Phenology-based sample generation for supervised crop type classification

Crop type identification and mapping have been a classic issue essential for agriculture characterisation and food security as well as crop health monitoring. Remote sensing was proven to be an efficient tool to define and study agriculture. The complementarity between optical and radar data can hugely improve crop type identification and mapping. This document presents an overview of more than 40 recent works between 2013 and 2019, related to crop type mapping using satellite imagery, and points the general techniques used. Different classification algorithms was studied and compared, as well as the comparison of the inputs, optimal bands and polarization for radar data, and process giving the best results. It is often concluded that fusion improves results when compared with single data using. Nevertheless, it had been challenging to combine datasets from sensors that had different physical concepts, and progress in this domain require the development of systematic procedures.

Crop type mapping by using transfer learning

LiDAR data are becoming increasingly available, which has opened up many new applications. One such application is crop type mapping. Accurate crop type maps are critical for monitoring water use, estimating harvests and in precision agriculture. The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field – often informed by data collected during ground and aerial surveys. However, manual digitizing and labeling is time-consuming, expensive and subject to human error. Automated remote sensing methods is a cost-effective alternative, with machine learning gaining popularity for classifying crop types. This study evaluated the use of LiDAR data, Sentinel-2 imagery, aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area. Different combinations of the three datasets were evaluated along with ten machine learning. The classification results were interpreted by comparing overall accuracies, kappa, standard deviation and f-score. It was found that LiDAR data successfully differentiated between different crop types, with XGBoost providing the highest overall accuracy of 87.8%. Furthermore, the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data, with LiDAR obtaining a mean overall accuracy of 84.3% and Sentinel-2 a mean overall accuracy of 83.6%. However, the combination of all three datasets proved to be the most effective at differentiating between the crop types, with RF providing the highest overall accuracy of 94.4%. These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.

Mapping and monitoring the distribution of croplands and crop types support policymakers and international organizations by reducing the risks to food security, notably from climate change and, for that purpose, remote sensing is routinely used. However, identifying specific crop types, cropland, and cropping patterns using space-based observations is challenging because different crop types and cropping patterns have similarity spectral signatures. This study applied a methodology to identify cropland and specific crop types, including tobacco, wheat, barley, and gram, as well as the following cropping patterns: wheat-tobacco, wheat-gram, wheat-barley, and wheat-maize, which are common in Gujranwala District, Pakistan, the study region. The methodology consists of combining optical remote sensing images from Sentinel-2 and Landsat-8 with Machine Learning (ML) methods, namely a Decision Tree Classifier (DTC) and a Random Forest (RF) algorithm. The best time-periods for differentiating cropland from other land cover types were identified, and then Sentinel-2 and Landsat 8 NDVI-based time-series were linked to phenological parameters to determine the different crop types and cropping patterns over the study region using their temporal indices and ML algorithms. The methodology was subsequently evaluated using Landsat images, crop statistical data for 2020 and 2021, and field data on cropping patterns. The results highlight the high level of accuracy of the methodological approach presented using Sentinel-2 and Landsat-8 images, together with ML techniques, for mapping not only the distribution of cropland, but also crop types and cropping patterns when validated at the county level. These results reveal that this methodology has benefits for monitoring and evaluating food security in Pakistan, adding to the evidence base of other studies on the use of remote sensing to identify crop types and cropping patterns in other countries.

