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Remote Sens., Volume 2, Issue 9 (September 2010), Pages 2040-2312

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Editorial

Jump to: Research, Review

Open AccessEditorial 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
Remote Sens. 2010, 2(9), 2305-2312; doi:10.3390/rs2092305
Received: 6 September 2010 / Revised: 17 September 2010 / Published: 27 September 2010
Cited by 16 | PDF Full-text (330 KB) | HTML Full-text | XML Full-text
Abstract
In an increasingly food insecure world, there is a critical need for us to have a comprehensive understanding of global croplands. The reality that the “green revolution” has ended is beginning to be felt around the World. Whereas, global population continues to [...] Read more.
In an increasingly food insecure world, there is a critical need for us to have a comprehensive understanding of global croplands. The reality that the “green revolution” has ended is beginning to be felt around the World. Whereas, global population continues to increase at a rate of about 100 million per year and is expected to reach around 10 billion by 2050, cropland areas are not increasing and have stagnated around 1.5 billion hectares globally. Indeed, cropland areas have even begun to decrease in some countries with important food contribution (e.g., USA) due to increasing demand of fertile arable lands for alternative uses such as bio-fuels, encroachment from urbanization, and industrialization. [...] Full article
(This article belongs to the Special Issue Global Croplands)
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Research

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Open AccessArticle A Seasonally Robust Empirical Algorithm to Retrieve Suspended Sediment Concentrations in the Scheldt River
Remote Sens. 2010, 2(9), 2040-2059; doi:10.3390/rs2092040
Received: 6 July 2010 / Revised: 5 August 2010 / Accepted: 19 August 2010 / Published: 27 August 2010
Cited by 4 | PDF Full-text (987 KB) | HTML Full-text | XML Full-text
Abstract
A seasonally robust algorithm for the retrieval of Suspended Particulate Matter (SPM) in the Scheldt River from hyperspectral images is presented. This algorithm can be applied without the need to simultaneously acquire samples (from vessels and pontoons). Especially in dynamic environments such [...] Read more.
A seasonally robust algorithm for the retrieval of Suspended Particulate Matter (SPM) in the Scheldt River from hyperspectral images is presented. This algorithm can be applied without the need to simultaneously acquire samples (from vessels and pontoons). Especially in dynamic environments such as estuaries, this leads to a large reduction of costs, both in equipment and personnel. The algorithm was established empirically using in situ data of the water-leaving reflectance obtained over the tidal cycle during different seasons and different years. Different bands and band combinations were tested. Strong correlations were obtained for exponential relationships between band ratios and SPM concentration. The best performing relationships are validated using airborne hyperspectral data acquired in June 2005 and October 2007 at different moments in the tidal cycle. A band ratio algorithm (710 nm/596 nm) was successfully applied to a hyperspectral AHS image of the Scheldt River to obtain an SPM concentration map. Full article
Open AccessArticle Flood Risk Mapping Using LiDAR for Annapolis Royal, Nova Scotia, Canada
Remote Sens. 2010, 2(9), 2060-2082; doi:10.3390/rs2092060
Received: 10 July 2010 / Revised: 19 August 2010 / Accepted: 20 August 2010 / Published: 1 September 2010
Cited by 8 | PDF Full-text (7450 KB) | HTML Full-text | XML Full-text
Abstract
A significant portion of the Canadian Maritime coastline has been surveyed with airborne Light Detection and Ranging (LiDAR). The purpose of these surveys has been to map the risk of flooding from storm surges and projected long-term sea‑level rise from climate change [...] Read more.
