Mapping Biophysical Variables From Solar and Thermal Infrared Remote Sensing: Focus on Agricultural Landscapes With Spatial Heterogeneity

This letter closes a Special Stream that focuses on spatial heterogeneity when mapping biophysical variables over agricultural landscape from solar and thermal infrared remote sensing. We propose an overview of the highlights from prior research, we report the main results of the Special Stream, and we discuss future directions. The main outcomes of the Special Stream are related to: 1) the impact on the remotely sensed signal of canopy vertical distribution, shadowing effects, and multiple scattering; 2) the notion of spatial resolution limit in relation to spatial heterogeneity; and 3) the definition of an optimal sampling strategy to spatialize ground measurements.

[1]  Craig Trotter,et al.  Extending a turbid medium BRDF model to allow sloping terrain with a vertical plant stand , 2000, IEEE Trans. Geosci. Remote. Sens..

[2]  F. Baret,et al.  Sensitivity of gap fraction to maize architectural characteristics based on 4D model simulations , 2007 .

[3]  W. Verhoef,et al.  An integrated model of soil-canopy spectral radiances, photosynthesis, fluorescence, temperature and energy balance , 2009 .

[4]  Guangjian Yan,et al.  Impact of sensor footprint on measurement of directional brightness temperature of row crop canopies , 2013 .

[5]  Guoqing Sun,et al.  Inversion of a lidar waveform model for forest biophysical parameter estimation , 2006, IEEE Geoscience and Remote Sensing Letters.

[6]  Michele Meroni,et al.  Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series , 2008 .

[7]  Jean-Pierre Wigneron,et al.  Multidimensional Disaggregation of Land Surface Temperature Using High-Resolution Red, Near-Infrared, Shortwave-Infrared, and Microwave-L Bands , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Athanassios I. Sfougaris,et al.  electing landscape metrics as indicators of spatial heterogeneity — A omparison among Greek landscapes , 2013 .

[9]  W. Verhoef,et al.  PROSPECT+SAIL models: A review of use for vegetation characterization , 2009 .

[10]  F. Baret,et al.  Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data , 2006 .

[11]  Ji Zhou,et al.  Disaggregation of remotely sensed land surface temperature: Literature survey, taxonomy, issues, and caveats , 2013 .

[12]  M. Wijk,et al.  Using Information Theory to Determine Optimum Pixel Size and Shape for Ecological Studies: Aggregating Land Surface Characteristics in Arctic Ecosystems , 2009, Ecosystems.

[13]  Mathias Disney,et al.  Monte Carlo ray tracing in optical canopy reflectance modelling , 2000 .

[14]  Jennifer L. Dungan,et al.  Comparison of regression and geostatistical methods for mapping Leaf Area Index (LAI) with Landsat ETM+ data over a boreal forest. , 2005 .

[15]  Andrew N. French,et al.  Disaggregation of GOES land surface temperatures using surface emissivity , 2009 .

[16]  S. Garrigues,et al.  Multiscale geostatistical analysis of AVHRR, SPOT-VGT, and MODIS global NDVI products , 2008 .

[17]  F. Baret,et al.  Estimating Canopy Characteristics from Remote Sensing Observations: Review of Methods and Associated Problems , 2008 .

[18]  Miina Rautiainen,et al.  Retrieval of seasonal dynamics of forest understory reflectance in a Northern European boreal forest from MODIS BRDF data , 2012 .

[19]  Gérard Dedieu,et al.  The MISTIGRI thermal infrared project: scientific objectives and mission specifications , 2013 .

[20]  Pablo J. Zarco-Tejada,et al.  Estimating Radiation Interception in Heterogeneous Orchards Using High Spatial Resolution Airborne Imagery , 2014, IEEE Geoscience and Remote Sensing Letters.

[21]  J. Privette,et al.  Modeling and Inversion in Thermal Infrared Remote Sensing over Vegetated Land Surfaces , 2008 .

[22]  C. Atzberger,et al.  Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery , 2012 .

[23]  Pablo J. Zarco-Tejada,et al.  Spatial Resolution Effects on Chlorophyll Fluorescence Retrieval in a Heterogeneous Canopy Using Hyperspectral Imagery and Radiative Transfer Simulation , 2013, IEEE Geoscience and Remote Sensing Letters.

[24]  Xavier Briottet,et al.  Simulating Space Lidar Waveforms From Smaller-Footprint Airborne Laser Scanner Data for Vegetation Observation , 2014, IEEE Geoscience and Remote Sensing Letters.

[25]  R. Myneni,et al.  Investigation of a model inversion technique to estimate canopy biophysical variables from spectral and directional reflectance data , 2000 .

