Showing papers on "Leaf area index published in 2017"

Climate mitigation from vegetation biophysical feedbacks during the past three decades

Abstract: The surface air temperature response to vegetation changes has been studied for the extreme case of land-cover change; yet, it has never been quantified for the slow but persistent increase in leaf area index (LAI) observed over the past 30 years (Earth greening) Here we isolate the fingerprint of increasing LAI on surface air temperature using a coupled land–atmosphere global climate model prescribed with satellite LAI observations We find that the global greening has slowed down the rise in global land-surface air temperature by 009 ± 002 °C since 1982 This net cooling effect is the sum of cooling from increased evapotranspiration (70%), changed atmospheric circulation (44%), decreased shortwave transmissivity (21%), and warming from increased longwave air emissivity (−29%) and decreased albedo (−6%) The global cooling originated from the regions where LAI has increased, including boreal Eurasia, Europe, India, northwest Amazonia, and the Sahel Increasing LAI did not, however, significantly change surface air temperature in eastern North America and East Asia, where the effects of large-scale atmospheric circulation changes mask local vegetation feedbacks Overall, the sum of biophysical feedbacks related to the greening of the Earth mitigated 12% of global land-surface warming for the past 30 years

Satellites reveal contrasting responses of regional climate to the widespread greening of Earth

Abstract: Changes in vegetation cover associated with the observed greening may affect several biophysical processes, whose net effects on climate are unclear. We analyzed remotely sensed dynamics in leaf area index (LAI) and energy fluxes in order to explore the associated variation in local climate. We show that the increasing trend in LAI contributed to the warming of boreal zones through a reduction of surface albedo and to an evaporation-driven cooling in arid regions. The interplay between LAI and surface biophysics is amplified up to five times under extreme warm-dry and cold-wet years. Altogether, these signals reveal that the recent dynamics in global vegetation have had relevant biophysical impacts on the local climates and should be considered in the design of local mitigation and adaptation plans.

Using Sentinel-2 Data for Retrieving LAI and Leaf and Canopy Chlorophyll Content of a Potato Crop

Abstract: Leaf area index (LAI) and chlorophyll content, at leaf and canopy level, are important variables for agricultural applications because of their crucial role in photosynthesis and in plant functioning. The goal of this study was to test the hypothesis that LAI, leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC) of a potato crop can be estimated by vegetation indices for the first time using Sentinel-2 satellite images. In 2016 ten plots of 30 × 30 m were designed in a potato field with different fertilization levels. During the growing season approximately 10 daily radiometric field measurements were used to determine LAI, LCC, and CCC. These radiometric determinations were extensively calibrated against LAI2000 and chlorophyll meter (SPAD, soil plant analysis development) measurements for potato crops grown in the years 2010–2014. Results for Sentinel-2 showed that the weighted difference vegetation index (WDVI) using bands at 10 m spatial resolution can be used for estimating the LAI (R2 of 0.809; root mean square error of prediction (RMSEP) of 0.36). The ratio of the transformed chlorophyll in reflectance index and the optimized soil-adjusted vegetation index (TCARI/OSAVI) showed to be a good linear estimator of LCC at 20 m (R2 of 0.696; RMSEP of 0.062 g·m−2). The performance of the chlorophyll vegetation index (CVI) at 10 m spatial resolution was slightly worse (R2 of 0.656; RMSEP of 0.066 g·m−2) compared to TCARI/OSAVI. Finally, results showed that the green chlorophyll index (CIgreen) was an accurate and linear estimator of CCC at 10 m (R2 of 0.818; RMSEP of 0.29 g·m−2). Results for CIgreen were better than for the red-edge chlorophyll index (CIred-edge, R2 of 0.576, RMSE of 0.43 g·m−2). Our results show that Sentinel-2 bands at 10 m spatial resolution are suitable for estimating LAI, LCC, and CCC, avoiding the need for red-edge bands that are only available at 20 m. This is an important finding for applying Sentinel-2 data in precision agriculture.

