Multitemporal Monitoring of Plant Area Index in the Valencia Rice District with PocketLAI

Leaf area index (LAI) is a key biophysical parameter used to determine foliage cover and crop growth in environmental studies in order to assess crop yield. Frequently, plant canopy analyzers (LAI-2000) and digital cameras for hemispherical photography (DHP) are used for indirect effective plant area index (PAIeff) estimates. Nevertheless, these instruments are expensive and have the disadvantages of low portability and maintenance. Recently, a smartphone app called PocketLAI was presented and tested for acquiring PAIeff measurements. It was used during an entire rice season for indirect PAIeff estimations and for deriving reference high-resolution PAIeff maps. Ground PAIeff values acquired with PocketLAI, LAI-2000, and DHP were well correlated (R2 = 0.95, RMSE = 0.21 m2/m2 for Licor-2000, and R2 = 0.94, RMSE = 0.6 m2/m2 for DHP). Complementary data such as phenology and leaf chlorophyll content were acquired to complement seasonal rice plant information provided by PAIeff. High-resolution PAIeff maps, which can be used for the validation of remote sensing products, have been derived using a global transfer function (TF) made of several measuring dates and their associated satellite radiances.

[1]  R. Myneni,et al.  Intercomparison and sensitivity analysis of Leaf Area Index retrievals from LAI-2000, AccuPAR, and digital hemispherical photography over croplands , 2008 .

[2]  Hao Hu,et al.  Modeling Leaf Image, Chlorophyll Fluorescence, Reflectance From SPAD Readings , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  Irene Marzolff,et al.  Unmanned Aerial Vehicle (UAV) for Monitoring Soil Erosion in Morocco , 2012, Remote. Sens..

[4]  Lin Zhu,et al.  Inter-Comparison and Validation of the FY-3A/MERSI LAI Product Over Mainland China , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  Jb Miller,et al.  A formula for average foliage density , 1967 .

[6]  J. Chen,et al.  Evaluation of hemispherical photography for determining plant area index and geometry of a forest stand , 1991 .

[7]  Guijun Yang,et al.  Newly Combined Spectral Indices to Improve Estimation of Total Leaf Chlorophyll Content in Cotton , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  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.

[9]  C. Justice,et al.  Validating MODIS Terrestrial Ecology Products: Linking In Situ and Satellite Measurements , 1999 .

[10]  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..

[11]  Fernando Vicente-Guijalba,et al.  Influence of Incidence Angle on the Coherent Copolar Polarimetric Response of Rice at X-Band , 2015, IEEE Geoscience and Remote Sensing Letters.

[12]  Wolfram Mauser,et al.  Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping , 2012, Remote. Sens..

[13]  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 .

[14]  P. Lancashire,et al.  A uniform decimal code for growth stages of crops and weeds , 1991 .

[15]  Daniela Stroppiana,et al.  Analysis of rice sample size variability due to development stage, nitrogen fertilization, sowing technique and variety using the visual jackknife , 2006 .

[16]  Guangjian Yan,et al.  Validating GEOV1 Fractional Vegetation Cover Derived From Coarse-Resolution Remote Sensing Images Over Croplands , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Olli Nevalainen,et al.  Monitoring Changes in Rice Cultivated Area from SAR and Optical Satellite Images in Ben Tre and Tra Vinh Provinces in Mekong Delta, Vietnam , 2014, Remote. Sens..

[18]  Manuel Campos-Taberner,et al.  Development of an earth observation processing chain for crop bio-physical parameters at local scale , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[19]  Frank Veroustraete,et al.  Chlorophyll content mapping of urban vegetation in the city of Valencia based on the hyperspectral NAOC index , 2014 .

[20]  J. Chen,et al.  Defining leaf area index for non‐flat leaves , 1992 .

[21]  J. Hicke,et al.  Global synthesis of leaf area index observations: implications for ecological and remote sensing studies , 2003 .

[22]  Jw Wilson.  Estimation of foliage denseness and foliage angle by inclined point quadrats , 1963 .

