI was stronger than in potted ones. The relationships between LAI and PCA (R2=0.44) or LAI and canopy volume (R2=0.77) were both significant. The results allowed to confirm the two main hypotheses behind this experiment: i) leaf Chl concentration had a greater impact than LAI on NDVI values obtained from vine canopy pixels (NDVIUAV), whereas NDVISAT was more affected by LAI; ii) The canopy volume from UAV images was a better predictor of LAI than NDVI and the resulting relationship showed a better temporal stability.

Upscaling field-measured seasonal ground vegetation patterns with Sentinel-2 images in boreal ecosystems

ABSTRACT Aboveground biomass (AGB) and leaf area index (LAI) are key variables of ecosystem processes and functioning. Knowledge is lacking on how well the seasonal patterns of ground vegetation AGB and LAI can be detected by satellite images in boreal ecosystems. We conducted field measurements between May and September during one growing season to investigate the seasonal development of ground vegetation AGB and LAI of seven plant functional types (PFTs) across seven vegetation types (VTs) within three peatland and forest study areas in northern Finland. We upscaled field-measured AGB and LAI with Sentinel-2 (S2) imagery by applying random forest (RF) regressions. Field-measured AGB peaked around the first week of August and, in most cases, one to two weeks later than LAI. Regarding PFTs, deciduous vascular plants had clear unimodal seasonal patterns, while the AGB and LAI of evergreen vegetation and mosses remained steady over the season. Remote sensing regression models explained 24.2–50.2% of the AGB (RMSE: 78.8–198.7 g m−2) and 48.5–56.1% of the LAI (RMSE: 0.207–0.497 m2 m−2) across sites. Peatland-dominant sites and VTs had a higher prediction accuracy. S2-predicted peak dates of AGB and LAI were one to three weeks earlier than the field-based ones. Our findings suggest that boreal ground vegetation seasonality varies among PFTs and VTs and that S2 time series data can be applied to monitor its spatiotemporal patterns, especially in treeless regions.

Effects of global climate change on the hydrological cycle and crop growth under heavily irrigated management – A comparison between CMIP5 and CMIP6

Quantifying the impact of global climate change on the water cycle and crop production is essential for water resource management and agricultural production planning. This study compared multiple GCM (General Circulation Model) projections of CMIP5 (Coupled Model Intercomparison Project 5) and CMIP6 and further used the selected GCMs to quantify the impact of future climate change on hydrology and crop production under intensive irrigation management in the North China Plain using an improved SWAT model (SWAT-MAD). Taylor’s skill score was used firstly to screen out six groups of GCMs with better simulation performance from 10 pairs of homologous GCMs of CMIP5 and CMIP6. The selected GCMs of CMIP5 and CMIP6 were further used to drive SWAT-MAD for a robust evaluation of climate change impacts. Results showed that during winter wheat growing season, average actual evapotranspiration (ETa) increased by 3%, 3%, 4%, and 5%, respectively, under 2041–2070 RCP4.5, 2041–2070 SSP2-4.5, 2041–2100 RCP4.5, and 2071–2100 SSP2-4.5 scenarios, compared to the historical period (1971–2000). During summer maize growing season, those changes in ETa were 4%, 2%, −0.2%, and −3%. Predicted future precipitation, air temperatures, and surface runoff could increase, while irrigation could decrease as precipitation increased. The dynamic patterns of leaf area index of winter wheat and summer maize indicated that there is a tendency for early emergence and maturity of both crops in the future, and the daily total biomass elevated with a corresponding increase in final yields. The highest increases in yields of winter wheat and summer maize were 18.9% and 16.7%. The findings not only contribute to enhancing the confidence of future projections using CMIP6 but also facilitate our understanding of the relative uncertainty of GCMs. This study provides technical and data support for pre-selections of GCMs and decision making in best management practices for groundwater conservation and agricultural production.

