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Remote Sensing in Food Production and Food Security

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (30 September 2014) | Viewed by 143864

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Special Issue Editor

Prof. Dr. Bingfang Wu

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Guest Editor

State Key Lab. of Remote Sensing Science, Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing, China
Interests: remote sensing; agricultural monitoring; water resources monitoring; ecology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Food security is one of the most essential factors for our physical wellbeing; it is a fundamental prerequisite for a healthy and happy life. Food security is a broad concept that goes beyond crop production because achieving it requires accounting for spatial and time variability, as well as physical and economic access.

Over the last two decades, traditional ground-based systems have benefited significantly from the addition of satellite remote sensing based inputs. Many national and international systems have developed this technology for monitoring crop conditions and assessing crop production; such monitoring occurs at different scales, which range from the sub-national, to national and global levels. As technology advances, a growing range of satellite remote sensing data has become both more available and affordable for operational use. As a result, indicators and methods have proliferated, some of which are innovative and powerful; development of these methods and indicators benefits from new analytical techniques, growing data processing power, and the availability of long-term time series data, which further improve the confidence in new products. .

With this Special Issue, we inventory state-of-the-art research that addresses operational methods for monitoring crop conditions and food production. The focus is on multi-source satellite-based data indicator(s), at sub-national, national, and global scales; crop biophysical parameter-based crop yield models, and the integration of crop models with satellite-based inputs; and indices-based crop area estimation (which specifically emphasizes production monitoring and food security analyses via operational systems). Paradoxically, while the sophistication of research products keeps increasing, operational work continues to suffer from the lack of reliable data on such basic variables as crop phenology, crop distribution, and biomass. Therefore, we also welcome contributions to the establishment of satellite-based (or ground-based, or mixed) reference databases.

In line with the broad definition of food security, we also invite scientists working on spatial economics, crop production risk assessments, and related subjects to consider submitting their work involving satellite inputs.

Overviews of national and international operational systems are welcome, as well as papers describing new technology and measurement concepts/sensors.

Prof. Bingfang Wu
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

national, regional, and international crop monitoring systems, food security and early warning systems
international and regional initiatives, and outlooks for crop monitoring and experimentation
cropland area estimation and crop identification
cropping intensity, patterns, and phenology
yield and production forecasts
biomass and harvest index
environmental conditions of crop growth
soil moisture and agricultural water management
early crop stage indices and early warning
agricultural food risk assessments
integration of crop models and satellite-based techniques at data and product levels
integration of gis techniques, geostatistical methods, economic, and remote sensing indicators for food security

Published Papers (11 papers)

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Research

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35033 KiB

Open AccessArticle

Global Crop Monitoring: A Satellite-Based Hierarchical Approach

by Bingfang Wu, René Gommes, Miao Zhang, Hongwei Zeng, Nana Yan, Wentao Zou, Yang Zheng, Ning Zhang, Sheng Chang, Qiang Xing and Anna Van Heijden

Remote Sens. 2015, 7(4), 3907-3933; https://doi.org/10.3390/rs70403907 - 01 Apr 2015

Cited by 79 | Viewed by 15642

Abstract

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 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. Full article

(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)

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Open AccessArticle

Meeting Earth Observation Requirements for Global Agricultural Monitoring: An Evaluation of the Revisit Capabilities of Current and Planned Moderate Resolution Optical Earth Observing Missions

by Alyssa K. Whitcraft, Inbal Becker-Reshef, Brian D. Killough and Christopher O. Justice

Remote Sens. 2015, 7(2), 1482-1503; https://doi.org/10.3390/rs70201482 - 29 Jan 2015