[26]  P. Lagacherie,et al.  Regional predictions of eight common soil properties and their spatial structures from hyperspectral Vis–NIR data , 2012 .

[27]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[28]  Miina Rautiainen,et al.  Modeling the Spectral Signature of Forests: Application of Remote Sensing Models to Coniferous Canopies , 2008 .

[29]  Xingfa Gu,et al.  Modeling directional brightness temperature over a maize canopy in row structure , 2003, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477).

[30]  F. L. Dimet,et al.  Multitemporal-patch ensemble inversion of coupled surface-atmosphere radiative transfer models for land surface characterization , 2008 .

[31]  Chenghu Zhou,et al.  Spatial Sampling Design for Estimating Regional GPP With Spatial Heterogeneities , 2014, IEEE Geoscience and Remote Sensing Letters.

[32]  Miina Rautiainen,et al.  Seasonal Contribution of Understory Vegetation to the Reflectance of a Boreal Landscape at Different Spatial Scales , 2013, IEEE Geoscience and Remote Sensing Letters.

[33]  F. Baret,et al.  Quantifying spatial heterogeneity at the landscape scale using variogram models , 2006 .

[34]  Hideki Kobayashi,et al.  Spatial Scale and Landscape Heterogeneity Effects on FAPAR in an Open-Canopy Black Spruce Forest in Interior Alaska , 2014, IEEE Geoscience and Remote Sensing Letters.

[35]  F. J. García-Haro,et al.  Derivation of high-resolution leaf area index maps in support of validation activities: Application to the cropland Barrax site , 2009 .

[36]  Pierre Defourny,et al.  A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing , 2010 .

[37]  Dominique Guyon,et al.  Directional Anisotropy of Brightness Surface Temperature Over Vineyards: Case Study Over the Medoc Region (SW France) , 2014, IEEE Geoscience and Remote Sensing Letters.

[38]  Glynn C. Hulley,et al.  Directional Viewing Effects on Satellite Land Surface Temperature Products Over Sparse Vegetation Canopies—A Multisensor Analysis , 2013, IEEE Geoscience and Remote Sensing Letters.

[39]  Nadine Gobron,et al.  Horizontal radiation transport in 3-D forest canopies at multiple spatial resolutions: Simulated impact on canopy absorption , 2006 .

[40]  C. Woodcock,et al.  Multiscale analysis and validation of the MODIS LAI product: I. Uncertainty assessment , 2002 .

[41]  Yasushi Yamaguchi,et al.  Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area , 2006 .

[42]  Jin Chen,et al.  Scale Effect of Vegetation-Index-Based Spatial Sharpening for Thermal Imagery: A Simulation Study by ASTER Data , 2012, IEEE Geoscience and Remote Sensing Letters.

[43]  Zhongbo Su,et al.  Retrieval of canopy component temperatures through Bayesian inversion of directional thermal measurements , 2009 .

[44]  F. Baret,et al.  Crop specific green area index retrieval from MODIS data at regional scale by controlling pixel-target adequacy , 2011 .

[45]  J. Chen Spatial Scaling of a Remotely Sensed Surface Parameter by Contexture , 1999 .

[46]  Pingheng Li,et al.  Canopy vertical heterogeneity plays a critical role in reflectance simulation , 2013 .

[47]  Ranga B. Myneni,et al.  The impact of gridding artifacts on the local spatial properties of MODIS data : Implications for validation, compositing, and band-to-band registration across resolutions , 2006 .

[48]  L. Spadavecchia,et al.  Upscaling leaf area index in an Arctic landscape through multiscale observations , 2008 .

[49]  Shunlin Liang,et al.  Numerical experiments on the spatial scaling of land surface albedo and leaf area index , 2000 .

[50]  Ranga B. Myneni,et al.  Modeling lidar waveforms with time‐dependent stochastic radiative transfer theory for remote estimations of forest structure , 2003 .

[51]  Nadine Gobron,et al.  Uniqueness of multiangular measurements. I. An indicator of subpixel surface heterogeneity from MISR , 2002, IEEE Trans. Geosci. Remote. Sens..

[52]  Jean-Luc Widlowski,et al.  Third Radiation Transfer Model Intercomparison (RAMI) exercise: Documenting progress in canopy reflectance models , 2007 .

[53]  G. Hay,et al.  Remote Sensing Contributions to the Scale Issue , 1999 .

[54]  Wout Verhoef,et al.  A practical algorithm to infer soil and foliage component temperatures from bi-angular ATSR-2 data , 2003 .

[55]  J. Chen,et al.  Global mapping of foliage clumping index using multi-angular satellite data , 2005 .