A significant portion of the Canadian Maritime coastline has been surveyed with airborne Light Detection and Ranging (LiDAR). The purpose of these surveys has been to map the risk of flooding from storm surges and projected long-term sea‑level rise from climate change and to include projects in all three Maritime Provinces: Prince Edward Island, New Brunswick, and Nova Scotia. LiDAR provides the required details in order to map the flood inundation from 1 to 2 m storm surge events, which cause coastal flooding in many locations in this region when they occur at high tide levels. The community of Annapolis Royal, Nova Scotia, adjacent to the Bay of Fundy, has been surveyed with LiDAR and a 1 m DEM (Digital Elevation Model) was constructed for the flood inundation mapping. Validation of the LiDAR using survey grade GPS indicates a vertical accuracy better than 30 cm. A benchmark storm, known as the Groundhog Day storm (February 1–3, 1976), was used to assess the flood maps and to illustrate the effects of different sea-level rise projections based on climate change scenarios if it were to re-occur in 100 years time. Near shore bathymetry has been merged with the LiDAR and local wind observations used to model the impact of significant waves during this benchmark storm. Long-term (ca. greater than 30 years) time series of water level observations from across the Bay of Fundy in Saint John, New Brunswick, have been used to estimate return periods of water levels under present and future sea-level rise conditions. Results indicate that under current sea-level rise conditions this storm has a 66 year return period. With a modest relative sea-level (RSL) rise of 80 cm/century this decreases to 44 years and, with a possible upper limit rise of 220 cm/century, this decreases further to 22 years. Due to the uncertainty of climate change scenarios and sea-level rise, flood inundation maps have been constructed at 10 cm increments up to the 9 m contour which represents an upper flood limit estimate in 100 years, based on the highest predicted tide, plus a 2 m storm surge and a RSL of 220 cm/century. Full article
(This article belongs to the Special Issue LiDAR)
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Open AccessArticle Terrain Segmentation of Egypt from Multi-Temporal Night LST Imagery and Elevation Data
Remote Sens. 2010, 2(9), 2083-2096; doi:10.3390/rs2092083
Received: 30 June 2010 / Revised: 23 August 2010 / Accepted: 24 August 2010 / Published: 2 September 2010
Cited by 4 | PDF Full-text (1220 KB) | HTML Full-text | XML Full-text
Abstract
Monthly night averaged land surface temperature (LST) MODIS imagery was analyzed throughout a year-period (2006), in an attempt to segment the terrain of Egypt into regions with different LST seasonal variability, and represent them parametrically. Regions with distinct spatial and temporal LST [...] Read more.
Monthly night averaged land surface temperature (LST) MODIS imagery was analyzed throughout a year-period (2006), in an attempt to segment the terrain of Egypt into regions with different LST seasonal variability, and represent them parametrically. Regions with distinct spatial and temporal LST patterns were outlined using several clustering techniques capturing aspects of spatial, temporal and temperature homogeneity or differentiation. Segmentation was supplemented, taking into consideration elevation, morphological features and landcover information. The northern coastal region along the Mediterranean Sea occupied by lowland plain areas corresponds to the coolest clusters indicating a latitude/elevation dependency of seasonal LST variability. On the other hand, for the inland regions, elevation and terrain dissection plays a key role in LST seasonal variability, while an east to west variability of clusters’ spatial distribution is evident. Finally, elevation biased clustering revealed annual LST differences among the regions with the same physiographic/terrain characteristics. Thermal terrain segmentation outlined the temporal variation of LST during 2006, as well as the spatial distribution of LST zones. Full article
(This article belongs to the Special Issue Multi-Temporal Remote Sensing)
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Open AccessArticle On the Critical Behaviour of Observed and Simulated Spatial Soil Moisture Fields during SGP97
Remote Sens. 2010, 2(9), 2097-2110; doi:10.3390/rs2092097
Received: 25 June 2010 / Revised: 30 July 2010 / Accepted: 30 August 2010 / Published: 2 September 2010
Cited by 1 | PDF Full-text (364 KB) | HTML Full-text | XML Full-text
Abstract
The aircraft-based ESTAR soil moisture fields from the Southern Great Plains 1997 (SGP97) Hydrology Experiment are compared to the simulated ones obtained by Bertoldi et al. [1] with the GEOtop model [2], with a particular focus on their capability in capturing [...] Read more.