Cited by 68 | Viewed by 8918

Abstract

Agriculture is a highly dynamic process in space and time, with many applications requiring data with both a relatively high temporal resolution (at least every 8 days) and fine-to-moderate (FTM < 100 m) spatial resolution. The relatively infrequent revisit of FTM optical satellite observatories coupled with the impacts of cloud occultation have translated into a barrier for the derivation of agricultural information at the regional-to-global scale. Drawing upon the Group on Earth Observations Global Agricultural Monitoring (GEOGLAM) Initiative’s general satellite Earth observation (EO) requirements for monitoring of major production areas, Whitcraft et al. (this issue) have described where, when, and how frequently satellite data acquisitions are required throughout the agricultural growing season at 0.05°, globally. The majority of areas and times of year require multiple revisits to probabilistically yield a view at least 70%, 80%, 90%, or 95% clear within eight days, something that no present single FTM optical observatory is capable of delivering. As such, there is a great potential to meet these moderate spatial resolution optical data requirements through a multi-space agency/multi-mission constellation approach. This research models the combined revisit capabilities of seven hypothetical constellations made from five satellite sensors—Landsat 7 Enhanced Thematic Mapper (Landsat 7 ETM+), Landsat 8 Operational Land Imager and Thermal Infrared Sensor (Landsat 8 OLI/TIRS), Resourcesat-2 Advanced Wide Field Sensor (Resourcesat-2 AWiFS), Sentinel-2A Multi-Spectral Instrument (MSI), and Sentinel-2B MSI—and compares these capabilities with the revisit frequency requirements for a reasonably cloud-free clear view within eight days throughout the agricultural growing season. Supplementing Landsat 7 and 8 with missions from different space agencies leads to an improved capacity to meet requirements, with Resourcesat-2 providing the largest incremental improvement in requirements met. The best performing constellation can meet 71%–91% of the requirements for a view at least 70% clear, and 45%–68% of requirements for a view at least 95% clear, varying by month. Still, gaps exist in persistently cloudy regions/periods, highlighting the need for data coordination and for consideration of active EO for agricultural monitoring. This research highlights opportunities, but not actual acquisition rates or data availability/access; systematic acquisitions over actively cropped agricultural areas as well as a policy which guarantees continuous access to high quality, interoperable data are essential in the effort to meet EO requirements for agricultural monitoring. Full article

(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)

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Open AccessArticle

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

by Alyssa K. Whitcraft, Inbal Becker-Reshef and Christopher O. Justice

Remote Sens. 2015, 7(2), 1461-1481; https://doi.org/10.3390/rs70201461 - 29 Jan 2015

Cited by 107 | Viewed by 8435

Abstract

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, 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. Full article

(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)

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Open AccessArticle

Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project

by Andrew Nelson, Tri Setiyono, Arnel B. Rala, Emma D. Quicho, Jeny V. Raviz, Prosperidad J. Abonete, Aileen A. Maunahan, Cornelia A. Garcia, Hannah Zarah M. Bhatti, Lorena S. Villano, Pongmanee Thongbai, Francesco Holecz, Massimo Barbieri, Francesco Collivignarelli, Luca Gatti, Eduardo Jimmy P. Quilang, Mary Rose O. Mabalay, Pristine E. Mabalot, Mabel I. Barroga, Alfie P. Bacong, Norlyn T. Detoito, Glorie Belle Berja, Frenciso Varquez, Wahyunto, Dwi Kuntjoro, Sri Retno Murdiyati, Sellaperumal Pazhanivelan, Pandian Kannan, Petchimuthu Christy Nirmala Mary, Elangovan Subramanian, Preesan Rakwatin, Amornrat Intrman, Thana Setapayak, Sommai Lertna, Vo Quang Minh, Vo Quoc Tuan, Trinh Hoang Duong, Nguyen Huu Quyen, Duong Van Kham, Sarith Hin, Touch Veasna, Manoj Yadav, Chharom Chin and Nguyen Hong Ninh Show full author list Hide full author list

Remote Sens. 2014, 6(11), 10773-10812; https://doi.org/10.3390/rs61110773 - 06 Nov 2014

Cited by 149 | Viewed by 21962

Abstract

Rice is the most important food security crop in Asia. Information on its seasonal extent forms part of the national accounting of many Asian countries. Synthetic Aperture Radar (SAR) imagery is highly suitable for detecting lowland rice, especially in tropical and subtropical regions, where pervasive cloud cover in the rainy seasons precludes the use of optical imagery. Here, we present a simple, robust, rule-based classification for mapping rice area with regularly acquired, multi-temporal, X-band, HH-polarized SAR imagery and site-specific parameters for classification. The rules for rice detection are based on the well-studied temporal signature of rice from SAR backscatter and its relationship with crop stages. We also present a procedure for estimating the parameters based on “temporal feature descriptors” that concisely characterize the key information in the rice signatures in monitored field locations within each site. We demonstrate the robustness of the approach on a very large dataset. A total of 127 images across 13 footprints in six countries in Asia were obtained between October 2012, and April 2014, covering 4.78 m ha. More than 1900 in-season site visits were conducted across 228 monitoring locations in the footprints for classification purposes, and more than 1300 field observations were made for accuracy assessment. Some 1.6 m ha of rice were mapped with classification accuracies from 85% to 95% based on the parameters that were closely related to the observed temporal feature descriptors derived for each site. The 13 sites capture much of the diversity in water management, crop establishment and maturity in South and Southeast Asia. The study demonstrates the feasibility of rice detection at the national scale using multi-temporal SAR imagery with robust classification methods and parameters that are based on the knowledge of the temporal dynamics of the rice crop. We highlight the need for the development of an open-access library of temporal signatures, further investigation into temporal feature descriptors and better ancillary data to reduce the risk of misclassification with surfaces that have temporal backscatter dynamics similar to those of rice. We conclude with observations on the need to define appropriate SAR acquisition plans to support policies and decisions related to food security. Full article

