Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs

Many remote sensing applications are devoted to the agricultural sector. Representative case studies are presented in the special issue "Advances in Remote Sensing of Agriculture". To complement the examples published within the special issue, a few main applications with regional to global focus were selected for this review, where remote sensing contributions are traditionally strong. The selected applications are put in the context of the global challenges the agricultural sector is facing: minimizing the environmental impact, while increasing production and productivity. Five different applications have been selected, which are illustrated and described: (1) biomass and yield estimation, (2) vegetation vigor and drought stress monitoring, (3) assessment of crop phenological development, (4) crop acreage estimation and cropland mapping and (5) mapping of disturbances and land use/land cover (LULC) changes. Many other applications exist, such as precision agriculture and irrigation management (see other special issues of this journal), but were not included to keep the paper concise. The paper starts with an overview of the main agricultural challenges. This section is followed by a brief overview of existing operational monitoring systems. Finally, in the main part of the paper, the mentioned applications are described and illustrated. The review concludes with some key recommendations.

[1]  Y. Hirosawa,et al.  Application of standardized principal component analysis to land-cover characterization using multitemporal AVHRR data , 1996 .

[2]  Clement Atzberger,et al.  Exploiting the Classification Performance of Support Vector Machines with Multi-Temporal Moderate-Resolution Imaging Spectroradiometer (MODIS) Data in Areas of Agreement and Disagreement of Existing Land Cover Products , 2012, Remote. Sens..

[3]  P. Beck,et al.  Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI , 2006 .

[4]  S. Schneider,et al.  Climate Change 2007 Synthesis report , 2008 .

[5]  J. Mustard,et al.  Wavelet analysis of MODIS time series to detect expansion and intensification of row-crop agriculture in Brazil , 2008 .

[6]  S. Polasky,et al.  Environmental, economic, and energetic costs and benefits of biodiesel and ethanol biofuels. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[7]  María Amparo Gilabert,et al.  Environmental monitoring and crop forecasting in the Sahel through the use of NOAA NDVI data. A case study: Niger 1986–89 , 1993 .

[8]  David B Lobell,et al.  Greenhouse gas mitigation by agricultural intensification , 2010, Proceedings of the National Academy of Sciences.

[9]  C. Tamminga,et al.  Mental Illness in Hotel Residents , 2013 .

[10]  Philip K. Thornton,et al.  The potential impacts of climate change on maize production in Africa and Latin America in 2055 , 2003 .

[11]  A. Strahler,et al.  Monitoring vegetation phenology using MODIS , 2003 .

[12]  Christopher J. Kucharik,et al.  Data and monitoring needs for a more ecological agriculture , 2011 .

[13]  Jesslyn F. Brown,et al.  Measuring phenological variability from satellite imagery , 1994 .

[14]  J. Townshend,et al.  Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers , 1998 .

[15]  R. Sonnenschein,et al.  Land use change and its effects on vegetation trends and fire patterns in Mediterranean rangelands , 2013 .

[16]  Nazzareno Pierdicca,et al.  Use of Satellite Radar Bistatic Measurements for Crop Monitoring: A Simulation Study on Corn Fields , 2013, Remote. Sens..

[17]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[18]  Michele Meroni,et al.  Evaluation of Agreement Between Space Remote Sensing SPOT-VEGETATION fAPAR Time Series , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[19]  D. Tilman,et al.  Global food demand and the sustainable intensification of agriculture , 2011, Proceedings of the National Academy of Sciences.

[20]  G. Likens,et al.  Technical Report: Human Alteration of the Global Nitrogen Cycle: Sources and Consequences , 1997 .

[21]  Agnès Bégué,et al.  Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI , 2013, Remote. Sens..

[22]  P. Eilers.  A perfect smoother. , 2003, Analytical chemistry.

[23]  S. Running,et al.  What does Remote Sensing Do for Ecology , 1991 .

[24]  John F. Mustard,et al.  A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data , 2007 .

[25]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[26]  Clement Atzberger,et al.  Correction: Rembold, F.; Atzberger, C.; Savin, I.; Rojas, O. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. RemoteSens 2013, 5, 1704-1733 , 2013, Remote. Sens..

[27]  Megan M. Lewis,et al.  Testing the Temporal Ability of Landsat Imagery and Precision Agriculture Technology to Provide High Resolution Historical Estimates of Wheat Yield at the Farm Scale , 2013, Remote. Sens..

[28]  Jan G. P. W. Clevers,et al.  A framework for monitoring crop growth by combining directional and spectral remote sensing information. , 1994 .

[29]  J. Mustard,et al.  Green leaf phenology at Landsat resolution: Scaling from the field to the satellite , 2006 .

[30]  M. E. Bauer.  LACIE: an experiment in global crop forecasting. , 1979 .