Detection of Irrigated Permanent Grasslands with Sentinel-2 Based on Temporal Patterns of the Leaf Area Index (LAI)

Conventional methods of crop mapping need ground truth information to train the classifier. Thanks to the frequent acquisition allowed by recent satellite missions (Sentinel 2), we can identify temporal patterns that depend on both phenology and crop management. Some of these patterns are specific to a given crop and thus can be used to map it. Thus, we can substitute ground truth information used in conventional methods with agronomic knowledge. This approach was applied to identify irrigated permanent grasslands (IPG) in the Crau area (Southern France), which play a crucial role in groundwater recharge. The grassland is managed by making three mows during the May–October period, which leads to a specific temporal pattern of leaf area index (LAI). The mowing detection algorithm was designed using the temporal LAI signal derived from Sentinel 2 observations. The algorithm includes some filtering to remove noise in the signal that might lead to false mowing detection. A pixel is considered a grassland if the number of detected mows is greater than 1. A data set covering five years (2016–2020) was used. The detection mowing number was conducted at the pixel level, and then the results were aggregated at the plot level. An evaluation data set including 780 plots was used to assess the performances of the classification. We obtained a Kappa index ranging between 0.94 and 0.99 according to the year. These results were better than other supervised classification methods that include training data sets. The analysis of land-use changes shows that misclassified plots concern grasslands managed less intensively with strong intra-parcel heterogeneity due to irrigation defects or year-round grazing. Time series analysis, therefore, allows us to understand different management practices. Real land-use change in use can be observed, but long time series are needed to confirm the change and remove ambiguities with heterogeneous grasslands.

Difference in seasonal peak timing of soybean far-red SIF and GPP explained by canopy structure and chlorophyll content

Recent advances in remotely sensed solar-induced chlorophyll fluorescence (SIF) have provided an exciting and promising opportunity for estimating gross primary production (GPP). Previous studies mainly focused on the linear correlation between SIF and GPP and the slope of the SIF-GPP relationship, both of which lack rigorous consideration of the seasonal trajectories of SIF and GPP. Here, we investigated the timing of seasonal peaks of far-red SIF and GPP in soybean fields by integrating tower data, satellite data, and process-based Soil Canopy Observation of Photosynthesis and Energy (SCOPE, v2.0) model simulations. We found inconsistency between the seasonal peak timing of far-red SIF and GPP in three of four soybean fields based on tower far-red SIF and eddy-covariance measurements. In particular, far-red SIF reached its seasonal maximum 14–17 days earlier than GPP. This far-red SIF-GPP difference in peak timing degraded the correlation between sunny-day far-red SIF and GPP at daily scale (Pearson r = 0.83–0.87 at the site with 14–17 days difference and Pearson r = 0.96 at the site with no difference), and it can be explained by a divergence in the seasonality between absorbed photosynthetic active radiation (APAR) and canopy chlorophyll content (ChlCanopy). We found that the seasonality of far-red SIF - a byproduct of the light reactions of photosynthesis - was primarily controlled by APAR, whereas GPP seasonality was dominated by ChlCanopy. Further, SCOPE model simulations showed that the seasonal patterns of leaf area index (LAI), leaf chlorophyll content (ChlLeaf) and leaf angle distribution (LAD) could affect the different peak timing of SIF and GPP and consequently the seasonal relationship between far-red SIF and GPP. A further increase in LAI after the fraction of light absorption (FPAR) saturates and a later peak of ChlLeaf compared to LAI results in a later peak of GPP compared to far-red SIF. More horizontal leaf angles can further exacerbate this difference. Our results advance mechanistic understanding of the SIF-GPP relationships and combining chlorophyll content information with SIF could potentially improve remote-sensing-based GPP estimation.

Experimental throughfall reduction has little effect on shoot and needle developmental patterns or leaf area dynamics in a young longleaf pine (Pinus palustris Mill.) plantation

Reduced precipitation and, consequently, low soil moisture are known to limit tree growth and function by affecting shoot and foliage development, as well as canopy-scale leaf area dynamics and litterfall. Longleaf pine (Pinus palustris Mill.) is considered one of the most drought-resistant forest species in the southeastern U.S. and could serve as a pathway to increase drought resistance of forests in the region. Still, reduced precipitation and low soil moisture impacts on longleaf canopy phenology and growth are not well understood. Over three years (2017–2019), we determined the effects of a 40% throughfall reduction (TR40) (relative to ambient throughfall treatment, TR0) on shoot and foliage phenology and growth in a young (12–14 yr. old) longleaf pine plantation. Each year, we repeatedly measured shoot and needle lengths on primary and secondary branch axes of multiple trees in each treatment plot. We fit growth curves for each tree and branch axis to estimate shoot and needle growth rate, growth start and cessation dates, growth duration (growth start - growth cessation), and final shoot and needle lengths. At the plot level, we documented temporal patterns of leaf area index (LAI) and litterfall to determine whether branch-scale phenological and growth responses to reduced water availability corresponded with temporal changes in LAI and litterfall. We observed significant and consistent differences in shoot and needle elongation patterns between primary and secondary branches. Timing of needle development varied among years and was generally later each successive year. However, shoot elongation patterns were relatively consistent across years. Although soil moisture was lower under throughfall reduction, shoot and needle growth patterns were not affected. LAI and litterfall patterns were also not affected by throughfall reduction. Our results indicate that reductions in rainfall amount (without changes in rainfall frequency or timing) may have little impact on shoot and needle phenology, canopy development, and litter production in established longleaf plantations.