The aircraft-based ESTAR soil moisture fields from the Southern Great Plains 1997 (SGP97) Hydrology Experiment are compared to the simulated ones obtained by Bertoldi et al. [1] with the GEOtop model [2], with a particular focus on their capability in capturing the critical point behaviour in their space-time dynamics (see [3]). The critical point behaviour should denote the transition of soil moisture spatial patterns from an unorganized to organized appearance, as conditions become wetter. The study region is the Little Washita watershed, located in the southwest Oklahoma, in the Southern Great Plains region of the USA. The case study takes place from June 27 to July 16 and encompasses wetting and drying cycles allowing for exploring the behaviour under transient conditions. Results show that the critical probability value is 0.85 for GEOtop, and 0.80 for ESTAR. The GEOtop patterns appear more fragmented, being more reluctant to organization, as confirmed by the higher value of critical probability. Such behaviour is probably inherited by the model’s parameterization: land use and soil classes impose additional spatial structures to those related to the meteorological forcings and the hillslope morphology, driving to higher degrees of heterogeneity. Full article
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Open AccessArticle Building Damage Estimation by Integration of Seismic Intensity Information and Satellite L-band SAR Imagery
Remote Sens. 2010, 2(9), 2111-2126; doi:10.3390/rs2092111
Received: 20 July 2010 / Revised: 4 August 2010 / Accepted: 30 August 2010 / Published: 8 September 2010
Cited by 11 | PDF Full-text (1503 KB) | HTML Full-text | XML Full-text
Abstract
For a quick and stable estimation of earthquake damaged buildings worldwide, using Phased Array type L-band Synthetic Aperture Radar (PALSAR) loaded on the Advanced Land Observing Satellite (ALOS) satellite, a model combining the usage of satellite synthetic aperture radar (SAR) imagery and [...] Read more.
For a quick and stable estimation of earthquake damaged buildings worldwide, using Phased Array type L-band Synthetic Aperture Radar (PALSAR) loaded on the Advanced Land Observing Satellite (ALOS) satellite, a model combining the usage of satellite synthetic aperture radar (SAR) imagery and Japan Meteorological Agency (JMA)-scale seismic intensity is proposed. In order to expand the existing C-band SAR based damage estimation model into L-band SAR, this paper rebuilds a likelihood function for severe damage ratio, on the basis of dataset from Japanese Earth Resource Satellite-1 (JERS-1)/SAR (L-band SAR) images observed during the 1995 Kobe earthquake and its detailed ground truth data. The model which integrates the fragility functions of building damage in terms of seismic intensity and the proposed likelihood function is then applied to PALSAR images taken over the areas affected by the 2007 earthquake in Pisco, Peru. The accuracy of the proposed damage estimation model is examined by comparing the results of the analyses with field investigations and/or interpretation of high-resolution satellite images. Full article
(This article belongs to the Special Issue Remote Sensing in Seismology)
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Open AccessCommunication Determination of Backscatter-Extinction Coefficient Ratio for LIDAR-Retrieved Aerosol Optical Depth Based on Sunphotometer Data
Remote Sens. 2010, 2(9), 2127-2135; doi:10.3390/rs2092127
Received: 5 August 2010 / Revised: 6 September 2010 / Accepted: 6 September 2010 / Published: 9 September 2010
Cited by 2 | PDF Full-text (622 KB) | HTML Full-text | XML Full-text
Abstract
Backscattered power data from the Doppler LIght Detection And Ranging (LIDAR) systems at the Hong Kong International Airport (HKIA) could be used to obtain the extinction coefficient of the troposphere by combining with the meteorological optical range (MOR) data from the nearby [...] Read more.
