A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing

Biradar, Chandrashekhar; Thenkabail, Prasad S.; Noojipady, Praveen; Li, Yuanjie; Dheeravath, Venkateswarlu; Turral, Hugh; Velpuri, Manohar; Gumma, Murali Krishna; Reddy, G. P. Obi; Cai, Xueliang; Xiao, Xiangming; Schull, Mitchell A.; Alankara, R.; Gunasinghe, Sarath; Mohideen, S. Kother

doi:10.1016/j.jag.2008.11.002

Cited by 162 publications

(96 citation statements)

References 21 publications

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“…Since traditional agricultural statistics on crop acreages are usually provided by the end of the season or later, in-season agricultural production managers lack necessary information about the current year's crops [9,10]. Alternatively, remote sensing satellites, owing to their synoptic and repetitive nature, have proven to be an effective means for mapping and monitoring crop extent [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Song

Zhou

et al. 2017

Remote Sensing

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Producing accurate crop maps during the current growing season is essential for effective agricultural monitoring. Substantial efforts have been made to study regional crop distribution from year to year, but less attention is paid to the dynamics of composition and spatial extent of crops within a season. Understanding how crops are distributed at the early developing stages allows for the timely adjustment of crop planting structure as well as agricultural decision making and management. To address this knowledge gap, this study presents an approach integrating object-based image analysis with random forest (RF) for mapping in-season crop types based on multi-temporal GaoFen satellite data with a spatial resolution of 16 meters. A multiresolution local variance strategy was used to create crop objects, and then object-based spectral/textural features and vegetation indices were extracted from those objects. The RF classifier was employed to identify different crop types at four crop growth seasons by integrating available features. The crop classification performance of different seasons was assessed by calculating F-score values. Results show that crop maps derived using seasonal features achieved an overall accuracy of more than 87%. Compared to the use of spectral features, a feature combination of in-season textures and multi-temporal spectral and vegetation indices performs best when classifying crop types. Spectral and temporal information is more important than texture features for crop mapping. However, texture can be essential information when there is insufficient spectral and temporal information (e.g., crop identification in the early spring). These results indicate that an object-based image analysis combined with random forest has considerable potential for in-season crop mapping using high spatial resolution imagery.

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Section: Introductionmentioning

confidence: 99%

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Song

Zhou

et al. 2017

Remote Sensing

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“…A relatively low margin remains then for a further increase of production in irrigation regime. The remaining 74 % of agricultural crops worldwide are grown in rainfed conditions, generally in semiarid environments, where 60 % of food and feed, such as cereal and fodder crops (Birard et al 2009), are produced.…”

Section: Introductionmentioning

confidence: 99%

Sustainability in Cereal Crop Production in Mediterranean Environments

Perniola

Lovelli

Arcieri

et al. 2015

The Sustainability of Agro-Food and Natural Resource Systems in the Mediterranean Basin