[31]  Norman C. Elliott,et al.  High spectral and spatial resolution hyperspectral imagery for quantifying Russian wheat aphid infestation in wheat using the constrained energy minimization classifier , 2014 .

[32]  D. Lloyd,et al.  A phenological classification of terrestrial vegetation cover using shortwave vegetation index imagery , 1990 .

[33]  John F. Hermance,et al.  Stabilizing high‐order, non‐classical harmonic analysis of NDVI data for average annual models by damping model roughness , 2007 .

[34]  Clement Atzberger,et al.  Object Based Image Analysis and Data Mining applied to a remotely sensed Landsat time-series to map sugarcane over large areas , 2012 .

[35]  F. Baret,et al.  Quantification of plant stress using remote sensing observations and crop models: the case of nitrogen management. , 2006, Journal of experimental botany.

[36]  G. Foody.  Assessing the accuracy of land cover change with imperfect ground reference data , 2010 .

[37]  Jan G. P. W. Clevers,et al.  Using Hyperspectral Remote Sensing Data for Retrieving Canopy Chlorophyll and Nitrogen Content , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[38]  Massimo Menenti,et al.  Mapping vegetation-soil-climate complexes in southern Africa using temporal Fourier analysis of NOAA-AVHRR NDVI data , 2000 .

[39]  F. J. Gallego,et al.  Stratified sampling of satellite images with a systematic grid of points , 2005 .

[40]  R. Naylor,et al.  Expanding the boundaries of agricultural development , 2011, Food Security.

[41]  Michele Meroni,et al.  Remote Sensing Based Yield Estimation in a Stochastic Framework - Case Study of Durum Wheat in Tunisia , 2013, Remote. Sens..

[42]  Limin Yang,et al.  COMPLETION OF THE 1990S NATIONAL LAND COVER DATA SET FOR THE CONTERMINOUS UNITED STATES FROM LANDSAT THEMATIC MAPPER DATA AND ANCILLARY DATA SOURCES , 2001 .

[43]  Bernardo Rudorff,et al.  Monitoring biennial bearing effect on coffee yield using modis remote sensing imagery , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[44]  S. Prince.  A model of regional primary production for use with coarse resolution satellite data , 1991 .

[45]  Per Jönsson,et al.  TIMESAT - a program for analyzing time-series of satellite sensor data , 2004, Comput. Geosci..

[46]  D. L. Seen,et al.  Mapping Fragmented Agricultural Systems in the Sudano-Sahelian Environments of Africa Using Random Forest and Ensemble Metrics of Coarse Resolution MODIS Imagery , 2012 .

[47]  Thomas Udelhoven,et al.  Retrieving the Bioenergy Potential from Maize Crops Using Hyperspectral Remote Sensing , 2013, Remote. Sens..

[48]  Felix Rembold,et al.  Analysis of GAC NDVI Data for Cropland Identification and Yield Forecasting in Mediterranean African Countries , 2001 .

[49]  Alfredo José Barreto Luiz,et al.  Objective Sampling Estimation of Crop Area Based on Remote Sensing Images , 2011 .

[50]  W. Sutherland,et al.  Reaping the Benefits: Science and the sustainable intensification of global agriculture , 2009 .

[51]  James E. McMurtrey,et al.  Relationship of spectral data to grain yield variation , 1980 .

[52]  D. Lobell,et al.  Cropland distributions from temporal unmixing of MODIS data , 2004 .

[53]  Stephan J. Maas,et al.  Remote sensing and crop production models: present trends , 1992 .

[54]  Mark Sullivan,et al.  Monitoring Global Croplands with Coarse Resolution Earth Observations: The Global Agriculture Monitoring (GLAM) Project , 2010, Remote. Sens..

[55]  James P. Verdin,et al.  A five‐year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States , 2007 .

[56]  Gary E. Johnson,et al.  The use of AVHRR data in operational agricultural assessment in Africa , 1987 .

[57]  Roger Y. Anderson,et al.  Harmonic analysis of varve time series , 1963 .

[58]  Alan H. Strahler,et al.  Change-vector analysis in multitemporal space: a tool to detect and categorize land-cover change pro , 1994 .

[59]  Jan G. P. W. Clevers,et al.  Using hyperspectral remote sensing data for retrieving total canopy chlorophyll and nitrogen content , 2011, 2011 3rd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS).

[60]  Paul R. Ehrlich,et al.  Human Appropriation of Renewable Fresh Water , 1996, Science.

[61]  Paul C. Doraiswamy,et al.  Spring Wheat Yield Assessment Using NOAA AVHRR Data , 1995 .

[62]  Antonino Maltese,et al.  Investigating the Relationship between X-Band SAR Data from COSMO-SkyMed Satellite and NDVI for LAI Detection , 2013, Remote. Sens..