MODIS phenology-derived, multi-year distribution of conterminous U.S. crop types

Crop type mapping provides essential information to control and make decisions related to agricultural practices and their regulations. To map crop types accurately, it is important to capture their phenological stages and fine spatial details, especially in a temporally and spatially heterogeneous landscape. The data availability of new generation multispectral sensors of Landsat-8 (L8) and Sentinel-2 (S2) satellites offers unprecedented options for such applications. Given this, our study aims to display how the synergistic use of these optical sensors can efficiently support crop type mapping research while integrating an object-based image analysis (OBIA). Through the applied methods, we used the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data-fusion model (FSDAF) to blend L8 and S2 data and obtain reliable normalized difference vegetation index (NDVI) datasets with fine spatial and temporal resolution. Then the crop phenological information was extracted using a Savitzky–Golay filter and fused NDVI time series. Finally, a model combining phenological metrics and fused reconstructed NDVI as classification features was developed using a random forest (RF) classifier/OBIA approach. The results show that the FSDAF method creates more accurate fused NDVI and keeps more spatial details than ESTARFM. The FSDAF model was then used to create fused, high-resolution time-series products that were able to extract crop phenology in single-crop fields while providing a very detailed pattern relative to that from individual sensor time-series data. Moreover, combined L8 and S2 data by FSDAF produced highly significant overall classification accuracies (90.03% for pixel-based RF to 93.12% OBIA RF), outperforming individual sensor use (82.57% for L8-only; 88.45% for S2-only). Our proposed workflow highlights the advantage of spatiotemporal fusing and OBIA environment in spatiotemporally heterogeneous areas and fragmented landscapes, which represents a promising step toward generating fast, accurate, and ready-to-use agricultural data products.

For semi-arid ecosystems, the remote sensing based crop type classification is often hampered by heterogeneous agricultural landscapes with mixed crops and small scale land use. Under these complicated conditions, the use of high spatio-temporal satellite time series data can improve classification results. In this context, Sentinel-2 for Agriculture (Sen2Agri) provides an open source processing system to exploit Sentinel-2 imagery in an operational way for crop monitoring. This research was conducted to explore and evaluate the potentiality of Sen2Agri system for crop type mapping in a highly fragmented and heterogeneous agricultural landscape in Haouz plain, Morocco. Based on the phenological information from field campaign, Sentinel-2 time series data for 2018 agricultural season, were processed to classify the main crop types dominated in the study area. After classification, accuracy assessment was undertaken based on generated crop map and resulted in Overall Accuracy (OA) of 85.6 and Kappa coefficients of 0.81.

Timely and accurate crop mapping is crucial for providing essential data support for agricultural production management. Reliable ground truth samples form the foundation for crop mapping using remote sensing imagery, a task that presents significant challenges in regions with limited sample availability. To address this issue, this study evaluates instance-based transfer learning methods, using the Hexi Corridor as a case study to explore crop mapping strategies in areas with scarce samples. High-confidence pixels from the United States Cropland Data Layer (CDL), along with high-density time series data derived from Sentinel-1, Sentinel-2, and Landsat-8 satellite imagery, as well as key vegetation indices, were selected as training samples for the source domain. Various algorithms, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and TrAdaBoost, were employed to transfer knowledge from the source domain to the target domain for crop type mapping. The results demonstrated that during the transfer learning process using only source domain data—without utilizing any target domain data—the overall classification accuracy reached 73.88%, with optimal accuracies for maize and alfalfa at 88.97% and 85.23%, respectively. As target domain data were gradually incorporated, the total accuracy for all models ranged from 0.77 to 0.92, with F1-scores ranging from 0.76 to 0.92, showing a consistent improvement in model performance. This study highlights the feasibility of employing transfer learning for crop mapping in the Hexi Corridor, demonstrating its potential to reduce labeling costs for target domain samples and providing a valuable reference for crop mapping in regions with limited sample availability.

Crop-type mapping using time-series remote sensing data is crucial for a wide range of agricultural applications. Crop mapping during the growing season is particularly critical in timely monitoring of the agricultural system. Most existing studies focusing on within-season crop mapping leverage historical remote sensing and crop type reference data for model building, due to the difficulty in obtaining timely crop type samples for the current growing season. Yet the crop type samples from previous years may not be used directly considering the diverse patterns of crop phenology across years and locations, which hampers the scalability and transferability of the model to the current season for timely crop mapping. This article proposes an innovative within-season emergence (WISE) phenology normalized deep learning model towards scalable within-season crop mapping. The crop time-series remote sensing data are first normalized by the WISE crop emergence dates before being fed into an attention-based one-dimensional convolutional neural network classifier. Compared to conventional calendar-based approaches, the WISE-phenology normalization approach substantially helps the deep learning crop mapping model accommodate the spatiotemporal variations in crop phenological dynamics. Results in Illinois from 2017 to 2020 indicate that the proposed model outperforms calendar-based approaches and yields over 90% overall accuracy for classifying corn and soybeans at the end of season. During the growing season, the proposed model can give satisfactory performance (85% overall accuracy) one to four weeks earlier than calendar-based approaches. With WISE-phenology normalization, the proposed model exhibits more stable performance across Illinois and can be transferred to different years with enhanced scalability and robustness.

