Estimating Winter Wheat Research Articles

Estimating Winter Wheat Plant Nitrogen Content by Combining Spectral and Texture Features Based on a Low-Cost UAV RGB System throughout the Growing Season

As prior information for precise nitrogen fertilization management, plant nitrogen content (PNC), which is obtained timely and accurately through a low-cost method, is of great significance for national grain security and sustainable social development. In this study, the potential of the low-cost unmanned aerial vehicle (UAV) RGB system was investigated for the rapid and accurate estimation of winter wheat PNC across the growing season. Specifically, texture features were utilized as complements to the commonly used spectral information. Five machine learning regression algorithms, including support vector machines (SVMs), classification and regression trees, artificial neural networks, K-nearest neighbors, and random forests, were employed to establish the bridge between UAV RGB image-derived features and ground-truth PNC, with multivariate linear regression serving as the reference. The results show that both spectral and texture features had significant correlations with ground-truth PNC, indicating the potential of low-cost UAV RGB images to estimate winter wheat PNC. The H channel, S4O6, and R_SE and R_EN had the highest correlation among the spectral indices, Gabor texture features, and grey level co-occurrence matrix texture features, with absolute Pearson’s correlation coefficient values of 0.63, 0.54, and 0.69, respectively. When the texture features were used together with spectral indices, the PNC estimation accuracy was enhanced, with the root mean square error (RMSE) decreasing from 2.56 to 2.24 g/kg, for instance, when using the SVM regression algorithm. The SVM regression algorithm with validation achieved the highest estimation accuracy, with a coefficient of determination (R2) of 0.62 and an RMSE of 2.15 g/kg based on the optimal feature combination of B_CON, B_M, G_DIS, H, NGBDI, R_EN, R_M, R_SE, S3O7, and VEG. Overall, this study demonstrated that the low-cost UAV RGB system could be successfully used to map the PNC of winter wheat across the growing season.

Wheat Yield Estimation Based on Unmanned Aerial Vehicle Multispectral Images and Texture Feature Indices

To obtain timely, accurate, and reliable information on wheat yield dynamics. The UAV DJI Wizard 4-multispectral version was utilized to acquire multispectral images of winter wheat during the tasseling, grouting, and ripening periods, and to manually acquire ground yield data. Sixteen vegetation indices were screened by correlation analysis, and eight textural features were extracted from five single bands in three fertility periods. Subsequently, models for estimating winter wheat yield were developed utilizing multiple linear regression (MLR), partial least squares (PLS), BP neural network (BPNN), and random forest regression (RF), respectively. (1) The results indicated a consistent correlation between the two variable types and yield across various fertility periods. This correlation consistently followed a sequence: heading period > filling period > mature stage. (2) The model’s accuracy improves significantly when incorporating both texture features and vegetation indices for estimation, surpassing the accuracy achieved through the estimation of a single variable type. (3) Among the various models considered, the partial least squares (PLS) model integrating texture features and vegetation indices exhibited the highest accuracy in estimating winter wheat yield. It achieved a coefficient of determination (R2) of 0.852, a root mean square error (RMSE) of 74.469 kg·hm−2, and a normalized root mean square error (NRMSE) of 7.41%. This study validates the significance of utilizing image texture features along with vegetation indices to enhance the accuracy of models estimating winter wheat yield. It demonstrates that UAV multispectral images can effectively establish a yield estimation model. Combining vegetation indices and texture features results in a more accurate and predictive model compared to using a single index.

Estimating winter wheat biomass by coupling the CBA-Wheat model and multi-spectral remote sensing

Estimating winter wheat biomass by coupling the CBA-Wheat model and multi-spectral remote sensing

Combining Texture, Color, and Vegetation Index from Unmanned Aerial Vehicle Multispectral Images to Estimate Winter Wheat Leaf Area Index during the Vegetative Growth Stage