Inconsistencies of interannual variability and trends in long-term satellite leaf area index products

Abstract: Understanding the long-term performance of global satellite leaf area index (LAI) products is important for global change research. However, few effort has been devoted to evaluating the long-term time-series consistencies of LAI products. This study compared four long-term LAI products (GLASS, GLOBMAP, LAI3g, and TCDR) in terms of trends, interannual variabilities, and uncertainty variations from 1982 through 2011. This study also used four ancillary LAI products (GEOV1, MERIS, MODIS C5, and MODIS C6) from 2003 through 2011 to help clarify the performances of the four long-term LAI products. In general, there were marked discrepancies between the four long-term LAI products. During the pre-MODIS period (1982-1999), both linear trends and interannual variabilities of global mean LAI followed the order GLASS>LAI3g>TCDR>GLOBMAP. The GLASS linear trend and interannual variability were almost 4.5 times those of GLOBMAP. During the overlap period (2003-2011), GLASS and GLOBMAP exhibited a decreasing trend, TCDR no trend, and LAI3g an increasing trend. GEOV1, MERIS, and MODIS C6 also exhibited an increasing trend, but to a much smaller extent than that from LAI3g. During both periods, the R2 of detrended anomalies between the four long-term LAI products was smaller than 0.4 for most regions. Interannual variabilities of the four long-term LAI products were considerably different over the two periods, and the differences followed the order GLASS>LAI3g>TCDR>GLOBMAP. Uncertainty variations quantified by a collocation error model followed the same order. Our results indicate that the four long-term LAI products were neither intraconsistent over time nor interconsistent with each other. These inconsistencies may be due to NOAA satellite orbit changes and MODIS sensor degradation. Caution should be used in the interpretation of global changes derived from the four long-term LAI products.

Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest

Abstract: Accurate estimation of terrestrial gross primary productivity (GPP) remains a challenge despite its importance in the global carbon cycle. Chlorophyll fluorescence (ChlF) has been recently adopted to understand photosynthesis and its response to the environment, particularly with remote sensing data. However, it remains unclear how ChlF and photosynthesis are linked at different spatial scales across the growing season. We examined seasonal relationships between ChlF and photosynthesis at the leaf, canopy, and ecosystem scales and explored how leaf-level ChlF was linked with canopy-scale solar-induced chlorophyll fluorescence (SIF) in a temperate deciduous forest at Harvard Forest, Massachusetts, USA. Our results show that ChlF captured the seasonal variations of photosynthesis with significant linear relationships between ChlF and photosynthesis across the growing season over different spatial scales (R2 = 0.73, 0.77, and 0.86 at leaf, canopy, and satellite scales, respectively; P < 0.0001). We developed a model to estimate GPP from the tower-based measurement of SIF and leaf-level ChlF parameters. The estimation of GPP from this model agreed well with flux tower observations of GPP (R2 = 0.68; P < 0.0001), demonstrating the potential of SIF for modeling GPP. At the leaf scale, we found that leaf Fq’/Fm’, the fraction of absorbed photons that are used for photochemistry for a light-adapted measurement from a pulse amplitude modulation fluorometer, was the best leaf fluorescence parameter to correlate with canopy SIF yield (SIF/APAR, R2 = 0.79; P < 0.0001). We also found that canopy SIF and SIF-derived GPP (GPPSIF) were strongly correlated to leaf-level biochemistry and canopy structure, including chlorophyll content (R2 = 0.65 for canopy GPPSIF and chlorophyll content; P < 0.0001), leaf area index (LAI) (R2 = 0.35 for canopy GPPSIF and LAI; P < 0.0001), and normalized difference vegetation index (NDVI) (R2 = 0.36 for canopy GPPSIF and NDVI; P < 0.0001). Our results suggest that ChlF can be a powerful tool to track photosynthetic rates at leaf, canopy, and ecosystem scales.

Multi-Spectral Imaging from an Unmanned Aerial Vehicle Enables the Assessment of Seasonal Leaf Area Dynamics of Sorghum Breeding Lines.

