Recently, time series remote sensing image (TSRSI) has been reported to be an effective resource to mapping fine land use/land cover (LULC), and deep learning, in particular, has been gaining growing attention in this field. However, existing deep learning methods often only learn features from either the temporal or spatial domain, neglecting the intercorrelation between temporal features, which may provide more information for classification, are not fully considered. In order to make full use of the relations between temporal features and to explore more objective features for improving classification accuracy, we proposed a feature relationship-based classification method. The method leverages the angles between features on the temporal curve to establish relationships between every pair and triplet of features, resulting in the creation of feature relationship matrices (FRMs) and feature relationship tensors (FRTs). Afterwards, a 2D-3D multi-scale convolutional neural network (2D-3D MSCNN) was designed to learn deep features from FRM and FRT, achieving the classification improvement of TSRSI. Our experiment was conducted on TSRSIs located in two counties, Sutter and Kings in California, United States. The experimental results indicate that compared to both deep learning and non-deep learning methods, the proposed approach achieves significant improvements in accuracy and LULC mapping, validating the effectiveness and feasibility of enhancing TSRSI classification accuracy through feature relationship learning.

Land Surface Temperature (LST) plays a crucial role in Earth’s energy balance and ecosystems. Various gap-filling methods have been developed to reconstruct seamless LST datasets to deal with the effect of data gaps caused by cloud cover, however, existing studies mainly focus on LST reconstruction under clear-sky conditions, rather than generating actual cloud-impacted LST. This study treats MODIS cloud-free pixels as known sample points. The deep forest (DF) algorithm is employed to establish a nonlinear relationship model between Himawari-8 cumulative downward surface shortwave radiation (DSSR), AMSR2 brightness temperature (TB) data, and other influencing factors on the sample points, as well as LST. This model is applied to cloud-covered pixels to obtain the LST of the underlying pixels, thereby reconstructing the real MODIS LST under the cloud over the Yellow River source region. The feasibility of this approach lies in the fact that cumulative DSSR incorporates the impact of cloud coverage on incoming solar radiation, and there exists a correlation between AMSR2 TB data and LST. The reconstruction results for January, April, July, and October of 2021 were validated against in situ 0 cm LST measurements from five meteorological stations. The results show that the reconstructed LST exhibits high consistency with in-situ measurements, with R2of 0.86, Bias of 0.62 K, and RMSE of 4.48 K. The results demonstrate the effectiveness of using DSSR and microwave data in LST reconstruction, accurately representing actual cloud-impacted LST.

Utilizing global navigation satellite system interferometric reflectometry (GNSS-IR) technology to obtain snow depth (SD) has the advantages of all-day, low cost and large amount of available data. At present, there is still a lack of in-depth research on the influence of weak surface fluctuation on SD inversion. In this paper, we investigate the influence of surface fluctuation on GNSS-IR SD retrieval by analyzing variation of reflection height in different azimuths through clustering based on different satellites during snow-free period, and the surface correction value of each cluster is obtained to correct SD in snowy period, the most probable value of daily SD is obtained by multi-azimuth and multi-satellite SD fusion. In order to prove the rationality and effectiveness of the proposed method, the data of two GNSS observation stations (AB33 and P351) with different elevations and different SD from the Plate Boundary Observation (PBO) are used to carry out experiments. The results show that the SD accuracy with multi-azimuth SD fusion after surface correction is improved significantly. The correlation coefficient (R) increased by 5.04%, the root mean square error (RMSE) decreased by 43.49%, and the mean absolute error (MAE) decreased by 47.62%. Additionally, the average R, RMSE, and MAE of multi-satellite SD fusion results are 0.99, 0.02m and 0.01m respectively. The average error (ME) of the two fusion methods is also significantly reduced. The study provides insightful new ideas for inverting SD using GNSS reflection signals.

In this paper, we demonstrate that traditional chaotic encryption schemes are vulnerable to the known-plaintext attack (KPA) with deep learning. Considering the decryption process as image restoration based on deep learning, we apply Convolutional Neural Network to perform known-plaintext attack on chaotic cryptosystems. We design a network to learn the operation mechanism of chaotic cryptosystems, and utilize the trained network as the decryption system. To prove the effectiveness, we select three existing chaotic encryption schemes as the attacked targets. The experimental results demonstrate that deep learning can be applied to known-plaintext attack against chaotic cryptosystems successfully. Compared with traditional attack methods for chaotic cryptosystems, the proposed method shows obvious advantages: (1) One neural network may be applied to cryptanalysis of various chaotic cryptosystems, not limited to specific one; (2) the proposed method is significantly convenient and cost-efficient. This paper provides a new idea for the cryptanalysis of chaotic cryptosystems.