Leaf Area Index (LAI) is a fundamental indicator of plant growth status in agronomy and environmental research. With the rapid development of drone technology, the estimation of crop LAI based on drone imagery and vegetation indices is becoming increasingly popular. However, there is still a lack of detailed research on the feasibility of using image texture to estimate LAI and the impact of combining texture indices with vegetation indices on LAI estimation accuracy. In this study, two key growth stages of winter wheat (i.e., the stages of green-up and jointing) were selected, and LAI was calculated using digital hemispherical photography. The feasibility of predicting winter wheat LAI was explored under three conditions: vegetation index, texture index, and a combination of vegetation index and texture index, at flight heights of 20 m and 40 m. Two feature selection methods (Lasso and recursive feature elimination) were combined with four machine learning regression models (multiple linear regression, random forest, support vector machine, and backpropagation neural network). The results showed that during the vegetative growth stage of winter wheat, the model combining texture information with vegetation indices performed better than the models using vegetation indices alone or texture information alone. Among them, the best prediction result based on vegetation index was RFECV-MLR at a flight height of 40 m (R2 = 0.8943, RMSE = 0.4139, RRMSE = 0.1304, RPD = 3.0763); the best prediction result based on texture index was RFECV-RF at a flight height of 40 m (R2 = 0.8894, RMSE = 0.4236, RRMSE = 0.1335, RPD = 3.0063); and the best prediction result combining texture and index was RFECV-RF at a flight height of 40 m (R2 = 0.9210, RMSE = 0.3579, RRMSE = 0.1128, RPD = 3.5575). The results of this study demonstrate that combining vegetation indices and texture from multispectral drone imagery can improve the accuracy of LAI estimation during the vegetative growth stage of winter wheat. In addition, selecting a flight height of 40 m can improve efficiency in large-scale agricultural field monitoring, as this study showed that drone data at flight heights of 20 m and 40 m did not significantly affect model accuracy.

Estimation of Winter Wheat SPAD Values Based on UAV Multispectral Remote Sensing

Unmanned aerial vehicle (UAV) multispectral imagery has been applied in the remote sensing of wheat SPAD (Soil and Plant Analyzer Development) values. However, existing research has yet to consider the influence of different growth stages and UAV flight altitudes on the accuracy of SPAD estimation. This study aims to optimize UAV flight strategies and incorporate multiple feature selection techniques and machine learning algorithms to enhance the accuracy of the SPAD value estimation of different wheat varieties across growth stages. This study sets two flight altitudes (20 and 40 m). Multispectral images were collected for four winter wheat varieties during the green-up and jointing stages. Three feature selection methods (Pearson, recursive feature elimination (RFE), and correlation-based feature selection (CFS)) and four machine learning regression models (elastic net, random forest (RF), backpropagation neural network (BPNN), and extreme gradient boosting (XGBoost)) were combined to construct SPAD value estimation models for individual growth stages as well as across growth stages. The CFS-RF (40 m) model achieved satisfactory results (green-up stage: R2 = 0.7270, RPD = 2.0672, RMSE = 1.1835, RRMSE = 0.0259; jointing stage: R2 = 0.8092, RPD = 2.3698, RMSE = 2.3650, RRMSE = 0.0487). For cross-growth stage modeling, the optimal prediction results for SPAD values were achieved at a flight altitude of 40 m using the Pearson-XGBoost model (R2 = 0.8069, RPD = 2.3135, RMSE = 2.0911, RRMSE = 0.0442). These demonstrate that the flight altitude of UAVs significantly impacts the estimation accuracy, and the flight altitude of 40 m (with a spatial resolution of 2.12 cm) achieves better SPAD value estimation than that of 20 m (with a spatial resolution of 1.06 cm). This study also showed that the optimal combination of feature selection methods and machine learning algorithms can more accurately estimate winter wheat SPAD values. In addition, this study includes multiple winter wheat varieties, enhancing the generalizability of the research results and facilitating future real-time and rapid monitoring of winter wheat growth.