Abstract: Genetic improvement in sorghum breeding programs requires the assessment of adaptation traits in small-plot breeding trials across multiple environments. Many of these phenotypic assessments are made by manual measurement or visual scoring, both of which are time consuming and expensive. This limits trial size and the potential for genetic gain. In addition, these methods are typically restricted to point estimates of particular traits such as leaf senescence or flowering and do not capture the dynamic nature of crop growth. In water-limited environments in particular, information on leaf area development over time would provide valuable insight into water use and adaptation to water scarcity during specific phenological stages of crop development. Current methods to estimate plant leaf area index (LAI) involve destructive sampling and are not practical in breeding. Unmanned aerial vehicles (UAV) and proximal-sensing technologies open new opportunities to assess these traits multiple times in large small-plot trials. We analyzed vegetation-specific crop indices obtained from a narrowband multi-spectral camera on board a UAV platform flown over a small pilot trial with 30 plots (10 genotypes with 3 replicates). Due to variable emergence we were able to assess the utility of these vegetation indices to estimate canopy cover and LAI over a large range of plant densities. We found good correlations between the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) with plant number per plot, canopy cover and LAI both during the vegetative growth phase (pre-anthesis) and at maximum canopy cover shortly after anthesis. We also analyzed the utility of time-sequence data to assess the senescence pattern of sorghum genotypes known as fast (senescent) or slow senescing (stay-green) types. The Normalized Difference Red Edge (NDRE) index which estimates leaf chlorophyll content was most useful in characterizing the leaf area dynamics / senescence patterns of contrasting genotypes. These methods to monitor dynamics of green and senesced leaf area are suitable for out-scaling to enhance phenotyping of additional crop canopy characteristics and likely crop yield responses among genotypes across large fields and multiple dates.

Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards

Abstract: Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of vine growth stages are too sparse to fully capture the spatial variability of vine conditions. In addition, traditional grape yield prediction methods are time consuming and require large amount grape samples. Remote sensing data provide detailed spatial and temporal information regarding vine development that is useful for vineyard management. In this study, Landsat surface reflectance products from 2013 and 2014 were used to map satellite-based Normalized Difference Vegetation Index (NDVI) and leaf area index (LAI) over two Vitis vinifera L. cv. Pinot Noir vineyards in California, USA. The spatial correlation between grape yield maps and the interpolated daily time series (LAI and NDVI) was quantified. NDVI and LAI were found to have similar performance as a predictor of spatial yield variability, providing peak correlations of 0.8 at specific times during the growing season, and the timing of this peak correlation differed for the two years of study. In addition, correlations with maximum and seasonal-cumulative vegetation indices were also evaluated, and showed slightly lower correlations with the observed yield maps. Finally, the within-season grape yield predictability was examined using a simple strategy in which the relationship between grape yield and vegetation indices were calibrated with limited ground measurements. This strategy has a strong potential to improve the accuracy and efficiency of yield estimation in comparison with traditional approaches used in the wine grape growing industry.

Potential of Sentinel-1 Radar Data for the Assessment of Soil and Cereal Cover Parameters.

Abstract: The main objective of this study is to analyze the potential use of Sentinel-1 (S1) radar data for the estimation of soil characteristics (roughness and water content) and cereal vegetation parameters (leaf area index (LAI), and vegetation height (H)) in agricultural areas. Simultaneously to several radar acquisitions made between 2015 and 2017, using S1 sensors over the Kairouan Plain (Tunisia, North Africa), ground measurements of soil roughness, soil water content, LAI and H were recorded. The NDVI (normalized difference vegetation index) index computed from Landsat optical images revealed a strong correlation with in situ measurements of LAI. The sensitivity of the S1 measurements to variations in soil moisture, which has been reported in several scientific publications, is confirmed in this study. This sensitivity decreases with increasing vegetation cover growth (NDVI), and is stronger in the VV (vertical) polarization than in the VH cross-polarization. The results also reveal a similar increase in the dynamic range of radar signals observed in the VV and VH polarizations as a function of soil roughness. The sensitivity of S1 measurements to vegetation parameters (LAI and H) in the VV polarization is also determined, showing that the radar signal strength decreases when the vegetation parameters increase. No vegetation parameter sensitivity is observed in the VH polarization, probably as a consequence of volume scattering effects.

Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery

Abstract: Leaf area index (LAI) is a significant biophysical variable in the models of hydrology, climatology and crop growth. Rapid monitoring of LAI is critical in modern precision agriculture. Remote sensing (RS) on satellite, aerial and unmanned aerial vehicles (UAVs) has become a popular technique in monitoring crop LAI. Among them, UAVs are highly attractive to researchers and agriculturists. However, some of the UAVs vegetation index (VI)—derived LAI models have relatively low accuracy because of the limited number of multispectral bands, especially as they tend to saturate at the middle to high LAI levels, which are the LAI levels of high-yielding wheat crops in China. This study aims to effectively estimate wheat LAI with UAVs narrowband multispectral image (400–800 nm spectral regions, 10 cm resolution) under varying growth conditions during five critical growth stages, and to provide the potential technical support for optimizing the nitrogen fertilization. Results demonstrated that the newly developed LAI model with modified triangular vegetation index (MTVI2) has better accuracy with higher coefficient of determination (Rc2 = 0.79, Rv2 = 0.80) and lower relative root mean squared error (RRMSE = 24%), and higher sensitivity under various LAI values (from 2 to 7), which will broaden the applied range of the new LAI model. Furthermore, this LAI model displayed stable performance under different sub-categories of growth stages, varieties, and eco-sites. In conclusion, this study could provide effective technical support to precisely monitor the crop growth with UAVs in various crop yield levels, which should prove helpful in family farm for the modern agriculture.

Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area

Abstract: In this study, a global land data assimilation system (LDAS-Monde) is applied over Europe and the Mediterranean basin to increase monitoring accuracy for land surface variables LDAS-Monde is able to ingest information from satellite-derived surface soil moisture (SSM) and leaf area index (LAI) observations to constrain the interactions between soil–biosphere–atmosphere (ISBA, Interactions between Soil, Biosphere and Atmosphere) land surface model (LSM) coupled with the CNRM (Centre National de Recherches Meteorologiques) version of the Total Runoff Integrating Pathways (ISBA-CTRIP) continental hydrological system It makes use of the CO2-responsive version of ISBA which models leaf-scale physiological processes and plant growth Transfer of water and heat in the soil rely on a multilayer diffusion scheme SSM and LAI observations are assimilated using a simplified extended Kalman filter (SEKF), which uses finite differences from perturbed simulations to generate flow dependence between the observations and the model control variables The latter include LAI and seven layers of soil (from 1 to 100 cm depth) A sensitivity test of the Jacobians over 2000–2012 exhibits effects related to both depth and season It also suggests that observations of both LAI and SSM have an impact on the different control variables From the assimilation of SSM, the LDAS is more effective in modifying soil moisture (SM) from the top layers of soil, as model sensitivity to SSM decreases with depth and has almost no impact from 60 cm downwards From the assimilation of LAI, a strong impact on LAI itself is found The LAI assimilation impact is more pronounced in SM layers that contain the highest fraction of roots (from 10 to 60 cm) The assimilation is more efficient in summer and autumn than in winter and spring Results shows that the LDAS works well constraining the model to the observations and that stronger corrections are applied to LAI than to SM A comprehensive evaluation of the assimilation impact is conducted using (i) agricultural statistics over France, (ii) river discharge observations, (iii) satellite-derived estimates of land evapotranspiration from the Global Land Evaporation Amsterdam Model (GLEAM) project and (iv) spatially gridded observation-based estimates of upscaled gross primary production and evapotranspiration from the FLUXNET network Comparisons with those four datasets highlight neutral to highly positive improvement

Genetic Progress in Grain Yield and Physiological Traits in Chinese Wheat Cultivars of Southern Yellow and Huai Valley since 1950

