Using Sentinel-1, Sentinel-2, and Planet Imagery to Map Crop Type of Smallholder Farms (original) (raw)
Related papers
Sustainability
Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of local economic activity and food security. However, their planted area estimates are unknown in most developing countries. This study explores the use of Sentinel-1 and Sentinel-2 data to map smallholder maize farms. The random forest (RF), support vector (SVM) machine learning algorithms and model stacking (ST) were applied. Results show that the classification of combined Sentinel-1 and Sentinel-2 data improved the RF, SVM and ST algorithms by 24.2%, 8.7%, and 9.1%, respectively, compared to the classification of Sentinel-1 data individually. Similarities in the estimated areas (7001.35 ± 1.2 ha for RF, 7926.03 ± 0.7 ha for SVM and 7099.59 ± 0.8 ha for ST) show that machine learning can estimate smallholder maize areas with high accuracies. The study concludes that the single-date Sentinel-1 data were insufficient to map smallholder maize farms. However, single-date Sentinel-1 combine...
2020
Accurate and detailed studies in crop mapping are crucial in precision agriculture, yield estimations, and crop monitoring. This study focused on exploring the utility of Sentinel-2 data in mapping of crop types and testing the two machine learning algorithms which are Random Forest and Support Vector Machine performance in classifying crop types in a heterogeneous agriculture landscape in Free state province, South Africa. Nine crop types were successfully classified. The utility and contribution of different bands for classification were evaluated using RF mean decrease GINI for variable importance. Validation of results was done using a confusion matrix which produced overall accuracy, errors and prediction measures. The best performance was attained by SVM with an overall accuracy of 95% and a kappa value of 94%. RF also performed fairly well with 85% of overall accuracy and kappa value of 83%. It was concluded that Sentinel-2 data performs better using the SVM classifier compar...
2019
Thesis (MPhil)--Stellenbosch University, 2019.ENGLISH SUMMARY : Spatially-explicit crop type information is useful for estimating agricultural production areas. Such information is used for various monitoring and decision-making applications, including crop insurance, food supply-demand logistics, commodity market forecasting and environmental modelling. Traditional methods, such as ground surveys and agricultural censuses, involve high production costs and are often labour intensive, which limit their use for timely and accurate crop type data production. Remote sensing, however, offers a dependable, cost-effective and timely way of mapping crop types. Although remote sensing approaches – particularly using multitemporal techniques – have been successfully employed for producing crop type information, this information is mostly available post-harvest. Thus, researchers and decision-makers have to wait several months after harvest to have such information, which is usually too late ...
SEN2-AGRI -CROP TYPE MAPPING PILOT STUDY USING SENTINEL-2 SATELLITE IMAGERY IN INDIA
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2019
Large-scale mapping and monitoring of agriculture land use are very important. It helps in forecast crop yields, assesses the factors influencing the crop stress and estimate the damage due to natural hazards. Also, more essentially, aids in calculating the irrigation water demand at the farm level and better water resource management. Recent developments in remote sensing satellite sensors spatial and temporal resolutions, global coverage and open access such as Sentinel-2, created new possibilities in mapping and monitoring land use/land cover features. The present study investigated the performance and applicability of Sen2-Agri system in the heterogeneous cropping system for operational crop type mapping at parcel resolution using time series Sentinel-2 multispectral satellite imagery. The parcel level crop type information was collected in the field by systematic sampling and used to train and validate the random forest (RF) classification in the system. The classification accuracy varied from 57% to 86% for different major crops. The overall classification accuracy was 70% with KAPPA index of 61%. The very small agriculture field size and persistent cloud cover are the major constraint to the improvement of classification accuracy. Combination of the time series imagery from multiple earth observation satellites for the monsoon cropping season and development of a robust system for in-situ data collection will further increase the mapping accuracy. Sen2-Agri system has the potential to handle a large amount of earth observation data and can be scaled up to the entire country, which will help in the efficient monitoring of crops.
Estimating Agricultural Crop Types and Fallow Lands Using Multi Temporal Sentinel-2A Imageries
Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 2017
Meeting the food and nutritional demands of ever growing human population will cause immense pressure on agricultural lands and natural resource bases across the world. This challenge can be met only by proper land and water management, which consists of crucial components like understanding cropping systems and crop fallow dynamics for sustainable intensification. In this work, a methodology was developed for crop and crop fallow land estimation using multi-temporal, high spatial resolution Sentinel-2A data in a test site of Odisha state, in India, comprising of two districts i.e., Bhadrak and Jajpur. Customized codes were written to find temporal variation pattern of NDVI values for each pixel in the study area. Observing the variation of NDVI over time, we have attempted to estimate crop life cycle duration and their type with rigorous field inputs. The cropland and fallow land intensification maps showed 10-different cropping pattern with classification accuracy of 83.33%, and kappa coefficient of 0.81. We observed that (1) kharif is the major crop in the study area, while rabi mainly grows in areas where external fresh water sources are available (2) a large portion of the area remains fallow for most part of the year as mapped from Sentinel 2A data. There is scope to utilise the fallow lands for multi-cropping with appropriate land and water management, through the government policy prescriptions. With Sentinel-2B sensor now on board, the temporal resolution of satellite-2 (2A and 2B combined) could improve leading to improved classification and upgradation of the algorithm followed here.
