Harmonizing and Combining Existing Land Cover/Land Use Datasets for Cropland Area Monitoring at the African Continental Scale

Vancutsem, Christelle; Marinho, Eduardo; Francois, Kayitakire; See, Linda; Fritz, Steffen

doi:10.3390/rs5010019

Cited by 113 publications

(105 citation statements)

References 18 publications

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“…Compared with traditional ground-based methods, such as visual examination and survey sampling, remote sensing that provides synoptic and repetitive observations of the land surface is well suited for agricultural mapping [4][5][6] and monitoring [7,8] large geographic areas. In particular, satellite-derived vegetation indices, as measures of plant chlorophyll abundance and vegetation radiation absorption [9], have proven to be closely related to crop growth in field studies and theoretical models [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

Xin

Gong

et al. 2013

Remote Sensing

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Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE) across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS) data by explicitly handling the following two issues: (1) field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17); and (2) contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated yields statistically correlate with the national survey data for rainfed counties in the require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas.

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Section: Introductionmentioning

confidence: 99%

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

Xin

Gong

et al. 2013

Remote Sensing

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“…Worldwide, cropland distribution estimates derived from GlobCover are more than 20% higher than those derived from MODIS [20,30,54]. These differences can be attributed to a number of factors, including the use of different classification algorithms with considerably diverse parameters, diverse satellite datasets used for different algorithms, dissimilar spatial resolutions, and the different temporal windows used to develop the land cover and cropland maps [9][10][11][12]28]. The land cover and cropland maps used for geospatial modeling can therefore have a theoretically huge influence on the outputs.…”

Section: Discussionmentioning

confidence: 99%

“…Due to the continuously growing wealth of low-and middle-income developing countries, an increase in annual food production of 60-70% will be required to feed their rapidly growing populations [1,3,7]. Mapping cropland extent can yield timely, updated, and accurate cropland information that provides essential inputs to crop monitoring systems and early warning systems, such as CropWatch, Global Information and Early Warning System (GIEWS), the Early Warning Crop Monitor and the Famine Early Warning Systems Network (FEWSNET), and Forecasting Agricultural output using Space, Agrometeorological and Land-based observations (FASAL) [9][10][11][12]. Such mapping represents an important step in agricultural production assessment and has direct benefits for the early forecasting of cropping pattern distribution and spread of diseases, and also provides information for climate change studies [13].…”

Section: Introductionmentioning

confidence: 99%

A Review of the Available Land Cover and Cropland Maps for South Asia

Patil¹,

Gumma²

2018

Agriculture

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A lack of accuracy, uniqueness and the absence of systematic classification of cropland categories, together with long-pending updates of cropland mapping, are the primary challenges that need to be addressed in developing high-resolution cropland maps for south Asia. In this review, we analyzed the details of the available land cover and cropland maps of south Asia on national and regional scales in south Asia and on a global scale. Here, we highlighted the methodology adopted for classification, datasets used, classification system used for classifying different land covers and croplands and the resolution of datasets available. This listed review of different available datasets can help the reader to know which datasets to be used in their study and to understand which methodology to be chosen to further developing the accurate high-resolution land cover and cropland maps for advanced studies and for better understanding of ground reality in a timely updated version. We tried to identify the major concerns, particularly the inadequacy of knowledge regarding the spatial distribution of major crop types within south Asia, which hinder policy and strategic investment and delay the efforts to improve food security for a rapidly growing human population at a time of constant market instability and changing global climate. The overall focus of this paper is on reviewing the need for timely updated high-resolution cropland maps of south Asia.

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“…Terminological standardization is not merely to introduce a Some of the products have used the LCCS in their development (Globcover, DRC map, Africover, GLCN and the MODIS-JRC dataset), while others have not [76]. The potential of Terra-ASTER data was systematically explored by LCCS classification in heterogeneous tree savannas of West Africa.…”

Section: Terminological Consistency (Terminology Standardization)mentioning

confidence: 99%

The Standardization and Harmonization of Land Cover Classification Systems towards Harmonized Datasets: A Review

Hui

Chen³

et al. 2017

IJGI

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A number of national, regional and global land cover classification systems have been developed to meet specific user requirements for land cover mapping exercises, independent of scale, nomenclature and quality. However, this variety of land-cover classification systems limits the compatibility and comparability of land cover data. Furthermore, the current lack of interoperability between different land cover datasets, often stemming from incompatible land cover classification systems, makes analysis of multi-source, heterogeneous land cover data for various applications a very difficult task. This paper provides a critical review of the harmonization of land cover classification systems, which facilitates the generation, use and analysis of land cover maps consistently. Harmonization of existing land cover classification systems is essential to improve their cross-comparison and validation for understanding landscape patterns and changes. The paper reviews major land cover classification standards according to different scales, summarizes studies on harmonizing land cover mapping, and discusses some research problems that need to be solved and some future research directions.

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Harmonizing and Combining Existing Land Cover/Land Use Datasets for Cropland Area Monitoring at the African Continental Scale

Cited by 113 publications

References 18 publications

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

A Production Efficiency Model-Based Method for Satellite Estimates of Corn and Soybean Yields in the Midwestern US

A Review of the Available Land Cover and Cropland Maps for South Asia

The Standardization and Harmonization of Land Cover Classification Systems towards Harmonized Datasets: A Review

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