The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing

Sheng-wei, Liu; Peng, Dailiang; Zhang, Bing; Chen, Zhengchao; Yu, Le; Chen, Junjie; Zheng, Shijun; Hu, Jinkang; Lou, Zihang; Chen, Yue; Yang, Shu

doi:10.3390/rs14040893

Cited by 16 publications

(10 citation statements)

References 70 publications

(78 reference statements)

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“…While land cover is available at an adequate resolution (i.e. field sale), it is error-prone, either because of erroneous declarations in the RGP (Magnin, 2019) or because of classification errors when EO based land cover maps are used (Liu et al, 2022). Interestingly, our results show that when a mismatch occurs, the fields in question exhibit high anomalies in retrieved parameters and are thus detectable.…”

Section: Importance Of High Spatial-resolutionmentioning

confidence: 79%

AgriCarbon-EO: v1.0.1: Large Scale and High Resolution Simulation of Carbon Fluxes by Assimilation of Sentinel-2 and Landsat-8 Reflectances using a Bayesian approach

Wijmer¹,

Bitar²,

Arnaud³

et al. 2023

Preprint

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Abstract. Soil carbon storage is a well identified climate change mitigation solution. The extensive in-situ monitoring of the soil carbon storage in cropland for agricultural policy and offset carbon markets is prohibitive, especially at intra-field scale. For this reason, comprehensive Monitoring, Reporting and Verification (MRV) of soil carbon and its explanatory variables at large scale needs to rely on remote sensing and modelling tools that provide the spatio-temporal dynamics of the carbon budget and it’s components at high resolution with associated uncertainties. In this paper, we present AgriCarbon-EO v1.0: an end-to-end processing chain that enables the estimation of carbon budget components of major crops and cover crops at intra-field resolution (10 m) and large scale (over 110×110 km) by assimilating remote sensing data in physically-based radiative transfert and agronomic models. The data assimilation in AgriCarbon-EO is based on a novel Bayesian approach that combines Normalised Importance Sampling (NIS) and Look-Up Table (LUT) generation. This approach propagates the 10 m uncertainties across the processing chain from the reflectances to the output variables. The chain considers as input a land cover map, multi-spectral reflectance maps from the Sentinel-2 and Landsat-8 satellites, and daily weather forcing. The PROSAIL radiative transfer model is inversed in a first step to obtain Green Leaf Area Index (GLAI). The GLAI time series are then assimilated into the SAFYE-CO2 crop model taking into consideration their uncertainty. The chain is applied over winter wheat in the south-west of France during the cropping seasons 2017 and 2019. We compare the results against the net ecosystem exchange measured at the FR-AUR ICOS site (RMSE = 1.69 - 2.4 gC m−2 , R2 = 0.88 - 0.88), biomass (RMSE = 250 g m−2 , R2 = 0.9), and combine harvester yield maps. We quantify the difference between pixel and field and pixel scale simulations of biomass (bias = -47 g m−2 , -39 % variability), and the impact of the number of remote sensing acquisitions on the outputs (-66 % of mean uncertainty of biomass). Finally, we conduct a coherency analysis at regional scale to test the consistency of the observed patterns with soil texture, altitude and exposition variability. Results show higher biomass for higher clay soils and earlier emergence and senescence for south western exposition.

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Section: Importance Of High Spatial-resolutionmentioning

confidence: 79%

AgriCarbon-EO: v1.0.1: Large Scale and High Resolution Simulation of Carbon Fluxes by Assimilation of Sentinel-2 and Landsat-8 Reflectances using a Bayesian approach

Wijmer¹,

Bitar²,

Arnaud³

et al. 2023

Preprint

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“…This demonstrates that the success of rainfed land cultivation relies mainly on rainfall, with factors like TS and GDD having secondary influence on yield. Research has shown that when it comes to dryland wheat cultivation in areas with low moisture, it is better to extend the growth period through autumn cultivation, rather than cultivate the crop in warm and temperate regions, as this is preferred over increasing GDD 25 .…”

Section: Discussionmentioning

confidence: 99%

Global insight into understanding wheat yield and production through Agro-Ecological Zoning

