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The Normalized Difference Vegetation Index (NDVI) is widely used for monitoring vegetation status, as accurate and reliable NDVI time series are crucial for understanding the relationship between environmental conditions, vegetation health, and productivity. Ground digital cameras have been recognized as important potential data sources for validating remote-sensing NDVI products. However, differences in the spectral characteristics and imaging methods between sensors onboard satellites and ground digital cameras hinder direct consistency analyses, thereby limiting the quantitative application of camera-based observations. To address this limitation and meet the needs of vegetation monitoring research and remote-sensing NDVI validation, this study implements a novel NDVI camera. The proposed camera incorporates narrowband dual-pass filters designed to precisely separate red and near-infrared (NIR) spectral bands, which are aligned with the configuration of sensors onboard satellites. Through software-controlled imaging parameters, the camera captures the real radiance of vegetation reflection, ensuring the acquisition of accurate NDVI values while preserving the evolving trends of the vegetation status. The performance of this NDVI camera was evaluated using a hyperspectral spectrometer in the Hulunbuir Grassland over a period of 93 days. The results demonstrate distinct seasonal characteristics in the camera-derived NDVI time series using the Green Chromatic Coordinate (GCC) index. Moreover, in comparison to the GCC index, the camera's NDVI values exhibit greater consistency with those obtained from the hyperspectral spectrometer, with a mean deviation of 0.04, and a relative root mean square error of 9.68%. This indicates that the narrowband NDVI, compared to traditional color indices like the GCC index, has a stronger ability to accurately capture vegetation changes. Cross-validation using the NDVI results from the camera and the PlanetScope satellite further confirms the potential of the camera-derived NDVI data for consistency analyses with remote sensing-based NDVI products, thus highlighting the potential of camera observations for quantitative applications The research findings emphasize that the novel NDVI camera, based on a narrowband spectral design, not only enables the acquisition of real vegetation index (VI) values but also facilitates the direct validation of vegetation remote-sensing NDVI products. [ABSTRACT FROM AUTHOR]

The last few decades have seen the emergence of many new methods for measuring leaf area index (LAI). One attractive method is to measure LAI by using a readily available simple instrument, such as a smartphone, as an alternative to traditional complicated and thus costly commercial instruments. We designed a novel integrated LAI measurement system, LAISmart, which is implemented by using two smart terminals communicating through a wireless connection in an integrated support infrastructure. The LAI is calculated by using the Beer gap fraction model on the assumption of spherical leaf angle distribution that takes the G value (the projection of unit foliage area on the plane perpendicular to the view direction θ) as constant at 0.5. The gap fraction is calculated by segmenting the image pixel into canopy and background. For the selection of image segment features, LAISmart provides two options, greenness index and blue band, which may be suited to different vegetation types. The proposed LAISmart was validated by measurements on four types of vegetation, i.e., Evergreen Needleleaf Forest (ENF), Deciduous Broadleaf Forest (DBF), Deciduous Needleleaf Forest (DNF), and broadleaf crop, on which 114 LAI values were calculated in the eight datasets. A pairwise comparison between LAISmart and LAI-2000 showed that the LAI values derived from LAISmart correlated strongly with those of the LAI-2000, with an R 2 value of 0.97, and had high accuracy in total, with a root–mean–square error (RMSE) of 0.45 m 2 /m 2 . The validation results revealed that the overestimation on LAI caused by underestimation of the G value might complement the bias caused by misclassification. Moreover, although there is no limitation on the shooting angle of LAISmart, it is recommended that LAISmart be used at the low zenith angle mode, e.g., upward- or downward-looking mode, rather than at the large zenith angle. Further validation will focus on other vegetation types such as grass and shrubs in order to improve the performance of LAISmart for different vegetation types. [ABSTRACT FROM AUTHOR]

