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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 - 1 Apr 2015

Cited by 94 | Viewed by 17492

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 [...] Read more.

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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3031 KiB

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 69 | Viewed by 9726

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 [...] Read more.

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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1205 KiB

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 118 | Viewed by 9371

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 [...] Read more.

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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20061 KiB

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 - 6 Nov 2014

Cited by 162 | Viewed by 23398

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, [...] Read more.

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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8790 KiB

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 99 | Viewed by 14525

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 [...] Read more.

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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Flowchart describing the crop yield modeling within the Integrated Canadian Crop Yield Forecaster (ICCYF) tool. Adapted from Newlands et al. [<a href="#b28-remotesensing-06-10193" class="html-bibr">28</a>]. Full article ">
Ecodistricts and Census Agricultural Regions (CARs) across the Western Canadian Prairie (encompassing the provinces of Alberta (AB), Saskatchewan (SK) and Manitoba (MB)). The highlighted CARs (i.e., 4607, 4609, 4610) are those with poorer ICCYF performance for spring wheat [<a href="#b28-remotesensing-06-10193" class="html-bibr">28</a>]. Full article ">
Distribution and variability of MODIS NDVI (A) and EVI (B) during the cropping season (May–August) based on historical data, 2000–2010 period. The ends of the boxplots indicate the upper and lower quantiles, the solid line indicates the median. The whiskers are 1.5 times of the box height towards upper and lower from the median. Asterisks are the outliers. Full article ">
Ranges of the root mean square error (RMSE, bottom), model efficiency index (middle), and mean absolute percentage error (MAPE, bottom) of spring wheat yield models at the ecodistrict scale, 2000–2010 period. (A) the input data set includes agroclimate (i.e., growing degree days (GDD), soil water availability (SWA), precipitation (P), and crop water deficit index (WDI)) and MODIS-NDVI indices; (B) same agroclimate indices as previously and MODIS-EVI indices. Note: year was included as an additional input variable in all cases. The ends of the boxplots indicate the upper and lower quantiles, the solid line indicates the median. The whiskers indicate the minimum and maximum values. Full article ">
2010 yield forecast of spring wheat- Spatial distribution of model error (CV %) for all ecodistricts and Census Agricultural Regions in yield forecasting using agroclimate and remote sensing indices. (A) agroclimate indices (GDD, P, SWA, WDI) and MODIS-NDVI; (B) same agroclimate indices as previously plus MODIS-EVI; (C) WDI, GDD, SWA and AVHRR-NDVI. MODIS NDVI/EVI values are the average of two consecutive 16-day periods, while AVHRR-NDVI indices are 3-week moving averages. n.a.: not applicable. Note: year was included as an additional input variable in all cases. The mapping of the model performance was based on the crop land extent map. Full article ">
Average values of model efficiency index (A) and mean absolute error percentage (B) of yield models in selected Census Agricultural Regions (CARs) and their corresponding ecodistricts (ECD) during the 2000–2010 period. CARUID and ECDUID stand for CAR and ECD unit identifiers, respectively. Striped bars represent model performance measures at CAR scale. The selected CARs (i.e., 4607, 4609 and 4610; see <a href="#f2-remotesensing-06-10193" class="html-fig">Figure 2</a>) are those with weak ICCYF performance at CAR scale [<a href="#b28-remotesensing-06-10193" class="html-bibr">28</a>]. The runs at the ECD scale are based on agroclimate (AgMet; i.e., GDD, P, SWA, WDI) and MODIS-NDVI/EVI indices. Whereas those at the CAR scale are based on agroclimate (GDD, SWA, WDI) and AVHRR-NDVI indices. Full article ">

13082 KiB

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 78 | Viewed by 12871

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, [...] Read more.

