Journal articles on the topic 'Early crop mapping'
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Kwak, Geun-Ho, Chan-won Park, Kyung-do Lee, Sang-il Na, Ho-yong Ahn, and No-Wook Park. "Potential of Hybrid CNN-RF Model for Early Crop Mapping with Limited Input Data." Remote Sensing 13, no. 9 (April 21, 2021): 1629. http://dx.doi.org/10.3390/rs13091629.
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When sufficient time-series images and training data are unavailable for crop classification, features extracted from convolutional neural network (CNN)-based representative learning may not provide useful information to discriminate crops with similar spectral characteristics, leading to poor classification accuracy. In particular, limited input data are the main obstacles to obtain reliable classification results for early crop mapping. This study investigates the potential of a hybrid classification approach, i.e., CNN-random forest (CNN-RF), in the context of early crop mapping, that combines the automatic feature extraction capability of CNN with the superior discrimination capability of an RF classifier. Two experiments on incremental crop classification with unmanned aerial vehicle images were conducted to compare the performance of CNN-RF with that of CNN and RF with respect to the length of the time-series and training data sizes. When sufficient time-series images and training data were used for the classification, the accuracy of CNN-RF was slightly higher or comparable with that of CNN. In contrast, when fewer images and the smallest training data were used at the early crop growth stage, CNN-RF was substantially beneficial and the overall accuracy increased by maximum 6.7%p and 4.6%p in the two study areas, respectively, compared to CNN. This is attributed to its ability to discriminate crops from features with insufficient information using a more sophisticated classifier. The experimental results demonstrate that CNN-RF is an effective classifier for early crop mapping when only limited input images and training samples are available.
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Khan, Haseeb Rehman, Zeeshan Gillani, Muhammad Hasan Jamal, Atifa Athar, Muhammad Tayyab Chaudhry, Haoyu Chao, Yong He, and Ming Chen. "Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery." Sensors 23, no. 4 (February 5, 2023): 1779. http://dx.doi.org/10.3390/s23041779.
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Climate change and the COVID-19 pandemic have disrupted the food supply chain across the globe and adversely affected food security. Early estimation of staple crops can assist relevant government agencies to take timely actions for ensuring food security. Reliable crop type maps can play an essential role in monitoring crops, estimating yields, and maintaining smooth food supplies. However, these maps are not available for developing countries until crops have matured and are about to be harvested. The use of remote sensing for accurate crop-type mapping in the first few weeks of sowing remains challenging. Smallholder farming systems and diverse crop types further complicate the challenge. For this study, a ground-based survey is carried out to map fields by recording the coordinates and planted crops in respective fields. The time-series images of the mapped fields are acquired from the Sentinel-2 satellite. A deep learning-based long short-term memory network is used for the accurate mapping of crops at an early growth stage. Results show that staple crops, including rice, wheat, and sugarcane, are classified with 93.77% accuracy as early as the first four weeks of sowing. The proposed method can be applied on a large scale to effectively map crop types for smallholder farms at an early stage, allowing the authorities to plan a seamless availability of food.
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Osman, Julien, Jordi Inglada, and Jean-François Dejoux. "Assessment of a Markov logic model of crop rotations for early crop mapping." Computers and Electronics in Agriculture 113 (April 2015): 234–43. http://dx.doi.org/10.1016/j.compag.2015.02.015.
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Hao, Pengyu, Huajun Tang, Zhongxin Chen, and Zhengjia Liu. "Early-season crop mapping using improved artificial immune network (IAIN) and Sentinel data." PeerJ 6 (August 31, 2018): e5431. http://dx.doi.org/10.7717/peerj.5431.
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Substantial efforts have been made to identify crop types by region, but few studies have been able to classify crops in early season, particularly in regions with heterogeneous cropping patterns. This is because image time series with both high spatial and temporal resolution contain a number of irregular time series, which cannot be identified by most existing classifiers. In this study, we firstly proposed an improved artificial immune network (IAIN), and tried to identify major crops in Hengshui, China at early season using IAIN classifier and short image time series. A time series of 15-day composited images was generated from 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Near-infrared (NIR) band and normalized difference vegetation index (NDVI) were selected as optimal bands by pair-wise Jeffries–Matusita distances and Gini importance scores calculated from the random forest algorithm. When using IAIN to identify irregular time series, overall accuracy of winter wheat and summer crops were 99% and 98.55%, respectively. We then used the IAIN classifier and NIR and NDVI time series to identify major crops in the study region. Results showed that winter wheat could be identified 20 days before harvest, as both the producer’s accuracy (PA) and user’s accuracy (UA) values were higher than 95% when an April 1–May 15 time series was used. The PA and UA of cotton and spring maize were higher than 95% with image time series longer than April 1–August 15. As spring maize and cotton mature in late August and September–October, respectively, these two crops can be accurately mapped 4–6 weeks before harvest. In addition, summer maize could be accurately identified after August 15, more than one month before harvest. This study shows the potential of IAIN classifier for dealing with irregular time series and Sentinel-1 and Sentinel-2 image time series at early-season crop type mapping, which is useful for crop management.
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Yi, Zhiwei, Li Jia, Qiting Chen, Min Jiang, Dingwang Zhou, and Yelong Zeng. "Early-Season Crop Identification in the Shiyang River Basin Using a Deep Learning Algorithm and Time-Series Sentinel-2 Data." Remote Sensing 14, no. 21 (November 7, 2022): 5625. http://dx.doi.org/10.3390/rs14215625.
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Timely and accurate crop identification and mapping are of great significance for crop yield estimation, disaster warning, and food security. Early-season crop identification places higher demands on the quality and mining of time-series information than post-season mapping. In recent years, great strides have been made in the development of deep-learning algorithms, and the emergence of Sentinel-2 data with a higher temporal resolution has provided new opportunities for early-season crop identification. In this study, we aimed to fully exploit the potential of deep-learning algorithms and time-series Sentinel-2 data for early-season crop identification and early-season crop mapping. In this study, four classifiers, i.e., two deep-learning algorithms (one-dimensional convolutional networks and long and short-term memory networks) and two shallow machine-learning algorithms (a random forest algorithm and a support vector machine), were trained using early-season Sentinel-2 images and field samples collected in 2019. Then, these algorithms were applied to images and field samples for 2020 in the Shiyang River Basin. Twelve scenarios with different classifiers and time intervals were compared to determine the optimal combination for the earliest crop identification. The results show that: (1) the two deep-learning algorithms outperformed the two shallow machine-learning algorithms in early-season crop identification; (2) the combination of a one-dimensional convolutional network and 5-day interval time-series Sentinel-2 data outperformed the other schemes in obtaining the early-season crop identification time and achieving early mapping; and (3) the early-season crop identification mapping time in the Shiyang River Basin was identified as the end of July, and the overall classification accuracy reached 0.83. In addition, the early identification time for each crop was as follows: the wheat was in the flowering stage (mid-late June); the alfalfa was in the first harvest (mid-late June); the corn was in the early tassel stage (mid-July); the fennel and sunflower were in the flowering stage (late July); and the melons were in the fruiting stage (around late July). This study demonstrates the potential of using Sentinel-2 time-series data and deep-learning algorithms to achieve early-season crop identification, and this method is expected to provide new solutions and ideas for addressing early-season crop identification monitoring.
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Croci, Michele, Giorgio Impollonia, Henri Blandinières, Michele Colauzzi, and Stefano Amaducci. "Impact of Training Set Size and Lead Time on Early Tomato Crop Mapping Accuracy." Remote Sensing 14, no. 18 (September 11, 2022): 4540. http://dx.doi.org/10.3390/rs14184540.
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Estimating key crop parameters (e.g., phenology, yield prediction) is a prerequisite for optimizing agrifood supply chains through the use of satellite imagery, but requires timely and accurate crop mapping. The moment in the season and the number of training sites used are two main drivers of crop classification performance. The combined effect of these two parameters was analysed for tomato crop classification, through 125 experiments, using the three main machine learning (ML) classifiers (neural network, random forest, and support vector machine) using a response surface methodology (RSM). Crop classification performance between minority (tomato) and majority (‘other crops’) classes was assessed through two evaluation metrics: Overall Accuracy (OA) and G-Mean (GM), which were calculated on large independent test sets (over 400,000 fields). RSM results demonstrated that lead time and the interaction between the number of majority and minority classes were the two most important drivers for crop classification performance for all three ML classifiers. The results demonstrate the feasibility of preharvest classification of tomato with high performance, and that an RSM-based approach enables the identification of simultaneous effects of several factors on classification performance. SVM achieved the best grading performances across the three ML classifiers, according to both evaluation metrics. SVM reached highest accuracy (0.95 of OA and 0.97 of GM) earlier in the season (low lead time) and with less training sites than the other two classifiers, permitting a reduction in cost and time for ground truth collection through field campaigns.