• DBH can be measured accurately in a noncontact mode, at low cost and portability. • Various factors are considered to improve the generalizability of the measurement. • Performance of DBH measurement was improved by the DBH calculation model. The measurement of tree diameter at breast height (DBH) is the basis for estimating forest timber volume, biomass, and carbon fluxes. The traditional contact methods of measuring DBH are time-consuming and labor-intensive. Thus, it is important to realize a low-cost and rapid method for measuring DBH. In this paper, a non-contact method was proposed by integrating passive (a smartphone) and active optical sensors (a laser ranger). With this device, the horizontal distance from the sensor to the tree trunk acquired by the laser ranger and the image of the target tree acquired by the smartphone were collected simultaneously. An autodetection algorithm was employed to identify the tree trunk within the image, and the diameter of the tree was then measured in combination with the horizontal distance based on the photogrammetry principle. The performance of the proposed method was validated using measuring tapes across 371 trees, the main species of which were Italian Poplar (Populus euramevicana) and Pine (Pinus tabuliformis) with diameters ranging from 6 to 51 cm. To investigate the factors that might affect the method, the results were further analyzed under four different conditions, i.e., varied illumination conditions, urban and natural forest conditions and different tree species with varied surface texture features. The results suggested that the measurements using the proposed device were in good agreement with those of the traditional contact method, with an absolute mean error (MAE) of 1.12 cm and RMSE of 1.55 cm. The attraction of the proposed method is that it is low-cost, portable, easy to use and sufficiently accurate. It is also expected that the proposed method can facilitate the measurement of DBH-related canopy structure parameters, such as tree volume, and other parameters, such as tree height, with little adaptation to the current version. [ABSTRACT FROM AUTHOR]

Plant leaf area index (LAI) is an important structural parameter for describing the ability of the canopy to intercept solar radiation, and it plays an important role in the estimation of forest biomass. To overcome the difficulties of current ground measurement instruments in automatically collecting plant LAI, we have designed a continuous LAI measurement system: LAINet. The main part of LAINet includes the distributed Below Node (BN), which is deployed below the canopy and receives transmitted solar radiation, the Above Node (AN), which is used to receive downward radiation above the canopy, and the Central Node (CN), which is used as a data reception and control node. Communication among the nodes is implemented through the Zigbee protocol, while data exchange between the CN and the remote data center server is completed through the General Packet Radio Service (GPRS) network. The design of the LAINet framework and the algorithm for estimating the LAI from a multi-point directional transmittance is presented in this paper. The sensors of LAINet are calibrated on two clear days, and the performance of the proposed system is compared in time series field experiments. Comparison of the nodes’ sensor results indicates that under the condition of the same solar radiation, the response between the different sensors is of high consistence, and each node has a stable calibration coefficient across different dates. In the field coniferous forest experiment, upon comparing with TRAC (Tracing Radiance and Architecture of Canopies) instruments and the results obtained using different data processing algorithms, it is found that the LAINet system can produce the best results with a minimum root mean square error (RMSE = 0.34) and a higher determination coefficient ( R 2 = 0.81). We adopt a multi-day window for aggregating the daily observed LAI to remove the noise that occurs from daily weather variation, and it indicates that the aggregated time series LAI can capture the process of the coniferous needles’ growth and defoliation properly. Thus, it is confirmed that LAINet can be used as a feasible method to conduct automatic ground observations, especially in the scenario where continuous observation is required. [ABSTRACT FROM AUTHOR]

This study intended to investigate the reflectance model for a crop stressed by the heavy metal copper. Forty-six groups of copper-treated leaves were measured in a laboratory experiment. Through these measurements, we obtained leaf reflectance spectra from 400 to 2500 nm, information on the biochemical components of leaves, and their corresponding scanning electron microscopy images. We then developed a new reflectance spectral model based on the classic broadleaf radiative transfer model – PROSPECT. Compared with the PROSPECT model, the new model primarily considers the addition of the specific absorption coefficient of the copper ion. The scattering process is described by a refractive index (n) and a leaf structure parameter (N). The absorption is modelled with pigment concentration, water content, dry matter content, copper ion contamination, and the corresponding specific spectral absorption coefficients (,,, and). Thus, reflectance spectral modelling is an inversion procedure to calculate the above six parameters accurately. For model validation, 16 leaves from the laboratory experiment were used. The validation showed that the inversion values foragree very well with the published absorption spectra of cupric chloride solutions. The linear regression analysis between the simulated and measured reflectance values provides a correlation coefficient of approximately 0.9441, and the root-mean-square error was less than 0.1 from 400 to 2500 nm. [ABSTRACT FROM AUTHOR]