Dadrasi,

Chaichi,

Nehbandani

et al. 2023

Sci Rep

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Global food security requires food production to be increased in the future decades. Agro-Ecological Zones (AEZ) methodology is a successful approach used in land evaluation studies to support sustainable agricultural development. This approach can facilitate finding suitable areas for wheat production on a global scale. This study was conducted based on a climate zone map, soil data, wheat cultivation area, yield, and production under irrigated and rainfed conditions, worldwide. The results for irrigated wheat indicated that there is an area of 59.5 Mha with an average yield of 4.02 t/ha which leads to the production of about 239.6 Mt of grain yield. Furthermore, climate zones (CZs) of 8002, 5203, 5302, 5403, and 8102 have the highest wheat production with an average of 20.7, 14.2, 13.3, 11.8, 11.5 Mt, respectively. The highest amount of irrigated wheat production has been achieved in soil type code 17 (Loam LF120), which has a cultivation area of around 23.6 Mha and a production of about 106.8 Mt. Rainfed wheat production is 410 Mt, and the cultivation area is 160.2 Mha. The highest rainfed wheat production with an average of 17 Mt was related to the 3702 CZ, followed by the other CZs (3802, 1303, 1203, 3602, 4602, etc.). The soil codes 11 (Loam HF120), 10 (Loam HF180), and 14 (Loam MF120) showed the highest rainfed wheat production. The findings of this study can be useful for agricultural scientists, authorities, and decision-makers around the world to find suitable lands to expand wheat cultivation and also to find new locations for increasing global wheat production to feed the increasing population in the world.

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“…Previous studies have explored correlations between multitemporal VIs and crop yields based on Random Forest (RF), Support Vector Machines (SVM), etc. However, these traditional deep learning-based methods require large, extensive data, limiting their application to yield predictions with fewer data [73][74][75][76]. Therefore, the BHT-ARIMA model applied in yield estimation has greater potential than other methods.…”

Section: Discussionmentioning

confidence: 99%

Winter Wheat Yield Estimation Based on Optimal Weighted Vegetation Index and BHT-ARIMA Model

Deng

Zhang

et al. 2022

Remote Sensing

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This study aims to use remote sensing (RS) time-series data to explore the intrinsic relationship between crop growth and yield formation at different fertility stages and construct a high-precision winter wheat yield estimation model applicable to short time-series RS data. Sentinel-2 images were acquired in this study at six key phenological stages (rejuvenation stage, rising stage, jointing stage, heading stage, filling stage, filling-maturity stage) of winter wheat growth, and various vegetation indexes (VIs) at different fertility stages were calculated. Based on the characteristics of yield data continuity, the RReliefF algorithm was introduced to filter the optimal vegetation index combinations suitable for the yield estimation of winter wheat for all fertility stages. The Absolutely Objective Improved Analytic Hierarchy Process (AOIAHP) was innovatively proposed to determine the proportional contribution of crop growth to yield formation in six different phenological stages. The selected VIs consisting of MTCI(RE2), EVI, REP, MTCI(RE1), RECI(RE1), NDVI(RE1), NDVI(RE3), NDVI(RE2), NDVI, and MSAVI were then fused with the weights of different fertility periods to obtain time-series weighted data. For the characteristics of short time length and a small number of sequences of RS time-series data in yield estimation, this study applied the multiplexed delayed embedding transformation (MDT) technique to realize the data augmentation of the original short time series. Tucker decomposition was performed on the block Hankel tensor (BHT) obtained after MDT enhancement, and the core tensor was extracted while preserving the intrinsic connection of the time-series data. Finally, the resulting multidimensional core tensor was trained with the Autoregressive Integrated Moving Average (ARIMA) model to obtain the BHT-ARIMA model for wheat yield estimation. Compared to the performance of the BHT-ARIMA model with unweighted time-series data as input, the weighted time-series input significantly improves yield estimation accuracy. The coefficients of determination (R2) were improved from 0.325 to 0.583. The root mean square error (RMSE) decreased from 492.990 to 323.637 kg/ha, the mean absolute error (MAE) dropped from 350.625 to 255.954, and the mean absolute percentage error (MAPE) decreased from 4.332% to 3.186%. Besides, BHT-ARMA and BHT-CNN models were also used to compare with BHT-ARIMA. The results indicated that the BHT-ARIMA model still had the best yield prediction accuracy. The proposed method of this study will provide fast and accurate guidance for crop yield estimation and will be of great value for the processing and application of time-series RS data.

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The Accuracy of Winter Wheat Identification at Different Growth Stages Using Remote Sensing

Cited by 16 publications

References 70 publications

AgriCarbon-EO: v1.0.1: Large Scale and High Resolution Simulation of Carbon Fluxes by Assimilation of Sentinel-2 and Landsat-8 Reflectances using a Bayesian approach

AgriCarbon-EO: v1.0.1: Large Scale and High Resolution Simulation of Carbon Fluxes by Assimilation of Sentinel-2 and Landsat-8 Reflectances using a Bayesian approach

Global insight into understanding wheat yield and production through Agro-Ecological Zoning

Winter Wheat Yield Estimation Based on Optimal Weighted Vegetation Index and BHT-ARIMA Model

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