Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data

Ahmadian, Nima; Ullmann, Tobias; Verrelst, Jochem; Borg, Erik; Zölitz, Reinhard; Conrad, Christopher

doi:10.1007/s41064-019-00076-x

Cited by 7 publications

(11 citation statements)

References 55 publications

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“…In addition, we computed mathematical band combinations (MBC) of both polarization in terms of addition (VH+VV), subtraction (VH-VV, VV-VH), ratio (VH/VV, VV/VH), and multiplication (VH * VV). These MBCs were reported to counteract radiometric instability and vegetation moisture variations introduced by using original polarization (e.g., Omar et al, 2017;Ahmadian et al, 2019) and hence are likely able to improve the performance of our empirical model. The definition of Kennaugh's matrix and its elements for dual-pol S1 data was used based on the calculation by Schmitt et al (2015) and Ullmann et al (2017).…”

Section: Sar-based Parametersmentioning

confidence: 90%

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

et al. 2021

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Reliable near-surface soil moisture (θ) information is crucial for supporting risk assessment of future water usage, particularly considering the vulnerability of agroforestry systems of Mediterranean environments to climate change. We propose a simple empirical model by integrating dual-polarimetric Sentinel-1 (S1) Synthetic Aperture Radar (SAR) C-band single-look complex data and topographic information together with in-situ measurements of θ into a random forest (RF) regression approach (10-fold cross-validation). Firstly, we compare two RF models' estimation performances using either 43 SAR parameters (θNovSAR) or the combination of 43 SAR and 10 terrain parameters (θNovSAR+Terrain). Secondly, we analyze the essential parameters in estimating and mapping θ for S1 overpasses twice a day (at 5 a.m. and 5 p.m.) in a high spatiotemporal (17 × 17 m; 6 days) resolution. The developed site-specific calibration-dependent model was tested for a short period in November 2018 in a field-scale agroforestry environment belonging to the “Alento” hydrological observatory in southern Italy. Our results show that the combined SAR + terrain model slightly outperforms the SAR-based model (θNovSAR+Terrain with 0.025 and 0.020 m3 m−3, and 89% compared to θNovSAR with 0.028 and 0.022 m3 m−3, and 86% in terms of RMSE, MAE, and R2). The higher explanatory power for θNovSAR+Terrain is assessed with time-variant SAR phase information-dependent elements of the C2 covariance and Kennaugh matrix (i.e., K1, K6, and K1S) and with local (e.g., altitude above channel network) and compound topographic attributes (e.g., wetness index). Our proposed methodological approach constitutes a simple empirical model aiming at estimating θ for rapid surveys with high accuracy. It emphasizes potentials for further improvement (e.g., higher spatiotemporal coverage of ground-truthing) by identifying differences of SAR measurements between S1 overpasses in the morning and afternoon.

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Section: Sar-based Parametersmentioning

confidence: 90%

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

et al. 2021

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“…Manual production estimation is tedious and can produce significant errors. In exploring more scientific and precise methods, scientists have begun to use crop growth models and machine algorithms to study the crop growth process and yield ( Liaqat et al, 2017 ; Mehta et al, 2018 ; Ahmadian et al, 2019 ; Li et al, 2021 ). At present, many crop growth models have been applied, such as APSIM, DSSAT, WOFOST, etc., which can accurately simulate crop growth stage by changing the parameter Settings of crop growth stage ( Rosa, Souza & Tsukahara, 2020 ; Zhao et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Effects of fallow tillage on winter wheat yield and predictions under different precipitation types

Feng¹,

Lin²,

Yu³

et al. 2021

PeerJ

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In northern China, precipitation that is primarily concentrated during the fallow period is insufficient for the growth stage, creates a moisture shortage, and leads to low, unstable yields. Yield prediction in the early growth stages significantly informs field management decisions for winter wheat (Triticum aestivum L.). A 10-year field experiment carried out in the Loess Plateau area tested how three tillage practices (deep ploughing (DP), subsoiling (SS), and no tillage (NT)) influenced cultivation and yield across different fallow periods. The experiment used the random forest (RF) algorithm to construct a prediction model of yields and yield components. Our results revealed that tillage during the fallow period was more effective than NT in improving yield in dryland wheat. Under drought condition, DP during the fallow period achieved a higher yield than SS, especially in drought years; DP was 16% higher than SS. RF was deemed fit for yield prediction across different precipitation years. An RF model was developed using meteorological factors for fixed variables and soil water storage after tillage during a fallow period for a control variable. Small error values existed in the prediction yield, spike number, and grains number per spike. Additionally, the relative error of crop yield under fallow tillage (5.24%) was smaller than that of NT (6.49%). The prediction error of relative meteorological yield was minimum and optimal, indicating that the model is suitable to explain the influence of meteorological factors on yield.

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“…However, studies examining the assimilation of aboveground biomass are rare and often use field measurements instead of estimates based on remote sensing data [17]- [19]. However, different authors have shown the potential of SAR data to estimate above ground biomass components of different crops using simple regressions or machine learning approaches [20]- [24]. Moreover, authors also showed that the simultaneous assimilation of optical and SAR data often allowed a better optimization of agrometeorological models [19], [25], [26].…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Multisensor Optical and Multiorbital SAR Satellite Data in a Simplified Agrometeorological Model for Rapeseed Crops Monitoring

Alliès¹,

Roumiguié²,

Fieuzal³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper investigates the potential of the assimilation of both Synthetic Aperture Radar (SAR) -derived Dry Mass (DM) and optically-derived Green Area Index (GAI) in an agro-meteorological model (i.e. SAFY) for a better rapeseed crops modeling. The GAI was derived from both 566 Sentinel-2 and 149 Landsat-8 images, whereas DM was derived from 884 Sentinel-1 images acquired from six different orbits. The ground data were collected during 3 agricultural years on 43 rapeseed fields located in three study areas in France with contrasted pedoclimatic conditions. Results show that the temporal evolutions of both DM and GAI can be accurately simulated over the 43 monitored rapeseed fields (R 2 = 0.84 and 0.92, respectively and relative Root Mean Square Error, RMSEr = 41 % and 28 %, respectively) for the best satellite configuration. Tested assimilation scenarios reveal that the concomitant assimilation of SAR and optical data allows a significant better control of the model than the assimilation of SAR or optical data alone. Small differences in the simulations of the model are observed when it is controlled by either multisensor optical and/or multiorbital SAR data or monosensor optical and/or mono-orbital SAR data. The discrepancies of performance between fields push towards the strengthening of this study by considering other rapeseed fields with ground observations acquired worldwide for the calibration of SARderived DM. These results are however promising in view of the development of a near-real time assimilation scheme as a decision support tool for farmers and decision makers.

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Biomass Assessment of Agricultural Crops Using Multi-temporal Dual-Polarimetric TerraSAR-X Data

Cited by 7 publications

References 55 publications

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

Effects of fallow tillage on winter wheat yield and predictions under different precipitation types

Assimilation of Multisensor Optical and Multiorbital SAR Satellite Data in a Simplified Agrometeorological Model for Rapeseed Crops Monitoring

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