Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

Leaf area index (LAI) and leaf chlorophyll content (Chll) represent key biophysical and biochemical controls on water, energy and carbon exchange processes in the terrestrial biosphere. In combination, LAI and Chll provide critical information on vegetation density, vitality and photosynthetic potentials.However, simultaneous retrieval of LAI and Chll fromspace observations is extremely challenging. Regularization strategies are required to increase the robustness and accuracy of retrieved properties and enable more reliable separation of soil, leaf and canopy parameters. To address these challenges, the REGularized canopy reFLECtance model (REGFLEC) inversion system was refined to incorporate enhanced techniques for exploiting ancillary LAI and temporal information derived from multiple satellite scenes. In this current analysis, REGFLEC is applied to a time-series of Landsat data. A novel aspect of the REGFLEC approach is the fact that no site-specific data are required to calibrate the model, which may be run in a largely automated fashion using information extracted entirely from image-based and other widely available datasets. Validation results, based upon in-situ LAI and Chll observations collected over maize and soybean fields in centralNebraska for the period 2001–2005, demonstrate Chll retrievalwith a relative root-mean-square-deviation (RMSD) on the order of 19% (RMSD = 8.42 μg cm−2). While Chll retrievals were clearly influenced by the version of the leaf optical properties model used (PROSPECT), the application of spatio-temporal regularization constraints was shown to be critical for estimating Chll with sufficient accuracy. REGFLEC also reproduced the dynamics of in-situ measured LAI well (r2 = 0.85), but estimates were biased low, particularly over maize (LAI was underestimated by ~36 %). This disparity may be attributed to differences between effective and true LAI caused by significant foliage clumping not being properly accounted for in the canopy reflectance model (SAIL). Additional advances in the retrieval of canopy biophysical and leaf biochemical constituents will require innovative use of existing remote sensing data within physically realistic canopy reflectancemodels along with the ability to exploit the enhanced spectral and spatial capabilities of upcoming satellite systems

Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA’s Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky–Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta.E.A. was supported by the predoctoral scholarship, grant number ACIF/2019/187, funded by the Generalitat Valenciana and co-funded by the European Social Fund. J.V. and S.B. were supported by the European Research Council (ERC) under the ERC-2017-STG SENTIFLEX project, grant number 755617. J.V. was additionally supported by a Ramón y Cajal Contract (Spanish Ministry of Science, Innovation and Universities). S.B. was additionally supported by the Generalitat Valenciana SEJIGENT program (SEJIGENT/2021/001) and European Union—NextGenerationEU (ZAMBRANO 21-04)

Crop monitoring and yield estimation using polarimetric SAR and optical satellite data in southwestern Ontario

Optical satellite data have been proven as an efficient source to extract crop information and monitor crop growth conditions over large areas. In local- to subfield-scale crop monitoring studies, both high spatial resolution and high temporal resolution of the image data are important. However, the acquisition of optical data is limited by the constant contamination of clouds in cloudy areas. This thesis explores the potential of polarimetric Synthetic Aperture Radar (SAR) satellite data and the spatio-temporal data fusion approach in crop monitoring and yield estimation applications in southwestern Ontario. Firstly, the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) was investigated. The results show that the SAR backscatters are affected by many factors unrelated to the crop canopy such as the incidence angle and the soil background and the degree of sensitivity varies with the crop types, growing stages, and the polarimetric SAR parameters. Secondly, the Minimum Noise Fraction (MNF) transformation, for the first time, was applied to multitemporal Radarsat-2 polarimetric SAR data in cropland area mapping based on the random forest classifier. An overall classification accuracy of 95.89% was achieved using the MNF transformation of the multi-temporal coherency matrix acquired from July to November. Then, a spatio-temporal data fusion method was developed to generate Normalized Difference Vegetation Index (NDVI) time series with both high spatial and high temporal resolution in heterogeneous regions using Landsat and MODIS imagery. The proposed method outperforms two other widely used methods. Finally, an improved crop phenology detection method was proposed, and the phenology information was then forced into the Simple Algorithm for Yield Estimation (SAFY) model to estimate crop biomass and yield. Compared with the SAFY model without forcing the remotely sensed phenology and a simple light use efficiency (LUE) model, the SAFY incorporating the remotely sensed phenology can improve the accuracy of biomass estimation by about 4% in relative Root Mean Square Error (RRMSE). The studies in this thesis improve the ability to monitor crop growth status and production at subfield scale

