Grassland Leaf Area Index Research Articles

Quantifying key vegetation parameters from Sentinel-3 and MODIS over the eastern Eurasian steppe with a Bayesian geostatistical model

Accurate estimation of grassland leaf area index (LAI), fractional vegetation cover (FVC), and aboveground biomass (AGB) is fundamental in grassland studies. The newly launched Ocean and Land Color Imager (OLCI) sensor onboard Sentinel-3 (S3) provides images with comparable spatial and spectral resolution with MODIS data. However, the use of S3 OLCI imageries for vegetation variable estimation is rarely evaluated. This study evaluated the potential of S3 OLCI and MODIS data for estimating grassland LAI, FVC, and AGB in the eastern Eurasian steppe. A Bayesian spatial model (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) was used to address spatial autocorrelation of in-situ observation data and to enhance our predictions. Our results showed that the models based on S3 OLCI data presented higher accuracy than models with MODIS data. The RMSEs decreased by 3.7–10.8 %, 3.7–7.5 %, and 1.6–14.2 % for LAI, FVC, and AGB predictions, respectively. Through combinations of multiple predictors, we confirmed the robustness of red edge bands for grassland variable estimation, the models employing red edge variables yielded 3.5 %, 3.2 %, and 0.4 % lower RMSEs than models with conventional visible and NIR bands for LAI, FVC, and AGB prediction, respectively. INLA-SPDE spatial model produced lower bias and higher prediction accuracy than random forest and random forests kriging method in most of the models; the INLA-SPDE predicted LAI and FVC maps also showed a better agreement with ground observations than MODIS and PROBA-V land products.

An Approach for Joint Estimation of Grassland Leaf Area Index and Leaf Chlorophyll Content from UAV Hyperspectral Data

Leaf area index (LAI) and leaf chlorophyll content (Cab) are two important indicators of vegetation growth. Due to the high-coupling of spectral signals of leaf area and chlorophyll content, simultaneous retrieval of LAI and Cab from remotely sensed date is always challenging. In this paper, an approach for joint estimation of grassland LAI and Cab from unmanned aerial vehicle (UAV) hyperspectral data was proposed. Firstly, based on a PROSAIL model, 15 typical hyperspectral vegetation indices (VIs) were calculated and analyzed to identify optimal VIs for LAI and Cab estimation. Secondly, four pairs of VIs were established and their discreteness was also calculated for building a two-dimension matrix. Thirdly, a two-layer VI matrix was generated to determine the relationship of VIs with LAI values and Cab values. Finally, LAI and Cab were jointly retrieved according to the cells of the two-layer matrix. The retrieval reduced the cross-influence between LAI and Cab. Compared with the VI empirical model and the single-layer VI matrix, the accuracy of LAI and Cab retrieved from UAV hyperspectral data based on the two-layer VI matrix was significantly improved (for LAI: R2 = 0.73, RMSE = 0.91 m2/m2 and u(SD) = 0.82 m2/m2; for Cab: R2 = 0.79, RMSE = 11.7 μg/cm2 and u(SD) = 10.84 μg/cm2). The proposed method has the potential for rapid retrieval of LAI and Cab from hyperspectral data. As a method similar to look-up table, the two-layer matrix can be used directly for LAI and Cab estimation without the need for prior measurements for training.

Forest, Crop and Grassland Leaf Area Index Estimation Using Remote Sensing: A Review of Current Research Methods, Sensors, Estimation Models and Accomplishments

Leaf area index (LAI) is an important parameter in plant ecophysiology; it can be used to quantify foliage directly and as a measure of the photosynthetic active area and, thus, the area subject to transpiration in vegetation. The aim of this paper was to review work on remote sensing methods of estimating LAI across different forest ecosystems, crops and grasslands in terms of remote sensing platforms, sensors and models. To achieve this aim, scholarly articles with the title or keywords “Leaf Area Index estimation” or “LAI estimation” were searched on Google Scholar and Web of Science with a date range between 2010 and 2020. The study’s results revealed that during the last decade, the use of remote sensing to estimate and map LAI increased for crops and natural forests. However, there is still a need for more research concerning commercial forests and grasslands, as the number of studies remains low. Of the 84 studies related to forests, 60 were related to natural forests and 24 were related to commercial forests. In terms of model types, empirical models were most often used for estimating the LAI of forests, followed by physical models.