Crop type information is essential for many practical applications, yet its mapping is often constrained by inherent characteristics of most farming areas, such as fragmentation and small farm plots, changes in crop morphology across the season, and cloud cover. This study investigated whether these limitations could be overcome by using time-series of Synthetic Aperture Radar (SAR) and crop phenological information combined with different Dynamic Time Warping implementation strategies. Focusing on a fragmented landscape with small farm plots in the Netherlands, we used Sentinel-1 dual polarimetry (VV + VH) and TerraSAR-X single polarimetry (HH) images and the Dutch Basic Registration of Crop Plots (BRP) dataset for training and validation. Image pre-processing was followed by the generation of radar vegetation indices and polarimetric decomposition. Crop-specific responses to incident radar signal were analyzed, as well as the accuracy of the crop classification by Time-Weighted Dynamic Time Warping (twDTW) using either backscatter bands only or backscatter bands in combination with derived indices and polarimetric decomposition features. In addition, two further modified Dynamic Time Warping strategies, namely Variable Time Weight Dynamic Time Warping (vtwDTW) and Angular Metric for Shape Similarity (AMSS), were tested for their performance. Furthermore, we investigated the accuracy performance of a decision level fusion of TerraSAR-X and Sentinel-1 classification outputs. Results show that even in a fragmented landscape with relatively small plots (around 0.08 ha), crop types can be successfully mapped by using decision level fusion of the twDTW results of both sensors, reaching an accuracy of 77.1%. When using twDTW on Sentinel-1 (VV + VH) only, including Ratio, MRVI, and DPSVI indices, overall accuracy reached 69.5%; without those indices, accuracy was slightly lower (67.5%). Merging six different grain crops with similar leaf geometry into winter grains and summer grains improved classification accuracy to 80.6%. Our findings demonstrate that twDTW on SAR imagery allows to map crop types in fragmented landscapes with relatively small farm plots, offering potential for crop type mapping in areas with smallholder farming.

The invasion of Ukraine by Russian forces was expected to have global impact on food trade and security, since Ukraine is a breadbasket cereals and oil seeds producer. The NASA Harvest « Rapid Agricultural Assessment for Policy Support » (RAAPS) team was triggered early in the conflict to provide answers to the following questions : (i) How much winter cereals, winter oil seeds and summer crops were planted in Ukraine during the 2021-2022 cropping season?(ii) What proportion of those crops fell under the Russian occupied area? (iii) How much cropland was left unplanted in 2022 due to the war?As insights had to be produced within season, the NASA Harvest RAAPS team produced the first ever, Ukraine scale in-season crop type map based on Planet Labs 3 meter spatial -, 4 bands spectral -, and daily  temporal – resolution data.  Since   no   labeled   datasets   were   available   early   enough   in-season  for applying supervised machine learning techniques, cropland was progressively mapped   into   four   classes   (winter   cereals,   rapeseed,   summer   crops   and barren/non cultivated plots), using semi-supervised clustering techniques and heuristical thresholdings. Expert domain  knowledge  allowed to cope  with missing ground truth training data. First, active cropland was separated into winter crops and potential summer crops. K-means clustering of April and May Planet images, followed by visual cluster assignment, allowed to efficiently separate green crops (winter crops) from barren soils (potential summer crops). Then, another K-means clustering allowed to split winter crops into winter cereals and rapeseed as of end of May, based  on the intense yellow flowering signal of the latter. Finally a set of NDVI based heuristics was applied on potential summer crops in order to assess if green-up happened or not. Crops which   did   not   green   up   as   of   the   11th   of   July   2022   were   considered barren/non-planted. Road side ground surveyed crop type information collected in free Ukraine has been provided by Kussul & al. (2022) in August 2022. Validation against this data provided an overall accuracy of 94 % and a mean F1-score of 91 % for winter cereals, rapeseed and summer crops. No unplanted fields  were collected as part of the ground campaign. Several assessments of proportional area per crop type and occupation status were performed throughout the growing season, as occupation boundaries kept moving. As of the 11th of July 2022, 23.03 % of Ukraines cropland was occupied. 55.29 % of all detected barren fields were located within occupied territories, mainly scattered around the front line. 33.9 % of all winter crops were under occupied territory when harvest ready (mid July). This crop type map was used for computing harvested area, estimating yield and   for   production computation. Following NASA EarthObservatory articles were published,   providing   information   to   the   public   and   private   sector :   (i) https://earthobservatory.nasa.gov/images/150025/measuring-wars-effect-on-a-global-breadbasket    (ii) https://earthobservatory.nasa.gov/images/150590/larger-wheat-harvest-in-ukraine-than-expected 