[23]  Michael Marshall,et al.  Developing in situ Non-Destructive Estimates of Crop Biomass to Address Issues of Scale in Remote Sensing , 2015, Remote. Sens..

[24]  Gérard Dedieu,et al.  Estimation of leaf area and clumping indexes of crops with hemispherical photographs , 2008 .

[25]  T. Nilson.  A theoretical analysis of the frequency of gaps in plant stands , 1971 .

[26]  Thomas J. Jackson,et al.  Vegetation water content during SMEX04 from ground data and Landsat 5 Thematic Mapper imagery , 2008 .

[27]  Raoul Lemeur,et al.  A critical review of light models for estimating the shortwave radiation regime of plant canopies , 1974 .

[28]  K. Moffett,et al.  Remote Sens , 2015 .

[29]  Chi-Farn Chen,et al.  A Phenology-Based Classification of Time-Series MODIS Data for Rice Crop Monitoring in Mekong Delta, Vietnam , 2013, Remote. Sens..

[30]  Clement Atzberger,et al.  Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs , 2013, Remote. Sens..

[31]  Anatoly A. Gitelson,et al.  Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content , 2012 .

[32]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[33]  F. Baret,et al.  GEOV1: LAI, FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 2: Validation and intercomparison with reference products , 2013 .

[34]  J. Ross.  The radiation regime and architecture of plant stands , 1981, Tasks for vegetation sciences 3.

[35]  Luca Gatti,et al.  Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from 13 Demonstration Sites across Asia in the RIICE Project , 2014, Remote. Sens..

[36]  J. R. Evans.  Photosynthesis and nitrogen relationships in leaves of C3 plants , 2004, Oecologia.

[37]  Stefano Amaducci,et al.  Empirical Estimation of Leaf Chlorophyll Density in Winter Wheat Canopies Using Sentinel-2 Spectral Resolution , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[38]  Gregory Asner,et al.  Mapping Savanna Tree Species at Ecosystem Scales Using Support Vector Machine Classification and BRDF Correction on Airborne Hyperspectral and LiDAR Data , 2012, Remote. Sens..

[39]  N. Breda.  Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. , 2003, Journal of experimental botany.

[40]  A. B. McBratney,et al.  The “Null Hypothesis” of Precision Agriculture Management , 2000, Precision Agriculture.

[41]  Fernando Vicente-Guijalba,et al.  Polarimetric Response of Rice Fields at C-Band: Analysis and Phenology Retrieval , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[42]  F. Baret,et al.  LAI and fAPAR CYCLOPES global products derived from VEGETATION. Part 2: validation and comparison with MODIS collection 4 products , 2007 .

[43]  F. Baret,et al.  GAI estimates of row crops from downward looking digital photos taken perpendicular to rows at 57.5° zenith angle: Theoretical considerations based on 3D architecture models and application to wheat crops , 2010 .

[44]  C. Francone,et al.  Comparison of leaf area index estimates by ceptometer and PocketLAI smart app in canopies with different structures , 2014 .

[45]  J. V. Stafford,et al.  Implementing precision agriculture in the 21st century. , 2000 .

[46]  Frédéric Baret,et al.  Review of methods for in situ leaf area index determination Part I. Theories, sensors and hemispherical photography , 2004 .

[47]  F. Baret,et al.  Review of methods for in situ leaf area index (LAI) determination: Part II. Estimation of LAI, errors and sampling , 2004 .

[48]  Raffaele Casa,et al.  Development of an app for estimating leaf area index using a smartphone. Trueness and precision determination and comparison with other indirect methods , 2013 .

[49]  Hongliang Fang,et al.  Seasonal variation of leaf area index (LAI) over paddy rice fields in NE China: Intercomparison of destructive sampling, LAI-2200, digital hemispherical photography (DHP), and AccuPAR methods , 2014 .

[50]  B. Martínez,et al.  Accuracy assessment of fraction of vegetation cover and leaf area index estimates from pragmatic methods in a cropland area , 2009 .