Modeling the Leaf Area Index of Inner Mongolia Grassland Based on Machine Learning Regression Algorithms Incorporating Empirical Knowledge

Leaf area index (LAI) is one of the key biophysical indicators for characterizing the growth and status of vegetation and is also used in modeling earth system processes. Machine learning algorithms (MLAs) such as random forest regression (RFR), artificial neural network regression (ANNR) and support vector regression (SVR) based on satellite data have been widely used for the estimation of LAI. However, the selection of input variables has a great impact on the estimation performance of MLAs. In this study, we aimed to improve the LAI inversion model of Inner Mongolia grassland based on MLAs incorporating empirical knowledge. Firstly, we used the ANNR, SVR and RFR approaches, respectively, to rank the input variables including vegetation indices, climate factors, soil factors and topography factors and found that Normalized Difference Phenology Index (NDPI) contributed the most to LAI estimation. Secondly, we selected four sets of input variables, namely, all variables—A, model selected variables—B, overlapping variables—C and self-defined variables—D, respectively. Subsequently, we built twelve LAI estimation models (RFR-A, RFR-B, RFR-C, etc.) based on three MLAs and four sets of input variables. The evaluation of them showed the RFR produced higher prediction accuracy, followed by ANNR and SVR. Furthermore, the RFR-D presented the highest accuracy in predicting LAI (R2 = 0.55, RMSE = 0.37 m2/m2, MAE = 0.29 m2/m2). Finally, we compared our results with MODIS LAI and GEOV2 LAI products and found that all of them showed a similar spatial distribution of grassland LAI in the four sub-regions covering all grassland types, but our model exhibited larger LAI values in the desert steppe and smaller LAI values in the others. These findings demonstrated that MLAs incorporating empirical knowledge could improve the accuracy of modelling LAI and further study is necessary to reduce the uncertainty in LAI mapping in grassland.

Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data

An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.

Comparison of Direct and Indirect Determination of Leaf Area Index in Permanent Grassland

Indirect, non-destructive methods to derive biophysical parameters, such as leaf area index (LAI), are of major importance for optimal grassland growth modelling and management. In this study, we compared different methods for the estimation of LAI in permanent grassland including (i) two direct methods, (ii) two indirect optical methods (AccuPAR and LAI-2200C), (iii) a proximal (field spectrometer) and a satellite remote sensing approach using Sentinel-2 (S-2) data, both based on radiative transfer modelling (RTM) of vegetation. To consider the seasonal variability of LAI sufficiently, we performed in situ measurements weekly during the entire growing season of 2018 and 2019. The RTM-based methods showed the lowest root-mean-square error (RMSE) when compared with direct green LAI measurements, which ranged from 1.68 to 7.85. The indirect optical methods resulted in higher RMSE values but in similar high correlation coefficients (r). The comparison between the indirect optical methods showed that the AccuPAR and the LAI-2200C highly correlate with a systematic underestimation of the AccuPAR in the upper range of LAI values. As expected, S-2 and the field spectrometer showed the highest correlation (RMSE: 0.40 and r: 0.95). Generally, we observed a significant influence of the seasonal changes of the canopy structure and morphology on the estimation accuracy.

VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

Red-edge (RE) spectral vegetation indices (SVIs)—combining bands on the sharp change region between near infrared (NIR) and visible (VIS) bands—alongside with SVIs solely based on NIR-shoulder bands (wavelengths 750–900 nm) have been shown to perform well in estimating leaf area index (LAI) from proximal and remote sensors. In this work, we used RE and NIR-shoulder SVIs to assess the full potential of bands provided by Sentinel-2 (S-2) and Sentinel-3 (S-3) sensors at both temporal and spatial scales for grassland LAI estimations. Ground temporal and spatial observations of hyperspectral reflectance and LAI were carried out at two grassland sites (Monte Bondone, Italy, and Neustift, Austria). A strong correlation (R2 > 0.8) was observed between grassland LAI and both RE and NIR-shoulder SVIs on a temporal basis, but not on a spatial basis. Using the PROSAIL Radiative Transfer Model (RTM), we demonstrated that grassland structural heterogeneity strongly affects the ability to retrieve LAI, with high uncertainties due to structural and biochemical PTs co-variation. The RENDVI783.740 SVI was the least affected by traits co-variation, and more studies are needed to confirm its potential for heterogeneous grasslands LAI monitoring using S-2, S-3, or Gaofen-5 (GF-5) and PRISMA bands.