Retrieval of biophysical parameters from multi-sensoral remote sensing data, assimilated into the crop growth model CERES-Wheat

This study investigated the possibilities and constraints for an integrated use of a crop growth model (CERES-Wheat) and earth observation techniques. The assimilation of information derived from earth observation sensors into crop growth models enables regional applications and may also help to improve the profound knowledge of the different involved processes and interactions. Both techniques can contribute to improved use of resources, reduced crop production risks, minimised environmental degradation, and increased farm income. Up to now, crop growth modelling and remote sensing techniquices mostly have been used separately for the assessment of agricultural applications. Crop growth models have made valuable contributions to, e.g., yield forecasting or to management decision support systems. Likewise, remote sensing techniques were successfully utilized in classification of agricultural areas or in the quantification of vegetation characteristics at various spatial and temporal scales. Multisensoral remote sensing approaches for the quantification biophysical variables are rarely realized. Normally the fusion of the data sources is based on the use of one sensor for classification purposes and the other one for the extraction of the desired parameters, based on the map classified previously. Pixel-based fusions between multispectral and SAR data is seldom realised for the assessment of quantitative parameters. The integration of crop growth models and remote sensing techniques by assimilating remotely sensed parameters into the models, is also still an issue of research. Especially, the integration of, e.g., multi-sensor biophysical parameter time-series for the improvement of the model performance, might feature a high potential. The starting point of the presented study was the question, if it is possible to derive the values of important crop variables from various remote sensing data? For the retrieval of these quantitative parameters by the use of various multispectral remote sensing sensors, intercalibration issues between the different retrieved vegetation indices had to be taken into account, in order to assure the comparability. Features influencing the vegetation indices are, e.g., the sensor geometry (like viewing- and solar-angle), atmospherical conditions, topography and spatial or radiometric resolution. However, the factors taken into account within this study are the spectral characteristics of the different sensors, like band position, bandwidth and centre wavelengths, which are described by the relative spectral response functions. Due to different RSR functions of the sensor bands, measured spectral differences occur, because the sensors record different components of the reflectance’s spectra from the monitored targets. These are then also introduced into the derived vegetation indices. The chosen cross-calibration method, intercalibrated the assessed Normalized Difference Vegetation Index and the Weighted Difference Vegetation Index between the various sensor pairs by regression, based on simulated multispectral sensors. Differences between the various assessed remote sensing sensors decreased form around 7% to below 1%. The intercalibration also had a positive impact on the later biophysical retrieval performance, producing sounder retrieval results. For the retrieval of the biophysical parameters empirical and semi-empirical models were assessed. The results indicate that the semi-empirical CLAIR model outperforms the empirical approaches. Not only for the Leaf Area Index retrieval, but also in the cases of all other assessed parameters. Concerning the other remote sensing data type used, the SAR data, it was analysed what potential different polarizations and incidence angles have for the extraction of the quantitative parameters. It became obvious that especially high incidence angles, as provided by the satellite Envisat ASAR, produce sounder retrieval results than lower incidence angles, due to a smaller amount of received soil signal. In the context of the assessed polarizations, sound results for the VV polarization could only be achieved for the retrieval of fresh biomass and the plant water content. For the ASAR sensor modelling fresh biomass and LAI using the HV polarization or the dry biomass using the ratio (HH/HV) was appropriate. As roughness aspects also have an influence on the retrieval performance from biophysical parameters using SAR data, the impact of soil surface and vegetation roughness was additionally considered. Best results were achieved, when also considering roughness features, however due to the need of regional modelling it is more appropriate not to consider them. For the calibration and re-tuning of crop growth models information about important phenological events such as heading/flowering is rather important. After this stage reproductive growth begins, whereby the number of kernels per plant is often calculated from plant weight at flowering and kernel weight is calculated from time and temperature available for dry matter distribution. By the use of the SAR VV time-series