• Camera effective viewing zenith angle was enlarged by an inclined smartphone. • Directional gap fraction was computed from image of inclined smartphone camera. • Mean tilt angle was retrieved by matching contact number with theory G function. • Leaf area index was estimated more accurately after mean tilt angle was estimated. Measurements of leaf area index (LAI) are important for modeling microclimate in vegetation research. Among the instruments for measuring the LAI, smartphone cameras are becoming an attractive alternative to special LAI instruments. However, the narrow full field of view (FOV) of the common smartphones offer only an effective viewing zenith angle (VZA) of less than 35° when the camera is pointing straight up. To overcome this limitation, we propose a method to estimate LAI from an inclined smartphone camera that can enlarge the range of the sensor's effective VZA. With the directional gap fractions extracted from the images taken by the inclined smartphone camera, a curve matching algorithm is used to iteratively search for the simulated G functions, i.e. functions of mean tilt angle (MTA) and VZA. The MTAs corresponding to the matched G functions are selected as ancillary parameters to help calculate the LAI. The proposed method is validated using data collected over crops and trees by a LAI-2200 instrument and a Huawei Honor 7 smartphone. The results reveal that an inclination angle of 30° from zenith is superior to other angles of 0, 45 and 60°. A good agreement between the LAI measurements from the proposed method and those from the LAI-2200 supports the accurate estimation of MTAs. The success of the MTA estimates and thus LAI measurements is attributed to the enlarged VZA ranging from 4° to 60°, and this VZA is comparable with that of the LAI-2200 instrument. The attraction of the proposed method is that it does not rely on the empiric al G value or MTA, providing an affordable alternative to traditional commercial instruments. Future efforts can be directed to automatically capture images when the smartphone is inclined to the desired angle. [ABSTRACT FROM AUTHOR]

Abstract: Remotely sensed data is the main source of vegetation leaf area index (LAI) information on the regional to global scale. Many validation results have revealed that the accuracy of the retrieved LAI is often affected by the cloud cover of imagery, instrument problems, and inversion algorithms. Ground meteorological station data, characterized by relatively high accuracy and time continuity compared with remote sensing data, can provide complementary information to remote sensing observations. In this paper, we combine the potential advantages of both types of data in order to improve LAI retrievals in the Heihe River Basin, an arid and semi-arid area in northwest China where Moderate Resolution Imaging Spectroradiometer (MODIS) LAI values are significantly underestimated. A dynamic Bayesian network (DBN) is used to integrate these two data types for time series LAI estimation. Results show that the square of correlation coefficient between LAI values estimated by our DBN method (referred to as DBN LAI) and field measured LAI values is 0.76, with a root mean square error of 0.78. The DBN LAI are closer to field measurements than the MODIS LAI standard product values. Moreover, by introducing ground meteorological station data using a dynamic process model, DBN LAI show better temporal consistency than the MODIS LAI. It is concluded that the quality of LAI retrievals can be improved by combining remote sensing data and ground meteorological station data using a filtering inference algorithm in a DBN framework. More importantly, the study provides a basis and method for utilizing ground meteorological station network data to estimate land surface parameters on a regional scale. [Copyright &y& Elsevier]

The leaf area index (LAI) is a key parameter in many meteorological, environmental and agricultural models. At present, global LAI products from several sensors have been released. These single sensor-based LAI products are generally discontinuous in time and cannot characterize the status of natural vegetation growth very well. In this study, by fusing Moderate Resolution Imaging Spectroradiometer (MODIS) and Satellite Pour l'Observation de la Terre (SPOT) VEGETATION products, time-series LAIs were used to train recurrent nonlinear autoregressive neural networks with exogenous inputs (NARXNNs) for six typical vegetation types. The exogenous inputs included time-series reflectances in the red, near-infrared and shortwave infrared bands as well as the corresponding sun-viewing angles. These NARXNNs subsequently served to predict the time-series LAI. The validation results show that the predicted LAI of the NARXNN is not only more continuous and stable than the MODIS LAI as a function of time but is also much closer to the ground truth. Thus, the proposed method may be helpful for improving the quality of the MODIS LAI. [ABSTRACT FROM PUBLISHER]

