LAI Estimation Research Articles

Temporal Dynamics of Canopy Properties and Carbon and Water Fluxes in a Temperate Evergreen Angiosperm Forest

The forest–atmosphere exchange of carbon and water is regulated by meteorological conditions as well as canopy properties such as leaf area index (LAI, m2 m−2), photosynthetic capacity (PC μmol m−2 s−1), or surface conductance in optimal conditions (Gs,opt, mmol m−2 s−1), which can vary seasonally and inter-annually. This variability is well understood for deciduous species but is poorly characterized in evergreen forests. Here, we quantify the seasonal dynamics of a temperate evergreen eucalypt forest with estimates of LAI, litterfall, carbon and water fluxes, and meteorological conditions from measurements and model simulations. We merged MODIS Enhanced Vegetation Index (EVI) values with site-based LAI measurements to establish a 17-year sequence of monthly LAI. We ran the Community Atmosphere Biosphere Land Exchange model (CABLE-POP (version r5046)) with constant and varying LAI for our site to quantify the influence of seasonal canopy dynamics on carbon and water fluxes. We observed that the peak of LAI occurred in late summer–early autumn, with a higher and earlier peak occurring in years when summer rainfall was greater. Seasonality in litterfall and allocation of net primary productivity (FNPP) to leaf growth (af, 0–1) drove this pattern, suggesting a complete renewal of the canopy before the timing of peak LAI. Litterfall peaked in spring, followed by a high af in summer, at the end of which LAI peaked, and PC and Gs,opt reached their maximum values in autumn, resulting from a combination of high LAI and efficient mature leaves. These canopy dynamics helped explain observations of maximum gross ecosystem production (FGEP) in spring and autumn and net ecosystem carbon loss in summer at our site. Inter-annual variability in LAI was positively correlated with Net Ecosystem Production (FNEP). It would be valuable to apply a similar approach to other temperate evergreen forests to identify broad patterns of seasonality in leaf growth and turnover. Because incorporating dynamic LAI was insufficient to fully capture the dynamics of FGEP, observations of seasonal variation in photosynthetic capacity, such as from solar-induced fluorescence, should be incorporated in land surface models to improve ecosystem flux estimates in evergreen forests.

Impacts of Variable Illumination and Image Background on Rice LAI Estimation Based on UAV RGB-Derived Color Indices

Variations in illumination and image background present challenges for using UAV RGB imagery. Existing studies often overlook these issues, especially in rice. To separately evaluate the impacts of illumination variation and image background on rice LAI assessment, this study utilized Retinex correction and image segmentation to eliminate illumination variations and background effects, and then analyzed the changes in color indices and their relationship with LAI before and after implementing these methods separately. The results indicated that both Retinex correction and image segmentation significantly enhanced the correlation between color indices and LAI at different growth stages as well as the accuracy of constructing a multivariate linear regression model separately. Our analysis confirmed the significance of accounting for variation in illumination and rice field backgrounds in LAI analysis when using UAV RGB images. Illumination variation and image background elements significantly degrade the accuracy of LAI estimation.

Development of a Model to Evaluate Water Conservation Function for Various Tree Species

The ecosystem services of forests, such as the water conservation function, are the combined results of diverse processes, and the modification of one part of a forest affects each ecosystem service separately via complex processes. It is necessary to develop an ecosystem service assessment model for various tree species to ensure proper forest management. In this study, a model to evaluate three ecosystem services, namely, the water supply, wood supply, and carbon sink, for various tree species in Japan is developed using many observation data from the previous literature. The integrated evaluation model consists of the forest model, hydrological model, and carbon stock assessment model. The forest model consists of the forest growth model and LAI estimation model, based on allometry. The results of the simulations for the major tree species yield the following findings: (1) Water supply varies with tree species but decreases until about 40 years of age, after which it is near constant. (2) Although beech has a larger LAI than needleleaf forests, water supply is not significantly different. (3) Broadleaf forests are more affected by thinning than needleleaf forests and tend to receive increased water supply as a result of processes such as thinning. This study enabled the evaluation of water conservation function in watersheds containing various tree species.