Effects of sowing dates, sowing rates, and nitrogen rates on growth and spectral indices in winter wheat

<p id="C3">To clarify the effects of sowing dates, sowing rates, and nitrogen rates on growth and spectral indices in winter wheat, a two-year winter wheat field experiment under different sowing dates, sowing rates, and nitrogen rates was conducted. We studied systematically the effects of three factors and their interactions on yield, leaf area index (LAI), and normalized difference red edge (NDRE) of winter wheat at critical growth stages. Furthermore, to facilitate real-time monitoring of winter wheat growth dynamics, we also established the appropriate time-series curves of winter wheat canopy NDRE under different yield levels. The results indicated that the change patterns of NDRE and LAI at critical growth stages were very consistent, and the response of three factors to the two indices at critical growth stages was basically the same in winter wheat. In 2018 and 2019, the yield, LAI, and NDRE of winter wheat at each growth stages decreased with the delay of sowing date. In 2019 and 2020, the yield, peaks of LAI and NDRE under late sowing date were the largest except for the filling stage. The LAI and NDRE of winter wheat at different growth stages in the two years increased with the increase of nitrogen rates. However, there was basically no significant difference among sowing rates. Among the three factors, the sowing dates and nitrogen rates had a significant influence on the time-series curves of winter wheat canopy NDRE. The NDRE time series curves of winter wheat were stretched with the increase of nitrogen rates, the descending part of NDRE time series curve shifted to the left with the delay of the sowing date. In 2018 and 2019, the peak values of NDRE time series curves of winter wheat were declined with the delay of the sowing date. In 2019 and 2020, the peak values of the NDRE time series curves of late sowing and over-late sowing winter wheat were higher than that of suitable sowing wheat. The data of two years were merged to establish suitable time-series curves of winter wheat canopy NDRE under three yield levels, and the yield levels were less than 6.75 t hm^-2, 6.75-8.25 t hm^-2, and higher than 8.25 t hm^-2, respectively. The peak values and width of the NDRE time-series curves increased with the increase of yield level. In summary, winter wheat should be sown early at an appropriate date, but if the accumulated temperature before winter was higher, the sowing date should be postponed appropriately. And the growth of late sowing winter wheat could be improved by increasing a certain amount of sowing rates and nitrogen rates. At the same time, these results could provide a technical support for monitoring the growth of winter wheat under different sowing dates and different yield levels.

Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+

Abstract. To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, and harvesting). The SWAT+ (Soil and Water Assessment Tool) model represents agricultural land by default in a generic way, where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and subtropical regions such as sub-Saharan Africa, where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for northeastern Africa. We evaluate the influence of the crop phenology representation on simulations of leaf area index (LAI) and evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR (Water Productivity through Open access of Remotely sensed derived data) ET data, respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations, especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural hydrologic response units (HRUs), which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in areas where they occur using regional to global hydrological models.

Estimating the phenological dynamics of irrigated rice leaf area index using the combination of PROSAIL and Gaussian Process Regression