Leaf area index (LAI) products retrieved from remote sensing observations have been widely used in the fields of ecosphere, atmosphere etc. However, because satellite-observed images are captured instantaneously and sometimes screened by cloud, some current LAI products are inherently discontinuous in time and their accuracy may not meet the needs of users well. To solve these problems, we proposed a dynamic Bayesian network (DBN)-based data fusion algorithm that integrates dynamic crop growth information, a canopy reflectance (CR) model and remote sensing observations from the perspective of Bayesian probability. Using the proposed algorithm, LAI was estimated using data sets from both field measurements for winter wheat in Beijing, China, and MODIS reflectance data at two American flux tower sites. Results showed good agreement between the LAI estimated by the DBN-based data fusion method and the true ground LAI, with a correlation coefficient of (R) 0.95 and 0.96, respectively, and a corresponding root mean square error (RMSE) of 0.35 and 0.49, respectively. In addition, the LAI estimated by the DBN-based data fusion method formed a continuous time series and was consistent with the variety law of vegetation growth at both plot and flux tower site scales. It has been demonstrated that the proposed DBN-based data fusion algorithm has the potential to be used to accurately estimate LAI and to fill the temporal gap by integrating information from multiple sources. [ABSTRACT FROM AUTHOR]

A hybrid inversion technique based on Bayesian network is proposed for estimating the biochemical and biophysical parameters of land surface vegetation from remotely sensed data. A Bayesian network is a unified knowledge-inferring process that can incorporate information derived from multiple sources including remote sensing and information derived from a priori knowledge. Using this inversion approach, content of chlorophyll a and chlorophyll b (Cab) and leaf area index (LAI) of winter wheat were estimated from data derived from simulations as well as field measurements. Estimations from the simulated data proved accurate, with root mean square errors (RMSEs) of 0.54 m2/m2 in LAI and 4.5 μg/cm2 in Cab. In validating the estimates against field measurements, it was found that prior knowledge of target parameters improved the accuracy of estimates, in terms of RMSEs from 0.73 to 0.22 m2/m2 in LAI and 9.6 to 4.0 μg/cm2 in Cab. Bayesian inference in this hybrid inversion scheme produces a posterior probability distribution, which can reveal such properties of the inferred results as updated information contained in the inversion result. Using entropy, the revision of posterior information about the parameters of interest was calculated. Including more data may allow more information to be retrieved about parameters in general. Exceptions were also observed where data from some viewing angles slightly reduced the information on the parameters of interest. It was also found that data from these viewing angles were less sensitive to the parameters. The method proposed here was also validated using LandSat ETM+ imagery provided by the BigFoot project. When used for mapping LAI with ETM+ imagery, the proposed method with an RMSE of 0.70 and a correlation of 0.67 produced a slightly better result than that from empirical regression. [Copyright &y& Elsevier]

The leaf area index (LAI) is an essential input parameter for quantitatively studying the energy and mass balance in soil-vegetation-atmosphere transfer systems. As an active remote sensing technology, light detection and ranging (LiDAR) provides a new method to describe forest canopy LAI. This paper reviewed the primary LAI retrieval methods using point cloud data (PCD) obtained by discrete airborne LiDAR scanner (DALS), its validation scheme, and its limitations. There are two types of LAI retrieval methods based on DALS PCD, i.e., the empirical regression and the gap fraction (GF) model. In the empirical model, tree height-related variables, LiDAR penetration indexes (LPIs), and canopy cover are the most widely used proxy variables. The height-related proxies are used most frequently; however, the LPIs proved the most efficient proxy. The GF model based on the Beer-Lambert law has been proven useful to estimate LAI; however, the suitability of LPIs is site-, tree species-, and LiDAR system-dependent. In the local validation in previous studies, poor scalability of both empirical and GF models in time, space, and across different DALS systems was observed, which means that field measurements are still needed to calibrate both types of models. The method to correct the impact from the clumping effect and woody material using DALS PCD and the saturation effect for both empirical and GF models still needs further exploration. Of most importance, further work is desired to emphasize assessing the transferability of published methods to new geographic contexts, different DALS sensors, and survey characteristics, based on figuring out the influence of each factor on the LAI retrieval process using DALS PCD. In addition, from a methodological perspective, taking advantage of DALS PCD in characterizing the 3D structure of the canopy, making full use of the ability of machine learning methods in the fusion of multisource data, developing a spatiotemporal scalable model of canopy structure parameters including LAI, and using multisource and heterogeneous data are promising areas of research. [ABSTRACT FROM AUTHOR]