this important stage could be successfully extracted. Further methods for pixel-based fused biophysical parameter estimations, using SAR and multispectral data were analysed. By this approach the different features, being monitored of the two systems, are combined for sounder parameter retrieval. The assessed method of combining the multi-sensoral information by linear regression did not bring sound results and was outperformed by single sensor use, only taking into account the multispectral information. Only for the parameter fresh biomass, modelling based on the NDIV and the ASAR ratio slightly outperformed the single sensor modelling approaches. The complex combined modelling by the use of the CLAIR and the Water Cloud Model featured no valid results. For the combination, by using the CLAIR model and multiple regression slight improvements, in contrast to the single multispectral sensor use, were achieved. Especially, during late phenological stages, the assessed VV information improved the modelling results, in comparison to only using the CLAIR model. All the findings could finally be successfully applied for regional estimations. Only the roughness features could not be applied, due to the fact, that it is hard to regionally assess this needed model input parameter. Regional parameter on the basis of remote sensing data, is the major advantage of this technique, due to the large spatial overview given. The second main question was, if it is possible to integrate the crop variables gained from multisensoral data into a crop growth model, increasing the final yield estimation accuracy. Thus far, beneficial linkages between both techniques have been often limited to land use classification via remote sensing for choosing the adequate model and quantification of crop growth and development curves using biophysical parameters derived from remote sensing images for model calibration. Only a few studies actually considered the potentials of remote sensing for model re-initialization of growth and development characteristics of a specific crop, as the here studied winter wheat. Overall, the integration of remotely sensed variables into the crop growth model CERES-Wheat led to an improved final yield estimation accuracy in comparison to an automatic input parameter setting. The assessed final yield bias for the automatic input parameter setting summed up to 6.6%. When re-initializing the most sensitive input parameters (sowing date and fertilizer application date) by the use of remotely sensed biophysical variables the biases ranged from 0.56% overestimation to 5.4% understimation, in dependence of the data series used for assimilation. Whereby, it was assessed that the combined dense data series, considering SAR and multispectral information, slightly outperformed the performance of the full multispectral data series. However, when analysing the assimilation of the multispectral data series in further detail, it became clear that the actually information from the phenological stage ripening declines the modelling performance and thus the final yield estimation accuracy. When neglecting the information from this phenological stage the reduced multispectral data series performed as sound as the dense data series containing SAR and multispectral information. Thus, when the appropriate phenological stages are monitored by multispectral data, additional SAR information does not lead to a model improvement. However, when important dates are not monitored by multispectral images, e.g., due to cloud coverage, the additionally considered SAR information was not able to appropriatly fill these important multispectral time gaps. They even had a more negeative influence on the modelling performance. Overall, the best results could be obtained by assimilating a multispectral data series, covering the crop development during the important phenological stages stem elongation and flowering (without ripening stage), into the CERES-Wheat model. Finally, the integration of remote sensing data in the point-based crop growth model allowed it‘s spatial application for prediction of wheat production at a more regional scale. This approach also outperformed another evaluated method of direct multi-sensoral regional yield estimation. This study has demonstrated that biophysical parameters can be retrieved from remote sensing data and led, when assimilated into a crop growth model, to an improved final yield estimation. However, overall the SAR information did not really have a significant positive effect on the multi-sensoral biophysical parameter retrieval and on the later assimilation process. Thus, overall SAR information should only be considered, when multispectral data acquisitions are tremendously hampered by cloud coverage. The assessed assimilation of remote sensing information into a crop growth model had a positive effect on the final yield estimation performance. The analysed method, combining remote sensing and crop growth model techniques, was succsessfully demonstrated and will gain even more importance in the future for, e.g., decision support systems fine-tuning fertilizer regimes and thus contributing to more environmentally sound and sustained agricultural production