A Three-Dimensional Conceptual Model for Estimating the Above-Ground Biomass of Winter Wheat Using Digital and Multispectral Unmanned Aerial Vehicle Images at Various Growth Stages

The timely and accurate estimation of above-ground biomass (AGB) is crucial for indicating crop growth status, assisting management decisions, and predicting grain yield. Unmanned aerial vehicle (UAV) remote sensing technology is a promising approach for monitoring crop biomass. However, the determination of winter wheat AGB based on canopy reflectance is affected by spectral saturation effects. Thus, constructing a generic model for accurately estimating winter wheat AGB using UAV data is significant. In this study, a three-dimensional conceptual model (3DCM) for estimating winter wheat AGB was constructed using plant height (PH) and fractional vegetation cover (FVC). Compared with both the traditional vegetation index model and the traditional multi-feature combination model, the 3DCM yielded the best accuracy for the jointing stage (based on RGB data: coefficient of determination (R2) = 0.82, normalized root mean square error (nRMSE) = 0.2; based on multispectral (MS) data: R2 = 0.84, nRMSE = 0.16), but the accuracy decreased significantly when the spike organ appeared. Therefore, the spike number (SN) was added to create a new three-dimensional conceptual model (n3DCM). Under different growth stages and UAV platforms, the n3DCM (RGB: R2 = 0.73–0.85, nRMSE = 0.17–0.23; MS: R2 = 0.77–0.84, nRMSE = 0.17–0.23) remarkably outperformed the traditional multi-feature combination model (RGB: R2 = 0.67–0.88, nRMSE = 0.15–0.25; MS: R2 = 0.60–0.77, nRMSE = 0.19–0.26) for the estimation accuracy of the AGB. This study suggests that the n3DCM has great potential in resolving spectral errors and monitoring growth parameters, which could be extended to other crops and regions for AGB estimation and field-based high-throughput phenotyping.

Application of UAS-Based Remote Sensing in Estimating Winter Wheat Phenotypic Traits and Yield During the Growing Season

Phenotyping approaches have been considered as a vital component in crop breeding programs to improve crops and develop new high-yielding cultivars. However, traditional field-based monitoring methods are expensive, invasive, and time-intensive. Moreover, data collected using satellite and airborne platforms are either costly or limited by their spatial and temporal resolution. Here, we investigated whether low-cost unmanned/unoccupied aerial systems (UASs) data can be used to estimate winter wheat (Triticum aestivum L.) nitrogen (N) content, structural traits including plant height, fresh and dry biomass, and leaf area index (LAI) as well as yield during different winter wheat growing stages. To achieve this objective, UAS-based red–green–blue (RGB) and multispectral data were collected from winter wheat experimental plots during the winter wheat growing season. In addition, for each UAS flight mission, winter wheat traits and total yield (only at harvest) were measured through field sampling for model development and validation. We then used a set of vegetation indices (VIs), machine learning algorithms (MLAs), and structure-from-motion (SfM) to estimate winter wheat traits and yield. We found that using linear regression and MLAs, instead of using VIs, improved the capability of UAS-derived data in estimating winter wheat traits and yield. Further, considering the costly and time-intensive process of collecting in-situ data for developing MLAs, using SfM-derived elevation models and red-edge-based VIs, such as CIre and NDRE, are reliable alternatives for estimating key winter wheat traits. Our findings can potentially aid breeders through providing rapid and non-destructive proxies of winter wheat phenotypic traits.

UAV-Based Hyperspectral and Ensemble Machine Learning for Predicting Yield in Winter Wheat

Winter wheat is a widely-grown cereal crop worldwide. Using growth-stage information to estimate winter wheat yields in a timely manner is essential for accurate crop management and rapid decision-making in sustainable agriculture, and to increase productivity while reducing environmental impact. UAV remote sensing is widely used in precision agriculture due to its flexibility and increased spatial and spectral resolution. Hyperspectral data are used to model crop traits because of their ability to provide continuous rich spectral information and higher spectral fidelity. In this study, hyperspectral image data of the winter wheat crop canopy at the flowering and grain-filling stages was acquired by a low-altitude unmanned aerial vehicle (UAV), and machine learning was used to predict winter wheat yields. Specifically, a large number of spectral indices were extracted from the spectral data, and three feature selection methods, recursive feature elimination (RFE), Boruta feature selection, and the Pearson correlation coefficient (PCC), were used to filter high spectral indices in order to reduce the dimensionality of the data. Four major basic learner models, (1) support vector machine (SVM), (2) Gaussian process (GP), (3) linear ridge regression (LRR), and (4) random forest (RF), were also constructed, and an ensemble machine learning model was developed by combining the four base learner models. The results showed that the SVM yield prediction model, constructed on the basis of the preferred features, performed the best among the base learner models, with an R2 between 0.62 and 0.73. The accuracy of the proposed ensemble learner model was higher than that of each base learner model; moreover, the R2 (0.78) for the yield prediction model based on Boruta’s preferred characteristics was the highest at the grain-filling stage.