• Leaf area index and mean tilt angle were measured from downward-looking photography. • Sun movement causes the variation of sunlit and shaded leaves and soil fractions. • Observed multi-directional sunlit and shaded fractions revealed the crop structure. Accurate, efficient, timely, and affordable measurements of crop structural parameters such as leaf area index (LAI) and mean tilt angle (MTA) are needed for crop growth modeling and precision field management. In this paper, a novel method was proposed to simultaneously measure corn (Zea mays L.) LAI and MTA using a low-cost indirect approach. The proposed method is based on multi-directional fractions of sunlit and shaded leaf and soil components obtained from nadir-viewing field photos captured under different solar angles. LAI and MTA were retrieved using a look-up-table (LUT) established using a Pov-Ray based geometrical canopy model. The method was validated using LAI-2200C field-measured data. Results showed that the estimated LAI were consistent with the LAI-2200C measurements, with a mean absolute error (MAE) of 0.11, relative MAE (RMAE) of 5%, and coefficient of determination (R2) of 0.89. The estimated average MTA was also close to that measured using the LAI-2200C, with the MAE of 5.9°, RMAE of 11% and R2 of 0.40. The proposed method provides accurate and efficient measurements of corn crop structural parameters. Thus, it is recommended as an affordable and effective field data collection method. [ABSTRACT FROM AUTHOR]

The leaf area index (LAI) is a crucial structural parameter of forest canopies. Light Detection and Ranging (LiDAR) provides an alternative to passive optical sensors in the estimation of LAI from remotely sensed data. However, LiDAR-based LAI estimation typically relies on empirical models, and such methods can only be applied when the field-based LAI data are available. Compared with an empirical model, a physically-based model—e.g., the Beer–Lambert law based light extinction model—is more attractive due to its independent dataset with training. However, two challenges are encountered when applying the physically-based model to estimate LAI from discrete LiDAR data: i.e., deriving the gap fraction and the extinction coefficient from the LiDAR data. We solved the first problem by integrating LiDAR and hyperspectral data to transfer the LiDAR penetration ratio to the forest gap fraction. For the second problem, the extinction coefficient was estimated from tiled (1 km × 1 km) LiDAR data by nonlinearly optimizing the cost function of the angular LiDAR gap fraction and simulated gap fraction from the Beer–Lambert law model. A validation against LAI-2000 measurements showed that the estimates were significantly correlated to the reference LAI with an R2 of 0.66, a root mean square error (RMSE) of 0.60 and a relative RMSE of 0.15. We conclude that forest LAI can be directly estimated by the nonlinear optimization method utilizing the Beer–Lambert model and a spectrally corrected LiDAR penetration ratio. The significance of the proposed method is that it can produce reliable remotely sensed forest LAI from discrete LiDAR and spectral data when field-measured LAI are unavailable. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is one of the most important canopy structure parameters utilized in process-based models of climate, hydrology, and biogeochemistry. In order to determine the reliability and applicability of satellite LAI products, it is critical to validate satellite LAI products. Due to surface heterogeneity and scale effects, it is difficult to validate the accuracy of LAI products. In order to improve the spatio-temporal accuracy of satellite LAI products, we propose a new multi-scale LAI product validation method based on a crop growth cycle. In this method, we used the PROSAIL model to derive Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) LAI data and Gaofen-1 (GF-1) for the study area. The Empirical Bayes Kriging (EBK) interpolation method was used to perform a spatial multi-scale transformation of Moderate Resolution Imaging Spectroradiometer (MODIS) LAI products, GF-1 LAI data, and ASTER LAI data. Finally, MODIS LAI satellite products were compared with field measured LAI data, GF-1 LAI data, and ASTER LAI data during the growing season of crop field. This study was conducted in the agricultural oasis area of the middle reaches of the Heihe River Basin in northwestern China and the Conghua District of Guangzhou in Guangdong Province. The results suggest that the validation accuracy of the multi-scale MODIS LAI products validated by ASTER LAI data were higher than those of the GF-1 LAI data and the reference field measured LAI data, showing a R2 of 0.758 and relative mean square error (RRMSE) of 28.73% for 15 m ASTER LAI and a R2 of 0.703 and RRMSE of 30.80% for 500 m ASTER LAI, which imply that the 15 m MODIS LAI product generated by the EBK method was more accurate than the 500 m and 8 m products. This study provides a new validation method for satellite remotely sensed products. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) is an important parameter to describe the capacity of forests to intercept light and thus affects the microclimate and photosynthetic capacity of canopies. In general, tropical forests have a higher leaf area index and it is a challenge to estimate LAI in a forest with a very dense canopy. In this study, it is assumed that the traditional Light Detection and Ranging (LiDAR)-derived fractional vegetation cover (fCover) has weak relationship with leaf area index in a dense forest. We propose a partial least squares (PLS) regression model using the height percentile metrics derived from airborne LiDAR data to estimate the LAI of a dense forest. Ground inventory and airborne LiDAR data collected in a selectively logged tropical forest area in Eastern Amazonia are used to map LAI from the plot level to the landscape scale. The results indicate that the fCover, derived from the first return or the last return, has no significant correlations with the ground-based LAI. The PLS model evaluated by the leave-one-out validation shows that the estimated LAI is significantly correlated with the ground-based LAI with an R2 of 0.58 and a root mean square error (RMSE) of 1.13. A data comparison indicates that the Moderate Resolution Imaging Spectrometer (MODIS) LAI underestimates the landscape-level LAI by about 22%. The MODIS quality control data show that in the selected tile, the cloud state is not the primary factor affecting the MODIS LAI performance; rather, the LAI from the main radiative transfer (RT) algorithm contributes much to the underestimation of the LAI in the tropical forest. In addition, the results show that the LiDAR-based LAI has a better response to the logging activities than the MODIS-based LAI, and that the leaf area reduction caused by logging is about 13%. In contrast, the MODIS-based LAI exhibits no apparent spatial correlation with the LiDAR-based LAI. It is suggested that the main algorithm of MODIS should be improved with regard to tropical forests. The significance of this study is the proposal of a framework to produce ground-based LAI using forest inventory data and determine the plot-level LAI at the airborne and satellite scale using LiDAR data. [ABSTRACT FROM AUTHOR]