Fusion of Optical and SAR Data Using Three Approaches for the Estimation of LAI With Modified Integral Equation Model

Fusion of Optical and SAR Data Using Three Approaches for the Estimation of LAI With Modified Integral Equation Model

Improving UAV-Based LAI Estimation for Forests Over Complex Terrain by Reducing Topographic Effects on Multispectral Reflectance

Improving UAV-Based LAI Estimation for Forests Over Complex Terrain by Reducing Topographic Effects on Multispectral Reflectance

Estimating canopy parameters of winter wheat at different stages using hyperspectral data combined with soil variables

ABSTRACT Remote sensing technology has many advantages in real-time estimating of crop growth. Crop growing estimation primarily establishes the direct statistical model between spectral data and crop biophysical variables. However, the model accuracy rarely improves with the increase in the number of independent variables owing to the multicollinearity; hence, the addition of a data set different from spectral variables may solve this problem. Considering the strong correlation between crop growth and soil environment, this study aims to investigate whether and how the addition of important soil variables can improve estimation model accuracy. This study collected LAI and Chlorophyll substitution index (SPAD value) of wheat canopy and canopy spectral data under different soil environments to quantify the correspondent relationship. Important spectral parameters (IPs) and important soil variables (ISVs) were selected by least absolute shrinkage and selection operator (LASSO) to establish linear and nonlinear models for wheat growth estimation and the effect of multiple soil variables in enhancing wheat growth estimation was tested. The results indicated LASSO can effectively reduce feature dimensionality for wheat growth estimation with maintaining model accuracy; the extra ISVs can improve the model accuracy due to the high collinearity of spectral parameters. The optimal models of wheat LAI estimation (R2 = 0.83, RMSE = 0.500) and SPAD estimation (R2 = 0.75, RMSE = 1.835) were constructed based on orthogonal partial least squares analysis (OPLS) by IPs and IPs+ISVs, respectively. Finally, we discussed the applicability of spectral parameters and soil variables. This research combines remote sensing features of crops with crucial growth variables to obtain an efficient and mechanical crop growth estimation.

Quantifying vegetation indices using terrestrial laser scanning: methodological complexities and ecological insights from a Mediterranean forest

Abstract. Accurate measurement of vegetation density metrics including plant, wood and leaf area indices (PAI, WAI and LAI) is key to monitoring and modelling carbon storage and uptake in forests. Traditional passive sensor approaches, such as digital hemispherical photography (DHP), cannot separate leaf and wood material, nor individual trees, and require many assumptions in processing. Terrestrial laser scanning (TLS) data offer new opportunities to improve understanding of tree and canopy structure. Multiple methods have been developed to derive PAI and LAI from TLS data, but there is little consensus on the best approach, nor are methods benchmarked as standard. Using TLS data collected in 33 plots containing 2472 trees of 5 species in Mediterranean forests, we compare three TLS methods (lidar pulse, 2D intensity image and voxel-based) to derive PAI and compare with co-located DHP. We then separate leaf and wood in individual tree point clouds to calculate the ratio of wood to total plant area (α), a metric to correct for non-photosynthetic material in LAI estimates. We use individual tree TLS point clouds to estimate how α varies with species, tree height and stand density. We find the lidar pulse method agrees most closely with DHP, but it is limited to single-scan data, so it cannot determine individual tree properties, including α. The voxel-based method shows promise for ecological studies as it can be applied to individual tree point clouds. Using the voxel-based method, we show that species explain some variation in α; however, height and plot density were better predictors. Our findings highlight the value of TLS data to improve fundamental understanding of tree form and function as well as the importance of rigorous testing of TLS data processing methods at a time when new approaches are being rapidly developed. New algorithms need to be compared against traditional methods and existing algorithms, using common reference data. Whilst promising, our results show that metrics derived from TLS data are not yet reliably calibrated and validated to the extent they are ready to replace traditional approaches for large-scale monitoring of PAI and LAI.