The growth of rice is a sequence of three different phenological phases. This sequence of change in rice phenology implies that the condition of the plant during the vegetative phase relates directly to the health of leaves functioning during the reproductive and ripening phases. As such, accurate monitoring is important towards understanding rice growth dynamics. Leaf Area Index (LAI) is an important indicator of rice yields and the availability of this information during key phenological phases can support more informed farming decisions. Satellite remote sensing has been adopted as a proxy to field measurements of LAI and with the launch of freely available high resolution Satellite images such as Sentinel-2, it is imperative that accurate retrieval methods are adopted towards monitoring LAI at irrigated rice fields. Here, we evaluate the potential of a hybrid radiative transfer model (i.e., PROSAIL - Gaussian Process Regression (GPR), for estimating the phenological dynamics of irrigated rice LAI using imager derived from the Sentinel-2 multispectral instrument. LAI field measurements were obtained from an experimental rice field in Nasarawa state, Nigeria during the dry season. We used the PROSAIL radiative transfer model to create a look up table (LUT) that was subsequently used to train a GPR model. Afterwards, we evaluated the potential of the hybrid modelling approach by assessing the overall model accuracy and the extent to which LAI was able to accurately predict LAI during key rice phenological phases. We compared the predicted hybrid GPR LAI values with LAI values generated from the SNAP toolbox, based on a hybrid Artificial Neural Network (ANN) modelling approach. Our results show that the overall predictive accuracy of the hybrid GPR model (R2 = 0.82, RMSE = 1.65) was more accurate than that of the hybrid ANN model (R2 = 0.66, RMSE = 3.89) for retrieving LAI values from Sentinel-2 imagery. Both models underestimated LAI values during the reproductive and ripening phases . However, the accuracy during the phenological phases were more significant when using the hybrid GPR model (P < 0.05). During the different phenological phases, the hybrid GPR model predicted LAI more accurately during the reproductive (R2 = 0.7) and ripening (R2 = 0.59) phases compared to the hybrid ANN reproductive and ripening phases. When monitoring LAI phenological profiles of both hybrid models, the hybrid GPR and ANN models underestimated LAI during the reproductive and ripening phases. However, the ANN model underestimations were statistically significantly greater than those for the hybrid GPR model (P < 0.05). Our results highlight the potential of hybrid GPR models for estimating the phenological dynamics of irrigated rice LAI from Sentinel-2 data. They provided more accurate estimation of LAI patterns from varying nitrogen and water applications than hybrid ANN models.

A recalibrated and tested LINTUL-Cassava simulation model provides insight into the high yield potential of cassava under rainfed conditions

Accurate assessments of the yield potential of cassava are needed to analyse yield gaps, define yield targets and set benchmarks for actual yields in Nigeria. This study evaluated the crop model LINTUL-Cassava under assumed potential growth and water-limited conditions in Nigeria. On-farm experiments were conducted at six locations across the three major cassava growing agro-ecologies of Western Africa (Tropical Rainforest – Ogoja and Ikom in Cross River state, Rainforest Transition Savanna – Ekpoma in Edo state and Guinea Savanna – Otukpo in Benue state) during two subsequent seasons (2016 – 2018). Treatments included fertilizer rates calculated to support the assumed potential yields of 90 t fresh storage root yield ha−1 y−1 (equivalent to 32 t DM ha−1, produced in a growing season of 12 months). Light interception (LI) and leaf area index (LAI) were measured each month. The weights of leaves, stems and storage roots were measured at 4 and 8 months after planting and at harvest, and radiation use efficiency (RUE) calculated. The Edo experiment from 2016 was without drought stress and was used to parameterise LINTUL-Cassava and calibrate assimilate partitioning as function of temperature sums. The average fraction of light intercepted during the season was 80 %, with a light extinction coefficient of 0.67 and a RUE of 2.8 g DM MJ−1 intercepted photosynthetically active radiation (IPAR). After calibration, the LINTUL-Cassava model described the crop growth and observed patterns of LAI well in the experiments in Cross River and Edo (2017). Simulated and observed storage root yield at 4 MAP (vegetative period), 8 MAP (mid-season) and at harvest were strongly correlated (R2 of 0.92), with a RMSE of 4.93 t DM ha−1. We ascertained that RUE of cassava was much higher than previously observed in Africa, with an average storage root yield of 39 ± 7 t DM ha−1. Consequently, potential yields are greater and yield gaps larger than expected or previously reported. We conclude that the LINTUL-Cassava model can provide an adequate estimate of storage root yield across major cassava growing agroecological zones in Nigeria under rainfed conditions.

High leaf area index inhibits net primary production in global temperate forest ecosystems.

