Changes In Spectral Reflectance Of Crop Canopies Due To Drought Stress

Huber, K.; Dorigo, Wouter; Bauer, Thomas; Eitzinger, Josef; Haumann, J.; Kaiser, G.; Linke, Rita; Postl, W.; Rischbeck, Pablo; Schneider, Werner; Suppan, Franz; Weihs, Philipp

doi:10.1063/1.2349352

Cited by 2 publications

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“…The need for a proper parameterization of C brown becomes apparent when observing the residual deviations in the red edge region and the absence of a distinct shoulder in the field spectra ( Figure 6). The same spectral shape is found in other publications that analyse dense crops like winter wheat [104] or grassland [51]. Solid assumptions about the biochemical influence of brown leaf pigments are necessary to improve the quality of the retrieval of other plant pigments as well as of LAI and ALIA, which are all sensitive in the far red and NIR.…”

Section: Discussionsupporting

confidence: 56%

Fitted PROSAIL Parameterization of Leaf Inclinations, Water Content and Brown Pigment Content for Winter Wheat and Maize Canopies

et al. 2019

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Decades after release of the first PROSPECT + SAIL (commonly called PROSAIL) versions, the model is still the most famous representative in the field of canopy reflectance modelling and has been widely used to obtain plant biochemical and structural variables, particularly in the agricultural context. The performance of the retrieval is usually assessed by quantifying the distance between the estimated and the in situ measured variables. While this has worked for hundreds of studies that obtained canopy density as a one-sided Leaf Area Index (LAI) or pigment content, little is known about the role of the canopy geometrical properties specified as the Average Leaf Inclination Angle (ALIA). In this study, we exploit an extensive field dataset, including narrow-band field spectra, leaf variables and canopy properties recorded in seven individual campaigns for winter wheat (4x) and silage maize (3x). PROSAIL outputs generally did not represent field spectra well, when in situ variables served as input for the model. A manual fitting of ALIA and leaf water (EWT) revealed significant deviations for both variables (RMSE = 14.5°, 0.020 cm) and an additional fitting of the brown leaf pigments (Cbrown) was necessary to obtain matching spectra at the near infrared (NIR) shoulder. Wheat spectra tend to be underestimated by the model until the emergence of inflorescence when PROSAIL begins to overestimate crop reflectance. This seasonal pattern could be attributed to an attenuated development of ALIAopt compared to in situ measured ALIA. Segmentation of nadir images of wheat was further used to separate spectral contributors into dark background, ears and leaves + stalks. It could be shown that the share of visible fruit ears from nadir view correlates positively with the deviations between field spectral measurement and PROSAIL spectral outputs (R² = 0.78 for aggregation by phenological stages), indicating that retrieval errors increase for ripening stages. An appropriate model parameterization is recommended to assure accurate retrievals of biophysical and biochemical products of interest. The interpretation of inverted ALIA as physical leaf inclinations is considered unfeasible and we argue in favour of treating it as a free calibration parameter.

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Section: Discussionsupporting

confidence: 56%

Fitted PROSAIL Parameterization of Leaf Inclinations, Water Content and Brown Pigment Content for Winter Wheat and Maize Canopies

et al. 2019

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“…One of the most critical applications of remote sensing (RS) technologies concerns detecting and monitoring changes occurring on Earth's surface using multi-temporal, remotely-sensed images [1][2][3][4][5][6][7][8][9][10][11][12]. In this context, optical RS sensors have historically extensively been used for addressing change detection (CD) with a variety of heterogeneous applications, including the hazard assessment of large earthquakes [13][14][15], crop growth monitoring [16,17], analysis of vegetation and forest changes [18][19][20][21][22], urban changes and urban sprawl detection [23][24][25][26], and snow cover monitoring [27][28][29][30]. Essentially, change detection is a process that analyzes two or more images captured over the same geographical area at different times to identify those significant land cover changes that have occurred.…”

Section: Introductionmentioning

confidence: 99%

Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations

et al. 2022

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This work aims to clarify the potential of incoherent and coherent change detection (CD) approaches for detecting and monitoring ground surface changes using sequences of synthetic aperture radar (SAR) images. Nowadays, the growing availability of remotely sensed data collected by the twin Sentinel-1A/B sensors of the European (EU) Copernicus constellation allows fast mapping of damage after a disastrous event using radar data. In this research, we address the role of SAR (amplitude) backscattered signal variations for CD analyses when a natural (e.g., a fire, a flash flood, etc.) or a human-induced (disastrous) event occurs. Then, we consider the additional pieces of information that can be recovered by comparing interferometric coherence maps related to couples of SAR images collected between a principal disastrous event date. This work is mainly concerned with investigating the capability of different coherent/incoherent change detection indices (CDIs) and their mutual interactions for the rapid mapping of “changed” areas. In this context, artificial intelligence (AI) algorithms have been demonstrated to be beneficial for handling the different information coming from coherent/incoherent CDIs in a unique corpus. Specifically, we used CDIs that synthetically describe ground surface changes associated with a disaster event (i.e., the pre-, cross-, and post-disaster phases), based on the generation of sigma nought and InSAR coherence maps. Then, we trained a random forest (RF) to produce CD maps and study the impact on the final binary decision (changed/unchanged) of the different layers representing the available synthetic CDIs. The proposed strategy was effective for quickly assessing damage using SAR data and can be applied in several contexts. Experiments were conducted to monitor wildfire’s effects in the 2021 summer season in Italy, considering two case studies in Sardinia and Sicily. Another experiment was also carried out on the coastal city of Houston, Texas, the US, which was affected by a large flood in 2017; thus, demonstrating the validity of the proposed integrated method for fast mapping of flooded zones using SAR data.

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Changes In Spectral Reflectance Of Crop Canopies Due To Drought Stress

Cited by 2 publications

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Fitted PROSAIL Parameterization of Leaf Inclinations, Water Content and Brown Pigment Content for Winter Wheat and Maize Canopies

Fitted PROSAIL Parameterization of Leaf Inclinations, Water Content and Brown Pigment Content for Winter Wheat and Maize Canopies

Change Detection Techniques with Synthetic Aperture Radar Images: Experiments with Random Forests and Sentinel-1 Observations

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