Comparing leaf area index estimates in a Mediterranean forest using field measurements, Landsat 8, and Sentinel-2 data

BackgroundLeaf area index (LAI) is a key indicator for the assessment of the canopy’s processes such as net primary production and evapotranspiration. For this reason, the LAI is often used as a key input parameter in ecosystem services’ modeling, which is emerging as a critical tool for steering upcoming urban reforestation strategies. However, LAI field measures are extremely time-consuming and require remarkable economic and human resources. In this context, spectral indices computed using high-resolution multispectral satellite imagery like Sentinel-2 and Landsat 8, may represent a feasible and economic solution for estimating the LAI at the city scale. Nonetheless, as far as we know, only a few studies have assessed the potential of Sentinel-2 and Landsat 8 data doing so in Mediterranean forest ecosystems. To fill such a gap, we assessed the performance of 10 spectral indices derived from Sentinel-2 and Landsat 8 data in estimating the LAI, using field measurements collected with the LI-COR LAI 2200c as a reference. We hypothesized that Sentinel-2 data, owing to their finer spatial and spectral resolution, perform better in estimating vegetation’s structural parameters compared to Landsat 8.ResultsWe found that Landsat 8-derived models have, on average, a slightly better performance, with the best model (the one based on NDVI) showing an R2 of 0.55 and NRMSE of 14.74%, compared to R2 of 0.52 and NRMSE of 15.15% showed by the best Sentinel-2 model, which is based on the NBR. All models were affected by spectrum saturation for high LAI values (e.g., above 5).ConclusionIn Mediterranean ecosystems, Sentinel-2 and Landsat 8 data produce moderately accurate LAI estimates during the peak of the growing season. Therefore, the uncertainty introduced using satellite-derived LAI in ecosystem services’ assessments should be systematically accounted for.

Exploring the performance of spectral and textural information for leaf area index estimation with homogeneous and heterogeneous surfaces

AbstractLeaf area index (LAI) is one of the key parameters of vegetation structure, which can be applied in monitoring vegetation growth status. Currently, abundant spatial information (e.g., textural information), provided by the developing remote sensing satellite techniques, could boost the accuracy of LAI estimation. Thus, the performance of spectral and textural information must be evaluated for different vegetation types of LAI estimation in different surface types. In this study, different spectral vegetation indices (SVIs) and grey‐level co‐occurrence matrix‐based textural variables under different moving window sizes were extracted from Landsat TM satellite data. First, the ability of different types of SVIs for LAI estimation in different surface types was analysed. Subsequently, the effect of different texture variables with different moving window sizes towards LAI estimation accuracy in different vegetation types was explored. Lastly, the performance of SVIs combined with textural information for the LAI estimation in different vegetation types was evaluated. Results indicated that SVIs performed better for LAI estimation in the homogeneous region than that in the heterogeneous region, and difference vegetation index was more remarkable for LAI estimation in different vegetation types than other SVIs. In addition, variations in texture variables and moving window sizes had a large influence on LAI estimation of natural vegetation with high canopy heterogeneity. SVI combined with textural information can efficiently improve the accuracy of LAI estimation in different vegetation types (R2 = 0.672, 0.455 and 0.523 for meadow, shrub and cantaloupe, respectively.) compared with SVI alone (R2 = 0.189, 0.064 and 0.431 for meadow, shrub and cantaloupe, respectively.). Especially for natural vegetation (meadow, shrub), the addition of textural information can greatly improve the accuracy of LAI estimation.

Optimization of Lai Estimation Method Based on Smartphones with Fisheye Lens

Leaf area index (LAI) is an important biological factor reflecting vegetation growth and forest ecosystem. LAI can be used to obtain plant health status, carbon cycle, and surrounding ecological environment effectively. In this study, the smartphone was equipped with a fisheye lens, and the optimization method was used to estimate LAI, which was compared with digital hemispherical photography (DHP) to investigate the possibility of the new method for LAI estimation. The hemispherical image was divided into blocks, and the optimized Otsu method was used for algorithm segmentation, which can effectively distinguish vegetation from the sky. Concurrently, when the gap fraction inversion LAI was performed, the linear inversion algorithm was improved based on single-angle inversion, and the LAI was obtained by inversion through the linear fitting of the mul-tiangle gap fraction. The experimental sample was located in Olympic National Forest Park in Beijing. Three coniferous mixed forests and three broadleaved forests were selected from the experimental sample. LAI measurements from smartphones were compared with those from DHP. In the samples for mixed coniferous forests, the values for coefficients of determination R^2 were 0.835, 0.802, and 0.809, and root mean square errors (REMS) were 0.137, 0.120, and 0.147. For the broadleaf forest samples, the values for R² were 0.629, 0.679, and 0.758, and REMS were 0.144, 0.135, and 0.137. The R^2 and RMES for the overall data was 0.810 and 0.134, respectively, and a good agreement between the LAI measurements from the proposed method and those from the DHP supports an accurate estimation. The results show that the use of a fisheye lens on a smartphone can effectively and accurately obtain tree canopy LAI. This provides a fast and effective new method to measure LAI of forest vegetation near the ground, which is of great significance for studying the interaction between plant growth status, ecological environment, and phenological changes.