Symposium franco-chinois de télédétection quantitative en agronomie et environnement. Bilan et perspectives de collaboration. Rapport de mission (26 au 30 mars 2000)

Ce rapport présente les principaux résultats d'un Symposium en Télédétection entre des équipes de chercheurs de l'INRA, du CIRAD, de l'Université de Lille et leurs homologues chinois de l'Institute of Remote Sensing Applications (IRSA) of Chinese Academy of Sciences (CAS), et du National Satellite Meteorological Center (NSMC). Les perspectives d'un programme de collaboration sont présentées avec deux axes majeurs correspondant à deux niveaux d'approche, régional et local en agriculture de précision. (Résumé d'auteur

Advances in Radar Remote Sensing of Agricultural Crops: A Review

There are enormous advantages of a review article in the field of emerging technology like radar remote sensing applications in agriculture. This paper aims to report select recent advancements in the field of Synthetic Aperture Radar (SAR) remote sensing of crops. In order to make the paper comprehensive and more meaningful for the readers, an attempt has also been made to include discussion on various technologies of SAR sensors used for remote sensing of agricultural crops viz. basic SAR sensor, SAR interferometry (InSAR), SAR polarimetry (PolSAR) and polarimetric interferometry SAR (PolInSAR). The paper covers all the methodologies used for various agricultural applications like empirically based models, machine learning based models and radiative transfer theorem based models. A thorough literature review of more than 100 research papers indicates that SAR polarimetry can be used effectively for crop inventory and biophysical parameters estimation such are leaf area index, plant water content, and biomass but shown less sensitivity towards plant height as compared to SAR interferometry. Polarimetric SAR Interferometry is preferable for taking advantage of both SAR polarimetry and SAR interferometry. Numerous studies based upon multi-parametric SAR indicate that optimum selection of SAR sensor parameters enhances SAR sensitivity as a whole for various agricultural applications. It has been observed that researchers are widely using three models such are empirical, machine learning and radiative transfer theorem based models. Machine learning based models are identified as a better approach for crop monitoring using radar remote sensing data. It is expected that the review article will not only generate interest amongst the readers to explore and exploit radar remote sensing for various agricultural applications but also provide a ready reference to the researchers working in this field

QUANTIFYING GRASSLAND NON-PHOTOSYNTHETIC VEGETATION BIOMASS USING REMOTE SENSING DATA

Non-photosynthetic vegetation (NPV) refers to vegetation that cannot perform a photosynthetic function. NPV, including standing dead vegetation and surface plant litter, plays a vital role in maintaining ecosystem function through controlling carbon, water and nutrient uptake as well as natural fire frequency and intensity in diverse ecosystems such as forest, savannah, wetland, cropland, and grassland. Due to its ecological importance, NPV has been selected as an indicator of grassland ecosystem health by the Alberta Public Lands Administration in Canada. The ecological importance of NPV has driven considerable research on quantifying NPV biomass with remote sensing approaches in various ecosystems. Although remote images, especially hyperspectral images, have demonstrated potential for use in NPV estimation, there has not been a way to quantify NPV biomass in semiarid grasslands where NPV biomass is affected by green vegetation (PV), bare soil and biological soil crust (BSC). The purpose of this research is to find a solution to quantitatively estimate NPV biomass with remote sensing approaches in semiarid mixed grasslands. Research was conducted in Grasslands National Park (GNP), a parcel of semiarid mixed prairie grassland in southern Saskatchewan, Canada. Multispectral images, including newly operational Landsat 8 Operational Land Imager (OLI) and Sentinel-2A Multi-spectral Instrument (MSIs) images and fine Quad-pol Radarsat-2 images were used for estimating NPV biomass in early, middle, and peak growing seasons via a simple linear regression approach. The results indicate that multispectral Landsat 8 OLI and Sentinel-2A MSIs have potential to quantify NPV biomass in peak and early senescence growing seasons. Radarsat-2 can also provide a solution for NPV biomass estimation. However, the performance of Radarsat-2 images is greatly affected by incidence angle of the image acquisition. This research filled a critical gap in applying remote sensing approaches to quantify NPV biomass in grassland ecosystems. NPV biomass estimates and approaches for estimating NPV biomass will contribute to grassland ecosystem health assessment (EHA) and natural resource (i.e. land, soil, water, plant, and animal) management