In the field of smart farming, automated crop type mapping is a challenging task to guarantee fast and automatic management of the agricultural sector. With the emergence of advanced technologies such as artificial intelligence and geospatial technologies, new concepts were developed to provide realistic solutions to precision agriculture. The present study aims to present a machine learning-based model for automated crop-type mapping with high accuracy. The proposed model is based on the use of both optical and radar satellite images for the classification of crop types with machine learning-based algorithms. Random Forest and Support Vector Machine, were employed to classify the time series of vegetation indices. Several indices extracted from both optical and radar data were calculated. Harmonical modelization was also applied to optical indices, and decomposed into harmonic terms to calculate the fitted values of the time series. The proposed model was implemented using the geospatial processing services of Google Earth Engine and tested with a case study with about 147 satellite images. The results show the annual variability of crops and allowed performing classifications and crop type mapping with accuracy that exceeds the performances of the other existing models.

Combining Sentinel-1 and Sentinel-2 Satellite Image Time Series for land cover mapping via a multi-source deep learning architecture

The creation of crop type maps from satellite data has proven challenging and is often impeded by a lack of accurate in situ data. Street-level imagery represents a new potential source of in situ data that may aid crop type mapping, but it requires automated algorithms to recognize the features of interest. This paper aims to demonstrate a method for crop type (i.e., maize, wheat and others) recognition from street-level imagery based on a convolutional neural network using a bottom-up approach. We trained the model with a highly accurate dataset of crowdsourced labelled street-level imagery using the Picture Pile application. The classification results achieved an AUC of 0.87 for wheat, 0.85 for maize and 0.73 for others. Given that wheat and maize are two of the most common food crops grown globally, combined with an ever-increasing amount of available street-level imagery, this approach could help address the need for improved global crop type monitoring. Challenges remain in addressing the noise aspect of street-level imagery (i.e., buildings, hedgerows, automobiles, etc.) and uncertainties due to differences in the time of day and location. Such an approach could also be applied to developing other in situ data sets from street-level imagery, e.g., for land use mapping or socioeconomic indicators.

With the latest development and increasing availability of high spatial resolution sensors, earth observation technology offers a viable solution for crop identification and management. There is a strong need to produce accurate, reliable and up to date crop type maps for sustainable agriculture monitoring and management. In this study, RapidEye, the first high-resolution multi-spectral satellite system that operationally provides a Red-edge channel, was used to test the potential of the data for crop type mapping. This study was investigated at a selected region mostly covered with agricultural fields locates in the low lands of Menemen (İzmir) Plain, TURKEY. The potential of the three classification algorithms such as Maximum Likelihood Classification, Support Vector Machine and Object Based Image Analysis is tested. Accuracy assessment of land cover maps has been performed through error matrix and kappa indexes. The results highlighted that all selected classifiers as highly useful (over 90%) in mapping of crop types in the study region however the object-based approach slightly outperforming the Support Vector Machine classification by both overall accuracy and Kappa statistics. The success of selected methods also underlines the potential of RapidEye data for mapping crop types in Aegean region.