Backscattered power data from the Doppler LIght Detection And Ranging (LIDAR) systems at the Hong Kong International Airport (HKIA) could be used to obtain the extinction coefficient of the troposphere by combining with the meteorological optical range (MOR) data from the nearby forward scatter sensor. The Range-height Indicator (RHI) scan of the LIDAR is then utilized to derive the vertical profile of extinction coefficient, which is integrated with height to obtain the aerosol optical depth (AOD). In the retrieval of extinction coefficient profile, there is a power exponent of unknown value relating the backscattered power and the extinction coefficient. This exponent (called the backscatter-extinction coefficient ratio) depends on the optical properties of the aerosol in the air, and is normally assumed to be 1. In the present study, the value of this ratio is established by comparing the AOD measurements by a hand-held sunphotometer and the LIDAR-based AOD estimate in one winter (October 2008 to January 2009), which is the season with the largest number of haze episodes, and one summer-winter-spring period of the following year (July 2009 to May 2010) at HKIA. It is found to be about 1.4. The sensitivity of extinction coefficient profile to the value of the ratio is also examined for two cases in the study period, one good visibility day and one hazy day. Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)
Open AccessArticle Meteorological Influence on Predicting Air Pollution from MODIS-Derived Aerosol Optical Thickness: A Case Study in Nanjing, China
Remote Sens. 2010, 2(9), 2136-2147; doi:10.3390/rs2092136
Received: 20 July 2010 / Revised: 30 August 2010 / Accepted: 5 September 2010 / Published: 13 September 2010
Cited by 7 | PDF Full-text (187 KB) | HTML Full-text | XML Full-text
Abstract
Whether the aerosol optical thickness (AOT) products derived from MODIS data can be used as a reliable proxy of air pollutants measured near the surface depends on meteorological influence. This study attempts to assess the influence of four meteorological parameters (air pressure, [...] Read more.
Whether the aerosol optical thickness (AOT) products derived from MODIS data can be used as a reliable proxy of air pollutants measured near the surface depends on meteorological influence. This study attempts to assess the influence of four meteorological parameters (air pressure, temperature, relative humidity, and wind velocity) on predicting air pollution from MODIS AOT data for the city of Nanjing, China. It is found that PM10 (particulate matter with a diameter Full article
(This article belongs to the Special Issue Atmospheric Remote Sensing)
Open AccessArticle What Do Observational Datasets Say about Modeled Tropospheric Temperature Trends since 1979?
Remote Sens. 2010, 2(9), 2148-2169; doi:10.3390/rs2092148
Received: 30 July 2010 / Revised: 3 September 2010 / Accepted: 14 September 2010 / Published: 15 September 2010
Cited by 25 | PDF Full-text (396 KB) | HTML Full-text | XML Full-text
Abstract
Updated tropical lower tropospheric temperature datasets covering the period 1979–2009 are presented and assessed for accuracy based upon recent publications and several analyses conducted here. We conclude that the lower tropospheric temperature (TLT) trend over these 31 years is [...] Read more.
Updated tropical lower tropospheric temperature datasets covering the period 1979–2009 are presented and assessed for accuracy based upon recent publications and several analyses conducted here. We conclude that the lower tropospheric temperature (TLT) trend over these 31 years is +0.09 ± 0.03 °C decade−1. Given that the surface temperature (Tsfc) trends from three different groups agree extremely closely among themselves (~ +0.12 °C decade−1) this indicates that the “scaling ratio” (SR, or ratio of atmospheric trend to surface trend: TLT/Tsfc) of the observations is ~0.8 ± 0.3. This is significantly different from the average SR calculated from the IPCC AR4 model simulations which is ~1.4. This result indicates the majority of AR4 simulations tend to portray significantly greater warming in the troposphere relative to the surface than is found in observations. The SR, as an internal, normalized metric of model behavior, largely avoids the confounding influence of short-term fluctuations such as El Niños which make direct comparison of trend magnitudes less confident, even over multi-decadal periods. Full article
(This article belongs to the Special Issue Remote Sensing in Climate Monitoring and Analysis)
Open AccessArticle A Study of the Correlation between Earthquakes and NOAA Satellite Energetic Particle Bursts
Remote Sens. 2010, 2(9), 2170-2184; doi:10.3390/rs2092170
Received: 16 July 2010 / Revised: 6 September 2010 / Accepted: 7 September 2010 / Published: 15 September 2010
Cited by 11 | PDF Full-text (704 KB) | HTML Full-text | XML Full-text
Abstract
Over the last two decades, potentially interesting phenomena in the ionosphere-magnetosphere transition region have been studied; anomalous particle fluxes detected by several space experiments and correlated with earthquakes. These phenomena are characterized by short-term increases in high energy particle counting rates, called [...] Read more.