Abstract: Understanding the key characteristics associated with genetic gains achieved through breeding is essential for improving yield-limiting factors and designing future breeding strategies in bread wheat (Triticum aestivum L.) cultivars. The objective of the present study was to investigate the genetic progress in yield-related and physiological traits in cultivars released from 1950 to 2012 for irrigated conditions in the southern Yellow and Huai Valleys Winter Wheat Zone. Field trials including 26 leading cultivars from 1950 to the present time were conducted at Zhengzhou and Zhoukou in Henan Province, during the 2013–2014 and 2014–2015 cropping seasons, providing data from four environments. Grain yield (GY) was significantly increased by the linear rate of 57.5 kg ha−1 yr−1 or 0.70% (R2 = 0.66, P < 0.01) and significantly correlated with increased thousandkernel weight (TKW) (r = 0.48, P < 0.05), spike number m−2 (r = 0.44, P < 0.05), kernels m−2 (r = 0.56, P < 0.01), aboveground biomass (AGBM) (r = 0.80, P < 0.01), harvest index (HI) (r = 0.84, P < 0.01), watersoluble carbohydrate at 10 d postanthesis (WSC-10) (r = 0.80, P < 0.01), and reduced plant height (PH) (r = −0.85, P < 0.01). There was no significant change in kernel number per spike, heading date, normalized difference in vegetation index at anthesis and at 10 d postanthesis, leaf area index at anthesis and at 10 d postanthesis, and canopy temperature depression at anthesis during the past 60 yr. Soil plant analysis development (SPAD) estimates of chlorophyll content at 10 d postanthesis (Chl-10) increased with year of release and were significantly correlated with GY (r = 0.69, P < 0.01), PH (r = −0.76, P < 0.01), AGBM (r = 0.52, P < 0.01), HI (r = 0.71, P < 0.01), and WSC-10 (r = 0.73, P < 0.01). Cultivars conferring Rht-D1b and RhtD1b + Rht8c showed increased GY, TKW, AGBM, HI, WSC-10, and Chl-10. Stem water solubility content can be used as a selection criterion for further improving yield potential. F. Gao, A. Rasheed, Y. Dong, Y. Xiao, X. Xia, and Z. He, Institute of Crop Science, National Wheat Improvement Center, Chinese Academy of Agricultural Sciences (CAAS), 12 Zhongguancun S. St., Beijing 100081, China; F. Gao, Key Laboratory of Soybean Biology, Soybean Research Institute, Ministry of Education, Northeast Agricultural Univ., Harbin, China; F. Gao, Keshan Agricultural Research Institute, Heilongjiang Academy of Agricultural Sciences, Keshan 161600, Heilongjiang, China; D. Ma, National Wheat Engineering Research Center, Henan Agricultural Univ., 2 Nongye Rd., Zhengzhou 450000, Henan Province, China; G. Yin, Zhoukou Academy of Agricultural Sciences, Zhoukou 466000, China; X. Wu, Key Lab. of Soybean Biology, China; A. Rasheed and Z. He, International Maize and Wheat Improvement Center (CIMMYT) China Office, c/o CAAS, 12 Zhongguancun S. St., Beijing 100081, China. F. Gao and D. Ma contributed equally to this work. Received 21 May 2016. Accepted 9 Dec. 2016. *Corresponding author (zhhecaas@163.com). Assigned to Associate Editor Ashish Saxena. Abbreviations: AGBM, aboveground biomass; Chl, SPAD estimates of chlorophyll content; Chl-10, SPAD estimates of chlorophyll content at 10 d postanthesis; Chl-A, SPAD estimates of chlorophyll content at anthesis; CTD, canopy temperature depression; CTD-10, canopy temperature depression at 10 d postanthesis; CTD-A, canopy temperature depression at anthesis; DPA, days postanthesis; GY, grain yield; HD, heading date; HI, harvest index; KASP, kompetitive allele specific polymerase chain reaction; KN, kernels m−2; KNS, kernel number per spike; LAI, leaf area index; LAI-10, leaf area index at 10 d postanthesis; LAI-A, leaf area index at anthesis; NDVI, normalized difference in vegetation index; NDVI-10, normalized difference in vegetation index at 10 d postanthesis; NDVI-A, normalized difference in vegetation index at anthesis; PH, plant height; SN, spike number m−2; SPAD, soil plant analysis development; TKW, thousand-kernel weight; WSC-10, stem water-soluble carbohydrate at 10 d postanthesis; YHVWWZ, the Yellow and Huai Valleys Winter Wheat Zone. Published in Crop Sci. 57:760–773 (2017). doi: 10.2135/cropsci2016.05.0362 © Crop Science Society of America | 5585 Guilford Rd., Madison, WI 53711 USA All rights reserved. Published March 3, 2017

Interacting Effects of Leaf Water Potential and Biomass on Vegetation Optical Depth