Retrieval of Grassland Aboveground Biomass through Inversion of the PROSAIL Model with MODIS Imagery

The estimation of aboveground biomass (AGB), an important indicator of grassland production, is crucial for evaluating livestock carrying capacity, understanding the response and feedback to climate change, and achieving sustainable development. Most existing grassland AGB estimation studies were based on empirical methods, in which field measurements are indispensable, hindering their operational use. This study proposed a novel physically-based grassland AGB retrieval method through the inversion of PROSAIL model against MCD43A4 imagery. This method relies on the basic understanding that grassland is herbaceous, and therefore AGB can be represented as the product of leaf dry matter content (Cm) and leaf area index (LAI), i.e., AGB = Cm × LAI. First, the PROSAIL model was parameterized according to the literature regarding grassland parameters retrieval, then Cm and LAI were retrieved using a lookup table (LUT) algorithm, finally, the retrieved Cm and LAI were multiplied to obtain the AGB. The method was assessed in Zoige Plateau, China. Results show that it could reproduce the reference AGB map, which is generated by upscaling the field measurements, in terms of magnitude (with RMSE and R-RMSE of 60.06 g·m−2 and 18.1%, respectively) and spatial distribution. The estimated AGB time series also agreed reasonably well with the expected temporal dynamic trends of the grassland in our study area. The greatest advantage of our method is its fully physical nature, i.e., no field measurement is needed. Our method has the potential for operational monitoring of grassland AGB at regional and even larger scales.

An agent-based model for assessing grazing strategies and institutional arrangements in Zeku, China

The assessment of grassland grazing strategies and institutional arrangements is essential for ensuring the sustainable development of grassland grazing systems. By employing per-pixel grazing information derived from remote sensing data, this paper presents an agent-based model of grassland grazing (ABMGG) for Zeku, China that was designed as a framework for assessing the effects of different combinations of grazing strategies and institutional arrangements on grassland status. By calibrating the parameter values of the ABMGG to the system status values under a policy that has already been implemented, the ABMGG can help us to understand grassland degradation in response to management interventions for each patch of land. In the Zeku implementation, it was found that although different grazing policy scenarios could not significantly improve or decrease the overall grassland leaf area index, a rotational group grazing scenario with a land market tenure system did produce a smaller number of severely degraded grass patches than other policy scenarios (except regional continuous grazing). This provides a new perspective on the consequences of grassland management practices where past research has concentrated more on overall grassland productivity. The ABMGG can extend the ability of policy assessment tools to a high resolution level with pixel-specific real-time remote sensing data, making the assessment results more accurate and representative.

Generalizability of gene expression programming and random forest methodologies in estimating cropland and grassland leaf area index

Leaf Area Index (LAI) is a very important structural attribute of ecosystems which affects the energy, water and carbon exchanges between the land surface and atmosphere. Direct measurement of LAI is costly and time consuming so indirect measurement approaches have been developed for determining its magnitude. The present paper aimed at modeling LAI in cropland and grassland sites using the available meteorological data through two heuristic data driven techniques, namely, gene expression programming (GEP) and random forest (RF). Different data set organizations were designed using local (temporal) and external (spatial) norms to provide a thoroughgoing data scanning strategy. The results showed that the external GEP and RF models (EGEP and ERF) might be suitable approaches for modeling LAI by average scatter index (SI) values of 0.275 and 0.270 (for cropland) and 0.273 and 0.279 (for grassland) when compared to the local GEP and RF models with average SI values of 0.207 and 0.204 (cropland), and 0.249 and 0.204 (grassland), respectively. The presented methodology allowed the evaluation in each site of models developed (trained) using local patterns and the models developed using the exogenous data (patterns from ancillary sites).

