Spatial Distribution Of Crops Research Articles

CROPGRIDS: a global geo-referenced dataset of 173 crops

CROPGRIDS is a comprehensive global geo-referenced dataset providing area information for 173 crops for the year 2020, at a resolution of 0.05° (about 5.6 km at the equator). It represents a major update of the Monfreda et al. (2008) dataset (hereafter MRF), the most widely used geospatial dataset previously available, covering 175 crops with reference year 2000 at 10 km spatial resolution. CROPGRIDS builds on information originally provided in MRF and expands it using 27 selected published gridded datasets, subnational data of 52 countries obtained from National Statistical Offices, and the 2020 national-level statistics from FAOSTAT, providing more recent harvested and crop (physical) areas for 173 crops at regional, national, and global levels. The CROPGRIDS data advance the current state of knowledge on the spatial distribution of crops, providing useful inputs for modelling studies and sustainability analyses relevant to national and international processes.

Genetic Programming for High-Level Feature Learning in Crop Classification

Information on crop spatial distribution is essential for agricultural monitoring and food security. Classification with remote-sensing time series images is an effective way to obtain crop distribution maps across time and space. Optimal features are the precondition for crop classification and are critical to the accuracy of crop maps. Although several approaches are available for extracting spectral, temporal, and phenological features for crop identification, these methods depend heavily on domain knowledge and human experiences, adding uncertainty to the final crop classification. This study proposed a novel Genetic Programming (GP) approach to learning high-level features from time series images for crop classification to address this issue. We developed a new representation of GP to extend the GP tree’s width and depth to dynamically generate either fixed or flexible informative features without requiring domain knowledge. This new GP approach was wrapped with four classifiers, i.e., K-Nearest Neighbor (KNN), Decision Tree (DT), Naive Bayes (NB), and Support Vector Machine (SVM), and was then used for crop classification based on MODIS time series data in Heilongjiang Province, China. The performance of the GP features was compared with the traditional features of vegetation indices (VIs) and the advanced feature learning method Multilayer Perceptron (MLP) to show GP effectiveness. The experiments indicated that high-level features learned by GP improved the classification accuracies, and the accuracies were higher than those using VIs and MLP. GP was more robust and stable for diverse classifiers, different feature numbers, and various training sample sets compared with classification using VI features and the classifier MLP. The proposed GP approach automatically selects valuable features from the original data and uses them to construct high-level features simultaneously. The learned features are explainable, unlike those of a black-box deep learning model. This study demonstrated the outstanding performance of GP for feature learning in crop classification. GP has the potential of becoming a mainstream method to solve complex remote sensing tasks, such as feature transfer learning, image classification, and change detection.

Spatiotemporal Evolution of Crop Planting Structure in the Black Soil Region of Northeast China: A Case Study in Hailun County

Detailed characteristics of crop planting structure (CPS) evolution can inform the optimization of the crop yield proportion in the black soil region of Northeast China (BSRNC). Choosing Hailun County as an example, this study sought to analyze the geographic characteristics of CPS evolution from 2000 to 2020. Our analysis produced new spatiotemporal information based on the remote-sensing interpretation data, namely, Landsat4-5 TM, Landsat7 ETM+, and Landsat8 OLI images. The study characterized the temporal and spatial dynamics of CPS. Our results showed the following: (1) Soybean and maize were the main crops, with a total land area of 70%; they alternated as the most dominant crop. (2) The distribution breadth and aggregation intensity of soybean and maize were spatially complementary; rice had the smallest distribution range but strong water aggregation. (3) The evolution pattern of CPS was the interconversion between a single type of soybean and maize. Our results indicate that the future CPS adjustment of BSRNC needs to consider the county-level optimization of crop area proportion and crop spatial distribution. This context has excellent implications in geographically informing policymaking to adjust county-level CPS of BSRNC, thus safeguarding food security.

Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data

Precise and timely information on crop spatial distribution over large areas is paramount to agricultural monitoring, food security, and policy development. Currently, automatically classifying crop types at a large scale is challenging due to the scarcity of ground data. Although previous studies have indicated that transductive transfer learning (TTL) is a promising method to address this problem, it performs poorly within regions where crop compositions and phenology differ largely. Here we transferred random forest classifiers trained in limited regions with diversified growing conditions and land covers to the rest of the study area where ground data are scarce, with more than 130,000 Sentinel-2 images processed using the Google Earth Engine (GEE) platform. We established the 10 m crop maps for four major crops (i.e., maize, rapeseed, winter, and spring Triticeae crops) across 10 European Union (EU) countries from 2018 to 2019. The final crop maps had a high accuracy with overall accuracy generally greater than 0.89, with user’s accuracy and producer’s accuracy ranging from 0.72 to 0.98. Moreover, the resulting maps were consistent with the NUTS-2 level official statistics, with R2 consistently greater than 0.9. We further analyzed the crop rotation patterns and found that the rotation intervals across these EU countries were generally at least one year. Maize was dominantly rotated with winter Triticeae crops or converted to other land covers in the following year. Rapeseed was generally grown in rotation with winter Triticeae crops, whereas the rotation patterns of winter and spring Triticeae crops were more diversified. Red Edge Position (REP) and Normalized Difference Yellow Index (NDYI) played significant roles in crop classification across the EU. This study highlights the potential of the developed TTL method for crop classification over large spatial extents where labeled data are limited and the differences in crop compositions and phenology are relatively large.

Characterizing Dominant Field-Scale Cropping Sequences for a Potato and Vegetable Growing Region in Central Wisconsin

Crop rotations are known to improve soil health by replenishing lost nutrients, increasing organic matter, improving microbial activity, and reducing disease risk and weed pressure. We characterized the spatial distribution of crops and dominant field-scale cropping sequences from 2008 to 2019 for the Wisconsin Central Sands (WCS) region, a major producer of potato and vegetables in the U.S. The dominant two- and three-year rotations were determined, with an additional focus on assessing regional potato rotation management. Our results suggest corn and soybean are the two most widely planted crops, occurring on 67% and 36% of all agricultural land at least once during the study period. The most frequent two- and three-year crop rotations include corn, soybean, alfalfa, sweet corn, potato, and beans, with continuous corn being the most dominant two- and three-year rotations (13.2% and 8.5% of agricultural land, respectively). While four- and five-year rotations for potato are recommended to combat pest and disease pressure, 23.2% and 65.9% of potato fields returned to that crop in rotation after two and three years, respectively. Furthermore, 5.6% of potato fields were planted continuously with that crop. Given potato’s high nitrogen (N) fertilizer requirements, the prevalence of sandy soils, and ongoing water quality issues, adopting more widespread use of four- or five-year rotations of potato with crops that require zero or less N fertilizer could reduce groundwater nitrate concentrations and improve water quality.

Analysis of Land Use/Cover Change and Driving Forces in the Selenga River Basin.

The Selenga River basin is an important section of the Sino-Mongolian Economic Corridor. It is an important connecting piece of the Eurasian Continental Bridge and an important part of Northeast Asia. Against the background of the evolution of the geopolitical pattern since the disintegration of the Soviet Union and global warming, based on the land cover data in the Selenga River basin from 1992, 2000, 2009, and 2015, this paper describes the dynamic changes in land use in the basin. Through a logistic model, the driving factors of land cover change were revealed, and the CA-Markov model was used to predict the land cover pattern of 2027. The results showed that (1) from 1992 to 2015, the agricultural population in the Selenga River basin continued to decrease, which led to a reduction in agricultural sown area. The intensification of climate warming and drying had a significant impact on the spatial distribution of crops. Grassland expansion mostly occurred in areas with relatively abundant rainfall, low temperature, and low human activity. (2) The simulation results showed that, according to the current development trend, the construction land area of the Selenga River basin will be slightly expanded in 2027, the area of arable land and grassland will be slightly reduced, and the areas of forest, water/wetland, and bare land will remain stable. In the future, human activities in the basin will increase in the process of the construction of the China-Mongolia-Russia economic corridor. Coupled with global warming, the land/cover of the basin will be affected by both man-made and natural disturbances, and attention should be paid to the possible risk of vegetation degradation.

