Crop planting and type proportion method for crop acreage estimation of complex agricultural landscapes

Wu, Bingfang; Li, Qiangzi

doi:10.1016/j.jag.2011.12.006

Cited by 68 publications

(43 citation statements)

References 27 publications

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“…In our particular case we rely on the studies of TRMM product validation [74][75][76], air temperature and PAR validation [48], and the applications of VCI, TCI and VHI [33,54,77,78]. In particular, CALF, crop type proportion and crop area have been validated intensively over last 10 years [3,52,62]. The same products used in this paper were used with success in other studies [2,19,20].…”

Section: Discussionmentioning

confidence: 99%

“…Data from 600 agro-meteorological sites over China are used to calibrate the yield model. The field work for validating CropWatch indicators takes place at twenty-eight global sites [1,52,[55][56][61][62][63][79][80][81][82][83][84][85][86][87][88][89][90][91][92][93][94][95].…”

Section: Discussionmentioning

confidence: 99%

“…For China, remotely sensed estimates of arable land area and CALF are combined with field survey based estimates of crop type proportion (specific type area to total cropped area, also referred to as "cropping structure") [61,62]. For other thirty key countries and provinces/states of eight large countries (the United States, Russia, India, Argentina, Brazil, Australia, Canada and Kazakhstan), CALF is used to estimate individual crop area using the following equation:…”

Section: Crop Production Indicatorsmentioning

confidence: 99%

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Global Crop Monitoring: A Satellite-Based Hierarchical Approach

Gommes

Zhang

et al. 2015

Remote Sensing

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Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, the CropWatch system has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The approach adopts a hierarchical system covering four spatial levels of detail: global, regional, national (thirty-one key countries including China) and "sub-countries" (for the nine largest countries). The thirty-one countries encompass more that 80% of both production and exports of maize, rice, soybean and wheat. The methodology resorts to climatic and remote sensing indicators at different scales. The global patterns of crop environmental growing conditions are first analyzed with indicators for rainfall, temperature, photosynthetically active radiation (PAR) as well as potential biomass. At the regional scale, the indicators pay more attention to crops and include Vegetation Health Index (VHI), Vegetation Condition Index (VCI), Cropped Arable Land Fraction (CALF) as well as Cropping Intensity (CI). Together, they characterize crop situation, farming intensity and stress. CropWatch carries out detailed crop condition analyses at the national scale with a comprehensive array of variables and indicators. The Normalized Difference Vegetation Index (NDVI), cropped areas and crop conditions are integrated to derive food production estimates. For the nine OPEN ACCESSRemote Sens. 2015, 7 3908 largest countries, CropWatch zooms into the sub-national units to acquire detailed information on crop condition and production by including new indicators (e.g., Crop type proportion). Based on trend analysis, CropWatch also issues crop production supply outlooks, covering both long-term variations and short-term dynamic changes in key food exporters and importers. The hierarchical approach adopted by CropWatch is the basis of the analyses of climatic and crop conditions assessments published in the quarterly "CropWatch bulletin" which provides accurate and timely information essential to food producers, traders and consumers.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Crop Production Indicatorsmentioning

confidence: 99%

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Global Crop Monitoring: A Satellite-Based Hierarchical Approach

Gommes

Zhang

et al. 2015

Remote Sensing

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“…In addition, three spectral indices, Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI) and Simple Ratio (SR), were calculated for each image. Previous research have shown the usefulness of these indices [29,45,46].…”

Section: Landsatmentioning

confidence: 99%

Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa

et al. 2017

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Food security is the topmost priority on the global agenda. Accurate agricultural statistics (i.e., cropped area) are essential for decision making and developing appropriate programs to achieve food security. However, derivation of these essential agricultural statistics, especially in developing countries, is fraught with many challenges including financial, logistical and human capacity limitations. This study investigated the use of fractional cover approaches in mapping cropland area in the heterogeneous landscape of West Africa. Discrete cropland areas identified from multi-temporal Landsat data were upscaled to MODIS resolution using random forest regression. Producer's accuracy and user's accuracy of the cropland class in the Landsat scale analysis averaged 95% and 94%, respectively, indicating good separability between crop and non-crop land. Validation of the fractional cropland cover map at MODIS resolution (MODIS_FCM) revealed an overall mean absolute error of 19%. Comparison of MODIS_FCM with the MODIS land cover product (e.g., MODIS_LCP) demonstrate the suitability of the proposed approach to cropped area estimation in smallholder dominant heterogeneous landscapes over existing global solutions. Comparison with official government statistics (i.e., cropped area) revealed variable levels of agreement and partly enormous disagreements, which clearly indicate the need to integrate remote sensing approaches and ground based surveys conducted by agricultural ministries in improving cropped area estimation. The recent availability of a wide range of open access remote sensing data is expected to expedite this integration and contribute missing information urgently required for regional assessments of food security in West Africa and beyond.

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“…Middle to high spatial resolution satellite data, e.g., Landsat TM/ETM+/OLI, SPOT and China Brazil Earth Resources Satellite (CBERS), are promising in capturing small patches of crop fields [14,15]. However, the cost and relatively long revisit cycles partially offset their advantages in spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images

et al. 2015

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Rice is one of the most important crops in the world; meanwhile, the rice field is also an important contributor to greenhouse gas methane emission. Therefore, it is important to get an accurate estimation of rice acreage for both food production and climate change related studies. The eastern plain region is one of the major single-cropped rice (SCR) growing areas in China. Subjected to the topography and intensified human activities, the rice fields are generally fragmented and irregular. How remote sensing can meet this challenge to accurately estimate the acreage of the rice in this region using medium-resolution imagery is the topic of this study. In this study, the applicability of the Chinese HJ-1A/B satellites and a two-band enhanced vegetation index (EVI2) was investigated. Field campaigns were carried out during the rice growing season and ground-truth data were collected for classification accuracy assessments in 2012. A stepwise classification strategy utilizing the EVI2 signatures during key phenology stages, i.e., the transplanting and the vegetative to reproductive transition phases, of the SCR was proposed, and the overall classification accuracy was 91.7%. The influence of the mixed pixel and boundary effects to classification accuracy was also investigated. This work demonstrates OPEN ACCESSRemote Sens. 2015, 7 3468 that the Chinese HJ-1A/B data are suitable data source to estimating SCR cropping area under complex land cover composition.

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Crop planting and type proportion method for crop acreage estimation of complex agricultural landscapes

Cited by 68 publications

References 27 publications

Global Crop Monitoring: A Satellite-Based Hierarchical Approach

Global Crop Monitoring: A Satellite-Based Hierarchical Approach

Multiscale Remote Sensing to Map the Spatial Distribution and Extent of Cropland in the Sudanian Savanna of West Africa

Rice Fields Mapping in Fragmented Area Using Multi-Temporal HJ-1A/B CCD Images

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