scholarly journals Spatial Distribution of Tree Species Governs the Spatio-Temporal Interaction of Leaf Area Index and Soil Moisture across a Forested Landscape

PLoS ONE ◽  
2013 ◽  
Vol 8 (3) ◽  
pp. e58704 ◽  
Author(s):  
Kusum J. Naithani ◽  
Doug C. Baldwin ◽  
Katie P. Gaines ◽  
Henry Lin ◽  
David M. Eissenstat
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Tree Species ◽  
Area Index ◽  
Spatio Temporal ◽  
Temporal Interaction

scholarly journals Assessment of Remotely Sensed Near-Surface Soil Moisture for Distributed Eco-Hydrological Model Implementation

Water ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 2613 ◽  
Author(s):  
Carlos Echeverría ◽  
Guiomar Ruiz-Pérez ◽  
Cristina Puertes ◽  
Luis Samaniego ◽  
Brian Barrett ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Surface Soil ◽  
Remotely Sensed ◽  
Validation Period ◽  
Surface Soil Moisture ◽  
Area Index ◽  
Spatio Temporal ◽  
Calibration Approach

The aim of this study was to implement an eco-hydrological distributed model using only remotely sensed information (soil moisture and leaf area index) during the calibration phase. Four soil moisture-based metrics were assessed, and the best alternative was chosen, which was a metric based on the similarity between the principal components that explained at least 95% of the soil moisture variation and the Nash-Sutcliffe Efficiency (NSE) index between simulated and observed surface soil moisture. The selected alternative was compared with a streamflow-based calibration approach. The results showed that the streamflow-based calibration approach, even presenting satisfactory results in the calibration period (NSE = 0.91), performed poorly in the validation period (NSE = 0.47) and Leaf Area Index (LAI) and soil moisture were neither sensitive to the spatio-temporal pattern nor to the spatial correlation in both calibration and validation periods. Hence, the selected soil moisture-based approach showed an acceptable performance in terms of discharges, presenting a negligible decrease in the validation period (ΔNSE = 0.1) and greater sensitivity to the spatio-temporal variables’ spatial representation.

scholarly journals Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

2020 ◽  
Vol 12 (13) ◽  
pp. 2148 ◽  
Author(s):  
Adnan Rajib ◽  
I Luk Kim ◽  
Heather E. Golden ◽  
Charles R. Lane ◽  
Sujay V. Kumar ◽  
...  
Keyword(s):  
Water Quality ◽  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Dynamics ◽  
Watershed Modeling ◽  
Remotely Sensed ◽  
Area Index ◽  
Daily Streamflow ◽  
Modis Lai

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershed in the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’s LAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

Studies on Transpiration Suppressants on Spring Sorghum in North-Western India in Relation to Soil Moisture Regimes. II. Effect on Growth and Nutrient Uptake

1984 ◽  
Vol 20 (2) ◽  
pp. 161-170
Author(s):  
D. Boobathi Babu ◽  
S. P. Singh
Keyword(s):  
Soil Moisture ◽  
Leaf Area Index ◽  
Nutrient Uptake ◽  
Leaf Area ◽  
Dry Matter ◽  
Western India ◽  
Area Index ◽  
Matter Production ◽  
Moisture Regimes ◽  
North Western

SUMMARYThe effects of irrigation and spraying of transpiration suppressants on growth and nutrient uptake by spring sorghum (CSH 6) have been investigated. Crop growth, measured by plant-height, leaf area index and dry matter production, and uptake of N, P and K increased with more frequent irrigation and in response to the spraying of transpiration suppressants. Foliar applications of atrazine at 200 g ha−1 and CCC at 300 ml ha−1 proved to be the best in this NW Indian location.

Field Competition between Ivyleaf Morningglory (Ipomea hederacea) and Soybeans (Glycine max)

Weed Science ◽  
1984 ◽  
Vol 32 (3) ◽  
pp. 364-370 ◽  
Author(s):  
Ronald C. Cordes ◽  
Thomas T. Bauman
Keyword(s):  
Soil Moisture ◽  
Glycine Max ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Dry Weight ◽  
Growth And Yield ◽  
Yield Reduction ◽  
Area Index ◽  
Soybean Yield ◽  
Competition Effects