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Tian, Haifeng, Yongjiu Wang, Ting Chen, Lijun Zhang, and Yaochen Qin. "Early-Season Mapping of Winter Crops Using Sentinel-2 Optical Imagery." Remote Sensing 13, no. 19 (September 24, 2021): 3822. http://dx.doi.org/10.3390/rs13193822.
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Sentinel-2 imagery is an unprecedented data source with high spatial, spectral and temporal resolution in addition to free access. The objective of this paper was to evaluate the potential of using Sentinel-2 data to map winter crops in the early growth stage. Analysis of three winter crop types—winter garlic, winter canola and winter wheat—was carried out in two agricultural regions of China. We analysed the spectral characteristics and vegetation index profiles of these crops in the early growth stage and other land cover types based on Sentinel-2 images. A decision tree classification model was built to distinguish the crops based on these data. The results demonstrate that winter garlic and winter wheat can be distinguished four months before harvest, while winter canola can be distinguished two months before harvest. The overall classification accuracy was 96.62% with a kappa coefficient of 0.95. Therefore, Sentinel-2 images can be used to accurately identify these winter crops in the early growth stage, making them an important data source in the field of agricultural remote sensing.
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Lussem, U., C. Hütt, and G. Waldhoff. "COMBINED ANALYSIS OF SENTINEL-1 AND RAPIDEYE DATA FOR IMPROVED CROP TYPE CLASSIFICATION: AN EARLY SEASON APPROACH FOR RAPESEED AND CEREALS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 959–63. http://dx.doi.org/10.5194/isprs-archives-xli-b8-959-2016.
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Timely availability of crop acreage estimation is crucial for maintaining economic and ecological sustainability or modelling purposes. Remote sensing data has proven to be a reliable source for crop mapping and acreage estimation on parcel-level. However, when relying on a single source of remote sensing data, e.g. multispectral sensors like RapidEye or Landsat, several obstacles can hamper the desired outcome, for example cloud cover or haze. Another limitation may be a similarity in optical reflectance patterns of crops, especially in an early season approach by the end of March, early April. Usually, a reliable crop type map for winter-crops (winter wheat/rye, winter barley and rapeseed) in Central Europe can be obtained by using optical remote sensing data from late April to early May, given a full coverage of the study area and cloudless conditions. These prerequisites can often not be met. By integrating dual-polarimetric SAR-sensors with high temporal and spatial resolution, these limitations can be overcome. SAR-sensors are not influenced by clouds or haze and provide an additional source of information due to the signal-interaction with plant-architecture. The overall goal of this study is to investigate the contribution of Sentinel-1 SAR-data to regional crop type mapping for an early season map of disaggregated winter-crops for a subset of the Rur-Catchment in North Rhine-Westphalia (Germany). For this reason, RapidEye data and Sentinel-1 data are combined and the performance of Support Vector Machine and Maximum Likelihood classifiers are compared. Our results show that a combination of Sentinel-1 and RapidEye is a promising approach for most crops, but consideration of phenology for data selection can improve results. Thus the combination of optical and radar remote sensing data indicates advances for crop-type classification, especially when optical data availability is limited.
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Lussem, U., C. Hütt, and G. Waldhoff. "COMBINED ANALYSIS OF SENTINEL-1 AND RAPIDEYE DATA FOR IMPROVED CROP TYPE CLASSIFICATION: AN EARLY SEASON APPROACH FOR RAPESEED AND CEREALS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B8 (June 24, 2016): 959–63. http://dx.doi.org/10.5194/isprsarchives-xli-b8-959-2016.
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Timely availability of crop acreage estimation is crucial for maintaining economic and ecological sustainability or modelling purposes. Remote sensing data has proven to be a reliable source for crop mapping and acreage estimation on parcel-level. However, when relying on a single source of remote sensing data, e.g. multispectral sensors like RapidEye or Landsat, several obstacles can hamper the desired outcome, for example cloud cover or haze. Another limitation may be a similarity in optical reflectance patterns of crops, especially in an early season approach by the end of March, early April. Usually, a reliable crop type map for winter-crops (winter wheat/rye, winter barley and rapeseed) in Central Europe can be obtained by using optical remote sensing data from late April to early May, given a full coverage of the study area and cloudless conditions. These prerequisites can often not be met. By integrating dual-polarimetric SAR-sensors with high temporal and spatial resolution, these limitations can be overcome. SAR-sensors are not influenced by clouds or haze and provide an additional source of information due to the signal-interaction with plant-architecture. The overall goal of this study is to investigate the contribution of Sentinel-1 SAR-data to regional crop type mapping for an early season map of disaggregated winter-crops for a subset of the Rur-Catchment in North Rhine-Westphalia (Germany). For this reason, RapidEye data and Sentinel-1 data are combined and the performance of Support Vector Machine and Maximum Likelihood classifiers are compared. Our results show that a combination of Sentinel-1 and RapidEye is a promising approach for most crops, but consideration of phenology for data selection can improve results. Thus the combination of optical and radar remote sensing data indicates advances for crop-type classification, especially when optical data availability is limited.
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HAO, Peng-yu, Hua-jun TANG, Zhong-xin CHEN, Qing-yan MENG, and Yu-peng KANG. "Early-season crop type mapping using 30-m reference time series." Journal of Integrative Agriculture 19, no. 7 (July 2020): 1897–911. http://dx.doi.org/10.1016/s2095-3119(19)62812-1.
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Demarez, Valérie, Florian Helen, Claire Marais-Sicre, and Frédéric Baup. "In-Season Mapping of Irrigated Crops Using Landsat 8 and Sentinel-1 Time Series." Remote Sensing 11, no. 2 (January 10, 2019): 118. http://dx.doi.org/10.3390/rs11020118.
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Numerous studies have reported the use of multi-spectral and multi-temporal remote sensing images to map irrigated crops. Such maps are useful for water management. The recent availability of optical and radar image time series such as the Sentinel data offers new opportunities to map land cover with high spatial and temporal resolutions. Early identification of irrigated crops is of major importance for irrigation scheduling, but the cloud coverage might significantly reduce the number of available optical images, making crop identification difficult. SAR image time series such as those provided by Sentinel-1 offer the possibility of improving early crop mapping. This paper studies the impact of the Sentinel-1 images when used jointly with optical imagery (Landsat8) and a digital elevation model of the Shuttle Radar Topography Mission (SRTM). The study site is located in a temperate zone (southwest France) with irrigated maize crops. The classifier used is the Random Forest. The combined use of the different data (radar, optical, and SRTM) improves the early classifications of the irrigated crops (k = 0.89) compared to classifications obtained using each type of data separately (k = 0.84). The use of the DEM is significant for the early stages but becomes useless once crops have reached their full development. In conclusion, compared to a “full optical” approach, the “combined” method is more robust over time as radar images permit cloudy conditions to be overcome.
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Toro, A. P. S. G. D., J. P. S. Werner, A. A. Dos Reis, J. C. D. M. Esquerdo, J. F. G. Antunes, A. C. Coutinho, R. A. C. Lamparelli, P. S. G. Magalhães, and G. K. D. A. Figueiredo. "EVALUATION OF EARLY SEASON MAPPING OF INTEGRATED CROP LIVESTOCK SYSTEMS USING SENTINEL-2 DATA." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2022 (May 31, 2022): 1335–40. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2022-1335-2022.