Holographic subsurface radar (HSR) is a promising geophysical electromagnetic technique to detect shallowly buried objects due to its high lateral resolution. However, the subsurface inspections and visualization of buried targets are prone to be impaired by strong clutter from the nonplanar surface reflections. In this article, a clutter mitigation method using dual-frequency cancellation and sparse feature enhancement is proposed for HSR data to distinguish the targets from background. The radar signals are received at two distinct frequencies under certain conditions to calculate the strong surface reflections. After cancellation of the estimated surface, a modified$\ell _{1}$regularization is utilized to further mitigate the residual clutter and highlight the target signature. The effectiveness of the proposed method is evaluated on both numerical simulation and radar signals collected from real HSR systems. The visual and quantitative results demonstrate that the proposed method successfully removes the nonplanar surface clutter with the targets preserved.

Atmospheric disturbance, sensor malfunctions, and other factors can cause serious gap pixels in MODIS normalized difference snow index (NDSI) products. In this paper, MODIS NDSI gap pixels are reconstructed in a highly heterogeneous area with drastic snow accumulation and melting changes using a long short-term memory (LSTM) network. Three LSTM-based MODIS NDSI gap pixel reconstruction schemes, i.e., forward, backward, and bidirectional LSTM networks that separately use earlier, subsequent, and integrated earlier and subsequent timestamp information, are developed. NDSI information for the gap pixel is restored using the long-term spatiotemporal information for this pixel and its adjacent pixels. A case study of NDSI reconstruction in the source area of the Yellow River, northwestern China, during the 2018–2019 snow season, demonstrates that all three LSTM-based schemes can reliably generate spatiotemporally continuous NDSI data with an accuracy comparable to that of the original MODIS NDSI products under clear-sky conditions. The bidirectional LSTM-based scheme, which has the best performance, can achieve a desirable overall accuracy of 89.93%, with an omission error of 3.82% and a commission error of 6.25%, in terms of dichotomous evaluation based on in situ snow depth observations. The R2, average RMSE, overestimation error, and underestimation error are 0.95, 5.13%, 5.39, and 6.40%, respectively, in terms of the continuous value assessment based on the gap pixels assumption. Our results demonstrate the reliability and feasibility of the LSTM-based schemes in recovering the missing values in MODIS NDSI products by deeply excavating the spatial continuity and long-time series dependence of the snow cover.

This article investigates how to select the optimal Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 OLI image pairs for MODIS fractional snow cover (FSC) mapping using an artificial neural network (ANN). Four issues are discussed, including date selection, location selection, priority of date and location, and global and regional monitoring of MODIS FSC with ANNs. We propose using the histogram quadratic distance to define the similarity between the ANN training and the target test scene, which was used to quantify the representativeness of the training samples. We use the case study of MODIS FSC mapping of North Xinjiang, China, in the 2014-2015 snow season as an example. Thirty-eight experiments were designed. The experimental results demonstrate that the ANN-based FSC estimation accuracy outperformed the MODIS FSC product, with an average RMSE of 0.17, R exceeds 0.8, and the total snow cover area was estimated more accurately in most cases. For a target test scene, we preliminarily inferred that the best method is to develop an ANN using image pairs of another location with the highest similarity in the same acquisition time, using historical image pairs of the target scene with the highest similarity is the second choice, and using historical image pairs from another location with a high similarity is the third choice. For global- and regional-scale MODIS FSC mapping with ANNs, we formulated the strategy of initially determining a reasonable location and subsequently selecting the acquisition date of the image pairs to guarantee that the training data set represents the entire study area well.