Abstract: Remotely sensed microwave observations of vegetation optical depth (VOD) have been widely used for examining vegetation responses to climate. Nevertheless, the relative impacts of phenological changes in leaf biomass and water stress on VOD have not been explicitly disentangled. In particular, determining whether leaf water potential (ψL) affects VOD may allow these data sets as a constraint for plant hydraulic models. Here we test the sensitivity of VOD to variations in ψL and present a conceptual framework that relates VOD to ψL and total biomass including leaves, whose dynamics are measured through leaf area index, and woody components. We used measurements of ψL from three sites across the US—a mixed deciduous forests in Indiana and Missouri and a pinon-juniper woodland in New Mexico—to validate the conceptual model. The temporal dynamics of X-band VOD were similar to those of the VOD signal estimated from the new conceptual model with observed ψL (R2 = 0.6–0.8). At the global scale, accounting for a combination of biomass and estimated ψL (based on satellite surface soil moisture data) increased correlations with VOD by ~ 15% and 30% compared to biomass and water potential, respectively. In wetter regions with denser and taller canopy heights, VOD has a higher correlation with leaf area index than with water stress and vice versa in drier regions. Our results demonstrate that variations in both phenology and ψL must be considered to accurately interpret the dynamics of VOD observations for ecological applications.

Seasonal variations of leaf and canopy properties tracked by ground-based NDVI imagery in a temperate forest

Abstract: Changes in plant phenology affect the carbon flux of terrestrial forest ecosystems due to the link between the growing season length and vegetation productivity. Digital camera imagery, which can be acquired frequently, has been used to monitor seasonal and annual changes in forest canopy phenology and track critical phenological events. However, quantitative assessment of the structural and biochemical controls of the phenological patterns in camera images has rarely been done. In this study, we used an NDVI (Normalized Difference Vegetation Index) camera to monitor daily variations of vegetation reflectance at visible and near-infrared (NIR) bands with high spatial and temporal resolutions, and found that the infrared camera based NDVI (camera-NDVI) agreed well with the leaf expansion process that was measured by independent manual observations at Harvard Forest, Massachusetts, USA. We also measured the seasonality of canopy structural (leaf area index, LAI) and biochemical properties (leaf chlorophyll and nitrogen content). We found significant linear relationships between camera-NDVI and leaf chlorophyll concentration, and between camera-NDVI and leaf nitrogen content, though weaker relationships between camera-NDVI and LAI. Therefore, we recommend ground-based camera-NDVI as a powerful tool for long-term, near surface observations to monitor canopy development and to estimate leaf chlorophyll, nitrogen status, and LAI.

Estimating Leaf Area Density of Individual Trees Using the Point Cloud Segmentation of Terrestrial LiDAR Data and a Voxel-Based Model

Abstract: The leaf area density (LAD) within a tree canopy is very important for the understanding and modeling of photosynthetic studies of the tree. Terrestrial light detection and ranging (LiDAR) has been applied to obtain the three-dimensional structural properties of vegetation and estimate the LAD. However, there is concern about the efficiency of available approaches. Thus, the objective of this study was to develop an effective means for the LAD estimation of the canopy of individual magnolia trees using high-resolution terrestrial LiDAR data. The normal difference method based on the differences in the structures of the leaf and non-leaf components of trees was proposed and used to segment leaf point clouds. The vertical LAD profiles were estimated using the voxel-based canopy profiling (VCP) model. The influence of voxel size on the LAD estimation was analyzed. The leaf point cloud’s extraction accuracy for two magnolia trees was 86.53% and 84.63%, respectively. Compared with the ground measured leaf area index (LAI), the retrieved accuracy was 99.9% and 90.7%, respectively. The LAD (as well as LAI) was highly sensitive to the voxel size. The spatial resolution of point clouds should be the appropriate estimator for the voxel size in the VCP model.

Exploring the Potential of WorldView-2 Red-Edge Band-Based Vegetation Indices for Estimation of Mangrove Leaf Area Index with Machine Learning Algorithms