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The continuous increase of the world population (a growth of about onethird is expected by 2050), together with an even larger increase in food demand (especially in emerging countries), will lead in the next 30 years to the necessity to produce 70 % more food. To keep the pace with food demand, global cereal production would need to increase by 40 % overall, or by some 900 million tons between the present and 2050. Single countries can either increase production or increase net imports or a combination of both.This new global emergency involves researchers, farmers, politicians, the agrofood industry, and all stakeholders, and the new challenge can be summarized as follows: to produce more, but in a sustainable way. The goal of "sustainable intensification" constitutes one of the priorities for the research in agriculture and one of the cornerstones of the new Common Agricultural Policy. In this context, all the techniques designed to maximize production through the more efficient use of resources are in line with the objectives of sustaining production with minimal impact.In Mediterranean environments, cereal crops are grown mainly in the semiarid and subhumid areas. In arid and semiarid areas dryland farming, techniques are of renewed interest in the view of sustainability. They are aimed to increase water accumulation in the soil, reduce runoff and soil evaporation losses, choose species and varieties able to make better use of rainwater, and rationalize fertilization plans, sowing dates, and weed and pest control.Fertilization plans should be based on well-defined principles of plant nutrition, soil chemistry, and chemistry of the fertilizer elements. Starting from the calculation of nutrient crop uptake (based on the actually obtainable yield), dose calculation must be corrected by considering the relationship between the availability of the trace elements in soil and the main physical and chemical parameters of the soil (pH, organic matter content, mineralization rate, C/N, ratio of solubilization of phosphorus, active lime content, presence of antagonist ions, etc.). The choice of traditional, minimum, or no-tillage is one of the most controversial aspects of agricultural research; much depends on soil texture, crop type (depending mainly on the characteristics of the root), rainfall regime (mainly intensity), longperiod tillage plan, structural stability of the soil (in function of the organic matter content), and all other variables and their interaction. In any case, minimum tillage and sod seeding are generally less expensive and evidence points to a lower production of CO 2 with respect to traditional tillage.Weed and pest control is also a critical point for the sustainability of cereal production: sustainability in control strategies involves minimizing the use of chemicals while safeguarding yields. To this end, priority should be given to preventive and nonchemical agronomic measures, while chemical means should be used only when the level of weeds and pests exceeds the threshold of eco...

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“…GE provides free access to high-resolution satellite imagery. In addition to being used as "pictures" for visualization, GE has been recognized as a significant resource for ground-truth data [44], improving visual interpretation, and even classifying complex LULC types [45]. In order to obtain better reference data, the time stamps of the GE images we chose were as close as possible to those of the original images It has been suggested that a minimum 50 samples (or pixels) of each class should be included in error analyses [16].…”

Section: Accuracy Assessmentmentioning

confidence: 99%

A Feature-Based Approach of Decision Tree Classification to Map Time Series Urban Land Use and Land Cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China

Hua

Zhang

Chen

et al. 2017

IJGI

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Accurate mapping of temporal changes in urban land use and land cover (LULC) is important for monitoring urban expansion and changes in LULC, urban planning, environmental management, and environmental modeling. In this study, we present a feature-based approach of the decision tree classification (FBA-DTC) method for mapping LULC based on spectral and topographic information. Landsat 5 TM and Land 8 OLI images were employed, and the technique was applied to the coastal city of Xiamen, China. The method integrates multi-spectral features such as SAVI (soil adjustment vegetation index), NDWI (normalized water index), MNDBaI (modified normalized difference barren index), BI (brightness index), and WI (wetness index), with topographic features including DEM and slope. In addition, the new approach distinguishes between fallow land and cropland, and separates high-rise buildings from beaches and water bodies. Several of the FBA-DTC parameters (or rules) from 1997 to 2015 remained constant (i.e., DEM and slope), whereas others changed slightly. WI was negatively related to percent area of beach land, and BI was negatively related to percent area of arable land. The FBA-DTC method had an average user's accuracy (UA) of 91.36% for built-up land, an average overall accuracy (OA) of 92.13%, and a Kappa coefficient (KC) of 0.90 for the period from 2003 to 2015, representing respective increases of 15.87%, 10.17%, and 0.13, compared with values calculated using maximum likelihood classification (MLC). Over the past 12 years, built-up land increased from 23.67% to 43.17% owing to occupation of coastal reclamation, arable land, and forest land. The FBA-DTC method presented here is a valuable technique for evaluating urban growth and changes in LULC classification for coastal cities.

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A global map of rainfed cropland areas (GMRCA) at the end of last millennium using remote sensing

Cited by 162 publications

References 21 publications

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

In-Season Crop Mapping with GF-1/WFV Data by Combining Object-Based Image Analysis and Random Forest

Sustainability in Cereal Crop Production in Mediterranean Environments

A Feature-Based Approach of Decision Tree Classification to Map Time Series Urban Land Use and Land Cover with Landsat 5 TM and Landsat 8 OLI in a Coastal City, China

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