Within limited growth age in some regions, forest production, including gross primary production (GPP) and net primary production (NPP), was linearly correlated with leaf area index (LAI). However, over wide range of growth age in the global scale, LAI patterns of forest production are unclear. Here, we compiled a subset from the Global Soil Respiration Database (SRDB) for global temperate forest ecosystems. The subset database mainly included forest production, soil respiration, and LAI data in 493 study sites over wide range of forest growth age (0-500years). The results showed that LAI initially increased rapidly, reached a peak at juvenility, decreased slowly until maturity, and again increased possibly with further forest aging (R2 = 0.21, P < 0.001). We found that the dynamics of both GPP and NPP across global temperate forest ecosystems were driven by LAI. GPP initially increased and subsequently stabilized with increasing LAI. NPP peaked at LAI of about 5.6m2m-2, and subsequently decreased. The decrease in NPP resulted from the asymptotic increase in GPP and the continuing decrease in the NPP/GPP ratio with increasing LAI. The decline in the NPP/GPP ratio resulted from the significant increase in autotrophic respiration (Ra), and especially after canopy closure, Ra increased more quickly with increasing LAI than GPP. These results will improve our understanding of the control of LAI on ecosystem production.

Sampling protocols of specific leaf area for improving accuracy of the estimation of forest leaf area index

Litterfall collection is a non-destructive direct method to estimate forest leaf area index (LAI) and validate indirect LAI products. However, the potential errors associated with the variation in specific leaf area (SLA) are rarely explored. Here, we measured the SLA of leaf litter for each tree species in a temperate deciduous forest using the litterfall collection method from 2012 to 2018, and assessed the spatial and temporal variation in SLA and its consequence on LAI estimates. The results showed that the spatial and temporal variation in SLA for the 10 major species across the seven years ranged from 0.8% to 24.3%, with the variation across the nine permanent plots (20 m × 30 m) being higher than that within plot (five 1-m2 traps), and interannual higher than seasonal. Across the 63 plot-years, the 10 simplified SLA sampling protocols introduced the errors in LAI by −12.4% to 22.2% relative to the reference protocol (sampling leaves from each trap at each collection date for the major species). Applying the SLA obtained from one trap in each plot, one plot, at the leaf fall peak for each year, or for a single year induced the errors in LAI by –3.7% to 2.9%, −6.4% to 14.7%, −9.2% to 4.7%, and −8.1% to 8.6%, respectively. Considering the trade-off between inter-plot and interannual variation, we recommend sampling from one-trap for each plot at the leaf-fall peak for measuring SLA and quantifying the spatial pattern of LAI, and sampling each species in the typical plot for measuring SLA and monitoring the temporal fluctuation in LAI. The tight relationship between the MODIS and ground reference LAI across the seven years (R2 = 0.77) validated the use of MODIS LAI to study the long-term change in forest LAI. These findings help to establish a standardized protocol for long-term accurately measuring forest LAI with the litterfall collection.

Peatland leaf-area index and biomass estimation with ultra-high resolution remote sensing

ABSTRACT There is fine-scale spatial heterogeneity in key vegetation properties including leaf-area index (LAI) and biomass in treeless northern peatlands, and hyperspectral drone data with high spatial and spectral resolution could detect the spatial patterns with high accuracy. However, the advantage of hyperspectral drone data has not been tested in a multi-source remote sensing approach (i.e. inclusion of multiple different remote sensing datatypes); and overall, sub-meter-level leaf-area index (LAI) and biomass maps have largely been absent. We evaluated the detectability of LAI and biomass patterns at a northern boreal fen (Halssiaapa) in northern Finland with multi-temporal and multi-source remote sensing data and assessed the benefit of hyperspectral drone data. We measured vascular plant percentage cover and height as well as moss cover in 140 field plots and connected the structural information to measured aboveground vascular LAI and biomass and moss biomass with linear regressions. We predicted both total and plant functional type (PFT) specific LAI and biomass patterns with random forests regressions with predictors including RGB and hyperspectral drone (28 bands in a spectral range of 500–900 nm), aerial and satellite imagery as well as topography and vegetation height information derived from structure-from-motion drone photogrammetry and aerial lidar data. The modeling performance was between moderate and good for total LAI and biomass (mean explained variance between 49.8 and 66.5%) and variable for PFTs (0.3–61.6%). Hyperspectral data increased model performance in most of the regressions, usually relatively little, but in some of the regressions, the inclusion of hyperspectral data even decreased model performance (change in mean explained variance between −14.5 and 9.1%-points). The most important features in regressions included drone topography, vegetation height, hyperspectral and RGB features. The spatial patterns and landscape estimates of LAI and biomass were quite similar in regressions with or without hyperspectral data, in particular for moss and total biomass. The results suggest that the fine-scale spatial patterns of peatland LAI and biomass can be detected with multi-source remote sensing data, vegetation mapping should include both spectral and topographic predictors at sub-meter-level spatial resolution and that hyperspectral imagery gives only slight benefits.