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Abstract. Various approaches were developed considering the need to increase agricultural productivity in cultivated areas without more deforestation, such as the Integrated Crop livestock systems (ICLS). The ICLS could be composed of annual crops followed by pastureland with the presence of cattle. Due to the high temporal dynamic of rotation between crops over the season, monitoring these areas is a big challenge. Also, agricultural organizations worldwide highlight the need for early-season maps for this kind of work. In this context, this study evaluated the potential of open data (Sentinel-2) data to map ICLS areas. The performance of two classifiers was evaluated: one of Machine Learning (random forest) and the other of Deep Learning (LSTM). Three different time windows of data were tested (Entire season, 180 days, and 120 days). Using the RF classifier, it was possible to achieve satisfactory results (Overall accuracy higher than 80%) for the early season (180 days). However, further studies are needed to explain better the lower(when compared to Random Forest) accuracy achieved by LSTM net (0.79 % for 180 days) and compare the results achieved here with results for a study area with different rates of cloud cover.
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Valero, Silvia, Ludovic Arnaud, Milena Planells, and Eric Ceschia. "Synergy of Sentinel-1 and Sentinel-2 Imagery for Early Seasonal Agricultural Crop Mapping." Remote Sensing 13, no. 23 (December 2, 2021): 4891. http://dx.doi.org/10.3390/rs13234891.
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The exploitation of the unprecedented capacity of Sentinel-1 (S1) and Sentinel-2 (S2) data offers new opportunities for crop mapping. In the framework of the SenSAgri project, this work studies the synergy of very high-resolution Sentinel time series to produce accurate early seasonal binary cropland mask and crop type map products. A crop classification processing chain is proposed to address the following: (1) high dimensionality challenges arising from the explosive growth in available satellite observations and (2) the scarcity of training data. The two-fold methodology is based on an S1-S2 classification system combining the so-called soft output predictions of two individually trained classifiers. The performances of the SenSAgri processing chain were assessed over three European test sites characterized by different agricultural systems. A large number of highly diverse and independent data sets were used for validation experiments. The agreement between independent classification algorithms of the Sentinel data was confirmed through different experiments. The presented results assess the interest of decision-level fusion strategies, such as the product of experts. Accurate crop map products were obtained over different countries in the early season with limited training data. The results highlight the benefit of fusion for early crop mapping and the interest of detecting cropland areas before the identification of crop types.
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Kumari, M., C. S. Murthy, V. Pandey, and G. D. Bairagi. "SOYBEAN CROPLAND MAPPING USING MULTI-TEMPORAL SENTINEL-1 DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 109–14. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-109-2019.
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<p><strong>Abstract.</strong> Soybean, a high value oilseed crop, is predominantly grown in the rainfed agro-ecosystem of central and peninsular India. Accurate and up-to-date assessment of the spatial distribution of soybean cultivated area is a key information requirement of all stakeholders including policy makers, soybean farmers and consumers. A methodology for timely assessment with high precision of soybean crop using satellite data is yet not operational in India. In this scenario, synthetic aperture radar (SAR) has been shown to be a reliable form of gathering crop information, especially during monsoon season. In this work, repeat coverage from the C-band Sentinel-1 satellite over Ujjain district, Madhya Pradesh is used for in-season soybean crop mapping along with other agricultural land-cover types. The data were processed through four steps: (a) data preprocessing, (b) constructing smooth time series backscatter data, (c) soybean crop classification using knowledge-based decision rule classifier and support vector machines (SVM) and (d) accuracy assessment. The results indicated that the smooth VH backscatter profiles reflected the temporal characteristics of soybean crop growing in the study region. Phenological characteristics were also derived from the smoothed S-1 VH backscatter time series to segregate early and late sown soybean. This information was used as an input to a decision-rule classifier and SVM in order to classify the input data into soybean and other crops. An overall accuracy of more than 80% using SVM and 75% using rule based classifier, in Ujjain district was achieved. These results demonstrate the scope for using time-series S-1 VH data to develop an operational soybean crop-monitoring framework.</p>
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Dong, Jie, Yangyang Fu, Jingjing Wang, Haifeng Tian, Shan Fu, Zheng Niu, Wei Han, Yi Zheng, Jianxi Huang, and Wenping Yuan. "Early-season mapping of winter wheat in China based on Landsat and Sentinel images." Earth System Science Data 12, no. 4 (November 25, 2020): 3081–95. http://dx.doi.org/10.5194/essd-12-3081-2020.
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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).
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Park, No-Wook, Min-Gyu Park, Geun-Ho Kwak, and Sungwook Hong. "Deep Learning-Based Virtual Optical Image Generation and Its Application to Early Crop Mapping." Applied Sciences 13, no. 3 (January 30, 2023): 1766. http://dx.doi.org/10.3390/app13031766.
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This paper investigates the potential of cloud-free virtual optical imagery generated using synthetic-aperture radar (SAR) images and conditional generative adversarial networks (CGANs) for early crop mapping, which requires cloud-free optical imagery at the optimal date for classification. A two-stage CGAN approach, including representation and generation stages, is presented to generate virtual Sentinel-2 spectral bands using all available information from Sentinel-1 SAR and Sentinel-2 optical images. The dual-polarization-based radar vegetation index and all available multi-spectral bands of Sentinel-2 imagery are particularly considered for feature extraction in the representation stage. A crop classification experiment using Sentinel-1 and -2 images in Illinois, USA, demonstrated that the use of all available scattering and spectral features achieved the best prediction performance for all spectral bands, including visible, near-infrared, red-edge, and shortwave infrared bands, compared with the cases that only used dual-polarization backscattering coefficients and partial input spectral bands. Early crop mapping with an image time series, including the virtual Sentinel-2 image, yielded satisfactory classification accuracy comparable to the case of using an actual time-series image set, regardless of the different combinations of spectral bands. Therefore, the generation of virtual optical images using the proposed model can be effectively applied to early crop mapping when the availability of cloud-free optical images is limited.
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de Castro, A. I., J. M. Peña, J. Torres-Sánchez, F. Jiménez-Brenes, and F. López-Granados. "Mapping Cynodon dactylon in vineyards using UAV images for site-specific weed control." Advances in Animal Biosciences 8, no. 2 (June 1, 2017): 267–71. http://dx.doi.org/10.1017/s2040470017000826.
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In Spain, the use of annual cover crops is a crop management practice for irrigated vineyards that allows controlling vineyard vigor and yield, which also leads to improve the crop quality. Recently, Cynodon dactylon (bermudagrass) has been reported to infest those cover crops and colonize the grapevine rows, resulting in significant yield and economic losses due to the competition for water and nutrients. From timely unmanned aerial vehicle (UAV) imagery, the objective of this research was to map C. dactylon patches in order to provide an optimized site-specific weed management. A quadrocopter UAV equipped with a point-and-shoot camera was used to collect a set of aerial red-green-blue (RGB) images over a commercial vineyard plot, coinciding with the dormant period of C. dactylon (February 2016). Object-based image analysis (OBIA) techniques were used to develop an innovative algorithm for early discrimination and mapping of C. dactylon, which had the ability to solve the limitation of spectral similarity of this weed with cover crops or bare soil. As a general result, the classified maps of the studied vineyard showed four main classes, i.e. vine, cover crop, C. dactylon and bare soil, with 85% overall accuracy. These weed maps allow developing new strategies for site-specific control of C. dactylon populations in the context of precision viticulture.
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Ibrahim, Esther Shupel, Philippe Rufin, Leon Nill, Bahareh Kamali, Claas Nendel, and Patrick Hostert. "Mapping Crop Types and Cropping Systems in Nigeria with Sentinel-2 Imagery." Remote Sensing 13, no. 17 (September 5, 2021): 3523. http://dx.doi.org/10.3390/rs13173523.
