Understanding the non‐linear response of summer evapotranspiration to clouds in a temperate forest under the impact of vegetation water content

Quantification of vegetation parameters such as Vegetation Optical Depth (VOD) and Vegetation Water Content (VWC) can be used for better irrigation management, yield forecasting, and soil moisture estimation. Since VOD is directly related to vegetation water content and canopy structure, it can be used as an indicator for VWC. Over the past few decades, optical and passive microwave satellite data have mostly been used to monitor VWC. However, recent research is using active data to monitor VOD and VWC benefitting from their high spatial and temporal resolution.Attenuation of the microwave signal through the vegetation layer is parametrized by the VOD. VOD is assumed to be linearly related to VWC with the proportionality constant being an empirical parameter b. For a given wavelength and polarization, b is assumed static and only parametrized as a function of vegetation type. The hypothesis of this study is that the VOD is not similar for dry and wet vegetation and the static linear relationship between attenuation and vegetation water content is a simplification of reality.The aim of this research is to understand the effect of surface canopy water on VOD estimation and the relationship between VOD and vegetation water content during the growing season of a corn canopy. In addition to studying the dependence of VOD on bulk VWC for dry and wet vegetation, the effect of different factors, such as different growth stages and internal vegetation water content is investigated using time series analysis.A field experiment was conducted in Florida, USA, for a full growing season of sweet corn. The corn field was scanned every 30 minutes with a truck-mounted, fully polarimetric, L-band radar. Pre-dawn vegetation water content was measured using destructive sampling three times a week for a full growing season. VWC could therefore be analyzed by constituent (leaf, stem, ear) or by height. Meteorological data, surface canopy water (dew or interception), and soil moisture were measured every 15 minutes for the entire growing season.The methodology of Vreugdenhil et al.&#160; [1], developed by TU Wien for ASCAT data, was adapted to present a new technique to estimate VOD from single-incidence angle backscatter data in each polarization. The results showed that the effect of surface canopy water on the VOD estimation increased by vegetation biomass accumulation and the effect was higher in the VOD estimated from the co-pol compared with the VOD estimated from the cross-pol.&#160;Moreover, the surface canopy water considerably affected the regression coefficient values (b-factor) of the linear relationship between VOD and VWC from dry and wet vegetation. This finding suggests that considering a similar b-factor for the dry and the wet vegetation will introduce errors in soil moisture retrievals. Furthermore, it highlights the importance of considering canopy wetness conditions when using tau-omega.<ul><li>[1] Vreugdenhil,W. A. Dorigo,W.Wagner, R. A. De Jeu, S. Hahn, andM. J. VanMarle, &#8220;Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval,&#8221; IEEE Transactions on Geoscience and Remote Sensing, vol. 54, pp. 3513&#8211;3531, 2016</li> </ul>

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Estimation of vegetation water content using hyperspectral vegetation indices: a comparison of crop water indicators in response to water stress treatments for summer maize

BMC Ecology ◽

10.1186/s12898-019-0233-0 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 14

Author(s):

F. Zhang ◽

G. Zhou

Keyword(s):

Water Stress ◽

Water Content ◽

Vegetation Indices ◽

Crop Water ◽

Summer Maize ◽

Vegetation Water Content ◽

Vegetation Water ◽

Response To Water

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Monitoring the Variation of Vegetation Water Content with Machine Learning Methods: Point–Surface Fusion of MODIS Products and GNSS-IR Observations

Remote Sensing ◽

10.3390/rs11121440 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1440 ◽

Cited By ~ 1

Author(s):

Qiangqiang Yuan ◽

Shuwen Li ◽

Linwei Yue ◽

Tongwen Li ◽

Huanfeng Shen ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Water Content ◽

Linear Regression Method ◽

Learning Methods ◽

Machine Learning Methods ◽

Vegetation Water Content ◽

Drought Prediction ◽

Vegetation Water ◽

Surface Fusion

Vegetation water content (VWC) is recognized as an important parameter in vegetation growth studies, natural disasters such as forest fires, and drought prediction. Recently, the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) has emerged as an important technique for monitoring vegetation information. The normalized microwave reflection index (NMRI) was developed to reflect the change of VWC based on this fact. However, NMRI uses local site-based data, and the sparse distribution hinders the application of NMRI. In this study, we obtained a 500 m spatially continuous NMRI product by integrating GNSS-IR site data with other VWC-related products using the point–surface fusion technique. The auxiliary data in the fusion process include the normalized difference vegetation index (NDVI), gross primary productivity (GPP), and precipitation. Meanwhile, the fusion performance of three machine learning methods, i.e., the back-propagation neural network (BPNN), generalized regression neural network (GRNN), and random forest (RF) are compared and analyzed. The machine learning methods achieve satisfactory results, with cross-validation R values of 0.71–0.83 and RMSEs of 0.025–0.037. The results show a clear improvement over the traditional multiple linear regression method, which achieves R (RMSE) values of only about 0.4 (0.045). It indicates that the machine learning methods can better learn the complex nonlinear relationship between NMRI and the input VWC-related index. Among the machine learning methods, the RF model obtained the best results. Long time-series NMRI images with a 500 m spatial resolution in the western part of the continental U.S. were then obtained. The results show that the spatial distribution of the NMRI product is consistent with a drought situation from 2012 to 2014 in the U.S., which verifies the feasibility of analyzing and predicting drought times and distribution ranges by using the 500 m fusion product.

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