Estimating Grassland Parameters from Sentinel-2: A Model Comparison Study

Grassland plays an important role in German agriculture. The interplay of ecological processes in grasslands secures important ecosystem functions and, thus, ultimately contributes to essential ecosystem services. To sustain, e.g., the provision of fodder or the filter function of soils, agricultural management needs to adapt to site-specific grassland characteristics. Spatially explicit information derived from remote sensing data has been proven instrumental for achieving this. In this study, we analyze the potential of Sentinel-2 data for deriving grassland-relevant parameters. We compare two well-established methods to calculate the aboveground biomass and leaf area index (LAI), first using a random forest regression and second using the soil–leaf-canopy (SLC) radiative transfer model. Field data were recorded on a grassland area in Brandenburg in August 2019, and were used to train the empirical model and to validate both models. Results confirm that both methods are suitable for mapping the spatial distribution of LAI and for quantifying aboveground biomass. Uncertainties generally increased with higher biomass and LAI values in the empirical model and varied on average by a relative RMSE of 11% for modeling of dry biomass and a relative RMSE of 23% for LAI. Similar estimates were achieved using SLC with a relative RMSE of 30% for LAI retrieval, and a relative RMSE of 47% for the estimation of dry biomass. Resulting maps from both approaches showed comprehensible spatial patterns of LAI and dry biomass distributions. Despite variations in the value ranges of both maps, the average estimates and spatial patterns of LAI and dry biomass were very similar. Based on the results of the two compared modeling approaches and the comparison to the validation data, we conclude that the relationship between Sentinel-2 spectra and grassland-relevant variables can be quantified to map their spatial distributions from space. Future research needs to investigate how similar approaches perform across different grassland types, seasons and grassland management regimes.

How Can We Represent Seasonal Land Use Dynamics in SWAT and SWAT+ Models for African Cultivated Catchments?

In SWAT and SWAT+ models, the variations in hydrological processes are represented by Hydrological Response Units (HRUs). In the default models, agricultural land cover is represented by a single growing cycle. However, agricultural land use, especially in African cultivated catchments, typically consists of several cropping seasons, following dry and wet seasonal patterns, and are hence incorrectly represented in SWAT and SWAT+ default models. In this paper, we propose a procedure to incorporate agricultural seasonal land-use dynamics by (1) mapping land-use trajectories instead of static land-cover maps and (2) linking these trajectories to agricultural management settings. This approach was tested in SWAT and SWAT+ models of Usa catchment in Tanzania that is intensively cultivated by implementing dominant dynamic trajectories. Our results were evaluated with remote-sensing observations for Leaf Area Index (LAI), which showed that a single growing cycle did not well represent vegetation dynamics. A better agreement was obtained after implementing seasonal land-use dynamics for cultivated HRUs. It was concluded that the representation of seasonal land-use dynamics through trajectory implementation can lead to improved temporal patterns of LAI in default models. The SWAT+ model had higher flexibility in representing agricultural practices, using decision tables, and by being able to represent mixed cropping cultivations.

FORests and HYdrology under Climate Change in Switzerland v1.0: a spatially distributed model combining hydrology and forest dynamics

