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Journal articles on the topic 'Early crop mapping'

Author: Grafiati

Published: 10 March 2023

Last updated: 31 July 2025

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1

You, Nanshan, Jinwei Dong, Jing Li, Jianxi Huang, and Zhenong Jin. "Rapid early-season maize mapping without crop labels." Remote Sensing of Environment 290 (May 2023): 113496. http://dx.doi.org/10.1016/j.rse.2023.113496.

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Mirzaei, Saham, Simone Pascucci, Maria Francesca Carfora, et al. "Early-Season Crop Mapping by PRISMA Images Using Machine/Deep Learning Approaches: Italy and Iran Test Cases." Remote Sensing 16, no. 13 (2024): 2431. http://dx.doi.org/10.3390/rs16132431.

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Despite its high importance for crop yield prediction and monitoring, early-season crop mapping is severely hampered by the absence of timely ground truth. To cope with this issue, this study aims at evaluating the capability of PRISMA hyperspectral satellite images compared with Sentinel-2 multispectral imagery to produce early- and in-season crop maps using consolidated machine and deep learning algorithms. Results show that the accuracy of crop type classification using Sentinel-2 images is meaningfully poor compared with PRISMA (14% in overall accuracy (OA)). The 1D-CNN algorithm, with 89%

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Li, Kaiyuan, Wenzhi Zhao, Jiage Chen, Liqiang Zhang, Duoduo Hu, and Qiao Wang. "Predicting Crop Growth Patterns with Spatial–Temporal Deep Feature Exploration for Early Mapping." Remote Sensing 15, no. 13 (2023): 3285. http://dx.doi.org/10.3390/rs15133285.

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The timely and accurate mapping of crops over large areas is essential for alleviating food crises and formulating agricultural policies. However, most existing classical crop mapping methods usually require the whole-year historical time-series data that cannot respond quickly to the current planting information, let alone for future prediction. To address this issue, we propose a novel spatial–temporal feature and deep integration strategy for crop growth pattern prediction and early mapping (STPM). Specifically, the STPM first learns crop spatial–temporal evolving patterns from historical d

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Wang, Yiqun, Hui Huang, and Radu State. "Early Crop Mapping Using Dynamic Ecoregion Clustering: A USA-Wide Study." Remote Sensing 15, no. 20 (2023): 4962. http://dx.doi.org/10.3390/rs15204962.

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Mapping target crops earlier than the harvest period is an essential task for improving agricultural productivity and decision-making. This paper presents a new method for early crop mapping for the entire conterminous USA (CONUS) land area using the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) data with a dynamic ecoregion clustering approach. Ecoregions, geographically distinct areas with unique ecological patterns and processes, provide a valuable framework for large-scale crop mapping. We conducted our dynamic ecoregion clustering by analyzing soil, cli

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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 (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 tim

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Wei, Peng, Huichun Ye, Shuting Qiao, et al. "Early Crop Mapping Based on Sentinel-2 Time-Series Data and the Random Forest Algorithm." Remote Sensing 15, no. 13 (2023): 3212. http://dx.doi.org/10.3390/rs15133212.

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Early-season crop mapping and information extraction is essential for crop growth monitoring and yield prediction, and it facilitates agricultural management and rapid response to agricultural disasters. However, training classifiers by remote sensing classification features for early crop prediction can be challenging, as early-season mapping can only use remote sensing image data during part of the crop growth period. In order to overcome this limitation, this study takes the Sanjiang Plain as an example to investigate the earliest identification time of rice, maize and soybean based on Sent

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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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Wen, Caiyun, Miao Lu, Ying Bi, et al. "Customized crop feature construction using genetic programming for early- and in-season crop mapping." Computers and Electronics in Agriculture 231 (April 2025): 109949. https://doi.org/10.1016/j.compag.2025.109949.

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Luo, Jiansong, Min Xie, Qiang Wu, et al. "Early Crop Identification Study Based on Sentinel-1/2 Images with Feature Optimization Strategy." Agriculture 14, no. 7 (2024): 990. http://dx.doi.org/10.3390/agriculture14070990.