Effectiveness of Common Preprocessing Methods of Time Series for Monitoring Crop Distribution in Kenya

Accurate crop identification and spatial distribution mapping are important for crop production estimation and famine early warning, especially for food-deficit African agricultural countries. By evaluating existing preprocessing methods for classification using satellite image time series (SITS) in Kenya, this study aimed to provide a low-cost method for cultivated land monitoring in sub-Saharan Africa that lacks financial support. SITS were composed of a set of MODIS Vegetation Indices (MOD13Q1) in 2018, and the classification method included the Support Vector Machine (SVM) and Random Forest (RF) classifier. Eight datasets obtained at three levels of preprocessing from MOD13Q1 were used in the classification: (1) raw SITS of vegetation indices (R-NDVI, R-EVI, and R-NDVI + R-EVI); (2) smoothed SITS of vegetation indices (S-NDVI); and (3) vegetation phenological data (P-NDVI, P-EVI, R-NDVI + P-NDVI, and P-NDVI-1). Both SVM and RF classification results showed that the “R-NDVI + R-EVI” dataset achieved the highest performance, while the three pure phenological datasets produced the lowest accuracy. Correlation analysis between variable importance and rainfall time series demonstrated that the vegetation index SITS during rainfall periods showed higher importance in RF classifiers, thus revealing the potential of saving computational costs. Considering the preprocessing cost of SITS and its negative impact on the classification accuracy, we recommend overlaying the original NDVI with the original EVI time series to map the crop distribution in Kenya.

Honeybee pollination benefits could inform solar park business cases, planning decisions and environmental sustainability targets

Renewable energy deployment has accelerated exponentially, taking up a growing area of land at a time of increasing land use pressure and environmental degradation. Land use change for renewable energy can have positive and negative environmental consequences, but robustly quantifying the effects is challenging. Here, we evaluate the monetary benefits of pollination services from installing honeybee hives in solar parks and discuss how they could inform policy and practice. If honeybee hives were installed in all existing solar parks within England, we estimate that the pollination service benefits for pollinator dependent field crops, top fruits and soft fruit would have been £5.9 million in 2017. This is grounded in honeybee pollination crop values of £4.81–£75.04 ha−1 for field crops and £635–£10,644 ha−1 for fruit. However, given the greater field crop land areas the total associated economic benefits were greater than for fruit. Honeybee pollination service benefits could theoretically be as high as £80 million per year if the spatial distribution of crops was altered. However, the viability of this is uncertain given other factors that influence crop location and the potential trade-offs with wild pollinators. We outline how honeybee pollination service benefits could contribute to solar park business cases, inform the planning process, and be used as environmental sustainability indicators by industry. Such energy-economic-ecosystem wins demonstrate the potential of incorporating environmental co-benefits into energy decarbonisation policies and a means of addressing the land-energy-ecosystem nexus.

Large-scale crop mapping from multi-source optical satellite imageries using machine learning with discrete grids

The spatial distribution of crops is an important agricultural parameter, which is used to derive important information about crop productivity and food security. However, crop mapping on a large scale is challenging due to the low spatio-temporal information of satellite data, sparse sampling, and poor computational efficiency for massive data. To alleviate these problems, this study proposes a method based on discrete grids with machine learning to integrate GaoFen-1 and Sentinel-2 imagery. First, the proposed method fuses multi-source satellite data with similar observation characteristics to improve the spatial and temporal coverage of satellites. Second, a data augmentation technique based on a discrete grid framework was proposed to solve the problem of sparse samples. Finally, a machine learning algorithm in a discrete grid was introduced to improve processing efficiency and ensure the crop classification precision of large-scale remote sensing images. An experiment in the Sanjiang Plain area (approximately 108900 km2) of Northeast China showed that the proposed scheme benefited from a high spatio-temporal multi-source dataset and achieved good performance. Compared with a single data source, the accuracy of crop mapping using multi-source optical remote sensing data is higher, attaining up to 86 and 88 % in 2017 and 2018, respectively. Furthermore, the advantages of machine learning in discrete grids over large-scale areas are validated by evaluating the accuracy of different classifiers, which indicates the suitability of discrete grids in data augmentation and large-scale crop mapping. Finally, discrete grid technology offers a possibility for crop mapping over large-scale areas, and improves the processing efficiency of remote sensing big data. The findings in this study can contribute to studies on large-scale crop classification and serve as a reference to them.

