A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches

Thenkabail, Prasad S.; Hanjra, Munir A.; Dheeravath, Venkateswarlu; Gumma, Murali Krishna

doi:10.3390/rs2010211

Cited by 93 publications

(67 citation statements)

References 105 publications

(184 reference statements)

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“…First, non-vegetation types (i.e., buildings and water) were excluded according to the higher NDVI values of vegetation types (i.e., woodland, grassland, maize and rice) during the phenophase of tasseling to milk of maize (DOY 193-257) with Formula (2): B1: mean(NDVI 193-257 ) > T 1 (2) Second, natural vegetation types (i.e., woodland and grassland) were excluded according to the lower NDVI values of crops (i.e., maize and rice) during the early stage of shooting of maize (DOY 129-153) with Formula (3):…”

Section: Comparison Of Different Features For Maize Identificationmentioning

confidence: 99%

“…Accurate data about the spatial distribution and planting area of maize is of great significance for crop yield estimation, agricultural production management, agricultural policy-making and food security under climate change [2][3][4][5]. The field survey is a traditional way to investigate crop distribution, but it is unable to meet the needs of modern crop investigation for a precise, comprehensive and efficient development aim [6].…”

Section: Introductionmentioning

confidence: 99%

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An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

et al. 2018

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Accurate data about the spatial distribution and planting area of maize is important for policy making, economic development, environmental protection and food security under climate change. This paper proposes a new identification method for spring maize based on spectral and phenological features derived from the moderate resolution imaging spectroradiometer (MODIS) land surface reflectance time-series data. The method focused on the spectral differences of different land cover types in the specific phenological phases of spring maize by testing the selections and combinations of classification metrics, feature extraction methods and classifiers. Taking Liaoning province, a representative planting region of spring maize in Northeast China, as the study area, the results indicated that the combined multiple metrics, including the red reflectance, near-infrared reflectance and normalized difference vegetation index (NDVI), were conducive to the maize identification and were better than any single metric. With regard to the feature extraction and selection, maize identification based on different phenological features selected with prior knowledge was more efficient than that based on statistical features derived from the principal component analysis. Compared with the maximum likelihood classification method, the decision tree classification based on expert knowledge was more suitable for phenological features selected from some prior knowledge. In summary, discriminant rules were defined with those phenological features from multiple metrics, and the decision tree classification was used to identify maize in the study area. The producer's accuracy of maize identification was 98.57%, and the user's accuracy was 81.18%. This method can be potentially applied to an operational identification of maize at large scales based on remote sensing time-series data.

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Section: Comparison Of Different Features For Maize Identificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

et al. 2018

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“…Meanwhile, it stretches out the growing season, as spring comes earlier and fall stays later, which can lead to increased water demand. With increasing urbanization, industrialization, and other demands on water, the pressure to reduce agricultural water use is also enhanced (Thenkabail et al, 2010). The question of how to improve WUE of croplands through appropriate farming practices such as conventional-till or no-till is critical.…”

Section: Introductionmentioning

confidence: 99%

Characterizing ecosystem water-use efficiency of croplands with eddy covariance measurements and MODIS products

Tang

Ding

et al. 2015

Ecological Engineering

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a b s t r a c tHow to quantify the variability in ecosystem water use efficiency (WUE) of croplands is of vital importance. Especially in the context of changing hydrologic environment, much attention need to be paid on how to use limited water to improve crop yield. However, the biophysical performances of environmental/biological controls, crop types, and effects of diverse farming practices on WUE remain unclear. Therefore, this study aims to address these questions through tower-based measurements from eddy covariance and satellite-based estimates from MODIS GPP and ET products at the two annual soybean/corn rotation fields. The results exhibited that temperature and solar radiation are the most important meteorological factors. MODIS WUE estimates captured the broad trend of 8-day tower-based observations. However, ecosystem WUE was overpredicted at the seedling stage and after harvest, and was severely underestimated at the peak periods of maize, which was mainly ascribed to the uncertainties in MODIS GPP. Finally, we evaluated the effects of two farming practices (conventional vs alternative) on WUE and carbon sequence (NEE). On annual time scale, WUE of both soybean and maize are improved obviously under the alternative management whereas contrasting effects are observed on NEE. But the overall carbon balance for both treatments is nearly identical over 2-year rotation.

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“…Additionally, remotesensing technology provides an opportunity to monitor global cropland in a spatially clear, cost-effective, well-organized and unbiased manner [14][15][16][17][18].…”

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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A Holistic View of Global Croplands and Their Water Use for Ensuring Global Food Security in the 21st Century through Advanced Remote Sensing and Non-remote Sensing Approaches

Cited by 93 publications

References 105 publications

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

Characterizing ecosystem water-use efficiency of croplands with eddy covariance measurements and MODIS products

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

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