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Reliable crop type maps from satellite data are an essential prerequisite for quantifying crop growth, health, and yields. However, such maps do not exist for most parts of Africa, where smallholder farming is the dominant system. Prevalent cloud cover, small farm sizes, and mixed cropping systems pose substantial challenges when creating crop type maps for sub-Saharan Africa. In this study, we provide a mapping scheme based on freely available Sentinel-2A/B (S2) time series and very high-resolution SkySat data to map the main crops—maize and potato—and intercropping systems including these two crops on the Jos Plateau, Nigeria. We analyzed the spectral-temporal behavior of mixed crop classes to improve our understanding of inter-class spectral mixing. Building on the Framework for Operational Radiometric Correction for Environmental monitoring (FORCE), we preprocessed S2 time series and derived spectral-temporal metrics from S2 spectral bands for the main temporal cropping windows. These STMs were used as input features in a hierarchical random forest classification. Our results provide the first wall-to-wall crop type map for this key agricultural region of Nigeria. Our cropland identification had an overall accuracy of 84%, while the crop type map achieved an average accuracy of 72% for the five relevant crop classes. Our crop type map shows distinctive regional variations in the distribution of crop types. Maize is the dominant crop, followed by mixed cropping systems, including maize–cereals and potato–maize cropping; potato was found to be the least prevalent class. Plot analyses based on a sample of 1166 fields revealed largely homogeneous mapping patterns, demonstrating the effectiveness of our classification system also for intercropped classes, which are temporally and spatially highly heterogeneous. Moreover, we found that small field sizes were dominant in all crop types, regardless of whether or not intercropping was used. Maize–legume and maize exhibited the largest plots, with an area of up to 3 ha and slightly more than 10 ha, respectively; potato was mainly cultivated on fields smaller than 0.5 ha and only a few plots were larger than 1 ha. Besides providing the first spatially explicit map of cropping practices in the core production area of the Jos Plateau, Nigeria, the study also offers guidance for the creation of crop type maps for smallholder-dominated systems with intercropping. Critical temporal windows for crop type differentiation will enable the creation of mapping approaches in support of future smart agricultural practices for aspects such as food security, early warning systems, policies, and extension services.
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Mohite, J. D., S. A. Sawant, S. Rana, and S. Pappula. "WHEAT AREA MAPPING AND PHENOLOGY DETECTION USING SYNTHETIC APERTURE RADAR AND MULTI MULTI-SPECTRAL REMOTE SENSING OBSERVATIONS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 123–27. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-123-2019.
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<p><strong>Abstract.</strong> In season crop area mapping is of significant importance for multiple reasons such as monitoring if crop health and residue burning areas, etc. Wheat is one of the important cereal crop cultivated all across the India, with Punjab-Haryana being the prime contributors to the total production. In this study we propose a method for early season Wheat area mapping using the combined use of temporal Sentinel-1 and 2 observations. Further, we propose a method to estimate the crop phenology parameter viz. sowing date using the early time series of Normalized Difference Vegetation Index (NDVI). Few districts from Haryana and Punjab have been selected. The Wheat sowing starts in month of Oct.&ndash;Nov. Considering the sowing window, images available during Oct.&ndash;Dec. 2017 have been chosen for early season Wheat area mapping. The field data for Wheat, other crops, forest, water and settlements classes is gathered using human participatory sensing and Google Earth Engine (GEE) platform and used for data analysis. We have assessed the performance of random forest classifier using 1. NDVI derived from Sentinel-2, 2. VV and VH backscatter obtained from Sentinel-1 and 3. Both NDVI and VV-VH backscatter. Results show the maximum classification accuracy of 88.31&thinsp;% when using combination of NDVI, VV and VH. However, accuracy drops to 87.19&thinsp;% and 79.16&thinsp;% while using NDVI and VV-VH respectively. Further, to estimate the sowing date we have considered the NDVI time-series during Oct.&ndash;Dec. for Wheat pixels. A method based on NDVI compositing is used with gradual increase of 0.1&ndash;0.15 at every 12 days for subsequent two images. We have found a good agreement between the estimated sowing dates and actual sowing dates.</p>
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Hao, Pengyu, Mingquan Wu, Zheng Niu, Li Wang, and Yulin Zhan. "Estimation of different data compositions for early-season crop type classification." PeerJ 6 (May 28, 2018): e4834. http://dx.doi.org/10.7717/peerj.4834.
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Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.
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Stanley, Thomas, Dalia B. Kirschbaum, George J. Huffman, and Robert F. Adler. "Approximating Long-Term Statistics Early in the Global Precipitation Measurement Era." Earth Interactions 21, no. 3 (April 1, 2017): 1–10. http://dx.doi.org/10.1175/ei-d-16-0025.1.
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Abstract Long-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMM’s successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities, such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.
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Guo, Yan, Haoming Xia, Xiaoyang Zhao, Longxin Qiao, and Yaochen Qin. "Estimate the Earliest Phenophase for Garlic Mapping Using Time Series Landsat 8/9 Images." Remote Sensing 14, no. 18 (September 8, 2022): 4476. http://dx.doi.org/10.3390/rs14184476.
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Garlic is the major economic crop in China. Timely and accurate identification and mapping of garlic are significant for garlic yield prediction and garlic market management. Previous studies on garlic mapping were mainly based on all observations of the entire growing season, so the resulting maps have a hysteresis. Here, we determined the optimal identification strategy and the earliest identifiable phenophase for garlic based on all available Landsat 8/9 time series imagery in Google Earth Engine. Specifically, we evaluated the performance of different vegetation indices for each phenophase to determine the optimal classification metrics for garlic. Secondly, we identified garlic using random forest algorithm and classification metrics of different time series lengths. Finally, we determined the earliest identifiable phenophase of garlic and generated an early-season garlic distribution map. Garlic could be identified as early as March (bud differentiation period) with an F1 of 0.91. Our study demonstrates the differences in the performance of vegetation indices at different phenophases, and these differences provide a new idea for mapping crops. The generated early-season garlic distribution map provides timely data support for various stakeholders.
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Murcia, Harold F., Sebastian Tilaguy, and Sofiane Ouazaa. "Development of a Low-Cost System for 3D Orchard Mapping Integrating UGV and LiDAR." Plants 10, no. 12 (December 17, 2021): 2804. http://dx.doi.org/10.3390/plants10122804.
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Growing evaluation in the early stages of crop development can be critical to eventual yield. Point clouds have been used for this purpose in tasks such as detection, characterization, phenotyping, and prediction on different crops with terrestrial mapping platforms based on laser scanning. 3D model generation requires the use of specialized measurement equipment, which limits access to this technology because of their complex and high cost, both hardware elements and data processing software. An unmanned 3D reconstruction mapping system of orchards or small crops has been developed to support the determination of morphological indices, allowing the individual calculation of the height and radius of the canopy of the trees to monitor plant growth. This paper presents the details on each development stage of a low-cost mapping system which integrates an Unmanned Ground Vehicle UGV and a 2D LiDAR to generate 3D point clouds. The sensing system for the data collection was developed from the design in mechanical, electronic, control, and software layers. The validation test was carried out on a citrus crop section by a comparison of distance and canopy height values obtained from our generated point cloud concerning the reference values obtained with a photogrammetry method. A 3D crop map was generated to provide a graphical view of the density of tree canopies in different sections which led to the determination of individual plant characteristics using a Python-assisted tool. Field evaluation results showed plant individual tree height and crown diameter with a root mean square error of around 30.8 and 45.7 cm between point cloud data and reference values.
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Yang, Yanjun, Bo Tao, Wei Ren, Demetrio P. Zourarakis, Bassil El Masri, Zhigang Sun, and Qingjiu Tian. "An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images." Remote Sensing 11, no. 10 (May 19, 2019): 1191. http://dx.doi.org/10.3390/rs11101191.
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Winter wheat is one of the major cereal crops in the world. Monitoring and mapping its spatial distribution has significant implications for agriculture management, water resources utilization, and food security. Generally, winter wheat has distinguished phenological stages during the growing season, which form a unique EVI (Enhanced Vegetation Index) time series curve and differ considerably from other crop types and natural vegetation. Since early 2000, the MODIS EVI product has become the primary dataset for satellite-based crop monitoring at large scales due to its high temporal resolution, huge observation scope, and timely availability. However, the intraclass variability of winter wheat caused by field conditions and agricultural practices might lower the mapping accuracy, which has received little attention in previous studies. Here, we present a winter wheat mapping approach that integrates the variables derived from the MODIS EVI time series taking into account intraclass variability. We applied this approach to two winter wheat concentration areas, the state of Kansas in the U.S. and the North China Plain region (NCP). The results were evaluated against crop-specific maps or statistical data at the state/regional level, county level, and site level. Compared with statistical data, the accuracies in Kansas and the NCP were 95.1% and 92.9% at the state/regional level with R2 (Coefficient of Determination) values of 0.96 and 0.71 at the county level, respectively. Overall accuracies in confusion matrix were evaluated by validation samples in both Kansas (90.3%) and the NCP (85.0%) at the site level. Comparisons with methods without considering intraclass variability demonstrated that winter wheat mapping accuracies were improved by 17% in Kansas and 15% in the NCP using the improved approach. Further analysis indicated that our approach performed better in areas with lower landscape fragmentation, which may partly explain the relatively higher accuracy of winter wheat mapping in Kansas. This study provides a new perspective for generating multiple subclasses as training inputs to decrease the intraclass differences for crop type detection based on the MODIS EVI time series. This approach provides a flexible framework with few variables and fewer training samples that could facilitate its application to multiple-crop-type mapping at large scales.