AGTOC: A novel approach to winter wheat mapping by automatic generation of training samples and one-class classification on Google Earth Engine

Accurate and timely acquisition of crop spatial distribution is a prerequisite for growth monitoring and yield forecasting. Currently, the automatic acquisition of crop distribution at large scales is still a challenge due to the time-consuming processing of remotely sensed imagery and manual collection of sufficient training samples. Although the advent of cloud computing platforms has proved to improve the efficiency and automation of crop type classification, how to obtain sufficient training samples in an efficient and cost-effective way remains unclear. In this research, we developed a new approach integrating the automatic generation of training samples and one-class machine learning classification (AGTOC) for mapping winter wheat over Jiangsu Province, China on Google Earth Engine (GEE). After extracting spatial objects from Sentinel-2 imagery in the season of 2017–2018, this method performed recognition of winter wheat objects based on the unique phenology and spectral features of winter wheat. Then the generated winter wheat objects were further refined and regarded as training samples for provincial winter wheat classification with one-class support vector machine (OCSVM). Furthermore, the transferability of AGTOC was evaluated by applying the classification approach to different seasons (2016–2017 & 2019–2020) and a different sensor (Landsat-8 OLI). According to independent ground truth data, the winter wheat mapping with AGTOC achieved an overall accuracy (OA) of 92.61%. When compared with agricultural census data, the winter wheat area accounted for 99% and 90% of the variability at the municipal and county levels. Furthermore, the OA achieved 88.94% and 90.17% while transferring the AGTOC from 2017–2018 to 2016–2017 and 2019–2020. The transferability of the AGTOC model to Landsat-8 OLI imagery of the same season yielded an OA of 85.98%. These results demonstrated that AGTOC exhibited high efficiency and accuracy across the province, different seasons and sensors without the need of extensive field visits for training sample collection. This proposed approach has great potential in the automatic mapping of winter wheat on GEE at country or global levels.

Super-resolution method using generative adversarial network for Gaofen wide-field-view images

Accurate information on the spatial distribution of crops is of great significance for scientific research and production practices. Such accurate information can be extracted from high-spatial-resolution optical remote sensing images. However, acquiring these images with a wide coverage is difficult. We established a model named multispectral super-resolution generative adversarial network (MS_SRGAN) for generating high-resolution 4-m images using Gaofen 1 wide-field-view (WFV) 16-m images. The MS_SRGAN model contains a generator and a discriminator. The generator network is composed of feature extraction units and feature fusion units with a symmetric structure, and the attention mechanism is introduced to constrain the spectral value of the feature map during feature extraction. The generator loss introduces feature loss to describe the feature difference of the image. This is realized using pre-trained discriminator parameters and a partial discriminator network. In addition to realizing feature loss, the discriminator network, which is a simple convolutional neural network, also realizes adversarial loss. Adversarial loss can provide some fake high frequency details to the generator to get a more sharpened image. In the Gaofen 1 WFV image test, the performance of MS_SRGAN was compared with that of Bicubic, EDSR, SRGAN, and ESRGAN. The results show that the spectral angle mapper (3.387) and structural similarity index measure (0.998) of MS_SRGAN are higher than those of the other models. In addition, the image obtained by MS_SRGAN is more realistic; its texture details and color distribution are closer to the reference image to a greater extent.

Assessment of the feasibility of hybrid renewable power for supply pumping system for irrigation