Smart, real-time, low-cost, and distributed ecosystem monitoring is essential for understanding and managing rapidly changing ecosystems. However, new techniques in the big data era have rarely been introduced into operational ecosystem monitoring, particularly for fragile ecosystems in remote areas. We introduce the Internet of Things (IoT) techniques to establish a prototype ecosystem monitoring system by developing innovative smart devices and using IoT technologies for ecosystem monitoring in isolated environments. The developed smart devices include four categories: large-scale and nonintrusive instruments to measure evapotranspiration and soil moisture, in situ observing systems for CO 2 and δ 13C associated with soil respiration, portable and distributed devices for monitoring vegetation variables, and Bi-CMOS cameras and pressure trigger sensors for terrestrial vertebrate monitoring. These new devices outperform conventional devices and are connected to each other via wireless communication networks. The breakthroughs in the ecosystem monitoring IoT include new data loggers and long-distance wireless sensor network technology that supports the rapid transmission of data from devices to wireless networks. The applicability of this ecosystem monitoring IoT is verified in three fragile ecosystems, including a karst rocky desertification area, the National Park for Amur Tigers, and the oasis-desert ecotone in China. By integrating these devices and technologies with an ecosystem monitoring information system, a seamless data acquisition, transmission, processing, and application IoT is created. The establishment of this ecosystem monitoring IoT will serve as a new paradigm for ecosystem monitoring and therefore provide a platform for ecosystem management and decision making in the era of big data. [ABSTRACT FROM AUTHOR]

Net primary productivity (NPP) is a key vegetation parameter and ecological indicator for tracking natural environmental change. High-quality Moderate Resolution Imaging Spectroradiometer Net primary productivity (MODIS-NPP) products are critical for assuring the scientific rigor of NPP analyses. However, obtaining high-quality MODIS-NPP products consistently is challenged by factors such as cloud contamination, heavy aerosol pollution, and atmospheric variability. This paper proposes a method combining the discrete wavelet transform (DWT) with an extended Kalman filter (EKF) for generating high-quality MODIS-NPP data. In this method, the DWT is used to remove noise in the original MODIS-NPP data, and the EKF is applied to the de-noised images. The de-noised images are modeled as a triply modulated cosine function that predicts the NPP data values when excessive cloudiness is present. This study was conducted in South China. By comparing measured NPP data to original MODIS-NPP and NPP estimates derived from combining the DWT and EKF, we found that the accuracy of the NPP estimates was significantly improved. The MODIS-NPP estimates had a mean relative error (RE) of 13.96% and relative root mean square error (rRMSE) of 15.67%, while the original MODIS-NPP had a mean RE of 23.58% and an rRMSE of 24.98%. The method combining DWT and EKF provides a feasible approach for generating new, high-quality NPP data in the absence of high-quality original MODIS-NPP data. [ABSTRACT FROM AUTHOR]