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Vaglio Laurin, Gaia, Claudio Belli, Roberto Bianconi, Pietro Laranci, and Dario Papale. "Early mapping of industrial tomato in Central and Southern Italy with Sentinel 2, aerial and RapidEye additional data." Journal of Agricultural Science 156, no. 3 (April 2018): 396–407. http://dx.doi.org/10.1017/s0021859618000400.
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AbstractTimely crop information, i.e. well before harvesting time and at first stages of crop development, can benefit farmers and producer organizations. The current case study documents the procedure to deliver early data on planted tomato to users, showing the potential of Sentinel 2 (S2) to map tomato at the very beginning of the crop season, which is a challenging task. Using satellite data, integrated with ground and aerial data, an initial estimate of area planted with tomato and early tomato maps were generated in seven main production areas in Italy. Estimates of the amount of area planted with tomato provided similar results either when derived from field surveys or from remote-sensing-based classification. Tomato early maps showed a producer accuracy >80% in seven cases out of nine, and a user accuracy >80% in five cases out of nine, with differences attributed to the varying agricultural characteristics and environmental heterogeneity of the study areas. The additional use of aerial data improved producer accuracy moderately. The ability to identify abrupt growth changes, such as those caused by natural hazards, was also analysed: S2 detected significant changes in tomato growth between a hailstorm-affected area and a control area. The study suggests that S2, with enhanced spectral capabilities and open data policy, represents very valuable data, allowing crop monitoring at an early development stage.
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Rußwurm, Marc, Nicolas Courty, Rémi Emonet, Sébastien Lefèvre, Devis Tuia, and Romain Tavenard. "End-to-end learned early classification of time series for in-season crop type mapping." ISPRS Journal of Photogrammetry and Remote Sensing 196 (February 2023): 445–56. http://dx.doi.org/10.1016/j.isprsjprs.2022.12.016.
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Ruangrak, Eaknarin, Xiaomei Su, Zejun Huang, Xiaoxuan Wang, Yanmei Guo, Yongchen Du, and Jianchang Gao. "Fine mapping of a major QTL controlling early flowering in tomato using QTL-seq." Canadian Journal of Plant Science 98, no. 3 (June 1, 2018): 672–82. http://dx.doi.org/10.1139/cjps-2016-0398.
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Early flowering is one of the major earliness traits in tomato and is also an important agronomical trait in crop plants; thus, this trait is important for plant breeding and crop improvement. With the innovation of rapid and cost-effective technologies, quantitative trait locus (QTL)-seq has become the preferred method of performing QTL identification. In the present study, we identified a candidate QTL of an early flowering trait in tomato (Solanum lycopersicum) using QTL-seq. Two DNA pools of the extreme phenotype of the F2 progeny from crosses between the ‘Bone MM’ cultivar (early flowering, P1) and ‘071-440’ cultivar (late flowering, P2) were bulked for sequencing and an alignment analysis. We observed 220 single nucleotide polymorphism markers, seven candidate QTLs, and genes that may be associated with early flowering located between 1.6 and 71.8 Mb on chromosome 1. Using traditional QTL analysis, the location of one QTL was confirmed in the physical region between 23.5 and 25.3 Mb, which corresponded to the region identified using QTL-seq, and was referred to as EF1 (Solyc01g017060). A real-time quantitative reverse transcription polymerase chain reaction analysis showed that EF1 was the most highly expressed among the candidate genes and significantly expressed in early flowering parents and furthermore, we found that EF1, which had a similar sequence to the Ycf2 gene, may relate to the early flowering phenotype.
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Roy, S., N. Singh, P. Kumar, M. M. Kimothi, and S. Mamatha. "INVENTORY AND ASSESSMENT OF CORIANDER CROP IN THE STATE OF RAJASTHAN USING MULTITEMPORAL REMOTE SENSING DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 315–20. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-315-2019.
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<p><strong>Abstract.</strong> The present study aims to develop the methodology for inventory and assessment of coriander crop in Rajasthan using remote sensing technique. Sentinel-2A optical data having a spatial resolution of 10&thinsp;m, from January&ndash;March, 2017 were considered for this study keeping in mind the crop calendar. It was found that coriander at its flowering stage gives a distinct light pink colour which helps it to differentiate from other crops. However it is difficult to separate other stages of coriander (early vegetative, mature stage) owing to its similarity in tonal pattern with mustard. The overall accuracy of single date image was found to be 63.29% and Kappa (K^) Coefficient as 0.5532. With the inclusion of multiple dates accuracy increased to 91.14% and Kappa (K^) Coefficient to 0.7436. This was because increase in information increases the possibility to separate crops from each other. This study demonstrates the feasibility of multi-temporal satellite data for accurate coriander crop mapping area estimation in multi-crop scenario with reasonable accuracy at the Block/district level and State level.</p>
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Taylor, JA, AD Mowat, AF Bollen, and BM Whelan. "Early season detection and mapping ofPseudomonas syringaepv.actinidaeinfected kiwifruit (Actinidia sp.) orchards." New Zealand Journal of Crop and Horticultural Science 42, no. 4 (September 3, 2014): 303–11. http://dx.doi.org/10.1080/01140671.2014.894543.
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Lin, Zhixian, Renhai Zhong, Xingguo Xiong, Changqiang Guo, Jinfan Xu, Yue Zhu, Jialu Xu, et al. "Large-Scale Rice Mapping Using Multi-Task Spatiotemporal Deep Learning and Sentinel-1 SAR Time Series." Remote Sensing 14, no. 3 (February 2, 2022): 699. http://dx.doi.org/10.3390/rs14030699.
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Timely and accurate cropland information at large spatial scales can improve crop management and support the government in decision making. Mapping the spatial extent and distribution of crops on a large spatial scale is challenging work due to the spatial variability. A multi-task spatiotemporal deep learning model, named LSTM-MTL, was developed in this study for large-scale rice mapping by utilizing time-series Sentinel-1 SAR data. The model showed a reasonable rice classification accuracy in the major rice production areas of the U.S. (OA = 98.3%, F1 score = 0.804), even when it only utilized SAR data. The model learned region-specific and common features simultaneously, and yielded a significant improved performance compared with RF and AtBiLSTM in both global and local training scenarios. We found that the LSTM-MTL model achieved a regional F1 score up to 10% higher than both global and local baseline models. The results demonstrated that the consideration of spatial variability via LSTM-MTL approach yielded an improved crop classification performance at a large spatial scale. We analyzed the input-output relationship through gradient backpropagation and found that low VH value in the early period and high VH value in the latter period were critical for rice classification. The results of in-season analysis showed that the model was able to yield a high accuracy (F1 score = 0.746) two months before rice maturity. The integration between multi-task learning and multi-temporal deep learning approach provides a promising approach for crop mapping at large spatial scales.
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Gao, Feng, Martha C. Anderson, David M. Johnson, Robert Seffrin, Brian Wardlow, Andy Suyker, Chunyuan Diao, and Dawn M. Browning. "Towards Routine Mapping of Crop Emergence within the Season Using the Harmonized Landsat and Sentinel-2 Dataset." Remote Sensing 13, no. 24 (December 14, 2021): 5074. http://dx.doi.org/10.3390/rs13245074.