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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Exemplary subsets (6.5 × 6.5 km) of the imagery and crop masks from the four test sites: Khorezm (KHO), Karakalpakstan (KKP), Kyzyl-Orda (KYZ), and Fergana Valley (FER). The imagery is displayed using a 5-2-1 band combination of RapidEye from June–July, contrast of the images is adjusted separately. The location of the sites in Middle Asia is shown below. Full article ">
Acquisition dates of the data sets from the RapidEye instrument utilized in this study. Nine images are available in KKP, eight images in the other landscapes. Full article ">
Flowchart to produce the convolved time series and pixel purity maps, respectively at different scales, and to identify pixel size requirements for crop identification. Full article ">
Examples of parameters chosen for crop identification for the pixel populations along the pixel size—pixel purity dimensions: (a) the number of pixels available for training the classifier (Ni), (b) median alpha quadratic entropy of the classified pixel populations (AQEi), and (c) class-wise classification accuracy (CAi). The values shown in the surfaces (b) and (c) are averaged over ten model runs. Note that the pixel purity axis is inverted in (a) and (b) for the sake of better visibility. Full article ">
Theoretical boundaries in ν − π space used to define the requirements for pixel populations to be used for supervised classification. Triangle indicates the position of maximum tolerable pixel size νmax, black filled square the minimum required pixel size νmin. Full article ">
Schematic example for the evolution of the amount of suitable pixel populations in KKP when increasing the thresholds. The first three images (from left to right) illustrate the effect of setting thresholds to 0.75, 0.80, and 0.85, respectively for CAi. Ni was increased from 50 to 100, and entropy values were set to 0.55, 0.50, and 0.45. The bottom image shows the pixel suitability map and the corresponding legend, which combines the three single suitability maps. Dark red was also assigned to pixel populations that did not fulfill any parameter. Full article ">
Suitable pixel populations for crop identification in KKP 2011. Green colors indicate suitable populations in the pixel size-pixel purity space, where all criteria defined above are met, yellow colors indicate that one criterion is not met, orange means two criteria are not met, and finally red colors indicate that three (or more) criteria are not met. Circle indicates the actual position of the best values achieved for CAi, the corresponding pixel size ν and purity π are given for each crop. Full article ">
Suitable pixel populations for selected crops in the four study sites. Green colors indicate suitable populations in the pixel size-pixel purity space, where all criteria defined above are met, yellow colors indicate that one criterion is not met, orange means two criteria are not met, and finally red colors indicate that three (or more) criteria are not met. Circle indicates the actual position of the best values achieved for CAi, the corresponding pixel size v and purity π are given for each crop. Rice was not present in FER, and cotton, wheat, and wheat-other was absent in KYZ. Full article ">
(a) Ranges of suitable pixel sizes for different crop types using unsupervised K-means clustering (right columns) compared to the RF algorithm (left columns) in the KKP landscape. (b) Ranges of suitable pixel sizes for selected crops in the four landscapes. The length of the bars correspond to the range of suitable pixel sizes, shades of green indicate different levels of suitability, e.g., dark green means that all level-III criteria defined in <a href="#t3-remotesensing-06-09034" class="html-table">Table 3</a> are fulfilled, light green that all level-I criteria are fulfilled. Full article ">

2314 KiB

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 49 | Viewed by 9042

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 [...] Read more.

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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(a) Distribution of the averaged Croplands and the Cropland/Natural Vegetation Mosaic domains between 2001 and 2011; (b) Study area with the major farming systems, as defined in [<a href="#b36-remotesensing-06-08541" class="html-bibr">36</a>]. The points refer to the locations of the 55 cropland maps from high-resolution images, and they are used as reference data to assess the MODIS LCP accuracy. Full article ">
The Pareto Boundary, the dashed blue line, divides the space into two regions. The region under the Pareto Boundary (in orange) is the unattainable region due to the low-resolution product. The region above the Pareto Boundary (in light green) is the attainable region. The distance between 0 and B is the “optimal” accuracy linked to the spatial resolution of the maps. The distance between the “optimal” accuracy B and the accuracy of the product A is an indicator of the performance of the classification algorithm (adapted from [<a href="#b43-remotesensing-06-08541" class="html-bibr">43</a>] with authors’ permission). Full article ">
Crop area averages (2001–2011) calculated from the MODIS LCP data (y-axis) plotted against the FAOSTAT/AGRHYMET data (x-axis) for (a) the national level (29 countries), where the red dot represents Burkina Faso; (b) the 12 regions (N1: level 1 below national) and (c) the 45 provinces (N2: level 2 below national) of Burkina Faso. The diagonal dashed lines represent the 1:1 lines. The MODIS LCP cropland area is equal to the sum of class 12 and class 14 with a weight of 0.5. Full article ">
Comparison of FAOSTAT/AGRHYMET dynamics (a) and the MODIS LCP dynamics (b) of crop areas between 2001 and 2011 for (1) the national level (29 countries of Sub-Saharan Africa); (2) the 12 regions (level 1 below national) and (3) the 45 provinces (level 2 below national) of Burkina Faso. Positive dynamics are in green colors, negative dynamics are in red/pink colors and no significant dynamics are in grey. “O” and “N” represent the Oudalan and Namentenga provinces. Full article ">
Pareto Boundary calculated within a square of 5 km × 5 km for two sites (see <a href="#f1-remotesensing-06-08541" class="html-fig">Figure 1</a>). The blue line represents the Pareto Boundary for a spatial resolution of 500 m. The black dot is the observed MODIS LCP accuracy, and the black triangle is the “optimal” accuracy. For each site, the country, the major farming system and the landscape metrics are given. The histograms indicate the distribution of the observed FScore, and the vertical red line represents the position of each site. Google images© are also provided for the two sites, and the crop domain is delimited by the red line. Full article ">
Normalized principal components analysis (PCA) performed on the six indicators of agricultural landscape fragmentation and the three accuracy indicators. (a) Correlation circle of variables for the first two PCA components; (b) PCA factorial map presenting 55 validation sites grouped into two classes as obtained from the K-Means clustering. Full article ">
Tables showing the landscape metrics computed for the MODIS LCP in each model for (a) omission errors and (b) FScores. The darker colors represent models that are more efficient. The BIC model selection statistic is optimum for the row at the top of the table. The resulting models and R2 values are given for N = 55 and p-value < 0.001. Full article ">
Map of user accuracy estimates for the MODIS LCP cropland classes. The user accuracy is defined as (1-omission errors) and is estimated from the crop fraction. The user accuracy is an indicator of uncertainties associated with the cropland classes of the MODIS LCP. The data were aggregated at a 20 km resolution for better visualization. Full article ">
Comparison of the “optimal” accuracy of the MODIS LCP (500 m) and the “optimal” accuracy obtained using the simulated data with 300 m, 30 m and 10 m spatial resolutions at the 55 validation sites for the cropland classes. The “optimal” accuracy was extracted from the Pareto Boundary; (a) omission errors and (b) commission errors. Full article ">