A Satellite-Based Method for National Winter Wheat Yield Estimating in China

Satellite-based models have tremendous potential for monitoring crop production because satellite data can provide temporally and spatially continuous crop growth information at large scale. This study used a satellite-based vegetation production model (i.e., eddy covariance light use efficiency, EC-LUE) to estimate national winter wheat gross primary production, and then combined this model with the harvest index (ratio of aboveground biomass to yield) to convert the estimated winter wheat production to yield. Specifically, considering the spatial differences of the harvest index, we used a cross-validation method to invert the harvest index of winter wheat among counties, municipalities and provinces. Using the field-surveyed and statistical yield data, we evaluated the model performance, and found the model could explain more than 50% of the spatial variations of the yield both in field-surveyed regions and most administrative units. Overall, the mean absolute percentage errors of the yield are less than 20% in most counties, municipalities and provinces, and the mean absolute percentage errors for the production of winter wheat at the national scale is 4.06%. This study demonstrates that a satellite-based model is an alternative method for crop yield estimation on a larger scale.

Transfer-learning-based approach for leaf chlorophyll content estimation of winter wheat from hyperspectral data

Leaf chlorophyll, as a key factor for carbon circulation in the ecosystem, is significant for the photosynthetic productivity estimation and crop growth monitoring in agricultural management. Hyperspectral remote sensing (RS) provides feasible solutions for obtaining crop leaf chlorophyll content (LCC) by the advantages of its repeated and high throughput observations. However, the data redundancy and the poor robustness of the inversion models are still major obstacles that prevent the widespread application of hyperspectral RS for crop LCC evaluation. For winter wheat LCC inversion from hyperspectral observations, this study described a novel hybrid method, which is based on the combination of amplitude- and shape- enhanced 2D correlation spectrum (2DCOS) and transfer learning. The innovative feature selection method, amplitude- and shape- enhanced 2DCOS, which originated from 2DCOS, additionally considered the relationships between external perturbations and hyperspectral amplitude and shape characteristics to enhance the dynamic spectrum response. To extract the representative LCC featured wavelengths, the amplitude- and shape- enhanced 2DCOS was conducted on the leaf optical PROperties SPECTra (PROSPECT) + Scattering from Arbitrarily Inclined Leaves (SAIL) (PROSAIL) simulated dataset, which covered most possible winter wheat canopy spectra. Nine wavelengths (i.e., 455, 545, 571, 615, 641, 662, 706, 728, and 756 nm) were then extracted as the sensitive wavelengths of LCC with the amplitude- and shape- enhanced 2DCOS. These wavelengths had specificity to LCC and showed good correlation with LCC from the aspect of photosynthesis mechanism, molecular structure, and optical properties. The transfer learning techniques based on the deep neural network was then introduced to transfer the knowledge learned from the PROSAIL simulated dataset to the inversion tasks of field measured LCC. Parts of the labeled samples in field observations were used to finetune the model pre-trained by the simulated dataset to improve the inversion accuracy of the winter wheat LCC in different field scenes, aiming to reduce the need for the field measured and labeled sample size. To further ascertain the universality, transferability and predictive ability of the proposed hybrid method, field samples collected from different locations at different phenological phases, including the jointing and heading stages in 2013, 2014, and 2018, were utilized as target tasks to validate the proposed hybrid method. Moreover, the LCC of winter wheat estimated with the proposed method was evaluated with the ground-based platform and the UAV-based platform to verify the model versatility for different monitoring platforms. Various validations demonstrated that the hybrid inversion method combining the amplitude- and shape- enhanced 2DCOS and the fine-tuned transfer learning model could effectively estimate winter wheat LCC with good accuracy and robustness, and can be extended to the detection and inversion of other key variables of crops.