The effect of satellite-derived surface soil moisture and leaf area index land data assimilation on streamflow simulations over France

Abstract. This study evaluates the impact of assimilating surface soil moisture (SSM) and leaf area index (LAI) observations into a land surface model using the SAFRAN–ISBA–MODCOU (SIM) hydrological suite. SIM consists of three stages: (1) an atmospheric reanalysis (SAFRAN) over France, which forces (2) the three-layer ISBA land surface model, which then provides drainage and runoff inputs to (3) the MODCOU hydro-geological model. The drainage and runoff outputs from ISBA are validated by comparing the simulated river discharge from MODCOU with over 500 river-gauge observations over France and with a subset of stations with low-anthropogenic influence, over several years. This study makes use of the A-gs version of ISBA that allows for physiological processes. The atmospheric forcing for the ISBA-A-gs model underestimates direct shortwave and long-wave radiation by approximately 5 % averaged over France. The ISBA-A-gs model also substantially underestimates the grassland LAI compared with satellite retrievals during winter dormancy. These differences result in an underestimation (overestimation) of evapotranspiration (drainage and runoff). The excess runoff flowing into the rivers and aquifers contributes to an overestimation of the SIM river discharge. Two experiments attempted to resolve these problems: (i) a correction of the minimum LAI model parameter for grasslands and (ii) a bias-correction of the model radiative forcing. Two data assimilation experiments were also performed, which are designed to correct random errors in the initial conditions: (iii) the assimilation of LAI observations and (iv) the assimilation of SSM and LAI observations. The data assimilation for (iii) and (iv) was done with a simplified extended Kalman filter (SEKF), which uses finite differences in the observation operator Jacobians to relate the observations to the model variables. Experiments (i) and (ii) improved the median SIM Nash scores by about 9 % and 18 % respectively. Experiment (iii) reduced the LAI phase errors in ISBA-A-gs but had little impact on the discharge Nash efficiency of SIM. In contrast, experiment (iv) resulted in spurious increases in drainage and runoff, which degraded the median discharge Nash efficiency by about 7 %. The poor performance of the SEKF originates from the observation operator Jacobians. These Jacobians are dampened when the soil is saturated and when the vegetation is dormant, which leads to positive biases in drainage and/or runoff and to insufficient corrections during winter, respectively. Possible ways to improve the model are discussed, including a new multi-layer diffusion model and a more realistic response of photosynthesis to temperature in mountainous regions. The data assimilation should be advanced by accounting for model and forcing uncertainties.

Estimating grassland LAI using the Random Forests approach and Landsat imagery in the meadow steppe of Hulunber, China

Leaf area index (LAI) is a key parameter for describing vegetation structures and is closely associated with vegetative photosynthesis and energy balance. The accurate retrieval of LAI is important when modeling biophysical processes of vegetation and the productivity of earth systems. The Random Forests (RF) method aggregates an ensemble of decision trees to improve the prediction accuracy and demonstrates a more robust capacity than other regression methods. This study evaluated the RF method for predicting grassland LAI using ground measurements and remote sensing data. Parameter optimization and variable reduction were conducted before model prediction. Two variable reduction methods were examined: the Variable Importance Value method and the principal component analysis (PCA) method. Finally, the sensitivity of RF to highly correlated variables was tested. The results showed that the RF parameters have a small effect on the performance of RF, and a satisfactory prediction was acquired with a root mean square error (RMSE) of 0.1956. The two variable reduction methods for RF prediction produced different results; variable reduction based on the Variable Importance Value method achieved nearly the same prediction accuracy with no reduced prediction, whereas variable reduction using the PCA method had an obviously degraded result that may have been caused by the loss of subtle variations and the fusion of noise information. After removing highly correlated variables, the relative variable importance remained steady, and the use of variables selected based on the best-performing vegetation indices performed better than the variables with all vegetation indices or those selected based on the most important one. The results in this study demonstrate the practical and powerful ability of the RF method in predicting grassland LAI, which can also be applied to the estimation of other vegetation traits as an alternative to conventional empirical regression models and the selection of relevant variables used in ecological models.