Detrimental effects on growth and yield of soybeans [Glycine max(L.) Merr. ‘Amsoy 77′] from density and duration of competition by ivyleaf morningglory [Ipomea hederacea(L.) Jacq. ♯3IPOHE] was evaluated in 1981 and 1982 near West Lafayette, IN. Ivyleaf morningglory was planted at densities of 1 plant per 90, 60, 30, and 15 cm of row in 1981 and 1 plant per 60, 30, 15, and 7.5 cm of row in 1982. Each density of ivyleaf morningglory competed for 22 to 46 days after emergence and the full season in 1981, and for 29 to 60 days after emergence and the full season in 1982. The best indicators of competition effects were leaf area index, plant dry weight, and yield of soybeans. Ivyleaf morningglory was more competitive during the reproductive stage of soybean growth. Photosynthetic irradiance and soil moisture measurements indicated that ivyleaf morningglory does not effectively compete for light or soil moisture. All densities of ivyleaf morningglory could compete with soybeans for 46 and 60 days after emergence in 1981 and 1982, respectively, without reducing soybean yield. Full-season competition from densities of 1 ivyleaf morningglory plant per 15 cm of row significantly reduced soybean yield by 36% in 1981 and 13% in 1982. The magnitude of soybean growth and yield reduction caused by a given density of ivyleaf morningglory was greater when warm, early season temperatures favored rapid weed development.

scholarly journals Spatially and Temporally Continuous Leaf Area Index Mapping for Crops through Assimilation of Multi-resolution Satellite Data

2019 ◽  
Vol 11 (21) ◽  
pp. 2517 ◽  
Author(s):  
Huaan Jin ◽  
Weixing Xu ◽  
Ainong Li ◽  
Xinyao Xie ◽  
Zhengjian Zhang ◽  
...  
Keyword(s):  
Time Series ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Landsat 8 ◽  
Area Index ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
Modis Lai ◽  
Temporal Profiles ◽  
Reflectance Data

As a key parameter that represents the structural characteristics and biophysical changes of crop canopy, the leaf area index (LAI) plays a significant role in monitoring crop growth and mapping yield. A considerable amount of farmland is dispersed with strong spatial heterogeneity. The existing time series satellite LAI products fail to capture spatial distributions and growth changes of crops due to coarse spatial resolutions and spatio-temporal discontinuities. Therefore, it becomes crucial for fine resolution LAI mapping in time series over crop areas. A two-stage data assimilation scheme was developed for dense time series LAI mapping in this study. A LAI dynamic model was first constructed using multi-year MODIS LAI data. This model coupled with the PROSAIL radiative transfer model, and MOD09A1 reflectance data were used to retrieve temporal LAI profiles at the 500 m resolution with the assistance of the very fast simulated annealing (VFSA) algorithm. Then, the LAI dynamics at the 500 m scale were incorporated as prior information into the Landsat 8 OLI reflectance data for time series LAI mapping at the 30 m resolution. Finally, the spatio-temporal continuities and retrieval accuracies of assimilated LAI values were assessed at the 500 m and 30 m resolutions respectively, using the MODIS LAI product, fine resolution LAI reference map and field measurements. The results indicated that the assimilated the LAI estimations at the 500 m scale effectively eliminated the spatio-temporal discontinuities of the MODIS LAI product and displayed reasonable temporal profiles and spatial integrity of LAI. Moreover, the 30 m resolution LAI retrievals showed more abundant spatial details and reasonable temporal profiles than the counterparts at the 500 m scale. The determination coefficient R2 between the estimated and field LAI values was 0.76 with a root mean square error (RMSE) value of 0.71 at the 30 m scale. The developed method not only improves the spatio-temporal continuities of the LAI at the 500 m scale, but also obtains 30 m resolution LAI maps with fine spatial and temporal consistencies, which can be expected to meet the needs of analysis on crop dynamic changes and yield mapping in fragmented and highly heterogeneous areas.

Analysis of water dynamics in the soil-plant-atmosphere continuum using a multi-sensor approach

2020 ◽  
Author(s):  
David Chaparro ◽  
Thomas Jagdhuber ◽  
Dara Entekhabi ◽  
María Piles ◽  
Anke Fluhrer ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Leaf Area Index ◽  
Water Content ◽  
Leaf Area ◽  
Optical Depth ◽  
Time Lag ◽  
Water Storage ◽  
Water Dynamics ◽  
Area Index ◽  
L Band