Crop identification is vital to make an inventory of the crops grown in a given area and their cultivation period. The Remote sensing (RS) techniques can provide information on the distribution of cultivated land, crop types, and areas for the agricultural sector's effective management.
 In remote sensing, various vegetation indices (VI) can analyze and evaluate multiple phenomena and themes. The Normalized Difference Vegetation Index (NDVI) is an essential and highly significant remote measurement widely used in agriculture for phenological monitoring and crop health (Ray and Dadhwal, 2001).
 In this work, we present a methodology for the contribution of NDVI from Landsat 7 (TM) and (ETM+) images to crop mapping in the Gharb region using a classification based on the pixel approach and estimating rice crop coefficient from NDVI.
 The classification results concern six main types of crops planted in this region (beet, maize, sugar cane, market gardening, cereals, and rice). The classification map showed differences in agricultural practices adopted by farmers in crop spatial distribution. The classification results showed the ability of this methodology to discriminate between crops.
 Crop coefficients were deduced from the NDVI extracted from the images. Due to meteorological data collected from the meteorological station TCSC of SK Tlet, the estimation of the reference evapotranspiration was made and subsequently the potential evapotranspiration of each crop during the agricultural season 2019-2020.
 The highest values for ETC were obtained when the crop was in its full development when water was mainly lost through transpiration after a slight decrease in the ratio values observed during the phase of the vegetative cycle (maturity).
 The water requirements (daily, monthly and annual) for the crops were determined and their electrical energy consumption.
 Renewable energy can be an effective solution to meet the energy needs of plots , greenhouses and large farms.
 A technical-economic study of different combinations of autonomous hybrid renewable energy systems (HRES) in order to meet the power supply needs of the above mentioned crops in the Gharb region. The renewable energy sources considered are solar, wind and biomass. The results show that for an average energy requirement of 92 kWh/day and a peak load of 6.5 kW, the unit energy cost of the optimal configuration scenario A (PV-wind-biomass-battery) is 0.19 $/kWh. Therefore, the design, development and implementation of the proposed system is a promising solution for the security of energy supply. For a 100% integration of renewable energy, the HRES produces electricity according to the following distribution: 11% from wind, 41% from solar and 48% from biomass.

Comparison of Regional Winter Wheat Mapping Results from Different Similarity Measurement Indicators of NDVI Time Series and Their Optimized Thresholds

Generally, there is an inconsistency between the total regional crop area that was obtained from remote sensing technology and the official statistical data on crop areas. When performing scale conversion and data aggregation of remote sensing-based crop mapping results from different administrative scales, it is difficult to obtain accurate crop planting area that match crop area statistics well at the corresponding administrative level. This problem affects the application of remote sensing-based crop mapping results. In order to solve the above problem, taking Fucheng County of Hebei Province in the Huanghuaihai Plain of China as the study area, based on the Sentinel-2 normalized difference vegetation index (NDVI) time series data covering the whole winter wheat growth period, the statistical data of the regional winter wheat planting area were regarded as reference for the winter wheat planting area extracted by remote sensing, and a new method for winter wheat mapping that is based on similarity measurement indicators and their threshold optimizations (WWM-SMITO) was proposed with the support of the shuffled complex evolution-University of Arizona (SCE-UA) global optimization algorithm. The accuracy of the regional winter wheat mapping results was verified, and accuracy comparisons with different similarity indicators were carried out. The results showed that the total area accuracy of the winter wheat area extraction by the proposed method reached over 99.99%, which achieved a consistency that was between the regional remote sensing-based winter wheat planting area and the statistical data on the winter wheat planting area. The crop recognition accuracy also reached a high level, which showed that the proposed method was effective and feasible. Moreover, in the accuracy comparison of crop mapping results based on six different similarity indicators, the winter wheat distribution that was extracted by root mean square error (RMSE) had the best recognition accuracy, and the overall accuracy and kappa coefficient were 94.5% and 0.8894, respectively. The overall accuracies of winter wheat that were extracted by similarity indicators, such as Euclidean distance (ED), Manhattan distance (MD), spectral angle mapping (SAM), and spectral correlation coefficient (SCC) were 94.1%, 93.9%, 93.3%, and 92.8%, respectively, and the kappa coefficients were 0.8815, 0.8776, 0.8657, and 0.8558, respectively. The accuracy of the winter wheat results extracted by the similarity indicator of dynamic time warping (DTW) was relatively low. The results of this paper could provide guidance and serve as a reference for the selection of similarity indicators in crop distribution extraction and for obtaining large-scale, long-term, and high-precision remote sensing-based information on a regional crop spatial distribution that is highly consistent with statistical crop area data.