Simultaneous Estimation of LAI, PAR, FAPAR, and Surface Albedo at Multiple Spatial Scales From Top-of-Atmosphere Satellite Observations With Different Spatial Resolutions

Simultaneous Estimation of LAI, PAR, FAPAR, and Surface Albedo at Multiple Spatial Scales From Top-of-Atmosphere Satellite Observations With Different Spatial Resolutions

Mapping mangrove leaf area index (LAI) by combining remote sensing images with PROSAIL‐D and XGBoost methods

AbstractLeaf area index (LAI) is a vital parameter reflecting vegetation structure, physio‐ecological process and growth development. Accurate estimation of mangrove LAI is fundamental for assessing the ecological restoration and sustainable development of mangrove ecosystems. To date, very few studies have explored the hybrid method of radiative transfer model (RTM) and machine‐learning model in retrieving mangrove LAI with different satellite sensors. This study investigated the capabilities of combining the PROSAIL‐D model, XGBoost (extreme gradient boosting) and remote sensing images in estimating mangrove LAI, considering the spatial resolutions and spectral vegetation indices (VIs) of Landsat‐8, Sentinel‐2, Worldview‐2 and Zhuhai‐1 images, and further explored the role of eco‐environmental factors in the spatial distribution of LAI in Gaoqiao Mangrove Reserve, China. The results showed that the Zhuhai‐1 acquires the best estimation accuracy ( (the determination coefficient of validation) = 0.86, RPD (residual prediction deviation) = 3.36 and RMSE (root mean square error) = 0.31), followed by Worldview‐2 ( = 0.84, RPD = 2.64 and RMSE = 0.33), Sentinel‐2 ( = 0.34, RPD = 1.33 and RMSE = 0.62) and Landsat‐8 ( = 0.29, RPD = 1.03 and RMSE = 0.71). The newly developed three‐band VIs ( with Landsat‐8, with Sentinel‐2, with Worldview‐2 and with Zhuhai‐1) were efficient estimators of mangrove LAI. Moreover, elevation and species composition could greatly affect the spatial distribution of mangrove LAI. We concluded that the hybrid method of PROSAIL‐D and XGBoost model using VIs derived from Zhuhai‐1 hyperspectral image could be deemed as basic method and input variables of mapping mangrove LAI, and could be effectively and widely applied in generating mangrove LAI products at the regional and national scales.

Combining Multiple Solution and Cost Function for Better LAI Estimation

پوشش گیاهی موتور محرک کرة زمین است؛ تبادلات انرژی و آب بین اتمسفر و زمین را کنترل می‌کند و در چرخه‏های جهانی انرژی، اکسیژن، دی‏اکسیدکربن و آب نقش مهمی دارد. پایش و مدیریت پوشش‌های گیاهی با استفاده از پارامتر‌های بیوفیزیکی و بیوشیمیایی آن، مانند LAI، انجام می‌پذیرد. شاخص سطح برگ (LAI) از مهم‌ترین پارامترهای پوشش گیاهی است که در اغلب مدل‌سازی‌ها مانند مدل‌سازی چرخه‌های آب، انرژی و کربن استفاده می‌شود. رویکردهای بازیابی متفاوتی، به‌منظور استخراج اطلاعات پارامترهای بیوفیزیکی از داده‌های سنجش از دوری، توسعه یافته است. در تحقیق حاضر، از روش فیزیکی معکوس مدل انتقال تابش PROSAIL، مبتنی‌بر جدول LUT، با هدف بازیابی متغیر LAI استفاده شده است. همچنین داده‌های زمینی برداشت‌شده طی کمپین SPARC 2003 برای ارزیابی صحت متغیر بازیابی‌شده به‌کار رفت. برای رفع مشکل ill-posed، چهار دسته از معیارهای هزینه با عنوان اندازه‌گیر اطلاعات (IM)، حداقل اختلاف (MC)، اندازه‌گیر زاویه (SAM) و خطای حداقل مربعات (LSE) به‌همراه نرمال‌سازی و میانگین بهترین جواب‌ها استفاده شد. نتایج بهبود تخمین متغیر LAI را با استفاده از معیار اندازه‌گیر اطلاعات (Kulbak-liebler)، به‌میزان 12% و با استفاده از 11% میانگین بهترین جواب‌ها نشان دادند. تابع هزینة LSE نیز در قیاس با حالت نرمال‌نشده، 7% بهبود یافت.

Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery

BackgroundLeaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.MethodsTo investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.ResultsThe results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat.ConclusionsThe method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

Improving leaf area index estimation accuracy of wheat by involving leaf chlorophyll content information

• A novel algorithm was proposed to improve leaf area index estimation accuracy. • Leaf chlorophyll content is useful information for leaf area index estimation. • Among three Sentinel-2 RE bands, only RE1 is sensitive to leaf chlorophyll content. Red-edge band is widely used for LAI estimation as it is highly correlated to vegetation growth conditions. Canopy reflectance is affected by both vegetation biophysical and biochemical characteristics. However, estimating LAI using satellite reflectance data as input rarely considers the influence of leaf chlorophyll content (LCC). This study tested the hypothesis whether LAI estimation accuracy can be improved by involving LCC information. Firstly, the sensitivities of seven PROSAIL simulated Sentinel-2 bands to LAI and LCC were investigated, and related vegetation indices (VIs) were constructed using these sensitive bands (including LAI-sensitive VIs and LCC-sensitive VIs). Then, the LAI estimation model taking sensitive VIs as input and LCC estimation model taking sensitive VIs as input were generated by random forest regression algorithm. Finally, the improved LAI estimation model involving LCC information was proposed using three different methods: (1) PROSAIL simulated LCC, (2) simulated LCC with noise, and (3) functional equation of LCC. The results indicated that the three LCC information introducing methods all improved the LAI estimation accuracy, while using the functional equation of LCC (growth equation) performed best with RMSE of 0.736, which is 11.54% higher when compared to the basic LAI estimation model.

LAI estimation across California vineyards using sUAS multi-seasonal multi-spectral, thermal, and elevation information and machine learning

LAI estimation across California vineyards using sUAS multi-seasonal multi-spectral, thermal, and elevation information and machine learning

Plant Growth and Lai Estimation Using Quantized Embedded Regression Models for High Through put Phenotyping

Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high- throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research. The influence of climate change, and due to its unpredictable nature, majority of agricultural crops have been affected in terms of production and maintenance. Hybrid and cost-effective crops are making their way into the market, but monitoring factors that affect the increase in yield of these crops, and conditions favorable for growth have to be manually monitored and structured to yield high throughput. Farmers are showing transition from traditional means to hydroponic systems for growing annual and perennial crops. These crop arrays possess growth patterns, which depend on environmental growth conditions in the hydroponic units. Semi-autonomous systems, which monitor these growths, may prove to be beneficial, reduce costs and maintenance efforts, and predict future yield beforehand to get an idea on how the crop would perform. These systems are also effective in understanding crop drools and wilt/diseases from visual systems and traits of plants. Forecasting or predicting the crop yield well ahead of its harvest time would assist the strategists and farmers for taking suitable measures for selling and storage. Accurate prediction of crop development stages plays an important role in crop production management. Such predictions will also support the allied industries for strategizing the logistics of their business. Several means and approaches of predicting and demonstrating crop yields have been developed earlier with changing rate of success, as these do not take into considerations the weather and its characteristics and are mostly empirical. Crop yield estimation is also affected by taking into account a few other factors. Plant diseases enormously affect the agricultural crop production and quality with huge economic losses to the farmers and the country. This in turn increases the market price of crops and food, which increase the purchase burden of customers. Therefore, early identification and diagnosis of plant diseases at every stage of plant life cycle is a very critical approach to protect and increase the crop yield. In this article, I propose an Embedded Machine Learning approach to predicting crop yield and biomass estimation of crops using an Image based Regression approach using edge Impulse that runs on Edge system, Sony Spresense, in real time. This utilizes few of the six Cortex M4F cores provided in the Sony Spresense board for Image processing, inferencing and predicting a regression output in real time. This system uses Image processing to analyze the plant in a semi-autonomous environment and predict the numerical serial of the biomass allocated to the plant growth. This numerical serial contains a threshold of biomass, which is then predicted for the plant. The biomass output is then also processed through a linear regression model to analyze efficacy and compared with the ground truth to identify pattern of growth. The image Regression and linear regression model contribute to an algorithm, which is finally used to test and predict biomass for each plant semi-autonomously.