Comparison of partial least squares and support vector regressions for predicting leaf area index on a tropical grassland using hyperspectral data

Leaf area index (LAI) is a key biophysical parameter commonly used to determine vegetation status, productivity, and health in tropical grasslands. Accurate LAI estimates are useful in supporting sustainable rangeland management by providing information related to grassland condition and associated goods and services. The performance of support vector regression (SVR) was compared to partial least square regression (PLSR) on selected optimal hyperspectral bands to detect LAI in heterogeneous grassland. Results show that PLSR performed better than SVR at the beginning and end of summer. At the peak of the growing season (mid-summer), during reflectance saturation, SVR models yielded higher accuracies (R2=0.902 and RMSE=0.371 m2 m−2) than PLSR models (R2=0.886 and RMSE=0.379 m2 m−2). For the combined dataset (all of summer), SVR models were slightly more accurate (R2=0.74 and RMSE=0.578 m2 m−2) than PLSR models (R2=0.732 and RMSE=0.58 m2 m−2). Variable importance on the projection scores show that most of the bands were located in the near-infrared and shortwave regions of the electromagnetic spectrum, thus providing a basis to investigate the potential of sensors on aerial and satellite platforms for large-scale grassland LAI prediction.

Predicting Grassland Leaf Area Index in the Meadow Steppes of Northern China: A Comparative Study of Regression Approaches and Hybrid Geostatistical Methods

Leaf area index (LAI) is a key parameter used to describe vegetation structures and is widely used in ecosystem biophysical process and vegetation productivity models. Many algorithms have been developed for the estimation of LAI based on remote sensing images. Our goal was to produce accurate and timely predictions of grassland LAI for the meadow steppes of northern China. Here, we compare the predictive power of regression approaches and hybrid geostatistical methods using Chinese Huanjing (HJ) satellite charge coupled device (CCD) data. The regression methods evaluated include partial least squares regression (PLSR), artificial neural networks (ANNs) and random forests (RFs). The two hybrid geostatistical methods were regression kriging (RK) and random forests residuals kriging (RFRK). The predictions were validated for different grassland types and different growing stages, and their performances were also examined by adding several groups of vegetation indices (VIs). The two hybrid geostatistical models (RK and RFRK) yielded the most accurate predictions (root mean squared error (RMSE) = 0.21 m2/m2 and 0.23 m2/m2 for RK and RFRK, respectively), followed by the RF model (RMSE = 0.27 m2/m2), which was the most accurate among the regression models. These three models also exhibited the best temporal performance across the duration of the growing season. The PLSR and ANN models were less accurate (RMSE = 0.33 m2/m2 and 0.35 m2/m2 for ANN and PLSR, respectively), and the PLSR model performed the worst (exhibiting varied temporal performance and unreliable prediction accuracy that was susceptible to ground conditions). By adding VIs to the predictor variables, the predictions of the PLSR and ANN models were obviously improved (RMSE improved from 0.35 m2/m2 to 0.28 m2/m2 for PLSR and from 0.33 m2/m2 to 0.28 m2/m2 for ANN); the RF and RFRK models did not generate more accurate predictions and the performance of the RK model declined (RMSE decreased from 0.21 m2/m2 to 0.32 m2/m2).

Comparison of Landsat 8 OLI and Landsat 7 ETM+ for estimating grassland LAI using model inversion and spectral indices: case study of Mpumalanga, South Africa