Abstract. We present FORHYCS (FORests and HYdrology under Climate Change in Switzerland), a distributed ecohydrological model to assess the impact of climate change on water resources and forest dynamics. FORHYCS is based on the coupling of the hydrological model PREVAH and the forest landscape model TreeMig. In a coupled simulation, both original models are executed simultaneously and exchange information through shared variables. The simulated canopy structure is summarized by the leaf area index (LAI), which affects local water balance calculations. On the other hand, an annual drought index is obtained from daily simulated potential and actual transpiration. This drought index affects tree growth and mortality, as well as a species-specific tree height limitation. The effective rooting depth is simulated as a function of climate, soil, and simulated above-ground vegetation structure. Other interface variables include stomatal resistance and leaf phenology. Case study simulations with the model were performed in the Navizence catchment in the Swiss Central Alps, with a sharp elevational gradient and climatic conditions ranging from dry inner-alpine to high alpine. In a first experiment, the model was run for 500 years with different configurations. The results were compared against observations of vegetation properties from national forest inventories, remotely sensed LAI, and high-resolution canopy height maps from stereo aerial images. Two new metrics are proposed for a quantitative comparison of observed and simulated canopy structure. In a second experiment, the model was run for 130 years under climate change scenarios using both idealized temperature and precipitation change and meteorological forcing from downscaled GCM-RCM model chains. The first experiment showed that model configuration greatly influences simulated vegetation structure. In particular, simulations where height limitation was dependent on environmental stress showed a much better fit to canopy height observations. Spatial patterns of simulated LAI were more realistic than for uncoupled simulations of the forest landscape model, although some model deficiencies are still evident. Under idealized climate change scenarios, the effect of the coupling varied regionally, with the greatest effects on simulated streamflow (up to 60 mm yr−1 difference with respect to a simulation with static vegetation parameters) seen at the valley bottom and in regions currently above the treeline. This case study shows the importance of coupling hydrology and vegetation dynamics to simulate the impact of climate change on ecosystems. Nevertheless, it also highlights some challenges of ecohydrological modeling, such as the need to realistically simulate the plant response to increased CO2 concentrations and process uncertainty regarding future land cover changes.

Spatiotemporal variation of vegetation dynamics and correlations with climatic factors in the Tibetan Plateau, China

The Tibetan Plateau has been recognized as one of the most sensitive areas responding to climate change, and has becomes a hotspot for coupled studies on terrestrial ecosystem variation and climate change. Leaf area index (LAI) is a key indicator that reflects vegetation dynamics and has been widely used to analyze the responses of vegetation to climate change. In this study, the spatiotemporal variation of LAI in the growing season and its correlations with climatic factors were analyzed. The results showed that the spatial pattern of LAI decreased from southeast to northwest. In terms of the temporal trend of LAI, 85% of the total study area experienced an increased trend. Additionally, 74% of the whole plateau will experience an improved vegetation growth in the future. Furthermore, temperature, precipitation and solar radiation all showed positive correlations with LAI for most of the study area. Our results effectively revealed the variation of LAI and its correlations with climatic factors. However, grassland in the plateau have been shown to have a greater and more rapid response to climatic fluctuations. Therefore, more managements should be made by local governments to improve the fragile environment, especially for areas with a decreasing LAI trend.

Modeling phenological controls on carbon dynamics in dryland sagebrush ecosystems

Dryland ecosystems play an important role in determining how precipitation anomalies affect terrestrial carbon fluxes at regional to global scales. Thus, to understand how climate change may affect the global carbon cycle, we must also be able to understand and model its effects on dryland vegetation. Dynamic Global Vegetation Models (DGVMs) are an important tool for modeling ecosystem dynamics, but they often struggle to reproduce seasonal patterns of plant productivity. Because the phenological niche of many plant species is linked to both total productivity and competitive interactions with other plants, errors in how process-based models represent phenology hinder our ability to predict climate change impacts. This may be particularly problematic in dryland ecosystems where many species have developed a complex phenology in response to seasonal variability in both moisture and temperature. Here, we examine how uncertainty in key parameters as well as the structure of existing phenology routines affect the ability of a DGVM to match seasonal patterns of leaf area index (LAI) and gross primary productivity (GPP) across a temperature and precipitation gradient. First, we optimized model parameters using a combination of site-level eddy covariance data and remotely-sensed LAI data. Second, we modified the model to include a semi-deciduous phenology type and added flexibility to the representation of grass phenology. While optimizing parameters reduced model bias, the largest gains in model performance were associated with the development of our new representation of phenology. This modified model was able to better capture seasonal patterns of both leaf area index (R2 = 0.75) and gross primary productivity (R2 = 0.84), though its ability to estimate total annual GPP depended on using eddy covariance data for optimization. The new model also resulted in a more realistic outcome of modeled competition between grass and shrubs. These findings demonstrate the importance of improving how DGVMs represent phenology in order to accurately forecast climate change impacts in dryland ecosystems.