Estimation Of Canopy Water Content Research Articles

Estimation of canopy water content for wheat through combining radiative transfer model and machine learning

Canopy water content (CWC) is an important indicator of crop growth. Currently, the hybrid approach, which combines the physical model and machine learning (ML) method, is commonly used for inversing CWC at the regional scale via optical imagery. Although this approach has good inversion accuracy, it suffers from ill-posed issues from the physical model due to the difference between actual and simulated scenes. Meanwhile, the ability of existing ML methods to resolve this difference remains unclear. To fill the above study gaps, we added different degrees of Gaussian noise into the simulated dataset to reduce the differences in spectral reflectance between that simulated by the PROSAIL-5B model and that measured from optimal imagery. Furthermore, this study also compared the performance of different ML approaches (neural network, NN; Gauss process regression, GPR; and kernel ridge regression, KRR) in the retrieval of wheat (Triticum aestivum L.) CWC based on PROSAIL-simulated datasets. Additionally, we also compared the inversion results for the CWC generated by the SNAP Toolbox with those estimated by the hybrid approach. The results showed that the aforementioned hybrid approaches performed well in the retrieval of wheat CWC when Gaussian noise with a mean of zero and a standard deviation of 0.06 (η: 0, 0.06) was added to the simulated dataset. The hybrid model based on GPR had the highest accuracy of estimation for the CWC (RMSE = 0.0096 g/cm2, RRMSE = 22.2%), when compared with NN or KRR (NN: RMSE = 0.0105 g/cm2, RRMSE = 26.9%; KRR: RMSE = 0.0099 g/cm2, RRMSE = 24.3%). It also outperformed the existing CWC-retrieval algorithms among the SNAP Toolbox (RMSE = 0.027 g/cm2, RRMSE=44.1%). Our results demonstrate that the hybrid approach in combination with GPR and the physical model can accurately retrieve the CWC of crops, thus confirming that the approach is suitable for estimating the CWC.

Atmospherically resistant vegetation water indices using the 970-nm water absorption feature

Atmospheric correction can introduce errors in surface spectral reflectance, and hence induces errors in plant water estimation from remote sensing water indices. We intend to develop water indices that are less impacted by atmospheric effects for plant water content estimation based on the 970-nm water absorption feature. A simulation study using the PROSAIL and 6S models showed that uncertainty in atmospheric water vapor (WV) content can induce large variation in existing 970-nm water indices, such as WI, NWI-1, and NWI-3. An attempt was made to incorporate atmospheric WV absorption at 940 nm to correct for the perturbation due to atmospheric WV variability, leading to the development of improved indices, named as ARWI, NARWI-1, and NARWI-3. The performance of these indices was evaluated using the simulated and field spectral reflectance data, as well as Hyperion and GF5 satellite data. Results showed that the new indices were resistant to uncertainty of WV and could be used to deliver improved estimation of canopy water content, with a smaller root-mean-square-error (ARWI: 7.4 mg/cm2, NARWI-1: 8.3 mg/cm2, and NARWI-3: 8.8 mg/cm2) compared to that obtained using the traditional water indices (WI: 8.9 mg/cm2, NWI-1: 9.4 mg/cm2, and NWI-3: 16.6 mg/cm2). The water indices developed in this study, although needing further assessment in wide application scenarios, have great potential for monitoring of vegetation water status using satellite hyperspectral data with reflectance measurement around 970 nm.

Modeling Winter Wheat Leaf Area Index and Canopy Water Content With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data

Leaf area index (LAI) and canopy water content (CWC) are important variables for monitoring crop growth and drought, which can be estimated from remotely sensed data. The goal of this study was to evaluate the suitability of the Sentinel-2 multispectral instrument (S2 MSI) data for winter wheat LAI and CWC estimation with three different inversion approaches in the main farming region in North China. During the winter wheat key growth stages in 2017, 22 fields, each with five independent samples, the total number of sample plot is 110, were designed for experimental measurements. In this study, the LAI and CWC were retrieved separately using empirical models through different spectral indices, neural network (NN) algorithms, and lookup table (LUT) methods based on the PROSAIL model. The accuracies of the estimated LAI and CWC were assessed through in situ measurements. The results show that the LUT inversion approach was more suitable for LAI and CWC estimation than the spectral index-based empirical model or the NN algorithm. With the LUT approach, LAI was obtained with a root mean square error (RMSE) of $\text{0.43}\,{\text{m}}^{\text{2}} \cdot {\text{m}}^{-\text{2}}$ and a relative RMSE (RRMSE) of 11% using seven S2 MSI bands, and CWC was obtained with an RMSE of $\text{0.41}\,{\text{kg}} \cdot {\text{m}}^{-\text{2}}$ , and an RRMSE of 32% using five S2 MSI bands. In all the three methods, S2 MSI was sensitive to LAI variation and able to reach higher accuracies when red edge bands were used. However, CWC inversion was still a challenge using S2 MSI data.