Many applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observation system, the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method, and Gaussian process regression (GPR). First, the LAINet wireless sensor network provides temporally continuous field measurements of LAI. Then, the CACAO approach generates synchronous reflectance data at high spatiotemporal resolution (30-m and 8-day) from the fusion of multitemporal MODIS and high spatial resolution Landsat satellite imagery. Finally, the GPR machine learning regression algorithm retrieves the LAI maps and their associated uncertainties. A case study in a cropland site in China showed that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R2 = 0.88 correlation with ground measurements as evaluated over the entire growing season. This paper demonstrates the potential of the joint use of newly developed software and hardware technologies in deriving concomitant LAI and uncertainty maps with high spatiotemporal resolution. It will contribute to precision agriculture, as well as to the retrieval and validation of LAI products. [ABSTRACT FROM AUTHOR]

Clumping index (CI) is a measure of foliage aggregation relative to a random distribution of leaves in space. The CI can help with estimating fractions of sunlit and shaded leaves for a given leaf area index (LAI) value. Both the CI and LAI can be obtained from global Earth Observation data from sensors such as the Moderate Resolution Imaging Spectrometer (MODIS). Here, the synergy between a MODIS-based CI and a MODIS LAI product is examined using the theory of spectral invariants, also referred to as photon recollision probability (‘ p -theory’), along with raw LAI-2000/2200 Plant Canopy Analyzer data from 75 sites distributed across a range of plant functional types. The p- theory describes the probability ( p -value) that a photon, having intercepted an element in the canopy, will recollide with another canopy element rather than escape the canopy. We show that empirically-based CI maps can be integrated with the MODIS LAI product. Our results indicate that it is feasible to derive approximate p -values for any location solely from Earth Observation data. This approximation is relevant for future applications of the photon recollision probability concept for global and local monitoring of vegetation using Earth Observation data. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI) products are now routinely generated from remote sensing and widely used in most land surface process models. Assessing the uncertainties associated with these LAI products is essential for their proper application. In validation activities, LAI reference maps serve as the bridge to upscale the field measurements to coarse resolution. Currently, the temporally continuous validation is attracting increasing attention. Therefore, there is an urgent requirement for the temporally continuous LAI reference maps. However, two main problems hinder the generation of temporally continuous LAI reference maps: 1) How to obtain temporally continuous field measurements, effectively and inexpensively? 2) How to obtain temporally continuous and fine spatial resolution satellite images which are synchronous with the field measurements? This paper proposed a method to address the above two problems based on the combination of the wireless sensor network technology and a data blending approach. Firstly, the temporally continuous effective LAI was obtained through the analysis of multi-angle gap fraction measured by LAINet observation system (developed with wireless sensor network technology), and the temporally continuous NDVI was reconstructed through CACAO (Consistent Adjustment of the Climatology to Actual Observations, a data blending approach). Then, a transfer function relating reconstructed NDVI to field measured LAI was calibrated through exponential function fitting. Finally, the temporally continuous LAI reference maps were generated by applying the calibrated transfer function to the reconstructed NDVI. Performances of the proposed method were evaluated over a crop site. Results show that the reconstructed LAI reference maps agree well with the original LAI reference maps derived from the Landsat-8 OLI NDVI (R 2 = 0.90, RMSE = 0.27 at 30 m resolution, R 2 = 0.97, RMSE = 0.09 at 1 km resolution). The derived temporally continuous LAI reference maps were then used as benchmark to validate the MOD15A2 LAI product. Generally, the MOD15A2 LAI has a relatively high accuracy (R 2 = 0.53, RMSE = 0.31), and captures the overall vegetation phenology well. But its value shows an obvious underestimation with a bias of −0.30. The results of this study contribute to the assessment of temporal dynamics of uncertainty in LAI products, which will benefit the long-term vegetation monitoring and data assimilation. [ABSTRACT FROM AUTHOR]

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques. [ABSTRACT FROM AUTHOR]

The fractional vegetation cover is an important parameter of the earth surface system. Its ground measurement is the basis in the remotely sensed data-based vegetation inversion modelling. At present, the ground measurement methods include mainly the human ocular estimation method, the sampling method and the photographic method. However the ocular estimation method has the issue of low accuracy, and the sampling method needs to conduct some complicated operations while the photographic method is restricted by the height at which the camera can be placed. This article proposes a method using a hand-held laser range finder to make quick observation on fractional vegetation cover of low shrub vegetation. Using binomial distribution, this model established a probability distribution model about the measurement errors to calculate the fractional vegetation cover with various sampling numbers. Two experiments and one simulation using a computer were done in order to validate the method. The result shows that the fractional vegetation cover obtained by using the hand-held laser range finder can meet the precision requirements. In addition to its high precision, this method is simple in operation and calculation if compared with the traditional ground measurement methods. [ABSTRACT FROM PUBLISHER]