In this study, the continuous daily ET at 90 m spatial resolution was estimated from the Surface Energy Balance System (SEBS) by using the land surface temperature and land surface reflectance of VNIR combining the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Space-borne Thermal Emission Reflectance Radiometer (ASTER) spatiotemporal characteristics obtained from the Temporal Adaptive Reflectance Fusion Model (STARFM). Performance of this scheme used to estimate ET at high spatiotemporal resolution was validate over a heterogeneous oasis-desert regions by using in-situ observations from automatic meteorological systems (AMS) and eddy covariance (EC) systems in the middle reach of Heihe River Basin of Northwest China. The error introduced during the data fusion process based on STARFM is in an acceptable range for the predicted LST at 90 m spatial resolution. The surface energy fluxes estimated from SEBS using predicted remotely sensed data combing MODIS and ASTER spatiotemporal characteristics are agree well with observed surface energy fluxes from EC systems for all land cover types. The continuous daily ET estimated based on SEBS and STARFM seems to produce the general ET trends reasonably well for all land covers.

Only using the post-earthquake PolSAR imagery to interpret collapsed buildings information is a rapid and effective disaster investigation means, which is also easy and fast for implementation of the earthquake damage assessment. This work is focused on rapid building earthquake damage information detection in urban areas using a single post-earthquake PolSAR image. In this paper, the Precision Weighted Multi-feature Fusion (PWMF) method is proposed to fuse multiple texture features for more accurate extraction of the collapsed buildings and the undamaged buildings. In addition, the algorithm of Optimization of Polarimetric Contrast Enhancement (OPCE) is employed to enhance the contrast ratio between the collapsed buildings and the oriented buildings in order to improve the extraction accuracy of the collapsed buildings and undamaged buildings. The building damage assessment is carried out at the city block scale according to the building collapse rate.

Analysis of MODIS Fractional Snow Cover (FSC) products during 2006 to 2007 snow seasons shows that the average cloud cover is above 50%. In the paper, an effective method based on similar pixel replacement by modified non-dominated sorting genetic algorithm (NSGA-II) is developed to solve the problem of cloud contamination. A cloud pixel is filled using an appropriate similar pixel within the remaining region of the image. We designed two experimental scheme based on “cloud hypothesis” and “actual” cloud cover during 2006 to 2007 snow seasons in Northern Xinjiang. Results show that the proposed method is qualified for cloud removal, with extremely well statistical accuracy in cloud hypothesis experimental verification, and there is also relatively high classification accuracy and consistency of MODIS derived Snow Covered Day (SCD) and In-situ observed SCD in actual experimental validation.

This study proposes a method for improving the estimation of surface turbulent fluxes in surface energy balance system (SEBS) model under water stress conditions using MODIS data. The normalized difference water index (NDWI) as an indicator of water stress is integrated into SEBS. To investigate the feasibility of the new approach, the desert-oasis region in the middle reaches of the Heihe River Basin (HRB) is selected as the study area. The proposed model is calibrated with meteorological and flux data over 2008- 2011 at the Yingke station and is verified with data from 16 stations of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) project in 2012. The results show that soil moisture significantly affects ET under water stress conditions in the study area. Adding the NDWI in SEBS can significantly improve the estimations of surface turbulent fluxes in water-limited regions especially for spare vegetation cover area. The daily ET maps generated by the new model also show improvements in drylands with low ET values. This study demonstrates that integrating the NDWI into SEBS as an indicator of water stress is an effective way to improve the assessment of the regional ET in semi-arid and arid regions.

Development and validation of hydrological cycle elements derived from remote sensing observations are of utmost importance for the study of hydrology at different scales, especially at watershed scale. This paper presents the progress we have made in developing and validating watershed scale hydrological cycle products, mainly including precipitation, snow cover area (SCA), soil moisture (SM), evapotranspiration (ET) and groundwater variation. Corresponding high quality remote sensing products (RSPs) have been produced. In addition, to validate the RSPs of water cycle variables, we established several ground observation networks which can provide extensive and high quality validation dataset. Our efforts significantly improve our understanding in watershed water cycle variables, and the developed water cycle products and validation data products have been widely used in several research domains, providing supporting for several key research projects. Based on these efforts, the developed and validated RSPs having been merged into hydrological and land surface models with the aid of land data assimilation method, to allow us to close the water cycle at the basin scale, and further improve our knowledge on terrestrial water study.