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Crop emergence is a critical stage for crop development modeling, crop condition monitoring, and biomass accumulation estimation. Green-up dates (or the start of the season) detected from remote sensing time series are related to, but generally lag, crop emergence dates. In this paper, we refine the within-season emergence (WISE) algorithm and extend application to five Corn Belt states (Iowa, Illinois, Indiana, Minnesota, and Nebraska) using routine harmonized Landsat and Sentinel-2 (HLS) data from 2018 to 2020. Green-up dates detected from the HLS time series were assessed using field observations and near-surface measurements from PhenoCams. Statistical descriptions of green-up dates for corn and soybeans were generated and compared to county-level planting dates and district- to state-level crop emergence dates reported by the National Agricultural Statistics Service (NASS). Results show that emergence dates for corn and soybean can be reliably detected within the season using the HLS time series acquired during the early growing season. Compared to observed crop emergence dates, green-up dates from HLS using WISE were ~3 days later at the field scale (30-m). The mean absolute difference (MAD) was ~7 days and the root mean square error (RMSE) was ~9 days. At the state level, the mean differences between median HLS green-up date and median crop emergence date were within 2 days for 2018–2020. At this scale, MAD was within 4 days, and RMSE was less than 5 days for both corn and soybeans. The R-squares were 0.73 and 0.87 for corn and soybean, respectively. The 2019 late emergence of crops in Corn Belt states (1–4 weeks to five-year average) was captured by HLS green-up date retrievals. This study demonstrates that routine within-season mapping of crop emergence/green-up at the field scale is practicable over large regions using operational satellite data. The green-up map derived from HLS during the growing season provides valuable information on spatial and temporal variability in crop emergence that can be used for crop monitoring and refining agricultural statistics used in broad-scale modeling efforts.
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Chaudhari, K., N. Nishant, G. Upadhyay, R. More, N. Singh, S. P. Vyas, and B. K. Bhattacharya. "CROP INVENTORY OF ORCHARD CROPS IN INDIA USING REMOTELY SENSED DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W6 (July 26, 2019): 269–75. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w6-269-2019.
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<p><strong>Abstract.</strong> The use of satellite remote sensing (RS) technologies for purpose of crop discrimination, mapping, area estimation, condition and yield assessment has been proved to be effective and efficient in terms of time and cost, having better consistency implemented with scientific approaches. However, application of satellite RS technology for horticultural crops in India has certain challenges due to scattered and small field sizes, comparatively short duration such as vegetable crops and mixed cropping. Hence the study was taken for developing research methodology for area assessment of three major fruit crops such as Banana, Mango and Citrus over 20 districts in four states viz. Gujarat, Madhya Pradesh, Uttar Pradesh and Bihar. Appropriate bio-window for analysing different crop types was selected and mapping of crops were done using pixel based hybrid classification i.e. un-supervised ISODATA clustering plus supervised MXL classification as well as object based classification of high resolution remote sensing data (Resourcesat LISS III and/or LISS IV, Cartosat – 1 PAN) followed by their accuracy assessment and their comparison with departmental reported statistics. Overall, the classification accuracy was more than 80% for all the crops. Deviation from statistics were in the range of 3 to 38%. Higher deviations from statistics were mostly due to use of lower resolution satellite data or mixing of crops having similar spectral signatures e.g. mango and sapota in Navsari and Valsad districts of Gujarat. It was very difficult to discriminate the young orchards of 2&ndash;3 years from other field crops due to mixed / inter cropping practices. The maps were checked and certified by respective State Horticulture Departments and were archived in VEADS, SAC and BHUVAN, NRSC geoportals of ISRO. RISAT – 1 (microwave) data were explored for the estimation of banana orchards in order to detect banana plantation at early stage and under cloudy sky conditions. There is huge potential of application in this sector using advanced observations from hyperspectral, thermal infrared sensors and advanced radars or LIDAR’s on-board upcoming satellites.</p>
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Wang, Xiaolei, Mei Hou, Shouhai Shi, Zirong Hu, Chuanxin Yin, and Lei Xu. "Winter Wheat Extraction Using Time-Series Sentinel-2 Data Based on Enhanced TWDTW in Henan Province, China." Sustainability 15, no. 2 (January 12, 2023): 1490. http://dx.doi.org/10.3390/su15021490.
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As a major world crop, the accurate spatial distribution of winter wheat is important for improving planting strategy and ensuring food security. Due to big data management and processing requirements, winter wheat mapping based on remote-sensing data cannot ensure a good balance between the spatial scale and map details. This study proposes a rapid and robust phenology-based method named “enhanced time-weighted dynamic time warping” (E-TWDTW), based on the Google Earth Engine, to map winter wheat in a finer spatial resolution, and efficiently complete the map of winter wheat at a 10-m resolution in Henan Province, China. The overall accuracy and Kappa coefficient of the resulting map are 97.98% and 0.9469, respectively, demonstrating its great applicability for winter wheat mapping. This research indicates that the proposed approach is effective for mapping large-scale planting patterns. Furthermore, based on comparative experiments, the E-TWDTW method has shown excellent robustness across lower quantities of training data and early season extraction ability. Therefore, it can provide early data preparation for winter wheat planting management in the early stage.
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Guo, Shuang, Tianquan Zhang, Yadi Xing, Xiaoyan Zhu, XianChun Sang, Yinghua Ling, Nan Wang, and Guang-hua He. "Identification and Gene Mapping of an early senescence leaf 4 Mutant of Rice." Crop Science 54, no. 6 (November 2014): 2713–23. http://dx.doi.org/10.2135/cropsci2013.12.0854.
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Wang, Yantong, Xiaowen Wang, Jia Xie, Wuzhong Yin, Ting Zhang, Xiaoyan Zhu, Peng Yu, et al. "Identification and Gene Mapping of an Early Senescent Leaf Mutant esl11 of Rice." Crop Science 58, no. 5 (August 23, 2018): 1932–41. http://dx.doi.org/10.2135/cropsci2018.03.0154.
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Lykhovyd, P. V. "Seasonal dynamics of normalized difference vegetation index in some winter and spring crops in the South of Ukraine." Agrology 4, no. 4 (2021): 187–93. http://dx.doi.org/10.32819/021022.
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Spatial crop monitoring using vegetation indices is one of the most promising technologies for crop mapping and remote phenological observations. The aim of the study was to determine the patterns of seasonal dynamics of the spatial normalized difference vegetation index for the main crops grown in the south of Ukraine and to connect it to their phenology. Remote sensing data provided by the OneSoil AI platform, which uses Sentinel-1 and Sentinel-2 imagery as a basis, was used to derive the monthly index values for the 2016–2021 growing season for nine selected crops grown in the experimental fields at the NAAS Institute of Irrigated Agriculture, Kherson, Ukraine. The fallow field was also included in the study to determine the cutoff values of the vegetation index, which are not representative of any healthy vegetation. It was determined that each crop has its unique pattern of the dynamics of the vegetation index, except for winter wheat and winter barley, which demonstrated quite similar models. The peak values of the vegetation index were observed in May for winter crops (wheat, barley, rapeseed) and early-spring crops (chickpea, peas), while the late-spring crops (grain corn, grain sorghum, soybeans, sunflower) reached the peak values in July. It is possible to suggest that the highest demand for mineral nutrition and watering will fall in the mentioned time periods of late spring and midsummer. Phenological monitoring revealed that the highest values of the spatial normalized difference vegetation index were observed in the following stages of crop growth, namely: winter wheat, winter barley – stem elongation; winter rapeseed – flowering; chickpea – branching; peas – budding and flowering; sunflower – stem growth; soybeans - pod formation; grain sorghum – panicle ejection and flowering; grain corn – panicle ejection and flowering. The results provide novel information for further implementation in the mathematical models for automation of crops recognition, mapping, and phenological observations based on the remote sensing data. Further scientific research in this direction will be aimed at increasing the spectrum of crops studied and a detailed investigation of the relationship between the value of the normalized difference vegetation index and their phenology.
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Dineshkumar, C., J. Satish Kumar, and S. Nitheshnirmal. "Rice Monitoring Using Sentinel-1 Data in the Google Earth Engine Platform." Proceedings 24, no. 1 (June 5, 2019): 4. http://dx.doi.org/10.3390/iecg2019-06206.
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Rice is the most essential and nutritional staple food crop worldwide. There is a need for accurate and timely rice mapping and monitoring, which is a pre-requisite for crop management and food security. Recent studies have utilized Sentinel-1 data for mapping and monitoring rice-growing areas. The present study was carried out in the Google Earth Engine (GEE), where the Sentinel-1data were used for monitoring the rice-growing area over Kulithalai taluk of Karur district, located along the Cauvery delta region. Normally, the production of rice in the study area starts in the late Samba Season where the long duration variety Cr1009 (130 days) is extensively grown. The results exhibit low backscattering values during the transplanting stage of VV and VH polarization (−15.19 db and −24 db), whereas maximum backscattering is experienced at the peak vegetation stage of VV and VH polarization (−7.42 and −16.9 db) and there is a decrease in the backscattering values after attaining the maturity stage. Amongst VH and VV polarization, VH polarization provides a consistently increasing trend in backscatter coefficients from the panicle initiation phase to the early milking phase, after which the crop attains its maturity phase, whereas in VV polarization, an early peak of backscatter coefficients is seen much earlier during the flowering phase itself. Thus, in this study, VV polarization gives better interpretation than VH polarization in the selected rice crop fields. The obtained results were cross-validated by collecting the ground truth values during the satellite data acquisition time, throughout the crop growing period from the selected rice fields.