Automatic Crop Classification in Northeastern China by Improved Nonlinear Dimensionality Reduction for Satellite Image Time Series

Accurate and timely information on the spatial distribution of crops is of great significance to precision agriculture and food security. Many cropland mapping methods using satellite image time series are based on expert knowledge to extract phenological features to identify crops. It is still a challenge to automatically obtain meaningful features from time-series data for crop classification. In this study, we developed an automated method based on satellite image time series to map the spatial distribution of three major crops including maize, rice, and soybean in northeastern China. The core method used is the nonlinear dimensionality reduction technique. However, the existing nonlinear dimensionality reduction technique cannot handle missing data, and it is not designed for subsequent classification tasks. Therefore, the nonlinear dimensionality reduction algorithm Landmark–Isometric feature mapping (L–ISOMAP) is improved. The advantage of the improved L–ISOMAP is that it does not need to reconstruct time series for missing data, and it can automatically obtain meaningful featured metrics for classification. The improved L–ISOMAP was applied to Landsat 8 full-band time-series data during the crop-growing season in the three northeastern provinces of China; then, the dimensionality reduction bands were inputted into a random forest classifier to complete a crop distribution map. The results show that the area of crops mapped is consistent with official statistics. The 2015 crop distribution map was evaluated through the collected reference dataset, and the overall classification accuracy and Kappa index were 83.68% and 0.7519, respectively. The geographical characteristics of major crops in three provinces in northeast China were analyzed. This study demonstrated that the improved L–ISOMAP method can be used to automatically extract features for crop classification. For future work, there is great potential for applying automatic mapping algorithms to other data or classification tasks.

Study on the optimization of staple crops spatial distribution in China under the influence of natural disasters

The spatial distribution of staple crops is related to the distribution of the agricultural industrial structure and national food security. Based on the interval-parameter stochastic programming method, the randomness and uncertainty of parameters are represented by intervals and the initial optimization model is established. In addition to conventional factors, such as land resources and food demand, the initial model also takes national and local agricultural supply policies as constraints. Furthermore, based on the two-stage interval-parameter stochastic programming method, natural disasters are regarded as a penalty in the second stage, and a two-stage model is established. The initial decision is modified so that the two-stage model can find a compromise optimization result under the constraints of feasibility and disaster impact. The initial model and the two-stage model are applied to optimize the staple crop spatial distribution in China’s provincial administrative districts. The initial optimization results show that rice is mainly planted in the southern region of China; wheat is planted in the central and eastern regions; maize is planted in the north-central and northeastern regions; and soybean is planted in the northeastern, middle-eastern and southwestern regions. Compared with the initial optimization results, the two-stage model optimization results showed that the area of wheat, rice and maize decreased while the area of soybean increased. Natural disasters led to crop yield reductions. In the initial model, the final yield of soybean could not meet the constraints. Therefore, the distribution area of soybean should be increased so that the soybean yield can still meet the constraints and ensure food security when the yield is reduced. The results of this study can provide a reference for adjusting the spatial distribution of staple crops at the provincial level in China from the perspective of food self-sufficiency.

Effective SAR image segmentation and classification of crop areas using MRG and CDNN techniques

ABSTRACT Crop classification is a significant requirement to estimate crop area, structure, and spatial distribution, as well as provide important input parameters for crop yield models. Different techniques were considered in this system and providing betterment in automation. But none of them gave promising results. So here, a Convolutional Deep Neural Network (CDNN) is proposed to identify the crop areas with the help of Synthetic-Aperture Radar (SAR) satellite images as well as the cultivation status of the crop. First, in training phase, the segmented image of the crop is preprocessed using HLS, then feature is extracted using BRIEF, then, they are classified using CDNN. Then after in testing phase, the input SAR image from the database is further processed using MRG algorithm and classified centered on the training results. After classification, the cultivation status of each classified crop can be identified by taking the Euclidean distance (ED) betwixt the standard parameters and resultant parameters of a specific crop. After computing ED, the ED is contrasted with the threshold value and the cultivation status of a particular crop can be identified. The results are analyzed to ascertain the performance shown by the proposed technique with other existent techniques.