Winter wheat planted area monitoring and yield modeling using MODIS data in the Huang-Huai-Hai Plain, China

Accurate and timely crop areas monitoring and yield modeling are of great significance for food security and sustainability of agro-ecosystems. This study provides a rapid and robust method for planted area monitoring and yield modeling of winter wheat based on Moderate Resolution Imaging Spectroradiometer (MODIS) data from 2001 to 2016 in the Huang-Huai-Hai Plain of China. Statistical data at the prefecture level are used for accuracy assessment of MODIS-derived winter wheat planted area and yield. The results show that the overall accuracy of estimated winter wheat planted area is 96.5% from 2001 to 2016. Besides, the winter wheat planted area presents an increasing trend in the Huang-Huai-Hai Plain, especially from 2009 to 2013. In terms of yield modeling, the coefficients of determination (R2) between the estimated yield and statistical data are all higher than 0.86, and the relative errors (RE) are <5%. The models can accurately estimate yield, with adequate NDVI data, before the harvesting dates. The rapid and robust method has the potential to be applied to winter wheat planted area monitoring and yield modeling in other regions.

Estimating winter wheat yield by assimilation of remote sensing data with a four-dimensional variation algorithm considering anisotropic background error and time window

To further improve the yield estimation accuracy of regional winter wheat, based on En4DVar algorithm, an anisotropic background error and time window four-dimensional variation (ABT-4DVar) algorithm was constructed by introducing a variation time window. Based on a comparison with the simulated crop yield without data assimilation at a single point scale and in a typical test area, the effectiveness and feasibility of the constructed ABT-4DVar algorithm were effectively verified. On this basis, using the official statistics as verification data, the ABT-4DVar algorithm was applied to a winter wheat yield estimation system by assimilating remotely sensed LAI into the WOFOST (WOrld FOod STudies) crop growth model at a regional scale. The yield estimation results of winter wheat in Hengshui city showed that the RE between the average estimated yield and official statistics was 3.18%, and the REs between the average estimated yields and the official statistics of the 11 counties were all within 14.80%. The yield estimation results of winter wheat in Hengshui city indicated that the ABT-4DVar algorithm had good performance in regional yield estimation and that the assimilation algorithm could provide technology for crop growth evaluation and yield simulation.

Estimation of Winter Wheat Yield in Arid and Semiarid Regions Based on Assimilated Multi-Source Sentinel Data and the CERES-Wheat Model.

The farmland area in arid and semiarid regions accounts for about 40% of the total area of farmland in the world, and it continues to increase. It is critical for global food security to predict the crop yield in arid and semiarid regions. To improve the prediction of crop yields in arid and semiarid regions, we explored data assimilation-crop modeling strategies for estimating the yield of winter wheat under different water stress conditions across different growing areas. We incorporated leaf area index (LAI) and soil moisture derived from multi-source Sentinel data with the CERES-Wheat model using ensemble Kalman filter data assimilation. According to different water stress conditions, different data assimilation strategies were applied to estimate winter wheat yields in arid and semiarid areas. Sentinel data provided LAI and soil moisture data with higher frequency (<14 d) and higher precision, with root mean square errors (RMSE) of 0.9955 m2 m−2 and 0.0305 cm3 cm−3, respectively, for data assimilation-crop modeling. The temporal continuity of the CERES-Wheat model and the spatial continuity of the remote sensing images obtained from the Sentinel data were integrated using the assimilation method. The RMSE of LAI and soil water obtained by the assimilation method were lower than those simulated by the CERES-Wheat model, which were reduced by 0.4458 m2 m−2 and 0.0244 cm3 cm−3, respectively. Assimilation of LAI independently estimated yield with high precision and efficiency in irrigated areas for winter wheat, with RMSE and absolute relative error (ARE) of 427.57 kg ha−1 and 6.07%, respectively. However, in rain-fed areas of winter wheat under water stress, assimilating both LAI and soil moisture achieved the highest accuracy in estimating yield (RMSE = 424.75 kg ha−1, ARE = 9.55%) by modifying the growth and development of the canopy simultaneously and by promoting soil water balance. Sentinel data can provide high temporal and spatial resolution data for deriving LAI and soil moisture in the study area, thereby improving the estimation accuracy of the assimilation model at a regional scale. In the arid and semiarid region of the southeastern Loess Plateau, assimilation of LAI independently can obtain high-precision yield estimation of winter wheat in irrigated area, while it requires assimilating both LAI and soil moisture to achieve high-precision yield estimation in the rain-fed area.