ABSTRACTThe leaf area index (LAI) is the key biophysical indicator used to assess the condition of rangeland. In this study, we investigated the implications of narrow spectral response, high radiometric resolution (12 bits), and higher signal-to-noise ratio of the Landsat 8 Operational Land Imager (OLI) sensor for the estimation of LAI. The Landsat 8 LAI estimates were compared to that of its predecessors, namely Landsat 7 Enhanced Thematic Mapper Plus (ETM+) (8 bits). Furthermore, we compared the radiative transfer model (RTM) and spectral indices approaches for estimating LAI on rangeland systems in South Africa. The RTM was inverted using artificial neural network (ANN) and lookup table (LUT) algorithms. The accuracy of the models was higher for Landsat 8 OLI, where ANN (root mean squared error, RMSE = 0. 13; R2 = 0. 89), LUT (RMSE = 0. 25; R2 = 0. 50), compared to Landsat 7 ETM+, where ANN (RMSE = 0. 35; R2 = 0. 60), LUT (RMSE = 0. 38; R2 = 0. 50). Compared to an empirical approach, the RTM provided higher accuracy. In conclusion, Landsat 8 OLI provides an improvement for the estimation of LAI over Landsat 7 ETM+. This is useful for rangeland monitoring.

Comparison of two inversion methods for leaf area index using HJ-1 satellite data in a temperate meadow steppe

Leaf area index (LAI) is one of the most important parameters for determining grassland canopy conditions. LAI controls numerous biological and physical processes in grassland ecosystems. Remote-sensing techniques are effective for estimating grassland LAI at a regional scale. Comparison of LAI inversion methods based on remote sensing is significant for accurate estimation of LAI in particular areas. In this study, we developed and compared two inversion models to estimate the LAI of a temperate meadow steppe in Hulunbuir, Inner Mongolia, China, based on HJ-1 satellite data and field-measured LAI data. LAI was measured from early June to late August in 2013, obtained from 326 sampling data. The back propagation (BP) neural network method proved better than the statistical regression model for estimating grassland LAI, the accuracy of the former being 82.8%. We then explored the spatio-temporal distribution in LAI of Stipa baicalensis Roshev. in the meadow steppe of Hulunbuir, including cut, grazed, and fenced plots. The LAI in the cut and grazed plots reflected the growth variations in S. baicalensis Roshev. However, because of the obvious litter layer, the LAI in the fenced plots was underestimated.

Comparative analysis of different retrieval methods for mapping grassland leaf area index using airborne imaging spectroscopy

Fine scale maps of vegetation biophysical variables are useful status indicators for monitoring and managing national parks and endangered habitats. Here, we assess in a comparative way four different retrieval methods for estimating leaf area index (LAI) in grassland: two radiative transfer model (RTM) inversion methods (one based on look-up-tables (LUT) and one based on predictive equations) and two statistical modelling methods (one partly, the other entirely based on in situ data). For prediction, spectral data were used that had been acquired over Majella National Park in Italy by the airborne hyperspectral HyMap instrument. To assess the performance of the four investigated models, the normalized root mean squared error (nRMSE) and coefficient of determination (R2) between estimates and in situ LAI measurements are reported (n=41). Using a jackknife approach, we also quantified the accuracy and robustness of empirical models as a function of the size of the available calibration data set. The results of the study demonstrate that the LUT-based RTM inversion yields higher accuracies for LAI estimation (R2=0.91, nRMSE=0.18) as compared to RTM inversions based on predictive equations (R2=0.79, nRMSE=0.38). The two statistical methods yield accuracies similar to the LUT method. However, as expected, the accuracy and robustness of the statistical models decrease when the size of the calibration database is reduced to fewer samples. The results of this study are of interest for the remote sensing community developing improved inversion schemes for spaceborne hyperspectral sensors applicable to different vegetation types. The examples provided in this paper may also serve as illustrations for the drawbacks and advantages of physical and empirical models.

Derivation of leaf area index for grassland within alpine upland using multi-temporal RapidEye data