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 36 | Viewed by 9709

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 [...] Read more.

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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Graphical abstract

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(a) The study area encompassing the main Sahel eco-regions. The herbaceous and cropland land covers are the union of GLC2000 (Global Land Cover 2000) herbaceous cover dominated classes (Classes 13 to 15) and cropland classes (Classes 16 to 18), respectively. Blue points in Senegal refer to the location of field measurements sites (zoom in (b)). Full article ">
Average start (SOS) (a), end (EOS) (b) and length (c) of the growing season and the cumulative Fraction of Absorbed Photosynthetically Active Radiation (CFAPAR) value (d) during the growing season. Average values are computed over the set of phenological variables extracted from the FAPAR time series (n = 15, years 1998–2012). Phenological variables and CFAPAR are shown for the herbaceous and cropland land covers (other classes in white) and the five main eco-regions of the Sahel (other regions in grey). Full article ">
Visualization of the correlation between CFAPAR vs. GSL, Peak value and ΔSOS in the RGB color space. The composite is based upon the coefficients of determination (R2) of the linear regression CFAPAR vs. GSL (Red), Peak (Green) and ΔSOS (Blue); no stretching is applied. Main land cover types are represented by the thick black lines (two simplified polygons, herbaceous cover in the north and crop cover in the south). Full article ">
Correlation coefficient between CFAPAR and GSL (a); Peak (b); ΔSOS (c). Land cover polygons (thick black lines) are as in <a href="#f3-remotesensing-06-05868" class="html-fig">Figure 3</a>. Pixels where the linear regression is not significant (n.s.) at p = 0.05 are masked in grey. Full article ">
Percentage of variation of the mean seasonal biomass production proxy (CFAPAR) for a one-day anomaly in the timing of SOS. The metric is computed as the slope of the linear regression CFAPAR vs. ΔSOS expressed as a percentage of the pixel-level mean CFAPAR. Pixels where the linear regression is not significant (n.s.) at p = 0.05 are masked in grey. Full article ">
(a) Linear regression statistics and scatterplot between measured biomass (years 2005–2011) and CFAPAR; (b) average of the site-level correlation coefficients and statistical significance: the green bar refers to the correlation between measured biomass and CFAPAR; the blue and yellow bars refer to the correlations of GSL, ΔSOS and Peak vs. measured biomass (blue) or CFAPAR (yellow), respectively. Error bars refer to ±1 standard deviation. Full article ">

5230 KiB

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 35 | Viewed by 14379

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 [...] Read more.

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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1141 KiB

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 - 3 Mar 2014

Cited by 39 | Viewed by 8761

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 [...] Read more.

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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(a) Location of Bangladesh in Southeast Asia; and (b) spatial extent of the 23 districts of Bangladesh where ground-based rice cultivation area information were available. Full article ">
(a) Schematic diagram of the proposed method; and (b) the details of formulating a mathematical model. Full article ">
Temporal dynamics of NDVI-based boro rice signatures over the entire growing season using data for (a) 2007; (b) 2008; (c) 2009; and (d) 2007–2009. Full article ">
Linear regression analysis between MODIS and ground-based boro rice cultivation areas at 23 districts (shown in <a href="#f1-remotesensing-06-01938" class="html-fig">Figure 1b</a>) during: (a) 2007; (b) 2008; and (c) 2009. Full article ">
Relation between ground and MODIS-based estimates of boro rice cultivation areas at 23 districts (shown in <a href="#f1-remotesensing-06-01938" class="html-fig">Figure 1b</a>) during: (a) 2010; (b) 2011; and (c) 2012. Full article ">
Example of MODIS-derived boro rice cultivation area (indicated using green shades) mapping during 2012. Full article ">

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 - 7 Nov 2014

Cited by 117 | Viewed by 24432

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 [...] Read more.

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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