Recently, five Global LAnd Surface Satellite (GLASS) products have been released: leaf area index (LAI), shortwave broadband albedo, longwave broadband emissivity, incident short radiation, and photosynthetically active radiation (PAR). The first three products cover the years 1982–2012 (LAI) and 1981–2010 (albedo and emissivity) at 1–5 km and 8-day resolutions, and the last two radiation products span the period 2008–2010 at 5 km and 3-h resolutions. These products have been evaluated and validated, and the preliminary results indicate that they are of higher quality and accuracy than the existing products. In particular, the first three products have much longer time series, and are therefore highly suitable for various environmental studies. This paper outlines the algorithms, product characteristics, preliminary validation results, potential applications and some examples of initial analysis of these products. [ABSTRACT FROM PUBLISHER]

We proposed a direct approach to validate hectometric and kilometric resolution leaf area index (LAI) products that involved the scaling up of field-measured LAI via the validation and recalibration of the decametric Sentinel-2 LAI product. We applied it over a test study area of maize crops in northern China using continuous field measurements of LAINet along the year 2019. Sentinel-2 LAI showed an overall accuracy of 0.67 in terms of Root Mean Square Error (RMSE) and it was used, after recalibration, as a benchmark to validate six coarse resolution LAI products: MODIS, Copernicus Global Land Service 1 km Version 2 (called GEOV2) and 300 m (GEOV3), Satellite Application Facility EUMETSAT Polar System (SAF EPS) 1.1 km, Global LAnd Surface Satellite (GLASS) 500 m and Copernicus Climate Change Service (C3S) 1 km V2. GEOV2, GEOV3 and MODIS showed a good agreement with reference LAI in terms of magnitude (RMSE ≤ 0.29) and phenology. SAF EPS (RMSE = 0.68) and C3S V2 (RMSE = 0.41), on the opposite, systematically underestimated high LAI values and showed systematic differences for phenological metrics: a delay of 6 days (d), 20 d and 24 d for the start, peak and the end of growing season, respectively, for SAF EPS and an advance of −4 d, −6 d and −6 d for C3S. [ABSTRACT FROM AUTHOR]

Accurate and continuous monitoring of leaf area index (LAI), a widely-used vegetation structural parameter, is crucial to characterize crop growth conditions and forecast crop yield. Meanwhile, advancements in collecting field LAI measurements have provided strong support for validating remote-sensing-derived LAI. This paper evaluates the performance of LAI retrieval from multi-source, remotely sensed data through comparisons with continuous field LAI measurements. Firstly, field LAI was measured continuously over periods of time in 2018 and 2019 using LAINet, a continuous LAI measurement system deployed using wireless sensor network (WSN) technology, over an agricultural region located at the Heihe watershed at northwestern China. Then, cloud-free images from optical satellite sensors, including Landsat 7 the Enhanced Thematic Mapper Plus (ETM+), Landsat 8 the Operational Land Imager (OLI), and Sentinel-2A/B Multispectral Instrument (MSI), were collected to derive LAI through inversion of the PROSAIL radiation transfer model using a look-up-table (LUT) approach. Finally, field LAI data were used to validate the multi-temporal LAI retrieved from remote-sensing data acquired by different satellite sensors. The results indicate that good accuracy was obtained using different inversion strategies for each sensor, while Green Chlorophyll Index (CIgreen) and a combination of three red-edge bands perform better for Landsat 7/8 and Sentinel-2 LAI inversion, respectively. Furthermore, the estimated LAI has good consistency with in situ measurements at vegetative stage (coefficient of determination R2 = 0.74, and root mean square error RMSE = 0.53 m2 m−2). At the reproductive stage, a significant underestimation was found (R2 = 0.41, and 0.89 m2 m−2 in terms of RMSE). This study suggests that time-series LAI can be retrieved from multi-source satellite data through model inversion, and the LAINet instrument could be used as a low-cost tool to provide continuous field LAI measurements to support LAI retrieval. [ABSTRACT FROM AUTHOR]