Rapidly and accurately obtaining collapsed buildings information of the earthquake-stricken areas can help to effectively guide the implementation of the emergency rescue and can reduce disaster losses and casualties. This work is focused on rapid building earthquake damage information detection in urban areas using a single post-earthquake PolSAR data. In this paper, the methods of polarization orientation angle (POA) compensation and Wishart supervised classification are employed to extract the collapsed buildings and undamaged buildings. In addition, the two parameters of the normalized difference of the dihedral component (NDDC) and the HH-HV Correlation Coefficient (ρHHHV) are proposed to improve the extraction accuracy of the collapsed buildings and undamaged buildings. The building damage assessment is carried out at the city block scale according to the building collapse rate.

A multilayer feedforward artificial neural network (ANN) is developed for mountainous fractional snow cover (FSC) mapping. This is trained with back propagation to learn the relationship between FSC and Moderate Resolution Imaging Spectroradiometer (MODIS) products (reflectance at seven bands, normalized difference snow index, land surface temperature (LST), and FSC) and elevation. In this paper, images from Landsat Enhanced Thematic Mapper Plus (ETM+) and MODIS products from three periods are chosen to test and validate the proposed method at the Heihe River Basin. Three binary snow cover maps derived from Landsat ETM+ images are used to calculate FSC. Two of these maps are first used to train, calibrate, and test the ANN. The other independent image is used to test the generalization ability of network. Results show that the ANN can easily incorporate auxiliary information to improve the accuracy of snow cover mapping effectively. It is also capable of mapping snow cover fraction in a complicated mountainous area with considerable generalization. For the nonindependent test set, the performance evaluation results show that the improvements of ANN-based methods are apparent compared with MODIS FSC products (higher correlation coefficient, lower root-mean-square error, and more accurate total snow cover area). For the temporal/temporal-spatial independent test set, ANN-based methods perform slightly worse than the nonindependent test set, but the accuracy of the ANN methods still shows some improvement. Elevation, LST, and FSC play more important roles in the training process of the ANN. Overall, experiment 8, which integrated all input information, is approved the best in all test sets.

Under global warming condition, how NPP change in China's terrestrial ecosystems was affected by climate change. In this study, we used a light-use efficiency model and linear regression model to describe and analyze the spatial and temporal patterns of terrestrial net primary productivity (NPP) in China during 2002-2010 and the drought effects as indicated by Palmer Drought Index (PDSI). First, we used the reconstructed 16-day 0.05°MODIS NDVI product (MOD13C1), 0.05°gridded GLDAS (Global Land Data Assimilation System) meteorological data to estimate the NPP in China. Then Based on regression analysis method, we analyzed the influence of drought on NPP change in China during 2002-2010. Our results suggest that NPP shows a significant change during the past decade and PDSI is likely a good indication for the change in china.

Snow can strongly influence the surface radiation balance, energy exchange and hydrological processes. Accurate fractional snow cover data plays an important role in many applications. As existing fractional snow cover (FSC) products (i.e. MOD10) are not accurate enough in mountainous areas, we developed a three-layers feed-forward artificial neural networks (ANN) for mountainous FSC mapping, which is trained with back-propagation to learn the relationship between FSC and eight different schemes of input information. In the study, an image from Landsat ETM+ and corresponding MODIS data products are chosen to train, validate and test the proposed method at the upstream of Heihe River Basin. The results showed that the ANN-based methods have higher R, lower RMSE and more accurate total snow cover area. Particularly, the Exp.8 combined all input information together achieved the best performance.

An ensemble-based data assimilation approach is developed to characterize the snow water equivalent (SWE) and underlying soil freeze–thaw state (including the soil surface temperature and both soil ice and liquid water content) using multifrequency passive and active microwave remote-sensing measurements. Its feasibility was examined using a synthetic test where passive microwave (1.4, 18.7, and 36.5 GHz) and active microwave [L-band (1.4 GHz), C-band (5.4 GHz), and Ku-band (12 GHz)] measurements at the point scale were individually and simultaneously assimilated to estimate the SWE and soil freeze–thaw state using an Ensemble Batch Smoother framework. The contribution of each channel in retrieving the true SWE, soil surface temperature, soil liquid water and ice content was investigated at the local-scale observation site of the National Aeronautics and Space Administration Cold Land Processes Experiments Field Campaign in northern Colorado during both the snow accumulation (Fall 2002–Winter 2003) and melt (Spring 2003) periods. All of the utilized passive and active measurements were found to contain valuable and complementary information for characterizing the SWE and freeze–thaw state of the underlying soil. L-band measurements were most effective for soil freeze–thaw state estimation, whereas higher frequencies were more effective at SWE characterization. In addition, results from the simultaneous assimilation of passive and active microwave data were compared to those from a modeling approach without assimilating microwave data (open loop). It was found that assimilating both passive and active microwave data decreased the errors that are associated with the open-loop approach. Finally, passive and active measurements were undersampled as expected from the overpasses of current and future satellite platforms. It was observed that the developed method can reliably estimate the soil freeze–thaw state and SWE, even with measurement sequences anticipated from the temporal frequency of existing and future satellites such as the Special Sensor Microwave/Imager, Soil Moisture Active Passive Mission, and Cold Regions Hydrology High-Resolution Observatory.