Estimation of Canopy Water Content by Means of Hyperspectral Indices Based on Drought Stress Gradient Experiments of Maize in the North Plain China

Here, we conducted drought stress gradient experiments of maize, and used ten water content related vegetation indices (VIs) to estimate widely variable canopy water content (CWC) and mean leaf equivalent water thickness at canopy level (\({\overline{EWT}}\)) based on in situ measurements of Lambertian equivalent reflectance and important biological and environmental factors during the 2013−2014 growing seasons in the North China Plain. Among ten VIs, the performances of green chlorophyll index (CIgreen), red edge chlorophyll index (CIred edge), and the red edge normalized ratio (NRred edge) were most sensitive to the variations of CWC and \({\overline{EWT}}\). Simulated drought in two differently managed irrigation years did not affect the sensitivities of VIs to the variations in CWC and \({\overline{EWT}}\). However, the relationships between CWC and VIs were more noticeable in 2014 than in 2013. In contrast, \({\overline{EWT}}\) and VIs were more closely related in 2013 than in 2014. CWC and relative soil water content (RSWC) obviously exhibited a two-dimensional trapezoid space, which illustrated that CWC was determined not only by soil water status but also by crop growth and stage of development. This study demonstrated that nearly half of the variation in CWC explained by spectral information was derived from the variation in leaf area index (LAI).

Leaf and canopy water content estimation in cotton using hyperspectral indices and radiative transfer models

In present study some vegetation indices for estimating leaf EWT and EWTcanopy were investigated using simulations and field measurements. Leaf and canopy spectral reflectance as well as leaf EWT and EWTcanopy were measured in cotton during the growing seasons of 2010 and 2011. The PROSPECT-5 model was coupled with the SAILH model to explore the performance of water-related vegetation indices for leaf EWT and EWTcanopy estimation. The vegetation indices evaluated were published formulations and new simple ratio vegetation indices formulated with wavebands at 1060nm and 1640nm. The sensitivities of these indices to leaf internal structural N and LAI effects were assessed. Simulation results indicated that all of the water-related vegetation indices were insensitive to leaf internal structural N, with the highest coefficient of determination R2<0.15 and the proposed index SR1640 (R1060/R1640) and published index SR2 (R1070/R1340) showed the lowest relationships (R2<0.35) with LAI of all the vegetation indices. Furthermore, coefficients of determination between simulated leaf EWT as well as EWTcanopy and vegetation indices tested revealed that the new simple-ratio vegetation indices proposed in this study (SR1060: R1640/R1060 and SR1640) were found to be significantly related with leaf EWT (R2>0.9; P<0.001) and EWTcanopy (R2>0.8; P<0.001). Results obtained with field measurements were in agreement with simulation results, with the coefficient of determination R2=0.5 (P<0.001) for leaf EWT and R2=0.57 (P<0.001) for EWTcanopy by the new simple ratio indices. This study provides a new candidate for leaf EWT and EWTcanopy estimation using hyperspectral vegetation indices.

Empirical and Physical Estimation of Canopy Water Content from CHRIS/PROBA Data

Efficient monitoring of Canopy Water Content (CWC) is a central feature in vegetation studies. The potential of hyperspectral high spatial resolution CHRIS/PROBA satellite data for the retrieval of CWC was here investigated using empirical and physical based approaches. Special attention was paid to the spectral band selection, inversion technique and training process. Performances were evaluated with ground measurements from the SEN3EXP field campaign over a range of crops. Results showed that the optimal band selection includes four spectral bands: one centered about 970 nm absorption feature which is sensible to Cw, and three bands in green, red and near infrared to estimate LAI and compensate from leaf- and canopy-level effects. A simple neural network with a single hidden layer of five tangent sigmoid transfer functions trained over PROSAIL radiative transfer simulations showed benefits in the retrieval performances compared with a look up table inversion approach (root mean square error of 0.16 kg/m2 vs. 0.22 kg/m2). The neural network inversion approach showed a good agreement and performances similar to an empirical up-scaling approach based on a multivariate iteratively re-weighted least squares algorithm, demonstrating the applicability of radiative transfer model inversion methods to CHRIS/PROBA for high spatial resolution monitoring of CWC.

Canopy Water Content Estimation for Typical Emerged Plant Community from Simulation Worldview-2 Data: A Case Study in Wild Duck Lake Wetland, Beijing

Quantitative estimation of vegetation water content with remote sensing technique is of great significance for vegetation physiological status and growth trend monitoring. It also provides a theoretical foundation for actual application of vegetation water content diagnosis using remote sensing images in Wild Duck Lake wetland. In this paper the NDVIs and SRs calculated from simulation WorldVeiw-2 curves can be used to model and predict canopy water content of typical emerged plant. The NDVIs and SRs involving the additional spectral bands of WorldView-2, such as the red-edge and near infrared regions of the electromagnetic spectrum, improve the prediction accuracy compared with the traditional NDVIs and SRs.

Estimation of canopy water content in Konza Prairie grasslands using synthetic aperture radar measurements during FIFE

This paper presents the development of an algorithm to retrieve the canopy water content of natural grasslands and pasture from synthetic aperture radar (SAR) measurements. The development of this algorithm involves three interrelated steps: (1) calibration of SAR data for ground topographic variations, (2) development and validation of backscatter model for grass canopies, and (3) estimation of canopy water content by inverting a backscattered model for cross‐polarized ratio. The polarimetric radar data acquired by the Jet Propulsion Laboratory AIRSAR system during the 1989 First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) are used for this study. The SAR data have been calibrated and corrected for the topographical effects by using the digital elevation map of the study area. The backscattering coefficients obtained from the SAR data for each pixel are related to the canopy and soil parameters by employing a discrete random media model for vegetation. The model simulations indicate that biomass variations and surface treatments (burned and unburned) of grass canopies affect the C‐band backscatter signal but does not influence the L‐band signal. This model is then validated and adjusted over training areas where ground measurements were collected. An inversion technique is proposed to estimate the canopy water content by using the cross‐polarized and copolarized ratios of the SAR data at C band. The result of the inversion algorithm shows a good agreement with the grass biomass data collected during FIFE 1989 intensive field campaign.