ADVANCED SOFT COMPUTING PARADIGM FOR CROP MAPPING USING REMOTE SENSING AND ARTIFICIAL INTELLIGENCE: A REVIEW
By
Benazir Meerasha
,
Benazir Meerasha
Research Scholar, Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences
, Coimbatore, Tamil Nadu
, India
Assistant Professor, Department of Electronics and Communication Engineering, Karunya institute of technology and science
, Coimbatore, Tamil Nadu
, India
High-precision crop type mapping is fundamental for agricultural monitoring, food security assessment, and sustainable land management. Recent breakthroughs in Earth observation and machine learning (ML) have greatly enhanced the potential for satellite data to capture crop phenology, spatial variability, and temporal variations. This paper conducts a systematic review of over 30 satellite-based crop type mapping studies, covering satellite data sources, multi-sensor fusion techniques, and classification models. The quantitative meta-analysis of the reviewed studies indicates that the fusion of optical and synthetic aperture radar (SAR) data can enhance overall classification accuracy by 0.2% to 0.6%, especially in areas with high spatial variability and frequent cloud cover. In addition, ensemble learning and deep learning models have been found to outperform conventional classifiers, with substantial improvements in both accuracy and robustness for various agro-ecological zones. Pixel-level fusion methods have been found to be the most effective means of enhancing crop type discrimination and area estimation.
Ajadi OA, Barr J, Liang SZ, Ferreira R, Kumpatla SP, Patel R, Swatantran A. Large-scale crop type and crop area mapping across Brazil using synthetic aperture radar and optical imagery. International Journal of Applied Earth Observation and Geoinformation. 2021 May 1;97:102294.
2.
Combined Landsat and L-Band SAR Data Improves Land Cover Classification and Change Detection in Dynamic Tropical Landscapes.
3.
Amani M, Kakooei M, Moghimi A, Ghorbanian A, Ranjgar B, Mahdavi S, Davidson A, Fisette T, Rollin P, Brisco B, Mohammadzadeh A. Application of google earth engine cloud computing platform, sentinel imagery, and neural networks for crop mapping in Canada. Remote Sensing. 2020 Oct 30;12(21):1-18.
4.
Ayhan B, Kwan C, Budavari B, Kwan L, Lu Y, Perez D, Li J, Skarlatos D, Vlachos M. Vegetation detection using deep learning and conventional methods. Remote Sensing. 2020 Aug 4;12(15):2-23.
5.
Biswas J, Jobaer MA, Haque SF, Shozib MS, Limon ZA. Mapping and monitoring land use land cover dynamics employing Google Earth Engine and machine learning algorithms on Chattogram, Bangladesh. Heliyon. 2023 Nov 1;9(11).
6.
Cai Y, Guan K, Peng J, Wang S, Seifert C, Wardlow B, Li Z. A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach. Remote sensing of environment. 2018 Jun 1; 210:35-47.
7.
Chakhar A, Hernández-López D, Ballesteros R, Moreno MA. Improving the accuracy of multiple algorithms for crop classification by integrating sentinel-1 observations with sentinel-2 data. Remote Sensing. 2021 Jan 12;13(2):243.
8.
Chen J, Zhang Z. An improved fusion of Landsat-7/8, Sentinel-2, and Sentinel-1 data for monitoring alfalfa: Implications for crop remote sensing. International Journal of Applied Earth Observation and Geoinformation. 2023 Nov 1; 124:103533.
9.
Ferchichi A, Abbes AB, Barra V, Farah IR. Forecasting vegetation indices from spatio-temporal remotely sensed data using deep learning-based approaches: A systematic literature review. Ecological Informatics. 2022 May 1; 68:101552.
10.
Crisóstomo de Castro Filho H, Abílio de Carvalho Júnior O, Ferreira de Carvalho OL, Pozzobon de Bem P, dos Santos de Moura R, Olino de Albuquerque A, Rosa Silva C, Guimaraes Ferreira PH, Fontes Guimarães R, Trancoso Gomes RA. Rice crop detection using LSTM, Bi-LSTM, and machine learning models from Sentinel-1 time series. Remote Sensing. 2020 Aug 18;12(16):1-25.
11.
Fu Y, Shen R, Song C, Dong J, Han W, Ye T, Yuan W. Exploring the effects of training samples on the accuracy of crop mapping with machine learning algorithm. Science of Remote Sensing. 2023 Jun 1; 7:100081.
12.
Ge S, Zhang J, Pan Y, Yang Z, Zhu S. Transferable deep learning model based on the phenological matching principle for mapping crop extent. International Journal of Applied Earth Observation and Geoinformation. 2021 Oct 1; 102:102451.
13.
Gibril MB, Bakar SA, Yao K, Idrees MO, Pradhan B. Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area. Geocarto international. 2017 Jul 3;32(7):735-48.
14.
Gong Z, Ge W, Guo J, Liu J. Satellite remote sensing of vegetation phenology: Progress, challenges, and opportunities. ISPRS Journal of Photogrammetry and Remote Sensing. 2024 Nov 1;217:149-64.
15.
Habibie MI, Nurda N, Sencaki DB, Putra PK, Prayogi H, Sutrisno D, Bintoro OB. The development land utilization and cover of the Jambi district are examined and forecasted using Google Earth Engine and CNN1D. Remote Sensing Applications: Society and Environment. 2024 Apr 1;34:101175.
16.
Kaplan G, Fine L, Lukyanov V, Malachy N, Tanny J, Rozenstein O. Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index. Agricultural Water Management. 2023 Feb 1;276:108056.
17.
Kaplan G, Rozenstein O. Spaceborne estimation of leaf area index in cotton, tomato, and wheat using sentinel-2. Land. 2021 May 9;10(5):1-13.
18.