Land Surface Phenologies and Seasonalities Using Cool Earthlight in Temperate and Tropical Croplands

In today’s world of increasing food insecurity due to more frequent and extreme events (droughts, floods), a comprehensive understanding of global cropland dynamics is critically needed. Land surface parameters derived from the passive microwave Advanced Microwave Scanning Radiometer on EOS (AMSR-E) and AMSR2 data enable monitoring of cropland dynamics and they can complement visible to near infrared (VNIR) and thermal infrared (TIR) data. Passive microwave data are less sensitive to atmospheric effects, cloud contamination, and solar illumination constraints resulting in finer temporal resolution suitable to track the temporal progression of cropland cover development compared to the VNIR data that has coarser temporal resolution due to compositing to lessen the atmospheric effects. Both VNIR and TIR data have moderate to fine spatial resolution compared to passive microwaves, due to the faint microwave flux from the planetary surface. I used AMSR, MODIS, TRMM, and simplified surface energy balance (SSEB) data to study cropland dynamics from 2003-2015 in North Dakota, USA, the Canadian Prairie Provinces, Northern Eurasia, and East Africa: a contrast between crop exporting regions and a food insecure region. Croplands in the temperate region are better studied compared to that of the tropics. The objective of this research was to characterize cropland dynamics in the tropics based on the knowledge gained about the microwave products in the temperate croplands. This study also aimed at assessing the utility of passive microwave data for cropland dynamics study, especially for tropical cropland regions that are often cloud-obscured during the growing season and have sparse in situ data networks. Using MODIS land cover data, I identified 162 AMSR grid cells (25km*25km=625km2) dominated by croplands within the study regions. To fit the passive microwave time series data to environmental forcings, I used the convex quadratic (CxQ) model fit that has been successfully applied with the VNIR and TIR data to herbaceous vegetation in temperate and boreal ecoregions. Land surface dynamics in the thermally-limited temperate croplands were characterized as a function of temperature; whereas, a function of moisture to model land surface dynamics in the tropical croplands. In the temperate croplands, growing degree-day (GDD), NDVI, and vegetation optical depth (VOD) were modeled as a convex quadratic function of accumulated GDD (AGDD) derived from AMSR air temperature data, yielding high coefficients of determination (0.88≤ r2≤0.98) Deviations of GDD from the long term average CxQ model by site corresponded to peak VI producing negative residuals (arising from higher latent heat flux) and low VI at beginning and end of growing season producing positive residuals (arising from higher sensible heat flux). In Northern Eurasia, sites at lower latitude (44° - 48° N) that grow winter grains showed either a longer unimodal growing season or a bimodal growing season; whereas, sites at higher latitude (48° - 56° N) where spring grains are cultivated showed shorter, unimodal growing seasons. Peak VOD showed strong linear correspondence with peak greenness (NDVI) with r2\u3e0.8, but with a one-week lag. The AMSR data were able to capture the effects of the 2010 and 2007 heat waves that devastated grain production in southwestern Russia and Northern Kazakhstan, and Ukraine, respectively, better than the MODIS data. In East African croplands, the AMSR, TRMM, and SSEB datasets modeled as a convex quadratic function of cumulative water-vapor-days displayed distinct cropland dynamics in space and time, including unimodal and bimodal growing seasons. Interannual moisture variability is at its highest at the beginning of the growing season affecting planting times of crops. Moisture time to peak from AMSR and TRMM land surface parameters displayed strong correspondence (r2 \u3e 0.80) and logical lags among variables. Characterizing cropland dynamics based on the synergistic use of complementary remote sensing data should help to advance and improve agricultural monitoring in tropical croplands that are often associated with food insecurity

Remote Sensing of Biophysical Parameters

Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security)