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Kadam, Sunil A., Claudio O. Stöckle, Mingliang Liu, Zhongming Gao, and Eric S. Russell. "Suitability of Earth Engine Evaporation Flux (EEFlux) Estimation of Evapotranspiration in Rainfed Crops." Remote Sensing 13, no. 19 (September 28, 2021): 3884. http://dx.doi.org/10.3390/rs13193884.
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This study evaluated evapotranspiration (ET) estimated using the Earth Engine Evapotranspiration Flux (EEFlux), an automated version of the widely used Mapping Evapotranspiration at High Spatial Resolution with Internalized Calibration (METRIC) model, via comparison with ET measured using eddy covariance flux towers at two U.S. sites (St. John, WA, USA and Genesee, ID, USA) and for two years (2018 and 2019). Crops included spring wheat, winter pea, and winter wheat, all grown under rainfed conditions. The performance indices for daily EEFlux ET estimations combined for all sites and years dramatically improved when the cold pixel alfalfa reference ET fraction (ETrF) in METRIC was reduced from 1.05 (typically used for irrigated crops) to 0.85, with further improvement when the periods of early growth and canopy senescence were excluded. Large EEFlux ET overestimation during crop senescence was consistent in all sites and years. The seasonal absolute departure error was 51% (cold pixel ETrF = 1.05) and 23% (cold pixel ETrF = 0.85), the latter reduced to 12% when the early growth and canopy senescence periods were excluded. Departures of 10% are a reasonable expectation for methods of ET estimation, which EEFlux could achieve with more frequent satellite images, better daily weather data sources, automated adjustment of daily ETrF values during crop senescence, and a better understanding of the selection of adequate cold pixel ETrF values for rainfed crops.
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Young, Andrew, and Andy M. Jones. "A Possible Cursus Monument at Lovington, Itchen Valley." Hampshire Studies 74, no. 1 (December 1, 2019): 1–8. http://dx.doi.org/10.24202/hs2019001.
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This short paper reports on the discovery of a possible Neolithic cursus at Lovington. The potential cursus is a crop-mark site which was discovered on aerial photographs during the Hampshire South Downs Mapping project.<br/> This is a significant outcome as no other cursus monuments have previously been identified in Hampshire. Its relationship with the potential causewayed enclosure is also important given the apparent absence of Early Neolithic enclosures in Hampshire. The paper describes the crop-mark and reviews the evidence for the interpretation of the site as a cursus monument.
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Sun, Zhizhong, Dong Hu, Lijuan Xie, and Yibin Ying. "Detection of early stage bruise in apples using optical property mapping." Computers and Electronics in Agriculture 194 (March 2022): 106725. http://dx.doi.org/10.1016/j.compag.2022.106725.
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Wetterhall, F., H. C. Winsemius, E. Dutra, M. Werner, and E. Pappenberger. "Seasonal predictions of agro-meteorological drought indicators for the Limpopo basin." Hydrology and Earth System Sciences 19, no. 6 (June 2, 2015): 2577–86. http://dx.doi.org/10.5194/hess-19-2577-2015.
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Abstract. The rainfall in southern Africa has a large inter-annual variability, which can cause rain-fed agriculture to fail. The staple crop maize is especially sensitive to dry spells during the early growing season. An early prediction of the probability of dry spells and below normal precipitation can potentially mitigate damages through water management. This paper investigates how well ECMWF's seasonal forecasts predict dry spells over the Limpopo basin during the rainy season December–February (DJF) with lead times from 0 to 4 months. The seasonal forecasts were evaluated against ERA-Interim reanalysis data, which in turn were corrected with GPCP (EGPCP) to match monthly precipitation totals. The seasonal forecasts were also bias-corrected with the EGPCP using quantile mapping as well as post-processed using a precipitation threshold to define a dry day. The results indicate that the forecasts show skill in predicting dry spells in comparison with a climatological ensemble based on previous years. Quantile mapping in combination with a precipitation threshold improved the skill of the forecast. The skill in prediction of dry spells was largest over the most drought-sensitive region. Seasonal forecasts have the potential to be used in a probabilistic forecast system for drought-sensitive crops, though these should be used with caution given the large uncertainties.
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Saad El Imanni, Hajar, Abderrazak El Harti, Mohammed Hssaisoune, Andrés Velastegui-Montoya, Amine Elbouzidi, Mohamed Addi, Lahcen El Iysaouy, and Jaouad El Hachimi. "Rapid and Automated Approach for Early Crop Mapping Using Sentinel-1 and Sentinel-2 on Google Earth Engine; A Case of a Highly Heterogeneous and Fragmented Agricultural Region." Journal of Imaging 8, no. 12 (November 24, 2022): 316. http://dx.doi.org/10.3390/jimaging8120316.
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Accurate and rapid crop type mapping is critical for agricultural sustainability. The growing trend of cloud-based geospatial platforms provides rapid processing tools and cloud storage for remote sensing data. In particular, a variety of remote sensing applications have made use of publicly accessible data from the Sentinel missions of the European Space Agency (ESA). However, few studies have employed these data to evaluate the effectiveness of Sentinel-1, and Sentinel-2 spectral bands and Machine Learning (ML) techniques in challenging highly heterogeneous and fragmented agricultural landscapes using the Google Earth Engine (GEE) cloud computing platform. This work aims to map, accurately and early, the crop types in a highly heterogeneous and fragmented agricultural region of the Tadla Irrigated Perimeter (TIP) as a case study using the high spatiotemporal resolution of Sentinel-1, Sentinel-2, and a Random Forest (RF) classifier implemented on GEE. More specifically, five experiments were performed to assess the optical band reflectance values, vegetation indices, and SAR backscattering coefficients on the accuracy of crop classification. Besides, two scenarios were used to assess the monthly temporal windows on classification accuracy. The findings of this study show that the fusion of Sentinel-1 and Sentinel-2 data can accurately produce the early crop mapping of the studied area with an Overall Accuracy (OA) reaching 95.02%. The scenarios prove that the monthly time series perform better in terms of classification accuracy than single monthly windows images. Red-edge and shortwave infrared bands can improve the accuracy of crop classification by 1.72% when compared to only using traditional bands (i.e., visible and near-infrared bands). The inclusion of two common vegetation indices (The Normalized Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI)) and Sentinel-1 backscattering coefficients to the crop classification enhanced the overall classification accuracy by 0.02% and 2.94%, respectively, compared to using the Sentinel-2 reflectance bands alone. The monthly windows analysis indicated that the improvement in the accuracy of crop classification is the greatest when the March images are accessible, with an OA higher than 80%.
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Mahdianpari, Mohammadimanesh, McNairn, Davidson, Rezaee, Salehi, and Homayouni. "Mid-season Crop Classification Using Dual-, Compact-, and Full-polarization in Preparation for the Radarsat Constellation Mission (RCM)." Remote Sensing 11, no. 13 (July 3, 2019): 1582. http://dx.doi.org/10.3390/rs11131582.