Wheat yield predictions at a county and field scale with deep learning, machine learning, and google earth engine

To meet the challenges of climate change, increasing population and food demand, a timely, accurate and reliable estimation of crop yield at a large scale is more imperative than ever for crop management, food security evaluation, food trade and policy-making. In this study, taking the major winter wheat production regions of China as an example, we compared a traditional machine learning method (random forest, RF) and three deep learning (DL) models, including DNN (deep neural networks), 1D-CNN (1D convolutional neural networks), and LSTM (long short-term memory networks) to predict crop yields by integrating publicly available data within the GEE (Google Earth Engine) platform, including climate, satellite, soil properties, and spatial information data. The results showed that all four models could capture winter wheat yield variations in all the county-years, with R2 of recorded and simulated yields ranging from 0.83 to 0.90 and RMSE ranging from 561.18 to 959.62 kg/ha. They all performed well for winter wheat yield prediction at a county level from 2011 to 2015, with mean R2≥0.85 and RMSE ≤ 768 kg/ha. At a field level, the spatial pattern of estimated winter wheat yield could capture the spatial heterogeneity and yield differences between individual fields across a county fairly well. However, only the DNN and RF models had relatively good performance at the field level, with mean R2 values of 0.71, 0.66 and RMSE values of 1127 kg/ha and 956 kg/ha, respectively. The model comparisons showed that the performance of RF was not always worse than DL at both the county and field levels. Our findings demonstrated a scalable, simple and inexpensive framework for estimating crop yields at various scales in a timely manner and with reliable accuracy, which has important implications for crop yield forecasting, agricultural disaster monitoring, food trade policy, and food security warning.

Early-season mapping of winter wheat in China based on Landsat and Sentinel images

Abstract. Early-season crop identification is of great importance for monitoring crop growth and predicting yield for decision makers and private sectors. As one of the largest producers of winter wheat worldwide, China outputs more than 18 % of the global production of winter wheat. However, there are no distribution maps of winter wheat over a large spatial extent with high spatial resolution. In this study, we applied a phenology-based approach to distinguish winter wheat from other crops by comparing the similarity of the seasonal changes of satellite-based vegetation index over all croplands with a standard seasonal change derived from known winter wheat fields. Especially, this study examined the potential of early-season large-area mapping of winter wheat and developed accurate winter wheat maps with 30 m spatial resolution for 3 years (2016–2018) over 11 provinces, which produce more than 98 % of the winter wheat in China. A comprehensive assessment based on survey samples revealed producer's and user's accuracies higher than 89.30 % and 90.59 %, respectively. The estimated winter wheat area exhibited good correlations with the agricultural statistical area data at the municipal and county levels. In addition, the earliest identifiable time of the geographical location of winter wheat was achieved by the end of March, giving a lead time of approximately 3 months before harvest, and the optimal identifiable time of winter wheat was at the end of April with an overall accuracy of 89.88 %. These results are expected to aid in the timely monitoring of crop growth. The 30 m winter wheat maps in China are available via an open-data repository (DOI: https://doi.org/10.6084/m9.figshare.12003990, Dong et al., 2020a).

Estimated winter wheat yield reduction following spring freeze events in field conditions

Estimated winter wheat yield reduction following spring freeze events in field conditions

Estimating winter wheat yield based on a light use efficiency model and wheat variety data

Satellite-based models are important tools for monitoring regional and global crop yields, because remotely sensed data is able to offer temporally and spatially continuous crop growth information over large areas. However, tracking inter-annual variability in crop yields remains a major challenge. Taking this challenge, a light use efficiency model (EC-LUE) is used to estimate winter wheat yield with 30-m spatial resolution Landsat data. Moreover, we integrate winter wheat variety data by considering different percentages of harvested organs to total crop organs. Using county-level statistical yield data for Kansas, United States, model validation shows that the EC-LUE model combined with wheat variety data can effectively capture the spatial variations of winter wheat yields. Specifically, the proposed method significantly improves model simulation performance for the inter-annual variation of yields during 2008–2017 and can explain 82% of the inter-annual yield variation. This method proposed in this study proves to be available for future applications of crop yield estimations on a larger scale. The great potential of incorporating satellite observations with crop variety data to monitor crop yield, and improve agricultural management is therefore indicated.