Biophysical parameters such as leaf area index (LAI) are key variables for vegetation monitoring and particularly important for modelling energy and matter fluxes in the biosphere. Therefore LAI has been derived from remote sensing data operationally based on data with a somewhat coarse spatial resolution. This study aims at deriving high-spatial resolution (6.5 m) multi-temporal LAI for grasslands based on RapidEye data by statistical regressions between vegetation indices (VIs) and field samplings. However, the suitability of those data for grassland LAI derivation has not been tested to date. Thus, the potential of RapidEye data in general and its red edge band in particular are investigated, as well as the robustness of the established relationships for different points in time.LAI was measured repeatedly over summer 2011 at about 30 different meadows in the Bavarian alpine upland using the LAI-2000 and correlated with VI values. The best relationships resulted from using the ratio vegetation index and red edge indices (NDVIrededge, rededge ratio index 1, and relative length) in non-linear models. Thus the indices based on the red edge channel improved regression modelling. The associated transfer functions achieved R2 values ranging from 0.57 to 0.85. The temporal transferability of those transfer functions to other dates was shown to be limited, with the root mean square errors (RMSEs) of several scenes exceeding one. However, when the LAI ranges are similar, a reliable transfer is possible: for example, the transfer of the regression function based on early autumn measurements showed RMSEs of only 0.77–0.95 for the other scenes except for the high-density stage in July, when the LAI reaches unprecedented maximal values. Also, the combination of multi-temporal training data shows no saturation of the selected indices and enables a satisfactory LAI mapping of different dates (RMSE = 0.59 – 1.02).

Using Data of HJ1A HSI for Retrieving Grassland Leaf Area Index

Leaf area index(LAI) is one of the key parameters for quantifying the energy and mass exchange characteristics of terrestrial ecosystems.To validate the feasibility of using HJ-1A HSI data to retrieve leaf area index,HJ-1A HSI data was acquired from China Center for Resource Satellite Data and Applications while in situ LAI was collected with a LAI-2000 sensor.Based on the field experiment data,an evaluation of grass LAI retrieval methods was conducted using three vegetation indices including ratio vegetation index(RVI),normalized difference vegetation index(NDVI) and modified soil adjusted vegetation index(MSAVI).The results showed that the three vegetation indices derived from HSI data have good relationship with measured LAI.MSAVI gained the best result with R2 value of 0.622.While the cross validation result of MSAVI model was also permissible with R2 value of 0.547 and RMSE value of 0.202.All the above showed that the measured LAI and simulated LAI have good consistency.At last,the map of grass LAI,which could partially reflect the growing status of the pasture,was produced using HJ-1A HSI data and MSAVI model.

Hydrological consequences of declining land use and elevated CO2 in alpine grassland

Summary Large areas of alpine pastures and meadows currently face declining land use or abandonment, which leads to tall‐grass transition ecosystems with higher leaf area index (LAI), potentially increased evapotranspiration (ET) and thus, reduced water yield. Elevated atmospheric CO2, on the other hand, is known to reduce stomata opening and hence, leaf‐level transpiration, which may translate into higher soil moisture and enhanced total runoff. Here, we quantify these opposing effects of global change on the water balance of alpine grassland in a field experiment in the Swiss Alps (2440 m a.s.l.). Rates of ET and deep seepage (percolation water) of four alpine grassland types (dominated by Agrostis, Nardus, Carex or forbs) were measured using intact monoliths in 51 weighing lysimeters. A part of the monoliths was clipped to simulate sheep grazing during three seasons (2008–2010). Another set was exposed to elevated CO2 (580 ppm) using free‐air CO2 enrichment (FACE) during the 2009 growing season. Simulated grazing reduced bright day ET by on average −12% across all years, with the most pronounced effects in the high‐stature swards. Correspondingly, the higher biomass and LAI in unclipped grassland lowered the seasonal sum of deep seepage by −13% in a drier summer (2009) and by −5% in a rather wet summer (2010) compared to clipped swards. CO2 enrichment reduced ET in all grassland types by −3 to −7%, increased δ18O in foliage and enhanced soil moisture, but not deep seepage. Hence, future CO2 slightly counteracts the land use effects at canopy level, however, not in terms of water yield. Synthesis. Our results indicate that both grazing and elevated CO2 are mitigating the effects of dry spells on alpine vegetation. The net effect of the continuous decline in the land use and of elevated CO2 is negative for catchment water yield and thus, for potential hydroelectric power production. Although these economic ‘costs’ are rather moderate per hectare of alpine grassland, sums are substantial when scaled to the vast areas potentially affected in the Alps. These calculated ‘costs’ attribute economic value to the eco‐hydrological benefits of land care at these high elevations.