Weather and Crop Management Impact on Crop Yield Variability

This research is primarily designed to examine crop yield variation due to change in weather pattern and crop management activities by exploring a comprehensive list of factors (e.g. environmental, economic etc.). Given that existing literature indicates significant effects when farmland value is regressed on weather variables, a natural question is to ask whether major agricultural crop yields responds to change in historical weather pattern. To answer this question, this study relies on a state level panel dataset including agricultural and high-resolution weather data covering the period 1997-2018. Using a Seemingly Unrelated Regression (SUR) approach, this study estimates how crop spatial distribution patterns have impacted crop yield variation in response to weather in United States Greater Midwest region. Key findings indicate that changes in crop management activities correspond closely to estimate of both crop price and weather effects. Additionally, corn yield response to weather change varies with crop spatial distribution pattern, with distinct impacts on the magnitude and even the direction at the state level. Further, revenue of corn, soybeans, wheat and their respective lagged yields have positive and significant effect to respective crop yields. Finally, a major crop yield does vary across region due to variation of crop prices, precipitation and temperature. These findings have useful implications for agriculture sector on how historical weather trends have affected crop yield and distribution pattern.

Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network

Using satellite remote sensing has become a mainstream approach for extracting crop spatial distribution. Making edges finer is a challenge, while simultaneously extracting crop spatial distribution information from high-resolution remote sensing images using a convolutional neural network (CNN). Based on the characteristics of the crop area in the Gaofen 2 (GF-2) images, this paper proposes an improved CNN to extract fine crop areas. The CNN comprises a feature extractor and a classifier. The feature extractor employs a spectral feature extraction unit to generate spectral features, and five coding-decoding-pair units to generate five level features. A linear model is used to fuse features of different levels, and the fusion results are up-sampled to obtain a feature map consistent with the structure of the input image. This feature map is used by the classifier to perform pixel-by-pixel classification. In this study, the SegNet and RefineNet models and 21 GF-2 images of Feicheng County, Shandong Province, China, were chosen for comparison experiment. Our approach had an accuracy of 93.26%, which is higher than those of the existing SegNet (78.12%) and RefineNet (86.54%) models. This demonstrates the superiority of the proposed method in extracting crop spatial distribution information from GF-2 remote sensing images.

Regulating effect of agricultural landscape pattern on ecological pest control by natural enemies.

It's well known that regional agricultural landscape pattern can significantly regulate the relationship between plants, herbivorous insects and natural enemies. The improvement of the ecosystem services of regional agricultural landscape pattern has profound impacts on pest control, which has received much interests in current landscape ecology and pest control studies. This review systematically summarized the impacts of agricultural landscape pattern, as well as its succession on the diversity and ecological pests control of important natural enemies. From the perspective of different natural enemies, the impacts of agricultural landscape pattern on natural predators, parasi-toids, beneficial microorganisms and its ecological pests control functions were all recapped. We outlined crop spatial distribution and vegetation habitat management strategies. This review could provide useful insights in improving the ecosystem services of natural enemies in agricultural landscape based on the vegetation habitat management, and offer theoretical support for regional ecological agriculture in response to the strategy of 'chemical pesticide reduction application'.

Vegetation Indices for Discrimination of Soybean Areas: A New Approach

Core Ideas Automation of mapping of soybean areas. Use of remote sensors in the recognition of summer crop. Development of exclusive vegetation index for soybean. The aim of this study was to map areas cultivated with soybean [Glycine max (L.) Merr.] in Paraná state, Brazil, using mono‐ and multitemporal MODerate‐resolution imaging spectroradiometer (MODIS) images. We applied the vegetation index perpendicular crop enhancement index (PCEI) and threshold determination for the automation of soybean area discrimination by geo‐object (GEOBIA). For this mapping, vegetation indices (normalized difference vegetation index [NDVI], enhanced vegetation index [EVI], and crop enhancement index [CEI]) and the development of the PCEI were used with the aid of time‐series images from the TERRA/MODIS system‐sensor. A support analysis, based on geo‐objects and a decision tree based on data mining, was used to determine the new vegetation index. “Classification” and “merge region” algorithms and feature extraction were used for classification. To evaluate the precision of the classifications, the Kappa (κ) and overall accuracy (OA) parameters were applied. Regarding the ground line, R and R2 were above 0.92 and 0.84, respectively (p < 0.01). The test results indicate that the proposed methodology is efficient for mapping soybean distribution, with 0.80 for the Kappa parameter, an appropriate crop spatial distribution, and no over‐ or underestimation of areas. Thus, this study allows automated mapping of areas cultivated with soybean crops at large scales.