Look-up-table approach for leaf area index retrieval from remotely sensed data based on scale information

2018 ◽  
Vol 57 (03) ◽  
pp. 1 ◽  
Author(s):  
Xiaohua Zhu ◽  
Chuanrong Li ◽  
Lingli Tang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Remotely Sensed ◽  
Remotely Sensed Data ◽  
Area Index ◽  
Look Up Table

Image based remote sensing method for modeling black-eyed beans (Vigna unguiculata) Leaf Area Index (LAI) and Crop Height (CH) over Cyprus

2013 ◽  
Vol 5 (1) ◽  
pp. 1-11 ◽  
Author(s):  
Giorgos Papadavid ◽  
Dionysia Fasoula ◽  
Michael Hadjimitsis ◽  
P. Skevi Perdikou ◽  
Diofantos Hadjimitsis
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Remotely Sensed ◽  
Theoretical Background ◽  
Crop Canopy ◽  
Remotely Sensed Data ◽  
Area Index ◽  
Crop Height ◽  
Statistical Relationships

AbstractIn this paper, Leaf Area Index (LAI) and Crop Height (CH) are modeled to the most known spectral vegetation index — NDVI — using remotely sensed data. This approach has advantages compared to the classic approaches based on a theoretical background. A GER-1500 field spectro-radiometer was used in this study in order to retrieve the necessary spectrum data for estimating a spectral vegetation index (NDVI), for establishing a semiempirical relationship between black-eyed beans’ canopy factors and remotely sensed data. Such semi-empirical models can be used then for agricultural and environmental studies. A field campaign was undertaken with measurements of LAI and CH using the Sun-Scan canopy analyzer, acquired simultaneously with the spectroradiometric (GER1500) measurements between May and June of 2010. Field spectroscopy and remotely sensed imagery have been combined and used in order to retrieve and validate the results of this study. The results showed that there are strong statistical relationships between LAI or CH and NDVI which can be used for modeling crop canopy factors (LAI, CH) to remotely sensed data. The model for each case was verified by the factor of determination. Specifically, these models assist to avoid direct measurements of the LAI and CH for all the dates for which satellite images are available and support future users or future studies regarding crop canopy parameters.

scholarly journals Leaf Area Index Estimates Using Remotely Sensed Data and BRDF Models in a Semiarid Region

2000 ◽  
Vol 73 (1) ◽  
pp. 18-30 ◽  
Author(s):  
J Qi ◽  
Y.H Kerr ◽  
M.S Moran ◽  
M Weltz ◽  
A.R Huete ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Remotely Sensed ◽  
Semiarid Region ◽  
Remotely Sensed Data ◽  
Area Index

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.

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