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The timely and accurate mapping of crop types is crucial for agricultural insurance, futures, and assessments of food security risks. However, crop mapping is currently focused on the post-harvest period, and less attention has been paid to early crop mapping. In this study, the feasibility of using Sentinel-1 (S1) and Sentinel-2 (S2) data for the earliest identifiable time (EIT) for major crops (sunflower, maize, spring wheat, and melon) was explored in the Hetao Irrigation District (HID) of China, based on the Google Earth Engine (GEE) platform. An early crop identification strategy based on

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Wang, Yiqun, Hui Huang, and Radu State. "Cross Domain Early Crop Mapping with Label Spaces Discrepancies using MultiCropGAN." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-1-2024 (May 9, 2024): 241–48. http://dx.doi.org/10.5194/isprs-annals-x-1-2024-241-2024.

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Abstract. Mapping target crops before the harvest season for regions lacking crop-specific ground truth is critical for global food security. Utilizing multispectral remote sensing and domain adaptation methods, prior studies strive to produce precise crop maps in these regions (target domain) with the help of the crop-specific labelled remote sensing data from the source regions (source domain). However, existing approaches assume identical label spaces across those domains, a challenge often unmet in reality, necessitating a more adaptable solution. This paper introduces the Multiple Crop Ma

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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 (2020): 1897–911. http://dx.doi.org/10.1016/s2095-3119(19)62812-1.

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12

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 (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 combi

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13

Wang, Qun, Boli Yang, Luchun Li, Hongyi Liang, Xiaolin Zhu, and Ruyin Cao. "Within-Season Crop Identification by the Fusion of Spectral Time-Series Data and Historical Crop Planting Data." Remote Sensing 15, no. 20 (2023): 5043. http://dx.doi.org/10.3390/rs15205043.

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Crop mapping at an earlier time within the growing season benefits agricultural management. However, crop spectral information is very limited at the early crop phenological stages, leading to difficulties for within-season crop identification. In this study, we proposed a deep learning-based fusion method for crop mapping within the growing season, which first learned a priori information (i.e., pre-season crop types) from historical crop planting data and then integrated the a priori information with the satellite-derived crop types estimated from spectral times-series data. We expect that p

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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 (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 o

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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 (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-S

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Chen, Jiaqi, Xin Du, Chen Wang, et al. "Zonal Estimation of the Earliest Winter Wheat Identification Time in Shandong Province Considering Phenological and Environmental Factors." Agronomy 15, no. 6 (2025): 1463. https://doi.org/10.3390/agronomy15061463.

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Early-season crop mapping plays a critical role in yield estimation, agricultural management, and policy-making. However, most existing methods assign a uniform earliest identification time across provincial or broader extents, overlooking spatial heterogeneity in crop phenology and environmental conditions. This often results in delayed detection or reduced mapping accuracy. To address this issue, we proposed a zonal-based early-season mapping framework for winter wheat by integrating phenological and environmental factors. Aggregation zones across Shandong Province were delineated using Prin

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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 (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).

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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 (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 betwee

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19

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-d

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20

Khan, Haseeb Rehman, Zeeshan Gillani, Muhammad Hasan Jamal, et al. "Early Identification of Crop Type for Smallholder Farming Systems Using Deep Learning on Time-Series Sentinel-2 Imagery." Sensors 23, no. 4 (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 remai

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21

Liu, Tingting, Peipei Li, Feng Zhao, Jie Liu, and Ran Meng. "Early-Stage Mapping of Winter Canola by Combining Sentinel-1 and Sentinel-2 Data in Jianghan Plain China." Remote Sensing 16, no. 17 (2024): 3197. http://dx.doi.org/10.3390/rs16173197.

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The early and accurate mapping of winter canola is essential in predicting crop yield, assessing agricultural disasters, and responding to food price fluctuations. Although some methods have been proposed to map the winter canola at the flowering or later stages, mapping winter canola planting areas at the early stage is still challenging, due to the insufficient understanding of the multi-source remote sensing features sensitive for winter canola mapping. The objective of this study was to evaluate the potential of using the combination of optical and synthetic aperture radar (SAR) data for m

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22

Toro, A. P. S. G. D., J. P. S. Werner, A. A. Dos Reis, et al. "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 (Sentine

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23

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 (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 pl

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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 i

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Birinyi, Edina, Dániel Kristóf, Roland Hollós, Zoltán Barcza, and Anikó Kern. "Large-Scale Maize Condition Mapping to Support Agricultural Risk Management." Remote Sensing 16, no. 24 (2024): 4672. https://doi.org/10.3390/rs16244672.