<p>Changing climate patterns have increased hydrological extremes in many regions [1]. This impacts water and carbon cycles, potentially modifying vegetation processes and thus terrestrial carbon uptake. It is therefore crucial to understand the relationship between the main water pools linked to vegetation (i.e., soil moisture, plant water storage, and atmospheric water deficit), and how vegetation responds to changes of these pools. Hence, the goal of this research is to understand the water pools and fluxes in the soil-plant-atmosphere continuum (SPAC) and their relationship with vegetation responses.</p><p>Our study spans from April 2015 to March 2019 and is structured in two parts:</p><p>Firstly, relative water content (RWC) is estimated using a multi-sensor approach to monitor water storage in plants. This is at the core of our research approach towards water pool monitoring within SPAC. Here, we will present a RWC dataset derived from gravimetric moisture content (<em>mg</em>) estimates using the method first proposed in [2], and further validated in [3]. This allows retrieving RWC and <em>mg</em> independently from biomass influences. Here, we apply this method using a sensor synergy including (i) vegetation optical depth from SMAP L-band radiometer (L-VOD), (ii) vegetation height (VH) from ICESat-2 Lidar and (iii) vegetation volume fraction (d) from AQUARIUS L-band radar. RWC status and temporal dynamics will be discussed.</p><p>Secondly, water dynamics in the SPAC and their impact on leaf changes are analyzed. We will present a global, time-lag correlation analysis among: (i) the developed RWC maps, (ii) surface soil moisture from SMAP (SM), (iii) vapor pressure deficit (VPD; from MERRA reanalysis [4]), and (iv) leaf area index (LAI; from MODIS [5]). Resulting time-lag and correlation maps, as well as analyses of LAI dynamics as a function of SPAC, will be presented at the conference.</p><p> </p><p>References</p><p>[1] IPCC. (2013). Annex I: Atlas of global and regional climate projections. In: van Oldenborgh, et al. (Eds.) Climate Change 2013: The Physical Science Basis (pp. 1311-1393). Cambridge University Press.</p><p>[2] Fink, A., et al. (2018). Estimating Gravimetric Moisture of Vegetation Using an Attenuation-Based Multi-Sensor Approach. In IGARSS 2018 (pp. 353-356). IEEE.</p><p>[3] Meyer, T., et al. Estimating Gravimetric Water Content of a Winter Wheat Field from L-Band Vegetation Optical Depth, Remote Sens. 2019, 11(20), 2353</p><p>[4] NASA (2019). Modern-Era Retrospective analysis for Research and Applications, Version 2. Accessed 2020-01-14 from https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/.</p><p>[5] Myneni, R., et al. (2015). MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. Accessed 2020-01-14 from https://doi.org/10.5067/MODIS/MOD15A2H.006.</p>

Defoliation, leaf area index, and the water use of four temperate pasture species under irrigated and dryland conditions

1973 ◽  
Vol 24 (6) ◽  
pp. 783 ◽  
Author(s):  
GG Johns ◽  
A Lazenby
Keyword(s):  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Water Use ◽  
Leaf Senescence ◽  
Late Spring ◽  
Stomatal Control ◽  
Area Index ◽  
Autumn Period ◽  
Dry Conditions

Measurements were made over a 12-month period of the water use and leaf area index (LAI) of both dryland and irrigated monoculture swards of four temperate pasture species under two defoliation regimes. All four species used similar quantities of water on the dryland plots despite large differences in their ability to grow under such conditions. Even though very dry conditions prevailed during part of the study, the dryland swards generally failed to exploit reserves of soil moisture below a depth of c. 120 cm. The water use of the irrigated swards was sensitive to the manipulation of LAI by defoliation, while in contrast, dryland water use was not. On the irrigated swards, at an LAI of 1, a 1% decrease in LAI was associated with a 1% decrease in water use. This sensitivity of water use decreased as LAI increased until, at an LAI of 3 and above, water use appeared to be insensitive to charges in LAI. During the late spring to early autumn period both irrigated and dryland water use were significantly related to LAI. In this period, those irrigated and dryland swards which had common values of LAI generally used similar quantities of water. This finding indicated that stomatal control was ineffective in reducing water use per unit of leaf area. The quantity of dead herbage present in the swards suggests that pronounced leaf senescence (and hence reduction of leaf area) may have been a consequence of ineffective stomatal control of transpiration.

scholarly journals The Influence of Assimilating Leaf Area Index in a Land Surface Model on Global Water Fluxes and Storages

2019 ◽  
Author(s):  
Xinxuan Zhang ◽  
Viviana Maggioni ◽  
Azbina Rahman ◽  
Paul Houser ◽  
Yuan Xue ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Global Water

Abstract. Vegetation plays a fundamental role not only in the energy and carbon cycle, but also the global water balance by controlling surface evapotranspiration. Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water and carbon cycles. This study aims to assess to what extent a land surface model can be optimized through the assimilation of leaf area index (LAI) observations at the global scale. Two observing system simulation experiments (OSSEs) are performed to evaluate the efficiency of assimilating LAI through an Ensemble Kalman Filter (EnKF) to estimate LAI, evapotranspiration (ET), interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework effectively reduces errors in LAI simulations. LAI assimilation also improves the model estimates of all the water flux and storage variables considered in this study (ET, CIE, CWS, SSM, and TWS), even when the forcing precipitation is strongly positively biased (extremely wet condition). However, it tends to worsen some of the model estimated water-related variables (SSM and TWS) when the forcing precipitation is affected by a dry bias. This is attributed to the fact that the amount of water in the land surface model is conservative and the LAI assimilation introduces more vegetation, which requires more water than what available within the soil. Future work should investigate a multi-variate data assimilation system that concurrently merges both LAI and soil moisture (or TWS) observations.

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