High radiating efficiency of antenna is very crucial for GPR applications. However, the bow-tie antenna, which is widely used in impulse GPR, has very low radiating efficiency because remarkable energy fed into antenna is radiated as the form of end reflection. In this paper, we study how to design bow-tie antenna with high radiating efficiency for impulse GPR. We find that, if the bow-tie antenna is excited by a bipolar pulse, the radiation efficiency can be significantly improved by utilizing the energy in end reflections. And the improvement is implemented by optimizing the antenna length to superpose the main pulse with the end reflection of a radiated pulse.

In ground-penetrating radar imaging, the classic back-projection (BP) algorithm has an excellent reputation for imaging in layered mediums with convenience and robustness. However, the classic BP algorithm is time consuming and with a lot of artifacts, which have adverse effects on the following work like detection and recognition. A novel BP algorithm, which is both fast and with good effect of suppressing artifacts, is proposed in this letter. At first, an approved approximation method is used to calculate the position of refraction point with remarkable speed and satisfactory accuracy. Then, a lookup table is used to reduce the redundancy in classic BP algorithm. In order to achieve effective artifact suppression, a cross-correlation-based method is introduced. Experimental results of field data present the superiority of the proposed BP algorithm over its classic counterpart both in operation speed and artifact suppression.

Two sets of experiments were performed to identify, respectively, the impacts of using different grain size models and simplified representations of snowpack stratigraphy on the predicted grain size evolution and the resulting radiance predictions. Three different grain size models were examined with the grain size prediction used as inputs to the Microwave Emission Model of Layered Snowpacks (MEMLS) model for predicting radiobrightness (at different frequencies) from the snowpack. The varying mechanisms and treatment of grain size growth lead to differences between the three models. Despite the differences, when using best-fit relationships between grain size and the exponential correlation length parameter needed by MEMLS, the predicted brightness temperatures are similar. For all three models, it was found that a proportional model between grain size and correlation length outperformed a more physically based model and that the regression coefficients differed from those in previous studies. The predicted V-pol brightness temperature measurements from the three grain size models showed good agreement with each other, with inter-model differences on the order of ~ 5 K. At H-pol the biases significantly increase, which is most likely due to errors in predicted density and grain size in melt-freeze layers. In the experiments aimed at assessing the impact of stratigraphy on predicted radiances, it was found that there were limited additional errors introduced in using a pre-specified one-, three-, or five-layer scheme at 18.7 and 36.5 GHz (V-pol), while sizeable errors were introduced at 89 GHz (V-pol). The additional errors at H-pol were relatively small, yet the overall errors were still larger than V-pol.

Normalized difference vegetation index (NDVI) is the most widely used vegetation index due to its simplicity, ease of application, and wide-spread familiarity. Time-series NDVI products have been proven to be a powerful tool to learn from past events, monitor current natural-resource conditions, extract canopy biophysical parameters and forecast terrestrial ecosystems on different scales. However, the current NDVI product is still spatiotemporally discontinuous mainly due to cloud cover, seasonal snow and atmospheric variability. In this work, a simplified data assimilation method is proposed to reconstruct high-quality time-series MODIS NDVI data. Results indicate that the newly developed method is easy and effective in reconstructing high-quality MODIS NDVI time series.