Over the last two decades, potentially interesting phenomena in the ionosphere-magnetosphere transition region have been studied; anomalous particle fluxes detected by several space experiments and correlated with earthquakes. These phenomena are characterized by short-term increases in high energy particle counting rates, called particle bursts. In this work we have used the NOAA electron flux data to study the time correlation between particle rate fluctuations and earthquakes. With respect to previous studies, we have analyzed contiguous particle bursts in order to distinguish correlations with seismic activity from seasonal variations of particle flux and solar activity. Earthquake clustering was initially included to study the types and causes of false correlations. Full article
(This article belongs to the Special Issue Remote Sensing in Seismology)
Open AccessArticle Cereal Yield Modeling in Finland Using Optical and Radar Remote Sensing
Remote Sens. 2010, 2(9), 2185-2239; doi:10.3390/rs2092185
Received: 25 July 2010 / Revised: 3 September 2010 / Accepted: 3 September 2010 / Published: 16 September 2010
Cited by 9 | PDF Full-text (2068 KB) | HTML Full-text | XML Full-text
Abstract
During 1996–2006, the Ministry of Agriculture and Forestry in Finland (MAFF), MTT Agrifood Research and the Finnish Geodetic Institute performed a joint remote sensing satellite research project. It evaluated the applicability of optical satellite (Landsat, SPOT) data for cereal yield estimations in the annual crop inventory program. Four Optical Vegetation Indices models (I: Infrared polynomial, II: NDVI, III: GEMI, IV: PARND/FAPAR) were validated to estimate cereal baseline yield levels (yb) using solely optical harmonized satellite data (Optical Minimum Dataset). The optimized Model II (NDVI) yb level was 4,240 kg/ha (R2 0.73, RMSE 297 kg/ha) for wheat and 4390 kg/ha (R2 0.61, RMSE 449 kg/ha) for barley and Model I yb was 3,480 kg/ha for oats (R2 0.76, RMSE 258 kg/ha). Optical VGI yield estimates were validated with CropWatN crop model yield estimates using SPOT and NOAA data (mean R2 0.71, RMSE 436 kg/ha) and with composite SAR/ASAR and NDVI models (mean R2 0.61, RMSE 402 kg/ha) using both reflectance and backscattering data. CropWatN and Composite SAR/ASAR & NDVI model mean yields were 4,754/4,170 kg/ha for wheat, 4,192/3,848 kg/ha for barley and 4,992/2,935 kg/ha for oats. Full article
(This article belongs to the Special Issue Global Croplands)
Open AccessArticle A Hierarchical Spatio-Temporal Markov Model for Improved Flood Mapping Using Multi-Temporal X-Band SAR Data
Remote Sens. 2010, 2(9), 2240-2258; doi:10.3390/rs2092240
Received: 28 July 2010 / Revised: 10 September 2010 / Accepted: 10 September 2010 / Published: 17 September 2010
Cited by 15 | PDF Full-text (694 KB) | HTML Full-text | XML Full-text
Abstract
In this contribution, a hybrid multi-contextual Markov model for unsupervised near real-time flood detection in multi-temporal X-band synthetic aperture radar (SAR) data is presented. It incorporates scale-dependent, as well as spatio-temporal contextual information, into the classification scheme, by combining hierarchical marginal posterior [...] Read more.
In this contribution, a hybrid multi-contextual Markov model for unsupervised near real-time flood detection in multi-temporal X-band synthetic aperture radar (SAR) data is presented. It incorporates scale-dependent, as well as spatio-temporal contextual information, into the classification scheme, by combining hierarchical marginal posterior mode (HMPM) estimation on directed graphs with noncausal Markov image modeling related to planar Markov random fields (MRFs). In order to increase computational performance, marginal posterior-based entropies are used for restricting the iterative bi-directional exchange of spatio-temporal information between consecutive images of a time sequence to objects exhibiting a low probability, to be classified correctly according to the HMPM estimation. The Markov models, originally developed for inference on regular graph structures of quadtrees and planar lattices, are adapted to the variable nature of irregular graphs, which are related to information driven image segmentation. Entropy based confidence maps, combined with spatio-temporal relationships of potentially inundated bright scattering vegetation to open water areas, are used for the quantification of the uncertainty in the labeling of each image element in flood possibility masks. With respect to accuracy and computational effort, experiments performed on a bi-temporal TerraSAR-X ScanSAR data-set from the Caprivi region of Namibia during flooding in 2009 and 2010 confirm the effectiveness of integrating hierarchical as well as spatio-temporal context into the labeling process, and of adapting the models to irregular graph structures. Full article
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Open AccessArticle Landslide Catastrophes and Disaster Risk Reduction: A GIS Framework for Landslide Prevention and Management
Remote Sens. 2010, 2(9), 2259-2273; doi:10.3390/rs2092259
Received: 12 August 2010 / Revised: 30 August 2010 / Accepted: 14 September 2010 / Published: 21 September 2010
Cited by 6 | PDF Full-text (1202 KB) | HTML Full-text | XML Full-text
Abstract
As catastrophic phenomena, landslides often cause large-scale socio-economic destruction including loss of life, economic collapse, and human injury. In addition, landslides can impair the functioning of critical infrastructure and destroy cultural heritage and ecological systems. In order to build a more landslide [...] Read more.