Ketchum D, Jencso K, Maneta MP, Melton F, Jones MO, Huntington J. IrrMapper: A machine learning approach for high resolution mapping of irrigated agriculture across the Western US. Remote Sensing. 2020 Jul 20;12(14):1-23.
19.
Liu S, Chen Y, Ma Y, Kong X, Zhang X, Zhang D. Mapping ratoon rice planting area in Central China using Sentinel-2 time stacks and the phenology-based algorithm. Remote Sensing. 2020 Oct 16;12(20):1- 13.
20.
Liu S, Chen Y, Ma Y, Kong X, Zhang X, Zhang D. Mapping ratoon rice planting area in Central China using Sentinel-2 time stacks and the phenology-based algorithm. Remote Sensing. 2020 Oct 16;12(20):1- 13.
21.
Chong LU, ZHANG XL. Monthly composites from Sentinel-1 and Sentinel-2 images for regional major crop mapping with Google Earth Engine. Journal of Integrative Agriculture. 2021 Jul 1;20(7):1944-57.
22.
Luo C, Qi B, Liu H, Guo D, Lu L, Fu Q, Shao Y. Using time series sentinel-1 images for object-oriented crop classification in google earth engine. Remote Sensing. 2021 Feb 4;13(4):1-19.
23.
Mansaray LR, Huang W, Zhang D, Huang J, Li J. Mapping rice fields in urban Shanghai, southeast China, using Sentinel-1A and Landsat 8 datasets. Remote Sensing. 2017 Mar 10;9(3):1-23.
24.
Meerasha B, Sagayam M. Cotton Crop Classification using Optical and Microwave Remote Sensing Datasets in Google Earth Engine. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2025 Jul 30;48:1055-62.
25.
Mohite J, Sawant S, Pandit A, Pappula S. Integration of Sentinel 1 and 2 Observations for Mapping Early and Late Sowing of Soybean and Cotton Crop Using Deep Learning. InIGARSS 2020-2020 IEEE International Geoscience and Remote Sensing Symposium 2020 Sep 26 (pp. 1941-1944). IEEE.
26.
Nowakowski A, Mrziglod J, Spiller D, Bonifacio R, Ferrari I, Mathieu PP, Garcia-Herranz M, Kim DH. Crop type mapping by using transfer learning. International Journal of Applied Earth Observation and Geoinformation. 2021 Jun 1;98:102313.
27.
Pan L, Xia H, Yang J, Niu W, Wang R, Song H, Guo Y, Qin Y. Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation. 2021 Oct 1;102:102376.
28.
Pott LP, Amado TJ, Schwalbert RA, Corassa GM, Ciampitti IA. Crop type classification in Southern Brazil: Integrating remote sensing, crop modeling and machine learning. Computers and Electronics in Agriculture. 2022 Oct 1;201:107320.
29.
Ramalingam K, Ramathilagam AB, Murugesan P. Area Estimation of Cotton and Maize Crops in Perambalur District of Tamil Nadu Using Multi Date SENTINEL-1A SAR Data & Optical Data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2019 Jul 26;42:137-40.
30.
Sanli FB, Abdikan S, Esetlili MT, Sunar F. Evaluation of image fusion methods using PALSAR, RADARSAT-1 and SPOT images for land use/land cover classification. Journal of the Indian Society of Remote Sensing. 2017 Aug;45(4):591-601.
31.
Sarzynski T, Giam X, Carrasco L, Lee JS. Combining radar and optical imagery to map oil palm plantations in Sumatra, Indonesia, using the Google Earth Engine. Remote Sensing. 2020 Apr 10;12(7):1220.
32.
Skakun S, Kussul N, Shelestov AY, Lavreniuk M, Kussul O. Efficiency assessment of multitemporal Cband Radarsat-2 intensity and Landsat-8 surface reflectance satellite imagery for crop classification in Ukraine. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2015 Jul 31;9(8):3712-9.
33.
Sun L, Chen J, Guo S, Deng X, Han Y. Integration of time series sentinel-1 and sentinel-2 imagery for crop type mapping over oasis agricultural areas. Remote Sensing. 2020 Jan 2;12(1):1-27.
34.
Sun Y, Li ZL, Luo J, Wu T, Liu N. Farmland parcel-based crop classification in cloudy/rainy mountains using Sentinel-1 and Sentinel-2 based deep learning. International Journal of Remote Sensing. 2022 Feb 1;43(3):1054-73.
35.
Tamiminia H, Salehi B, Mahdianpari M, Quackenbush L, Adeli S, Brisco B. Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS journal of photogrammetry and remote sensing. 2020 Jun 1;164:152-70.
36.
Tariq A, Yan J, Gagnon AS, Riaz Khan M, Mumtaz F. Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. GeoSpatial Information Science. 2023 Jul 3;26(3):302-20.
37.
Vuorinne I, Heiskanen J, Pellikka PK. Assessing leaf biomass of agave sisalana using sentinel-2 vegetation indices. Remote Sensing. 2021 Jan 12;13(2):1-21.
38.
Yang G, Yu W, Yao X, Zheng H, Cao Q, Zhu Y, Cao W, Cheng T. AGTOC: A novel approach to winter wheat mapping by automatic generation of training samples and one-class classification on Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation. 2021 Oct 1;102:102446.
39.
Yuan Y, Lin L, Zhou ZG, Jiang H, Liu Q. Bridging optical and SAR satellite image time series via contrastive feature extraction for crop classification. ISPRS Journal of Photogrammetry and Remote Sensing. 2023 Jan 1;195:222-32.
40.
Zaji A, Liu Z, Xiao G, Sangha JS, Ruan Y. A survey on deep learning applications in wheat phenotyping. Applied Soft Computing. 2022 Dec 1;131:109761.
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