We have developed Chinese land data assimilation system (CLDAS). In this system, the Common Land Model (CoLM) is used to simulate land surface processes. The radiative transfer models of thawed and frozen soil, snow, and vegetation are used as observation operators to transfer model predictions into estimated brightness temperatures. The EnKF algorithm is implemented as data assimilation method to integrate modeling and observation. The system is capable of assimilating passive microwave remotely sensed data such as special sensor microwave/imager (SSM/I) and advanced microwave scanning radiometer enhanced for EOS (AMSR-E). In this study, we primarily compare the assimilation results of soil moisture with AMSR-E L3 surface soil moisture products and in situ observations from GAME-Tibet experimental fields. The results indicate that the relationship between the simulated and assimilated surface soil moisture with AMSR-E L3 surface soil moisture products is very low. In comparison with in situ observations from GAME-Tibet experimental fields, the assimilated results of soil moisture are better than the simulated results. Additionally, the assimilated results can describe the thawed-frozen cycle.

The estimation of evapotranspiration(ET) over heterogeneous land surface over arid and semi-arid region is quite complicated and significant. In this paper, NOAA/AVHRR remote sensing data, NCEP grid data and the meteorological stations data are used to estimate daily ET over the Heihe river basin by integrating Surface Energy Algorithm for Land model [1,2] and the Penman method[3]. As for clear days, Remote sensing model is used to retrieval the instantaneous ET to daily ET [4]. FAO-17 Penman method is also used to estimate the same day's reference crop ET by NCEP grid data and the meteorological stations data. The relationship between actual ET and the reference crop ET can be used to calculate the actual ET based on the results from FAO-17 Penman method on the cloudy days. These results are validated by observation data and other study in Heihe River Basin.

In this paper, the Common Land Model (CLM) and the Monteith type carbon model-C-FIX were applied to estimate the water use efficiency (WUE) of western China in 2002. The input data mainly included NCAR and Meteo France meteorological data sets, 1 km USGS soil and land cover data, the global 0.25deg monthly MODIS LAI data and the 1 km VGT-S10 NDVI products. Some field measurements of WUE for different plants in the study area were used for validation. The total annual NPP and actual ET in western China in 2002 were estimated about 0.96PgC and 2098km3H2O. The mean annual WUE per square meter was about 0.32gC/mm. The spatial pattern of the mean annual WUE in Western China as well as the seasonal WUE profiles of different ecosystems were illustrated and descript in detail. In general, the mean annual WUE of the main ecosystems in western China were ranked as: mountain forest > desert shrub and woodland > irrigated farmland > alpine meadow > gobi >= cold desert. This study could provide quantitative and spatially distributed data to help water resource utilization and managements in arid and semi-arid regions of western China.

A real-time recursive back projection (BP) imaging algorithm is presented in this paper. Real time imaging is an intense demand but a challenging task in impulse surface penetrating radar (ImpSPR)'s application. Based on 'delay-sum' operation in time domain, BP imaging algorithm can precisely focus the scattering intensity and obtain high quality subsurface profile. But it's heavy computation burden restricts it's application in ImpSPR's real-time processing. By minutely analyzing it's procedure, a recursive model of BP imaging algorithm is established and real-time BP imaging algorithm is educed subsequently. The computation complexity of both non real time BP imaging algorithm and real time BP imaging algorithm are analyzed. Through processing the experimental data obtained by a ImpSPR system- RadarEye, the imaging algorithm validates its capability at the aspect of ImpSPR's real time processing.

A field campaign was executed in a semi-arid grassland of northwest China from July 11th-July 15th, 2002. According to the VALERI (Validation of Land European Remote Sensing Instruments) sampling procedures, the leaf area index (LAI) were intensively measured within a homogenous 3/spl times/3 km/sup 2/ square by using LAI-2000 and TRAC instrument. A quarter scene of Landsat7 ETM+ with acquisition times close to the field campaign time was processed by proper geo-registration and atmospheric correction. Three kinds of spectral vegetation index including NDVI, SR and MSAVI in the sampling area were derived from the corrected ETM+ image. The two sets of LAI data measured with LAI-2000 and TRAC instrument at the same site were inter-compared. The relationships between the measured LAI and vegetation indices were investigated as well. The results elicit that the statistical relationships between measured LAI and the different vegetation indices are consistent. Among them, NDVI seems the most promising estimator for the extraction of LAI. In addition, the LAI-2000 seems to perform better for LAI measurement in the semi-arid grassland than the TRAC instrument.