(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)

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Open AccessArticle

Assessing the Performance of MODIS NDVI and EVI for Seasonal Crop Yield Forecasting at the Ecodistrict Scale

by Louis Kouadio, Nathaniel K. Newlands, Andrew Davidson, Yinsuo Zhang and Aston Chipanshi

Remote Sens. 2014, 6(10), 10193-10214; https://doi.org/10.3390/rs61010193 - 23 Oct 2014

Cited by 89 | Viewed by 13077

Abstract

Crop yield forecasting plays a vital role in coping with the challenges of the impacts of climate change on agriculture. Improvements in the timeliness and accuracy of yield forecasting by incorporating near real-time remote sensing data and the use of sophisticated statistical methods can improve our capacity to respond effectively to these challenges. The objectives of this study were (i) to investigate the use of derived vegetation indices for the yield forecasting of spring wheat (Triticum aestivum L.) from the Moderate resolution Imaging Spectroradiometer (MODIS) at the ecodistrict scale across Western Canada with the Integrated Canadian Crop Yield Forecaster (ICCYF); and (ii) to compare the ICCYF-model based forecasts and their accuracy across two spatial scales-the ecodistrict and Census Agricultural Region (CAR), namely in CAR with previously reported ICCYF weak performance. Ecodistricts are areas with distinct climate, soil, landscape and ecological aspects, whereas CARs are census-based/statistically-delineated areas. Agroclimate variables combined respectively with MODIS-NDVI and MODIS-EVI indices were used as inputs for the in-season yield forecasting of spring wheat during the 2000–2010 period. Regression models were built based on a procedure of a leave-one-year-out. The results showed that both agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI performed equally well predicting spring wheat yield at the ECD scale. The mean absolute error percentages (MAPE) of the models selected from both the two data sets ranged from 2% to 33% over the study period. The model efficiency index (MEI) varied between −1.1 and 0.99 and −1.8 and 0.99, respectively for the agroclimate + MODIS-NDVI and agroclimate + MODIS-EVI data sets. Moreover, significant improvement in forecasting skill (with decreasing MAPE of 40% and 5 times increasing MEI, on average) was obtained at the finer, ecodistrict spatial scale, compared to the coarser CAR scale. Forecast models need to consider the distribution of extreme values of predictor variables to improve the selection of remote sensing indices. Our findings indicate that statistical-based forecasting error could be significantly reduced by making use of MODIS-EVI and NDVI indices at different times in the crop growing season and within different sub-regions. Full article

(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)

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Open AccessArticle

Defining the Spatial Resolution Requirements for Crop Identification Using Optical Remote Sensing

by Fabian Löw and Grégory Duveiller

Remote Sens. 2014, 6(9), 9034-9063; https://doi.org/10.3390/rs6099034 - 23 Sep 2014

