Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project

Becker‐Reshef, Inbal; Justice, Christopher O.; Sullivan, M.; Vermote, Éric; Tucker, Compton J.; Anyamba, Assaf; Small, Jennifer; Pak, Edwin; Masuoka, E.; Schmaltz, Jeff; Hansen, Matthew C.; Pittman, Kyle; Birkett, C. M.; Williams, Derrick; Reynolds, Curt; Doorn, Bradley

doi:10.3390/rs2061589

Cited by 230 publications

(145 citation statements)

References 14 publications

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“…At the local and regional scales this information is a basic requirement to forecast yields and manage crop production [1], but also to design agricultural policies and manage subsidies (e.g., European Common Agricultural Policy (CAP) subsidies) [2]. At the continental and global scales this information is key to ensure food security, but can also impact the market prices of major staple crops, and even affect forecasts on climate dynamics and water and carbon balances [3].…”

Section: Introductionmentioning

confidence: 99%

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

Larrañaga

Álvarez‐Mozos

2016

Remote Sensing

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Polarimetric SAR images are a rich data source for crop mapping. However, quad-pol sensors have some limitations due to their complexity, increased data rate, and reduced coverage and revisit time. The main objective of this study was to evaluate the added value of quad-pol data in a multi-temporal crop classification framework based on SAR imagery. With this aim, three RADARSAT-2 scenes were acquired between May and June 2010. Once we analyzed the separability and the descriptive analysis of the features, an object-based supervised classification was performed using the Random Forests classification algorithm. Classification results obtained with dual-pol (VV-VH) data as input were compared to those using quad-pol data in different polarization bases (linear H-V, circular, and linear 45˝), and also to configurations where several polarimetric features (Pauli and Cloude-Pottier decomposition features and co-pol coherence and phase difference) were added. Dual-pol data obtained satisfactory results, equal to those obtained with quad-pol data (in H-V basis) in terms of overall accuracy (0.79) and Kappa values (0.69). Quad-pol data in circular and linear 45˝bases resulted in lower accuracies. The inclusion of polarimetric features, particularly co-pol coherence and phase difference, resulted in enhanced classification accuracies with an overall accuracy of 0.86 and Kappa of 0.79 in the best case, when all the polarimetric features were added. Improvements were also observed in the identification of some particular crops, but major crops like cereals, rapeseed, and sunflower already achieved a satisfactory accuracy with the VV-VH dual-pol configuration and obtained only minor improvements. Therefore, it can be concluded that C-band VV-VH dual-pol data is almost ready to be used operationally for crop mapping as long as at least three acquisitions in dates reflecting key growth stages representing typical phenology differences of the present crops are available. In the near future, issues regarding the classification of crops with small field sizes and heterogeneous cover (i.e., fallow and grasslands) need to be tackled to make this application fully operational.

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Section: Introductionmentioning

confidence: 99%

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

Larrañaga

Álvarez‐Mozos

2016

Remote Sensing

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“…Monitoring projects yielding agricultural information in near-real-time and covering a large number of crops are important [1,2]. This is particularly true for Brazil, which is one of the largest countries of the world and a main producer and exporter of agricultural commodities [3].…”

Section: Introductionmentioning

confidence: 99%

Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

et al. 2015

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Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in São Paulo State (Brazil) was used to evaluate the OPEN ACCESSRemote Sens. 2015, 7 14483proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km 2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ≈ 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map.

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“…Several countries and organizations currently employ crop monitoring systems to monitor their own countries' or regional and global crop production [3,4]. In the United States, the US Department of Agriculture (USDA) Foreign Agricultural Service (FAS) provides crop monitoring as part of its Global Agricultural Monitoring (GLAM) program [3]. The European Commission operates its AGRI4CAST program implemented by the European Commission Joint Research Center under the Monitoring Agricultural Resources (MARS) program [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…It is essential to facilitate food price stability for agriculture importers and exporters, especially when production shortfalls are anticipated [2]. Several countries and organizations currently employ crop monitoring systems to monitor their own countries' or regional and global crop production [3,4]. In the United States, the US Department of Agriculture (USDA) Foreign Agricultural Service (FAS) provides crop monitoring as part of its Global Agricultural Monitoring (GLAM) program [3].…”

Section: Introductionmentioning

confidence: 99%

“…In most cases, the crop condition is assessed by using various satellite-derived indices. The normalized difference vegetation index (NDVI), an indicator of the level of photosynthetic activity, reflecting whether the vegetation is stressed or not, is one of the most commonly used indices [1,3,4,11,[19][20][21][22][23]. Furthermore, the leaf area index (LAI), the fraction of absorbed photosynthetically active radiation (FAPAR), the temperature condition index (TCI), the vegetation condition index (VCI) and the normalized difference water index (NDWI) have been tested for their suitability and validity for crop condition and crop stress monitoring under different environmental conditions around the world [9,[24][25][26].…”

Section: Introductionmentioning

confidence: 99%

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Crop Condition Assessment with Adjusted NDVI Using the Uncropped Arable Land Ratio

Zhang

et al. 2014

Remote Sensing

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Abstract:Crop condition assessment in the early growing stage is essential for crop monitoring and crop yield prediction. A normalized difference vegetation index (NDVI)-based method is employed to evaluate crop condition by inter-annual comparisons of both spatial variability (using NDVI images) and seasonal dynamics (based on crop condition profiles). Since this type of method will generate false information if there are changes in crop rotation, cropping area or crop phenology, information on cropped/uncropped arable land is integrated to improve the accuracy of crop condition monitoring. The study proposes a new method to retrieve adjusted NDVI for cropped arable land during the growing season of winter crops by integrating 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data at 250-m resolution with a cropped and uncropped arable land map derived from the multi-temporal China Environmental Satellite (Huan Jing Satellite) charge-coupled device (HJ-1 CCD) images at 30-m resolution. Using the land map's data on cropped and uncropped arable land, a pixel-based uncropped arable land ratio (UALR) at 250-m resolution was generated. Next, the UALR-adjusted NDVI was produced by assuming that the MODIS reflectance value for each pixel is a linear mixed signal composed of the proportional reflectance of cropped and uncropped arable land. When UALR-adjusted NDVI data are used for crop condition assessment, results are expected to be more accurate, because: (i) pixels with only uncropped arable land are not included in the assessment; and (ii) the adjusted NDVI corrects for interannual variation in cropping area. On the provincial level, crop growing OPEN ACCESSRemote Sens. 2014, 6 5775 profiles based on the two kinds of NDVI data illustrate the difference between the regular and the adjusted NDVI, with the difference depending on the total area of uncropped arable land in the region. The results suggested that the proposed method can be used to improve the assessment of early crop condition, but additional evaluation in other major crop producing regions is needed to better assess the method's application in other regions and agricultural systems.

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Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project

Cited by 230 publications

References 14 publications

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

On the Added Value of Quad-Pol Data in a Multi-Temporal Crop Classification Framework Based on RADARSAT-2 Imagery

Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

Crop Condition Assessment with Adjusted NDVI Using the Uncropped Arable Land Ratio

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