Abstract: To accurately estimate leaf area index (LAI) in mangrove areas, the selection of appropriate models and predictor variables is critical. However, there is a major challenge in quantifying and mapping LAI using multi-spectral sensors due to the saturation effects of traditional vegetation indices (VIs) for mangrove forests. WorldView-2 (WV2) imagery has proven to be effective to estimate LAI of grasslands and forests, but the sensitivity of its vegetation indices (VIs) has been uncertain for mangrove forests. Furthermore, the single model may exhibit certain randomness and instability in model calibration and estimation accuracy. Therefore, this study aims to explore the sensitivity of WV2 VIs for estimating mangrove LAI by comparing artificial neural network regression (ANNR), support vector regression (SVR) and random forest regression (RFR). The results suggest that the RFR algorithm yields the best results (RMSE = 0.45, 14.55% of the average LAI), followed by ANNR (RMSE = 0.49, 16.04% of the average LAI), and then SVR (RMSE = 0.51, 16.56% of the average LAI) algorithms using 5-fold cross validation (CV) using all VIs. Quantification of the variable importance shows that the VIs derived from the red-edge band consistently remain the most important contributor to LAI estimation. When the red-edge band-derived VIs are removed from the models, estimation accuracies measured in relative RMSE (RMSEr) decrease by 3.79%, 2.70% and 4.47% for ANNR, SVR and RFR models respectively. VIs derived from red-edge band also yield better accuracy compared with other traditional bands of WV2, such as near-infrared-1 and near-infrared-2 band. Furthermore, the estimated LAI values vary significantly across different mangrove species. The study demonstrates the utility of VIs of WV2 imagery and the selected machine-learning algorithms in developing LAI models in mangrove forests. The results indicate that the red-edge band of WV2 imagery can help alleviate the saturation problem and improve the accuracy of LAI estimation in a mangrove area.

An improved SWAT vegetation growth module and its evaluation for four tropical ecosystems

Abstract: . The Soil and Water Assessment Tool (SWAT) is a globally applied river basin ecohydrological model used in a wide spectrum of studies, ranging from land use change and climate change impacts studies to research for the development of the best water management practices. However, SWAT has limitations in simulating the seasonal growth cycles for trees and perennial vegetation in the tropics, where rainfall rather than temperature is the dominant plant growth controlling factor. Our goal is to improve the vegetation growth module of SWAT for simulating the vegetation variables – such as the leaf area index (LAI) – for tropical ecosystems. Therefore, we present a modified SWAT version for the tropics (SWAT-T) that uses a straightforward but robust soil moisture index (SMI) – a quotient of rainfall (P) and reference evapotranspiration (ETr) – to dynamically initiate a new growth cycle within a predefined period. Our results for the Mara Basin (Kenya/Tanzania) show that the SWAT-T-simulated LAI corresponds well with the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI for evergreen forest, savanna grassland and shrubland. This indicates that the SMI is reliable for triggering a new annual growth cycle. The water balance components (evapotranspiration and streamflow) simulated by the SWAT-T exhibit a good agreement with remote-sensing-based evapotranspiration (ET-RS) and observed streamflow. The SWAT-T model, with the proposed vegetation growth module for tropical ecosystems, can be a robust tool for simulating the vegetation growth dynamics in hydrologic models in tropical regions.

Exploring Relationships between Canopy Architecture, Light Distribution, and Photosynthesis in Contrasting Rice Genotypes Using 3D Canopy Reconstruction

Abstract: The arrangement of leaf material is critical in determining the light environment, and subsequently the photosynthetic productivity of complex crop canopies However, links between specific canopy architectural traits and photosynthetic productivity across a wide genetic background are poorly understood for field grown crops The architecture of five genetically diverse rice varieties - four parental founders of a multi-parent advanced generation intercross (MAGIC) population plus a high yielding Philippine variety (IR64) - was captured at two different growth stages using a method for digital plant reconstruction based on stereocameras Ray tracing was employed to explore the effects of canopy architecture on the resulting light environment in high-resolution, whilst gas exchange measurements were combined with an empirical model of photosynthesis to calculate an estimated carbon gain and total light interception To further test the impact of different dynamic light patterns on photosynthetic properties, an empirical model of photosynthetic acclimation was employed to predict the optimal light-saturated photosynthesis rate (Pmax) throughout canopy depth, hypothesising that light is the sole determinant of productivity in these conditions First we show that a plant type with steeper leaf angles allows more efficient penetration of light into lower canopy layers and this, in turn, leads to a greater photosynthetic potential Second the predicted optimal Pmax responds in a manner that is consistent with fractional interception and leaf area index across this germplasm However measured Pmax, especially in lower layers, was consistently higher than the optimal Pmax indicating factors other than light determine photosynthesis profiles Lastly, varieties with more upright architecture exhibit higher maximum quantum yield of photosynthesis indicating a canopy-level impact on photosynthetic efficiency