[63]  Clement Atzberger,et al.  Evaluation of time-series and phenological indicators for land cover classification based on MODIS data , 2011, Remote Sensing.

[64]  P. Eilers,et al.  Evaluating the effectiveness of smoothing algorithms in the absence of ground reference measurements , 2011 .

[65]  Jerry C. Ritchie,et al.  The Agricultural Research Service's Remote Sensing Program: An Example of Interagency Collaboration , 2003 .

[66]  Bruno Mary,et al.  Elaboration of a nitrogen nutrition indicator for winter wheat based on leaf area index and chlorophyll content for making nitrogen recommendations , 2007 .

[67]  Terry L. Kastens,et al.  Image masking for crop yield forecasting using AVHRR NDVI time series imagery , 2005 .

[68]  Jiahua Zhang,et al.  Corn Area Extraction by the Integration of MODIS-EVI Time Series Data and China’s Environment Satellite (HJ-1) Data , 2014, Journal of the Indian Society of Remote Sensing.

[69]  J. Schaber,et al.  Responses of spring phenology to climate change , 2004 .

[70]  P. Atkinson,et al.  Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology , 2012 .

[71]  A. J. Stern,et al.  Application of MODIS derived parameters for regional crop yield assessment , 2005 .

[72]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[73]  S. Bruin,et al.  Analysis of monotonic greening and browning trends from global NDVI time-series , 2011 .

[74]  Patrick Hostert,et al.  Rapid land use change after socio-economic disturbances: the collapse of the Soviet Union versus Chernobyl , 2011 .

[75]  T. Udelhoven,et al.  Assessment of rainfall and NDVI anomalies in Spain (1989–1999) using distributed lag models , 2009 .

[76]  B. Metz.  The Intergovernmental Panel on Climate Change , 2011 .

[77]  C. Woodcock,et al.  Post-Soviet farmland abandonment, forest recovery, and carbon sequestration in western Ukraine , 2011 .

[78]  Bettina Baruth,et al.  Enhanced Processing of 1-km Spatial Resolution fAPAR Time Series for Sugarcane Yield Forecasting and Monitoring , 2013, Remote. Sens..

[79]  S. Robinson,et al.  Food Security: The Challenge of Feeding 9 Billion People , 2010, Science.

[80]  Michael A. Wulder,et al.  Opening the archive: How free data has enabled the science and monitoring promise of Landsat , 2012 .

[81]  Johanna Link,et al.  Mobile sensor platforms: categorisation and research applications in precision farming , 2013 .

[82]  Rob J Hyndman,et al.  Detecting trend and seasonal changes in satellite image time series , 2010 .

[83]  Dirk Pflugmacher,et al.  Remote Sensing How Normalized Difference Vegetation Index (ndvi) Trends from Advanced Very High Resolution Radiometer (avhrr) and Système Probatoire D'observation De La Terre Vegetation (spot Vgt) Time Series Differ in Agricultural Areas: an Inner Mongolian Case Study , 2012 .

[84]  Wfp,et al.  The State of Food Insecurity in the World , 2011 .

[85]  Philip Lewis,et al.  On the information content of multiple view angle (MVA) images , 1997 .

[86]  Steffen Fritz,et al.  Harmonizing and Combining Existing Land Cover/Land Use Datasets for Cropland Area Monitoring at the African Continental Scale , 2012, Remote. Sens..

[87]  Paul G Falkowski,et al.  The Evolution and Future of Earth’s Nitrogen Cycle , 2010, Science.

[88]  Genovese Giampiero,et al.  The Use of Remote Sensing Within the Mars Crop Yield Monitoring System of the European Commission , 2008 .

[89]  Rodolfo Dirzo,et al.  Global State of Biodiversity and Loss , 2003 .

[90]  Warren B. Cohen,et al.  Trajectory-based change detection for automated characterization of forest disturbance dynamics , 2007 .

[91]  Bernardo Rudorff,et al.  Monitoring biennial bearing effect on coffee yield using modis remote sensing imagery , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[92]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[93]  E. L. Hunter,et al.  An assessment of two classification methods for mapping Thames Estuary intertidal habitats using CASI data , 2002 .

[94]  R. Stöckli,et al.  European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset , 2004 .

[95]  Clement Atzberger,et al.  A time series for monitoring vegetation activity and phenology at 10-daily time steps covering large parts of South America , 2011, Int. J. Digit. Earth.

[96]  N. Pelletier,et al.  Forecasting potential global environmental costs of livestock production 2000–2050 , 2010, Proceedings of the National Academy of Sciences.

[97]  R. Myneni,et al.  Potential monitoring of crop production using a satellite-based Climate-Variability Impact Index , 2005 .

[98]  Elisabetta Carfagna,et al.  Action Plan of the Global Strategy to Improve Agricultural and Rural Statistics , 2012 .