Rapid mapping of winter wheat yield, protein, and nitrogen uptake using remote and proximal sensing

Farmers’ interest in precision nitrogen (N) fertilization has grown in the inland Pacific Northwest due to significant within-field spatial variability in yield and protein content. Mapping the spatial variability of yield, protein content, and total grain N uptake (Ng) can be a useful tool for post-harvest evaluation of N sufficiency that, in turn, can guide precision N fertilization. We evaluated the applicability of combining RapidEye satellite imagery-derived vegetation indices with topographic variables derived from high-resolution data obtained from proximal sensors to estimate winter wheat yield, protein content, and Ng. Results indicate that the normalized difference vegetation index (NDVI), the normalized difference red edge index (NDRE), terrain curvature, slope, aspect, topographic wetness index, and solar radiation are the most relevant co-variables that contribute to spatial variability of yield and Ng. Yield and Ng exhibited strong relationships with NDVI and NDRE from late June through early July during grain filling stage, with nearly all R2 > 0.6. We found greater yield and Ng values in concave-shaped terrain; 0-5° and 10-20° slopes; and flat, eastern, or northern aspects. In contrast, protein content exhibited weak relationships with vegetation indices and nearly all terrain factors. Prediction models demonstrated that these variables can provide good estimations of yield and Ng, with R2 of 0.848 and 0.864, respectively, and RMSE of 48.47 and 0.0136, respectively. Combining RapidEye-based NDRE with proximal sensor-based topographical factors shows great potential for accurately and efficiently estimating winter wheat yield and Ng, which can help guide N fertilizer management.

An Improved CASA Model for Estimating Winter Wheat Yield from Remote Sensing Images

The accurate and timely monitoring and evaluation of the regional grain crop yield is more significant for formulating import and export plans of agricultural products, regulating grain markets and adjusting the planting structure. In this study, an improved Carnegie–Ames–Stanford approach (CASA) model was coupled with time-series satellite remote sensing images to estimate winter wheat yield. Firstly, in 2009 the entire growing season of winter wheat in the two districts of Tongzhou and Shunyi of Beijing was divided into 54 stages at five-day intervals. Net Primary Production (NPP) of winter wheat was estimated by the improved CASA model with HJ-1A/B satellite images from 39 transits. For the 15 stages without HJ-1A/B transit, MOD17A2H data products were interpolated to obtain the spatial distribution of winter wheat NPP at 5-day intervals over the entire growing season of winter wheat. Then, an NPP-yield conversion model was utilized to estimate winter wheat yield in the study area. Finally, the accuracy of the method to estimate winter wheat yield with remote sensing images was verified by comparing its results to the ground-measured yield. The results showed that the estimated yield of winter wheat based on remote sensing images is consistent with the ground-measured yield, with R2 of 0.56, RMSE of 1.22 t ha−1, and an average relative error of −6.01%. Based on time-series satellite remote sensing images, the improved CASA model can be used to estimate the NPP and thereby the yield of regional winter wheat. This approach satisfies the accuracy requirements for estimating regional winter wheat yield and thus may be used in actual applications. It also provides a technical reference for estimating large-scale crop yield.

Estimating winter wheat area based on an SVM and the variable fuzzy set method

ABSTRACTThis paper proposed a method of extracting the winter wheat area by combining support vector machine (SVM) with variable fuzzy sets. This method mainly aims to deal with mixed pixels in remote sensing images. The SVM classifier can accurately identify pure winter wheat pixels with training samples. However, winter wheat area information in mixed pixels cannot be directly obtained because they contain multiple types of features. In order to estimate the winter wheat area information of mixed pixels in Landsat8 data, this paper introduced the normalized difference vegetation index (NDVI) and the variable fuzzy set method. Finally, this paper reasonably estimated the area of winter wheat from the mixed pixels in Landsat8 images. The accuracy assessment showed that the proposed method could extract winter wheat area information more accurately.