As catastrophic phenomena, landslides often cause large-scale socio-economic destruction including loss of life, economic collapse, and human injury. In addition, landslides can impair the functioning of critical infrastructure and destroy cultural heritage and ecological systems. In order to build a more landslide resistant and resilient society, an original GIS-based decision support system is put forth in order to help emergency managers better prepare for and respond to landslide disasters. The GIS-based landslide monitoring and management system includes a Central Repository System (CRS), Disaster Data Processing Modules (DDPM), a Command and Control System (CCS) and a Portal Management System (PMS). This architecture provides valuable insights into landslide early warning, landslide risk and vulnerability analyses, and critical infrastructure damage assessments. Finally, internet-based communications are used to support landslide disaster modelling, monitoring and management. Full article

Review

Jump to: Editorial, Research

Open AccessReview Remote Sensing of Irrigated Agriculture: Opportunities and Challenges
Remote Sens. 2010, 2(9), 2274-2304; doi:10.3390/rs2092274
Received: 29 July 2010 / Revised: 15 September 2010 / Accepted: 25 September 2010 / Published: 27 September 2010
Cited by 36 | PDF Full-text (815 KB) | HTML Full-text | XML Full-text
Abstract
Over the last several decades, remote sensing has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. The objective of this review, which summarizes the methods and the results of existing remote sensing studies, [...] Read more.
Over the last several decades, remote sensing has emerged as an effective tool to monitor irrigated lands over a variety of climatic conditions and locations. The objective of this review, which summarizes the methods and the results of existing remote sensing studies, is to synthesize principle findings and assess the state of the art. We take a taxonomic approach to group studies based on location, scale, inputs, and methods, in an effort to categorize different approaches within a logical framework. We seek to evaluate the ability of remote sensing to provide synoptic and timely coverage of irrigated lands in several spectral regions. We also investigate the value of archived data that enable comparison of images through time. This overview of the studies to date indicates that remote sensing-based monitoring of irrigation is at an intermediate stage of development at local scales. For instance, there is overwhelming consensus on the efficacy of vegetation indices in identifying irrigated fields. Also, single date imagery, acquired at peak growing season, may suffice to identify irrigated lands, although to multi-date image data are necessary for improved classification and to distinguish different crop types. At local scales, the mapping of irrigated lands with remote sensing is also strongly affected by the timing of image acquisition and the number of images used. At the regional and global scales, on the other hand, remote sensing has not been fully operational, as methods that work in one place and time are not necessarily transferable to other locations and periods. Thus, at larger scales, more work is required to indentify the best spectral indices, best time periods, and best classification methods under different climatological and cultural environments. Existing studies at regional scales also establish the fact that both remote sensing and national statistical approaches require further refinement with a substantial investment of time and resources for ground-truthing. An additional challenge in mapping irrigation across large areas occurs in fragmented landscapes with small irrigated and cultivated fields, where the spatial scale of observations is pitted against the need for high frequency temporal acquisitions. Finally, this review identifies passive and active microwave observations, advanced image classification methods, and data fusion including optical and radar sensors or with information from sources with multiple spatial and temporal characteristics as key areas where additional research is needed. Full article
(This article belongs to the Special Issue Global Croplands)

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