Remote sensing-based global crop monitoring: experiences with China's CropWatch system

Wu, Bingfang; Meng, Jun; Li, Qiangzi; Niu, Y.; Du, Xin; Zhang, Miao

doi:10.1080/17538947.2013.821185

Cited by 138 publications

(76 citation statements)

References 31 publications

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“…For crop yield and crop condition, for example, clear views are needed roughly weekly or at least biweekly, although even more frequent data are valuable [18][19][20][21][22]. Due to this requirement for frequently sampled data, global cropland monitoring to date has been predominately undertaken with coarse spatial resolution data (defined in the context of GEOGLAM as greater than 100 m) [23,24], with near-daily MODIS-class observations at 250-500 m and with broad spectral coverage providing the primary data source over the past decade [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39]. However, analyses relying upon coarse spatial resolution data to monitor cropland dynamics are often confronted with issues of subpixel heterogeneity [16,[40][41][42][43], with many small fields or highly heterogeneous landscapes having variability beneath the spatial resolution of the sensing instrument in use.…”

Section: Agricultural Monitoring: Spatial and Temporal Considerationsmentioning

confidence: 99%

“…However, analyses relying upon coarse spatial resolution data to monitor cropland dynamics are often confronted with issues of subpixel heterogeneity [16,[40][41][42][43], with many small fields or highly heterogeneous landscapes having variability beneath the spatial resolution of the sensing instrument in use. While moderate spatial resolution (defined in the context of GEOGLAM as 10-100 m) has been used extensively in national scale analyses of land cover, including cropped area and crop type mapping efforts [39,[44][45][46][47][48][49][50][51][52][53][54][55][56][57][58], their limited revisit frequency and/or limitations in on-board storage capacity have meant that these data have been too sparsely collected in time and often also in extent in order to be used for agricultural monitoring at broad scales across the globe [19].…”

Section: Agricultural Monitoring: Spatial and Temporal Considerationsmentioning

confidence: 99%

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A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

2015

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Global agricultural monitoring utilizes a variety of Earth observations (EO) data spanning different spectral, spatial, and temporal resolutions in order to gather information on crop area, type, condition, calendar, and yield, among other applications. Categorical requirements for space-based monitoring of major agricultural production areas have been articulated based on best practices established by the Group on Earth Observation's (GEO) Global Agricultural Monitoring Community (GEOGLAM) of Practice, in collaboration with the Committee on Earth Observation Satellites (CEOS). We present a method to transform generalized requirements for agricultural monitoring in the context of GEOGLAM into spatially explicit (0.05°) Earth observation (EO) requirements for multiple resolutions of data. This is accomplished through the synthesis of the necessary remote sensing-based datasets concerning where (crop mask, when (growing calendar, and how frequently imagery is required (considering cloud cover impact throughout the agricultural growing season. Beyond this provision of the framework and tools necessary to articulate these requirements, investigated in depth is the requirement for reasonably clear moderate spatial resolution (10-100 m) optical data within 8 days over global within-season croplands of all sizes, a data type prioritized by GEOGLAM and CEOS. Four definitions of "reasonably clear" are investigated: 70%, 80%, 90%, or 95% clear. The revisit frequency required (RFR) for a reasonably clear view varies greatly both geographically and throughout the growing season, as well as with the threshold of acceptable clarity. The global average RFR for a 70% clear view within 8 days is 3.9-4.8 days (depending on the month), 3.0-4.1 days for 80% clear, OPEN ACCESSRemote Sens. 2015, 7 1462 2.2-3.3 days for 90% clear, and 1.7-2.6 days for 95% clear. While some areas/times of year require only a single revisit (RFR = 8 days) to meet their reasonably clear requirement, generally the RFR, regardless of clarity threshold, is below to greatly below the 8 day mark, highlighting the need for moderate resolution optical satellite systems or constellations with revisit capabilities more frequent than 8 days. This analysis is providing crucial input for data acquisition planning for agricultural monitoring in the context of GEOGLAM.

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Section: Agricultural Monitoring: Spatial and Temporal Considerationsmentioning

confidence: 99%

Section: Agricultural Monitoring: Spatial and Temporal Considerationsmentioning

confidence: 99%

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

2015

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“…A robust agricultural monitoring system is then a prerequisite to promote informed decisions not only at executive or policy levels but also at the level of daily field management. Such a system could, for example, help to reduce price fluctuations by deciding on import and export needs for each crop [6], to establish agricultural insurance mechanisms, or to estimate the demand for agricultural inputs [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

et al. 2018

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Smallholder farmers cultivate more than 80% of the cropland area available in Africa. The intrinsic characteristics of such farms include complex crop-planting patterns, and small fields that are vaguely delineated. These characteristics pose challenges to mapping crops and fields from space. In this study, we evaluate the use of a cloud-based multi-temporal ensemble classifier to map smallholder farming systems in a case study for southern Mali. The ensemble combines a selection of spatial and spectral features derived from multi-spectral Worldview-2 images, field data, and five machine learning classifiers to produce a map of the most prevalent crops in our study area. Different ensemble sizes were evaluated using two combination rules, namely majority voting and weighted majority voting. Both strategies outperform any of the tested single classifiers. The ensemble based on the weighted majority voting strategy obtained the higher overall accuracy (75.9%). This means an accuracy improvement of 4.65% in comparison with the average overall accuracy of the best individual classifier tested in this study. The maximum ensemble accuracy is reached with 75 classifiers in the ensemble. This indicates that the addition of more classifiers does not help to continuously improve classification results. Our results demonstrate the potential of ensemble classifiers to map crops grown by West African smallholders. The use of ensembles demands high computational capability, but the increasing availability of cloud computing solutions allows their efficient implementation and even opens the door to the data processing needs of local organizations.

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“…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%

“…FAO has established the Global Information and Early-Warning System (GIEWS) [10], which focuses on food and agriculture at the global scale. The CropWatch system developed by the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), is designed specifically to use remote sensing data to assess national and global crop production and related indicators [4]. The U.S. Agency for International Development (USAID) Famine Early Warning System Network (FEWS-NET) collaborates with international, regional and national partners to provide timely and rigorous early warning and vulnerability information on emerging and evolving food security issues [11].…”

Section: Introductionmentioning

confidence: 99%

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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Remote sensing-based global crop monitoring: experiences with China's CropWatch system

Cited by 138 publications

References 31 publications

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

A Framework for Defining Spatially Explicit Earth Observation Requirements for a Global Agricultural Monitoring Initiative (GEOGLAM)

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

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

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