Cited by 67 | Viewed by 11744

Abstract

The past decades have seen an increasing demand for operational monitoring of crop conditions and food production at local to global scales. To properly use satellite Earth observation for such agricultural monitoring, high temporal revisit frequency over vast geographic areas is necessary. However, this often limits the spatial resolution that can be used. The challenge of discriminating pixels that correspond to a particular crop type, a prerequisite for crop specific agricultural monitoring, remains daunting when the signal encoded in pixels stems from several land uses (mixed pixels), e.g., over heterogeneous landscapes where individual fields are often smaller than individual pixels. The question of determining the optimal pixel sizes for an application such as crop identification is therefore naturally inclined towards finding the coarsest acceptable pixel sizes, so as to potentially benefit from what instruments with coarser pixels can offer. To answer this question, this study builds upon and extends a conceptual framework to quantitatively define pixel size requirements for crop identification via image classification. This tool can be modulated using different parameterizations to explore trade-offs between pixel size and pixel purity when addressing the question of crop identification. Results over contrasting landscapes in Central Asia demonstrate that the task of finding the optimum pixel size does not have a “one-size-fits-all” solution. The resulting values for pixel size and purity that are suitable for crop identification proved to be specific to a given landscape, and for each crop they differed across different landscapes. Over the same time series, different crops were not identifiable simultaneously in the season and these requirements further changed over the years, reflecting the different agro-ecological conditions the crops are growing in. Results indicate that sensors like MODIS (250 m) could be suitable for identifying major crop classes in the study sites, whilst sensors like Landsat (30 m) should be considered for object-based classification. The proposed framework is generic and can be applied to any agricultural landscape, thereby potentially serving to guide recommendations for designing dedicated EO missions that can satisfy the requirements in terms of pixel size to identify and discriminate crop types. Full article

(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)

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Open AccessArticle

How Reliable is the MODIS Land Cover Product for Crop Mapping Sub-Saharan Agricultural Landscapes?

by Louise Leroux, Audrey Jolivot, Agnès Bégué, Danny Lo Seen and Bernardin Zoungrana

Remote Sens. 2014, 6(9), 8541-8564; https://doi.org/10.3390/rs6098541 - 11 Sep 2014

Cited by 45 | Viewed by 8241

Abstract

Accurate cropland maps at the global and local scales are crucial for scientists, government and nongovernment agencies, farmers and other stakeholders, particularly in food-insecure regions, such as Sub-Saharan Africa. In this study, we aim to qualify the crop classes of the MODIS Land Cover Product (LCP) in Sub-Saharan Africa using FAO (Food and Agricultural Organisation) and AGRHYMET (AGRiculture, Hydrology and METeorology) statistical data of agriculture and a sample of 55 very-high-resolution images. In terms of cropland acreage and dynamics, we found that the correlation between the statistical data and MODIS LCP decreases when we localize the spatial scale (from R² = 0.86 *** at the national scale to R² = 0.26 *** at two levels below the national scale). In terms of the cropland spatial distribution, our findings indicate a strong relationship between the user accuracy and the fragmentation of the agricultural landscape, as measured by the MODIS LCP; the accuracy decreases as the crop fraction increases. In addition, thanks to the Pareto boundary method, we were able to isolate and quantify the part of the MODIS classification error that could be directly linked to the performance of the adopted classification algorithm. Finally, based on these results, (i) a regional map of the MODIS LCP user accuracy estimates for cropland classes was produced for the entire Sub-Saharan region; this map presents a better accuracy in the western part of the region (43%–70%) compared to the eastern part (17%–43%); (ii) Theoretical user and producer accuracies for a given set of spatial resolutions were provided; the simulated future Sentinel-2 system would provide theoretical 99% user and producer accuracies given the landscape pattern of the region. Full article

(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)

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888 KiB

Open AccessArticle

Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel

by Michele Meroni, Felix Rembold, Michel M. Verstraete, Rene Gommes, Anne Schucknecht and Gora Beye

Remote Sens. 2014, 6(6), 5868-5884; https://doi.org/10.3390/rs6065868 - 20 Jun 2014

Cited by 33 | Viewed by 9022

Abstract

In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting unfavorable crop and pasture conditions and informing stakeholders about impending food security risks. Despite growing evidence that vegetation productivity is directly related to phenology, most approaches to estimate such risks do not explicitly take into account the actual timing of vegetation growth and development. The date of the start of the season (SOS) or of the peak canopy density can be assessed by remote sensing techniques in a timely manner during the growing season. However, there is limited knowledge about the relationship between vegetation biomass production and these variables at the regional scale. This study describes the first attempt to increase our understanding of such a relationship through the analysis of phenological variables retrieved from SPOT-VEGETATION time series of the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). Two key phenological variables (growing season length (GSL); timing of SOS) and the maximum value of FAPAR attained during the growing season (Peak) are analyzed as potentially related to a proxy of biomass production (CFAPAR, the cumulative value of FAPAR during the growing season). GSL, SOS and Peak all show different spatial patterns of correlation with CFAPAR. In particular, GSL shows a high and positive correlation with CFAPAR over the whole Sahel (mean r = 0.78). The negative correlation between delays in SOS and CFAPAR is stronger (mean r = −0.71) in the southern agricultural band of the Sahel, while the positive correlation between Peak FAPAR and CFAPAR is higher in the northern and more arid grassland region (mean r = 0.75). The consistency of the results and the actual link between remote sensing-derived phenological parameters and biomass production were evaluated using field measurements of aboveground herbaceous biomass of rangelands in Senegal. This study demonstrates the potential of phenological variables as indicators of biomass production. Nevertheless, the strength of the relation between phenological variables and biomass production is not universal and indeed quite variable geographically, with large scattered areas not showing a statistically significant relationship. Full article