Agronomy, 2021
The European Commission introduces the Control by Monitoring through new technologies to manage Common Agricultural Policy funds through the Regulation 2018/746. The advances in remote sensing have been considered one of these new technologies, mainly since the European Space Agency designed the Copernicus Programme. The Sentinel-1 (radar range) and Sentinel-2 (optical range) satellites have been designed for monitoring agricultural problems based on the characteristics they provide. The data provided by the Sentinel 2 missions, together with the emergence of different scientific disciplines in artificial intelligence —especially machine learning— offer the perfect basis for identifying and classifying any crop and its phenological state. Our research is based on developing and evaluating a pixel-based supervised classification scheme to produce accurate rice crop mapping in a smallholder agricultural zone in Calasparra, Murcia, Spain. Several models are considered to obtain the mos...
Crop classification based on multi-temporal satellite remote sensing data for agro-advisory services
Land Surface Remote Sensing II, 2014
Using remote sensing images to classify crops to obtain spatial distribution of different crops is of great significance for crop yield estimation and agricultural policy formulation. Due to the phenomenon of the same spectrum from different materials or the phenomenon of the same materials with different spectrum, it is difficult to obtain accurate crop classification results from single-phase images. We take a farm in Lintong District of Xi'an as the research area. The crops in this study area are mostly cross-planted, and the planting area is small, so it is difficult for the traditional classification method. In order to increase classification accuracy, a multilevel classification method is proposed in this paper. The Sentinel-1 backscattering coefficient (Sigma) of image is used to pre-classify the ground in the study area, and the Sentinel-2 images which cover the crop growth cycle in the study area are used to construct a normalized vegetation index (NDVI) time series to distinguish the growth differences of different crops. Combined with field survey data and phenological characteristics of crops, on the basis of pre-classification, SVM (Support Vector Machine) method is used to classify Sentinel-2 images. The classification accuracy reaches 98.07%, which is much higher than the minimum distance, Mahalanobis distance, neural network, expert decision tree, object-oriented and other classification methods.
Agriculture, 2021
The quantity of land covered by various crops in a specific time span, referred to as a cropping pattern, dictates the level of agricultural production. However, retrieval of this information at a landscape scale can be challenging, especially when high spatial resolution imagery is not available. This study hypothesized that utilizing the unique advantages of multi-date and medium spatial resolution freely available Sentinel-2 (S2) reflectance bands (S2 bands), their vegetation indices (VIs) and vegetation phenology (VP) derivatives, and Sentinel-1 (S1) backscatter data would improve cropping pattern mapping in heterogeneous landscapes using robust machine learning algorithms, i.e., the guided regularized random forest (GRRF) for variable selection and the random forest (RF) for classification. This study’s objective was to map cropping patterns within three sub-counties in Murang’a County, a typical African smallholder heterogeneous farming area, in Kenya. Specifically, the perfor...
Geocarto International, 2019
Crop mapping is a challenging task due to spectral similarity of various crops. This study aims to: (1) identify major crops in Roorkee, India, using Sentinel-2A data. (2) test the efficacy of ensemble methods, i.e. extreme gradient boosting (Xgboost) Adaboost.M1, stochastic gradient boosting (SGB), random forest (RF) in comparison to support vector machine (SVM) for crop mapping. Results show that Xgboost outperformed with Overall accuracy of 86.91%, followed by RF whereas SVM produced lowest classification accuracy. All classifier's performance is significantly different on the basis of McNemar's test (chi-squared value >3.84). Here, major crops like wheat and sugarcane are identified at maximum accuracy of 88.04% and 85.95%, respectively. Derived variable importance revealed that Red-Edge2, Red-Edge3 and NIR band are most important predictor for crop classification. Interestingly, Red-Edge1 shown the least variable importance. Xgboost has great potential for classification in a heterogeneous agricultural environment need to be explored for future research.
arXiv (Cornell University), 2022
The integration of the modern Machine Learning (ML) models into remote sensing and agriculture has expanded the scope of the application of satellite images in the agriculture domain. In this paper, we present how the accuracy of crop type identification improves as we move from medium-spatiotemporal-resolution (MSTR) to high-spatiotemporal-resolution (HSTR) satellite images. We further demonstrate that high spectral resolution in satellite imagery can improve prediction performance for low spatial and temporal resolutions (LSTR) images. The F1-score is increased by 7% when using multispectral data of MSTR images as compared to the best results obtained from HSTR images. Similarly, when crop season based time series of multispectral data is used we observe an increase of 1.2% in the F1-score. The outcome motivates further advancements in the field of synthetic band generation.