[99]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[100]  J. G. Carnes,et al.  A semi-automatic technique for multitemporal classification of a given crop within a landsat scene , 1982, Pattern Recognit..

[101]  Peter M. Atkinson,et al.  The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India , 2010 .

[102]  Warren B. Cohen,et al.  Spatial, spectral and temporal patterns of tropical forest cover change as observed with multiple scales of optical satellite data , 2007 .

[103]  Clement Atzberger,et al.  Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets , 2013, Remote. Sens..

[104]  D. Legates,et al.  Crop identification using harmonic analysis of time-series AVHRR NDVI data , 2002 .

[105]  F. Hall,et al.  Global Crop Forecasting , 1980, Science.

[106]  Steffen Fritz,et al.  Geo-Wiki.Org: The Use of Crowdsourcing to Improve Global Land Cover , 2009, Remote. Sens..

[107]  J. Townshend,et al.  Detection of land cover changes using MODIS 250 m data , 2002 .

[108]  D. Roy,et al.  An overview of MODIS Land data processing and product status , 2002 .

[109]  A. Formaggio,et al.  Estimating soybean crop areas using spectral-temporal surfaces derived from MODIS images in Mato Grosso, Brazil , 2010 .

[110]  Damien Arvor,et al.  Classification of MODIS EVI time series for crop mapping in the state of Mato Grosso, Brazil , 2011 .

[111]  J. Hill,et al.  Trend analysis of Landsat-TM and -ETM+ imagery to monitor grazing impact in a rangeland ecosystem in Northern Greece , 2008 .

[112]  Per Jönsson,et al.  Seasonality extraction by function fitting to time-series of satellite sensor data , 2002, IEEE Trans. Geosci. Remote. Sens..

[113]  Clement Atzberger,et al.  Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection , 2013, Remote. Sens..

[114]  Clement Atzberger,et al.  Estimation of Leaf Area Index Using DEIMOS-1 Data: Application and Transferability of a Semi-Empirical Relationship between two Agricultural Areas , 2013, Remote. Sens..

[115]  Jörg Kaduk,et al.  A prognostic phenology scheme for global terrestrial carbon cycle models , 1996 .

[116]  Philippe Clastre,et al.  Identification de parcelles agricoles à partir de la forme de leur évolution radiométrique au cours de la saison de culture , 1996 .

[117]  S. Carpenter,et al.  Solutions for a cultivated planet , 2011, Nature.

[118]  Obi Reddy P. Gangalakunta,et al.  Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium , 2009 .

[119]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[120]  S. Running,et al.  A continental phenology model for monitoring vegetation responses to interannual climatic variability , 1997 .

[121]  Savin Igor,et al.  Using Low Resolution Satellite Imagery for Crop Monitoring and Yield Prediction at the Regional Scale , 2010 .

[122]  R. Lunetta,et al.  Land-cover change detection using multi-temporal MODIS NDVI data , 2006 .

[123]  Prasad S. Thenkabail,et al.  Global Croplands and their Importance for Water and Food Security in the Twenty-first Century: Towards an Ever Green Revolution that Combines a Second Green Revolution with a Blue Revolution , 2010, Remote. Sens..

[124]  Christopher Conrad,et al.  Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series , 2012, Remote. Sens..

[125]  Thomas Udelhoven,et al.  TimeStats: A Software Tool for the Retrieval of Temporal Patterns From Global Satellite Archives , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[126]  Björn Waske,et al.  Hypertemporal Classification of Large Areas Using Decision Fusion , 2009, IEEE Geoscience and Remote Sensing Letters.

[127]  Clement Atzberger,et al.  Vegetation Structure Retrieval in Beech and Spruce Forests Using Spectrodirectional Satellite Data , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[128]  Clement Atzberger,et al.  Potential of Multi-Angular Data Derived From a Digital Aerial Frame Camera for Forest Classification , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[129]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[130]  James S. Gerber,et al.  Erratum: Closing yield gaps through nutrient and water management (Nature (2012) 490 (254-257) DOI: 10.1038/nature11420) , 2013 .

[131]  A. Anyamba,et al.  Interannual variability of NDVI over Africa and its relation to El Niño/Southern Oscillation , 1996 .

[132]  N. Ramankutty,et al.  Closing yield gaps through nutrient and water management , 2012, Nature.

[133]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[134]  Clement Atzberger,et al.  Data service platform for MODIS Vegetation Indices time series processing at BOKU Vienna: current status and future perspectives , 2012, Remote Sensing.

[135]  Jennifer N. Hird,et al.  Noise reduction of NDVI time series: An empirical comparison of selected techniques , 2009 .

[136]  Clement Atzberger,et al.  Portability of neural nets modelling regional winter crop acreages using AVHRR time series , 2012 .

[137]  F. J. Gallego.  Remote sensing and land cover area estimation , 2004 .