(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)

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Open AccessArticle

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

by Miao Zhang, Bingfang Wu, Mingzhao Yu, Wentao Zou and Yang Zheng

Remote Sens. 2014, 6(6), 5774-5794; https://doi.org/10.3390/rs6065774 - 19 Jun 2014

Cited by 28 | Viewed by 13002

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 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. Full article

(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)

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Open AccessArticle

Development of a Remote Sensing-Based “Boro” Rice Mapping System

by Mostafa K. Mosleh and Quazi K. Hassan

Remote Sens. 2014, 6(3), 1938-1953; https://doi.org/10.3390/rs6031938 - 03 Mar 2014

Cited by 34 | Viewed by 8149

Abstract

Rice is one of the staple foods across the world, thus information about its production is essential for ensuring food security. Here, our objective was to develop a method for mapping “boro” rice (i.e., cultivated during the months January to May) in a Bangladeshi context. In this paper, we used a Moderate Resolution Imaging Spectroradiometer (MODIS)-derived 16-day composite of normalized difference vegetation index (NDVI) at 250 m spatial resolution in conjunction with ancillary datasets (i.e., land use map, crop calendar, and ground-based rice production information) during the period 2007–2012. The proposed method consisted of three procedures: (i) ISODATA clustering and determining the boro rice signatures in temporal dimension using data from the period 2007–2009; (ii) formulating a mathematical model for extracting the boro rice areas using data from the period 2007–2009; and (iii) model calibration using data from the period 2007–2009 and its validation using data from the period 2010–2012. The implementation of the abovementioned procedures revealed reasonable agreements between the model (i.e., MODIS-based) and ground-based estimates of boro rice area at both country (i.e., percentage error in the range −0.83–1.42%) and district levels (i.e., r² in the range 0.69–0.89) during the period 2010–2012. Our proposed method demonstrated its effectiveness in mapping rice system at the regional/country scale. Full article

(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)

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Review

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1297 KiB

Open AccessReview

The Potential and Uptake of Remote Sensing in Insurance: A Review

by Jan De Leeuw, Anton Vrieling, Apurba Shee, Clement Atzberger, Kiros M. Hadgu, Chandrashekhar M. Biradar, Humphrey Keah and Calum Turvey

Remote Sens. 2014, 6(11), 10888-10912; https://doi.org/10.3390/rs61110888 - 07 Nov 2014

Cited by 108 | Viewed by 22447

Abstract

Global insurance markets are vast and diverse, and may offer many opportunities for remote sensing. To date, however, few operational applications of remote sensing for insurance exist. Papers claiming potential application of remote sensing typically stress the technical possibilities, without considering its contribution to customer value for the insured or to the profitability of the insurance industry. Based on a systematic search of available literature, this review investigates the potential and actual support of remote sensing to the insurance industry. The review reveals that research on remote sensing in classical claim-based insurance described in the literature revolve around crop damage and flood and fire risk assessment. Surprisingly, the use of remote sensing in claim-based insurance appears to be instigated by government rather than the insurance industry. In contrast, insurance companies are offering various index insurance products that are based on remote sensing. For example, remotely sensed index insurance for rangelands and livestock are operational, while various applications in crop index insurance are being considered or under development. The paper discusses these differences and concludes that there is particular scope for application of remote sensing by the insurance industry in index insurance because (1) indices can be constructed that correlate well with what is insured; (2) these indices can be delivered at low cost; and (3) it opens up new markets that are not served by claim-based insurance. The paper finally suggests that limited adoption of remote sensing in insurance results from a lack of mutual understanding and calls for greater cooperation between the insurance industry and the remote sensing community. Full article

(This article belongs to the Special Issue Remote Sensing in Food Production and Food Security)

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