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Despite recent research on the potential of dual- (DP) and full-polarimetry (FP) Synthetic Aperture Radar (SAR) data for crop mapping, the capability of compact polarimetry (CP) SAR data has not yet been thoroughly investigated. This is of particular concern, given the availability of such data from RADARSAT Constellation Mission (RCM) shortly. Previous studies have illustrated potential for accurate crop mapping using DP and FP SAR features, yet what contribution each feature makes to the model accuracy is not well investigated. Accordingly, this study examined the potential of the early- to mid-season (i.e., May to July) RADARSAT-2 SAR images for crop mapping in an agricultural region in Manitoba, Canada. Various classification scenarios were defined based on the extracted features from FP SAR data, as well as simulated DP and CP SAR data at two different noise floors. Both overall and individual class accuracies were compared for multi-temporal, multi-polarization SAR data using the pixel- and object-based random forest (RF) classification schemes. The late July C-band SAR observation was the most useful data for crop mapping, but the accuracy of single-date image classification was insufficient. Polarimetric decomposition features extracted from CP and FP SAR data produced relatively equal or slightly better classification accuracies compared to the SAR backscattering intensity features. The RF variable importance analysis revealed features that were sensitive to depolarization due to the volume scattering are the most important FP and CP SAR data. Synergistic use of all features resulted in a marginal improvement in overall classification accuracies, given that several extracted features were highly correlated. A reduction of highly correlated features based on integrating the Spearman correlation coefficient and the RF variable importance analyses boosted the accuracy of crop classification. In particular, overall accuracies of 88.23%, 82.12%, and 77.35% were achieved using the optimized features of FP, CP, and DP SAR data, respectively, using the object-based RF algorithm.
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Varshney, Rajeev K., and Himabindu Kudapa. "Legume biology: the basis for crop improvement." Functional Plant Biology 40, no. 12 (2013): v. http://dx.doi.org/10.1071/fpv40n12_fo.
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Legumes represent the most valued food sources in agriculture after cereals. Despite the advances made in breeding food legumes, there is a need to develop and further improve legume productivity to meet increasing food demand worldwide. Several biotic and abiotic stresses affect legume crop productivity throughout the world. The study of legume genetics, genomics and biology are all important in order to understand the limitations of yield of legume crops and to support our legume breeding programs. With the advent of huge genomic resources and modern technologies, legume research can be directed towards precise understanding of the target genes responsible for controlling important traits for yield potential, and for resistance to abiotic and biotic stresses. Programmed and systematic research will lead to developing high yielding, stress tolerant and early maturing varieties. This issue of Functional Plant Biology is dedicated to ‘Legume Biology’ research covering part of the work presented at VI International Conference on Legume Genetics and Genomics held at Hyderabad, India, in 2012. The 13 contributions cover recent advances in legume research in the context of plant architecture and trait mapping, functional genomics, biotic stress and abiotic stress.
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López-Granados, Francisca, M. Teresa Gómez-Casero, José M. Peña-Barragán, Montserrat Jurado-Expósito, and Luis García-Torres. "Classifying Irrigated Crops as Affected by Phenological Stage Using Discriminant Analysis and Neural Networks." Journal of the American Society for Horticultural Science 135, no. 5 (September 2010): 465–73. http://dx.doi.org/10.21273/jashs.135.5.465.
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In Spain, water for agricultural use represents about 85% of the total water demand, and irrigated crop production constitutes a major contribution to the country's economy. Field studies were conducted to evaluate the potential of multispectral reflectance and seven vegetation indices in the visible and near-infrared spectral range for discriminating and classifying bare soil and several horticultural irrigated crops at different dates. This is the first step of a broader project with the overall goal of using satellite imagery with high spatial and multispectral resolutions for mapping irrigated crops to improve agricultural water use. On-ground reflectance data of bare soil and annual herbaceous crops [garlic (Allium sativum), onion (Allium cepa), sunflower (Helianthus annuus), bean (Vicia faba), maize (Zea mays), potato (Solanum tuberosum), winter wheat (Triticum aestivum), melon (Cucumis melo), watermelon (Citrillus lanatus), and cotton (Gossypium hirsutum)], perennial herbaceous crops [alfalfa (Medicago sativa) and asparagus (Asparagus officinalis)], deciduous trees [plum (Prunus spp.)], and non-deciduous trees [citrus (Citrus spp.) and olive (Olea europaea)] were collected using a handheld field spectroradiometer in spring, early summer, and late summer. Three classification methods were applied to discriminate differences in reflectance between the different crops and bare soil: stepwise discriminant analysis, and two artificial neural networks: multilayer perceptron (MLP) and radial basis function. On any of the sampling dates, the highest degree of accuracy was achieved with the MLP neural network, showing 89.8%, 91.1%, and 96.4% correct classification in spring, early summer, and late summer, respectively. The classification matrix from the MLP model using cross-validation showed that most crops discriminated in spring and late summer were 100% classifiable. For future works, we would recommend acquiring two multispectral satellite images taken in spring and late summer for monitoring and mapping these irrigated crops, thus avoiding costly field surveys.
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Fujino, Kenji, and Hiroshi Sekiguchi. "Mapping of QTLs conferring extremely early heading in rice (Oryza sativa L.)." Theoretical and Applied Genetics 111, no. 2 (June 7, 2005): 393–98. http://dx.doi.org/10.1007/s00122-005-2035-3.
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de Castro, Ana, Jorge Torres-Sánchez, Jose Peña, Francisco Jiménez-Brenes, Ovidiu Csillik, and Francisca López-Granados. "An Automatic Random Forest-OBIA Algorithm for Early Weed Mapping between and within Crop Rows Using UAV Imagery." Remote Sensing 10, no. 3 (February 12, 2018): 285. http://dx.doi.org/10.3390/rs10020285.
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Xing, Yadi, Dan Du, Yanhua Xiao, Tianquan Zhang, Xinlong Chen, Ping Feng, XianChun Sang, Nan Wang, and Guanghua He. "Fine Mapping of a New Lesion Mimic and Early Senescence 2 (lmes2 ) Mutant in Rice." Crop Science 56, no. 4 (July 2016): 1550–60. http://dx.doi.org/10.2135/cropsci2015.09.0541.
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Toro, Ana P. S. G. D. D., Inacio T. Bueno, João P. S. Werner, João F. G. Antunes, Rubens A. C. Lamparelli, Alexandre C. Coutinho, Júlio C. D. M. Esquerdo, Paulo S. G. Magalhães, and Gleyce K. D. A. Figueiredo. "SAR and Optical Data Applied to Early-Season Mapping of Integrated Crop–Livestock Systems Using Deep and Machine Learning Algorithms." Remote Sensing 15, no. 4 (February 18, 2023): 1130. http://dx.doi.org/10.3390/rs15041130.
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Regenerative agricultural practices are a suitable path to feed the global population. Integrated Crop–livestock systems (ICLSs) are key approaches once the area provides animal and crop production resources. In Brazil, the expectation is to increase the area of ICLS fields by 5 million hectares in the next five years. However, few methods have been tested regarding spatial and temporal scales to map and monitor ICLS fields, and none of these methods use SAR data. Therefore, in this work, we explored the potential of three machine and deep learning algorithms (random forest, long short-term memory, and transformer) to perform early-season (with three-time windows) mapping of ICLS fields. To explore the scalability of the proposed methods, we tested them in two regions with different latitudes, cloud cover rates, field sizes, landscapes, and crop types. Finally, the potential of SAR (Sentinel-1) and optical (Sentinel-2) data was tested. As a result, we found that all proposed algorithms and sensors could correctly map both study sites. For Study Site 1(SS1), we obtained an overall accuracy of 98% using the random forest classifier. For Study Site 2, we obtained an overall accuracy of 99% using the long short-term memory net and the random forest. Further, the early-season experiments were successful for both study sites (with an accuracy higher than 90% for all time windows), and no significant difference in accuracy was found among them. Thus, this study found that it is possible to map ICLSs in the early-season and in different latitudes by using diverse algorithms and sensors.
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Moeletsi, Mokhele Edmond. "Mapping of Maize Growing Period over the Free State Province of South Africa: Heat Units Approach." Advances in Meteorology 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/7164068.
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Temperature is one of the important environmental parameters that determines the development of a crop from one stage to another. It is integral in the calculation of heat units. In this study, the thermal index concept is used to determine the length of the growing period of short season, medium season, and medium-late season maize crop varieties for different sowing dates (1st dekad of October to 1st dekad of January). The results show high spatiotemporal variation in the median growing period for all three maize varieties. The length of the growing period for the short, medium, and medium-late season varieties is relatively short during October to early December with values in some areas of less than 100, 120, and 120 days, respectively. The duration of the planting period increases exponentially in most places starting from the 2nd dekad of November to 2nd dekad of December, depending on the region and crop variety. Long growing periods are likely to align maize growing period with dates of high frost risk and water shortages. Thus, appropriate choice of sowing date taking into consideration the thermal time requirements of the cultivar is crucial for proper growth and development of the maize crop.