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Crop condition mapping and yield loss detection are highly relevant scientific fields due to their economic importance. Here, we report a new, robust six-category crop condition mapping methodology based on five vegetation indices (VIs) using Sentinel-2 imagery at a 10 m spatial resolution. We focused on maize, the most drought-affected crop in the Carpathian Basin, using three selected years of data (2017, 2022, and 2023). Our methodology was validated at two different spatial scales against independent reference data. At the parcel level, we used harvester-derived precision yield data from s

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Ruangrak, Eaknarin, Xiaomei Su, Zejun Huang, et al. "Fine mapping of a major QTL controlling early flowering in tomato using QTL-seq." Canadian Journal of Plant Science 98, no. 3 (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 flowerin

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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 Apri

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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 Apri

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Dong, Jie, Yangyang Fu, Jingjing Wang, et al. "Early-season mapping of winter wheat in China based on Landsat and Sentinel images." Earth System Science Data 12, no. 4 (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 cropla

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Zhou, Xin, Jinfei Wang, Bo Shan, and Yongjun He. "Early-Season Crop Classification Based on Local Window Attention Transformer with Time-Series RCM and Sentinel-1." Remote Sensing 16, no. 8 (2024): 1376. http://dx.doi.org/10.3390/rs16081376.

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Crop classification is indispensable for agricultural monitoring and food security, but early-season mapping has remained challenging. Synthetic aperture radar (SAR), such as RADARSAT Constellation Mission (RCM) and Sentinel-1, can meet higher requirements on the reliability of satellite data acquisition with all-weather and all-day imaging capability to supply dense observations in the early crop season. This study applied the local window attention transformer (LWAT) to time-series SAR data, including RCM and Sentinel-1, for early-season crop classification. The performance of this integrati

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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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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 Norma

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Yu, Yunfei, Linghua Meng, Chong Luo, Beisong Qi, Xinle Zhang, and Huanjun Liu. "Early Mapping Method for Different Planting Types of Rice Based on Planet and Sentinel-2 Satellite Images." Agronomy 14, no. 1 (2024): 137. http://dx.doi.org/10.3390/agronomy14010137.

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In Northeast China, transplanted rice cultivation has been adopted to extend the rice growing season and boost yields, responding to the limitations of the cumulative temperature zone and high food demand. However, direct-seeded rice offers advantages in water conservation and labour efficiency. The precise and timely monitoring of the distribution of different rice planting types is key to ensuring food security and promoting sustainable regional development. This study explores the feasibility of mapping various rice planting types using only early-stage satellite data from the rice growing

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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 (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 resolut

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T. S., Sindhushree, Kavya D, Gajbe Harshit Jitendra, Nitin C. Tongali, B. Rajeswari, and Pallabi Phukan. "Digital Soil Mapping: A Review of Techniques, Applications and Emerging Trends." Journal of Scientific Research and Reports 31, no. 7 (2025): 1151–58. https://doi.org/10.9734/jsrr/2025/v31i73329.

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Digital Soil Mapping is a method of digital evaluation of soil. It consisting of numerous processes such as creation of soil information system, maintenance of digital data base etc. it involves the generating digital soil maps with the help of Geographic information System and Remote Sensing. Digital Soil mapping system embedded with the computer enabled techniques to produce detailed and accurate information. Digital Soil Mapping is an integral part of the Soil information System. It is emerged as a new methodology to describe the soil physical and spatial properties efficiently. It helps in

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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 (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

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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 (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

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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 (2014): 303–11. http://dx.doi.org/10.1080/01140671.2014.894543.