Development of LAI and fruit load estimation method using a three-dimensional shape measurement sensor

Development of LAI and fruit load estimation method using a three-dimensional shape measurement sensor

Full Parameterisation Matters for the Best Performance of Crop Models: Inter-comparison of a Simple and a Detailed Maize Model

Process-based crop growth models have become indispensable tools for investigating the effects of genetic, management, and environmental factors on crop productivity. One source of uncertainty in crop model predictions is model parameterization, i.e. estimating the values of model input parameters, which is carried out very differently by crop modellers. One simple (SSM-iCrop) and one detailed (APSIM) maize (Zea mays L.) model were partially or fully parameterized using observed data from a 2-year field experiment conducted in 2016 and 2017 at the UFT (Universitäts- und Forschungszentrum Tulln, BOKU) in Austria. Model initialisation was identical for both models based on field measurements. Partial parameterization (ParLevel_1) was first performed by estimating only those parameters related to crop phenology. Full parameterization (ParLevel_2) was then conducted by estimating parameters related to phenology plus those affecting dry mass production and partitioning, nitrogen uptake, and grain yield formation. With ParLevel_1, both models failed to provide accurate estimation of LAI, dry mass accumulation, nitrogen uptake and grain yield, but the performance of APSIM was generally better than SSM-iCrop. Full parameterization greatly improved the performance of both crop models, but it was more effective for the simple model, so that SSM-iCrop was equally well or even better compared to APSIM. It was concluded that full parameterization is indispensable for improving the accuracy of crop model predictions regardless whether they are simple or detailed. Simple models seem to be more vulnerable to incomplete parameterization, but they better respond to full parameterization. This needs confirmation by further research.

SASYA: An integrated framework for crop biophysical parameter retrieval and within-season crop yield prediction with SAR remote sensing data

Food security can be assured with a reasonable crop yield forecast at a national and regional scale. The agencies require advance estimates of production of major crops at a regional scale for taking various policy decisions. Hence, it is necessary to develop operational systems for crop monitoring and yield forecasting. Unlike the traditional annual crop yield estimates based on the statistics-based framework, remote sensing techniques are recognized as an effective approach for yield forecasting. In particular, the microwave remote sensing data have gained importance due to its all-weather imaging capability and sensitivity to crop phenological changes. In this present work, an integrated framework ‘SASYA-Space technology for Agricultural Systems and Yield forecasting Algorithm’ is introduced for crop yield forecasting using Synthetic Aperture Radar (SAR) remote sensing images. SASYA incorporates a model inversion scheme to estimate crop biophysical parameters (Leaf Area Index–LAI and wet biomass) simultaneously using a multi-output support vector regression (MSVR). The framework cross-link the MSVR based inversion module with a yield prediction module, which utilizes the WOrld FOod STudies (WOFOST) crop simulation model. The robustness of the framework is assessed for estimation of winter barley yield using L-band NISAR data simulated using full-pol L-band E-SAR airborne SAR data acquired during AgriSAR 2006 campaign over DEMMIN test site in Germany. The simulated NISAR data in HH-HV-VV, HH-HV, and VV-HV modes are first compared by investigating accuracies to estimate crop biophysical parameters with the inversion of Water Cloud Model. These LAI and wet biomass estimates are then used in SASYA framework to assimilate with WOFOST crop yield model to estimate grain yield. Different polarization modes HH-HV-VV, HH-HV, and VV-HV are also assessed to investigate best performance in grain yield estimation. Also, different test cases are considered to find the suitable acquisition dates based on crop phenological stages, which returns the best potential yield estimation. The SAR images during heading and early fruit development stage are found to be most important for yield prediction with lowest RMSE error in the range of 0.57–1.612 ton/ha. The LAI, biomass and potential yield map indicate promising results, which can be used further for crop risk assessment for that region.