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.

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 Enhanced Watershed Modeling by Incorporating Remotely Sensed Evapotranspiration and Leaf Area Index

2021 ◽  
Author(s):  
Sangchul Lee ◽  
Gregory W. McCarty ◽  
Glenn E. Moglen ◽  
Haw Yen ◽  
Fangni Lei ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Assessment Tool ◽  
Watershed Modeling ◽  
Remotely Sensed ◽  
Temporal Consistency ◽  
Remotely Sensed Data ◽  
Area Index ◽  
Predictive Uncertainty ◽  
Time Space

Abstract. Remotely sensed evapotranspiration (RS-ET) products have been widely adopted as additional constraints on hydrologic modeling to enhance the model predictability while reducing predictive uncertainty. However, vegetation parameters, responsible for key time/space variation in evapotranspiration (ET), are often calibrated without the use of suitable constraints. Remotely sensed leaf area index (RS-LAI) products are increasingly available and provide an opportunity to assess vegetation dynamics and improve calibration of associated parameters. The goal of this study is to assess the Soil and Water Assessment Tool (SWAT) predictive uncertainty in estimates of ET using streamflow and two remotely sensed products (i.e., RS-ET and RS-LAI). We explore how the application of RS-ET and RS-LAI products contributes to 1) reducing the parameter uncertainty; 2) improving the model capacity to predict the spatial distribution of ET and LAI at the sub-watershed level; and 3) assessing the model predictions of ET and LAI at the basic modeling unit (i.e., the hydrologic response unit [HRU]) under the assumption that ET and LAI are related in croplands. Our results suggest that most of the parameter sets with acceptable performances for two constraints (i.e., streamflow and RS-ET; 12 parameter sets) are also acceptable for three constraints (i.e., streamflow, RS-ET, and RS-LAI; 11 parameter sets) at the watershed level. This finding is likely because both the ET simulation algorithm and the RS-ET products consider LAI as an input variable. Relative to the watershed-level assessment, the number of parameter sets that satisfactorily characterize spatial patterns of ET and LAI at the sub-watershed level are reduced from 11 to 6. Among the 11 parameter sets acceptable for three constraints (i.e., streamflow, RS-ET and RS-LAI) at the sub-watershed level, two parameter sets appear to provide high spatial and temporal consistency between ET and LAI at the HRU level. These results suggested that use of multiple remotely sensed products as model constraints enables model evaluations at finer scales – thereby constraining acceptable parameter sets and accurately representing the spatial characteristics of hydrologic variables. As such, this study highlights the potential of remotely sensed data to increase the predictability and utility of hydrologic models.

scholarly journals An automated procedure for estimating the leaf area index (LAI) of woodland ecosystems using digital imagery, MATLAB programming and its application to an examination of the relationship between remotely sensed and field measurements of LAI

2008 ◽  
Vol 35 (10) ◽  
pp. 1070 ◽  
Author(s):  
Sigfredo Fuentes ◽  
Anthony R. Palmer ◽  
Daniel Taylor ◽  
Melanie Zeppel ◽  
Rhys Whitley ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Gap Analysis ◽  
Digital Camera ◽  
Field Measurements ◽  
Accurate Estimation ◽  
Image Capture ◽  
Area Index ◽  
Modis Lai ◽  
Matlab Programming

Leaf area index (LAI) is one of the most important variables required for modelling growth and water use of forests. Functional–structural plant models use these models to represent physiological processes in 3-D tree representations. Accuracy of these models depends on accurate estimation of LAI at tree and stand scales for validation purposes. A recent method to estimate LAI from digital images (LAID) uses digital image capture and gap fraction analysis (Macfarlane et al. 2007b) of upward-looking digital photographs to capture canopy LAID (cover photography). After implementing this technique in Australian evergreen Eucalyptus woodland, we have improved the method of image analysis and replaced the time consuming manual technique with an automated procedure using a script written in MATLAB 7.4 (LAIM). Furthermore, we used this method to compare MODIS LAI values with LAID values for a range of woodlands in Australia to obtain LAI at the forest scale. Results showed that the MATLAB script developed was able to successfully automate gap analysis to obtain LAIM. Good relationships were achieved when comparing averaged LAID and LAIM (LAIM = 1.009 – 0.0066 LAID; R2 = 0.90) and at the forest scale, MODIS LAI compared well with LAID (MODIS LAI = 0.9591 LAID – 0.2371; R2 = 0.89). This comparison improved when correcting LAID with the clumping index to obtain effective LAI (MODIS LAI = 1.0296 LAIe + 0.3468; R2 = 0.91). Furthermore, the script developed incorporates a function to connect directly a digital camera, or high resolution webcam, from a laptop to obtain cover photographs and LAI analysis in real time. The later is a novel feature which is not available on commercial LAI analysis softwares for cover photography. This script is available for interested researchers.

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.

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