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Wang, Gengze, Di Meng, Riqiang Chen, et al. "Automatic Rice Early-Season Mapping Based on Simple Non-Iterative Clustering and Multi-Source Remote Sensing Images." Remote Sensing 16, no. 2 (2024): 277. http://dx.doi.org/10.3390/rs16020277.

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Timely and accurate rice spatial distribution maps play a vital role in food security and social stability. Early-season rice mapping is of great significance for yield estimation, crop insurance, and national food policymaking. Taking Tongjiang City in Heilongjiang Province with strong spatial heterogeneity as study area, a hierarchical K-Means binary automatic rice classification method based on phenological feature optimization (PFO-HKMAR) is proposed, using Google Earth Engine platform and Sentinel-1/2, and Landsat 7/8 data. First, a SAR backscattering intensity time series is reconstructe

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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 (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 provi

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Yang, Yanjun, Bo Tao, Wei Ren, et al. "An Improved Approach Considering Intraclass Variability for Mapping Winter Wheat Using Multitemporal MODIS EVI Images." Remote Sensing 11, no. 10 (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

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Young, Andrew, and Andy M. Jones. "A Possible Cursus Monument at Lovington, Itchen Valley." Hampshire Studies 74, no. 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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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 (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 tw

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Niu, Jiahong, Yanan Guan, Xiao Yu, et al. "SiNF-YC2 Regulates Early Maturity and Salt Tolerance in Setaria italica." International Journal of Molecular Sciences 24, no. 8 (2023): 7217. http://dx.doi.org/10.3390/ijms24087217.

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Early maturity is an important agronomic trait in most crops, because it can solve the problem of planting in stubble for multiple cropping as well as make full use of light and temperature resources in alpine regions, thereby avoiding damage from low temperatures in the early growth period and early frost damage in the late growth period to improve crop yield and quality. The expression of genes that determine flowering affects flowering time, which directly affects crop maturity and indirectly affects crop yield and quality. Therefore, it is important to analyze the regulatory network of flo

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Cheng, Feifei, Bingwen Qiu, Peng Yang, et al. "Crop sample prediction and early mapping based on historical data: Exploration of an explainable FKAN framework." Computers and Electronics in Agriculture 237 (October 2025): 110689. https://doi.org/10.1016/j.compag.2025.110689.

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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 (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

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Dida, Gudeta. "Molecular Markers in Breeding of Crops: Recent Progress and Advancements." Open Access Journal of Microbiology & Biotechnology 7, no. 4 (2022): 1–11. http://dx.doi.org/10.23880/oajmb-16000244.

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A convectional plant breeder faces the challenge of how to more effectively and efficiently perform selection and accelerate breeding progress to satisfy the requirements of changing demands for crop cultivars. However, with the development and advancement of molecular marker technology, the fate of plant breeding has shifted from year to year. Recently, different types of molecular markers have been developed, and advancements in sequencing technologies have greatly increased plant improvement. To further our understanding of molecular markers, several reviews have been published in recent de

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Jaloliddinov, Jaloliddin, Xiangyu Tian, Yongqing Bai, et al. "Large-Scale Cotton Classification under Insufficient Sample Conditions Using an Adaptive Feature Network and Sentinel-2 Imagery in Uzbekistan." Agronomy 14, no. 1 (2023): 75. http://dx.doi.org/10.3390/agronomy14010075.

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Cotton (Gossypium hirsutum L.) is one of the main crops in Uzbekistan, which makes a major contribution to the country’s economy. The cotton industry has played a pivotal role in the economic landscape of Uzbekistan for decades, generating employment opportunities and supporting the livelihoods of countless individuals across the country. Therefore, having precise and up-to-date data on cotton cultivation areas is crucial for overseeing and effectively managing cotton fields. Nonetheless, there is currently no extensive, high-resolution approach that is appropriate for mapping cotton fields on

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Mahdianpari, Mohammadimanesh, McNairn, et al. "Mid-season Crop Classification Using Dual-, Compact-, and Full-polarization in Preparation for the Radarsat Constellation Mission (RCM)." Remote Sensing 11, no. 13 (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

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Saad El Imanni, Hajar, Abderrazak El Harti, Mohammed Hssaisoune, et al. "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 (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 landsc

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