Determination of land surface temperature and its lapse rate in the Satluj River basin using NOAA data

Temperature lapse rate (TLR), an essential parameter for snowmelt runoff analysis, was determined for the Satluj River basin in the Western Himalayas. National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) data sets were used to determine the land surface temperature (LST) of the region using the split‐window algorithm proposed by Coll and Caselles (Journal of Geophysical Research, 1997, 102, pp. 16697–16713). The LST was correlated with the elevation values obtained from a US Geological Survey digital elevation model (USGS‐DEM) of the same area and the trend showed an inverse relationship between LST and elevation. The TLRs for the study area on 2 February, 1 March, 26 March, 16 October, 1 November and 20 November 2004 were in the range 0.6–0.74°C/100 m. The results obtained were compared with lapse rates determined using Moderate Resolution Imaging Spectroradiometer (MODIS) LST maps. TLR determination in the past was based on air temperature data available from meteorological stations that are sparsely located in rugged terrain such as the Himalayas. As these measurements were point data and had been measured manually, they may have led to erroneous results. Satellite data, however, provide continuous and potentially unbiased recording provided an accurate radiometric calibration and atmospheric correction can be achieved. A previous TLR calculation using air temperature from meteorological stations for the western Himalayan region was found to be 0.65°C/100 m. Air temperature and LST from NOAA‐AVHRR and MODIS‐Terra data were found to be in good agreement. This type of study will be useful for snowmelt runoff modelling studies for the Himalayan region.     

The potential of Pathfinder AVHRR data for providing surrogate climatic variables across Africa and Europe for epidemiological applications

Surface climatic conditions are key determinants of arthropod vector distribution and abundance and consequently affect transmission rates of any diseases they may carry. Remotely sensed observations by satellite sensors are the only feasible means of obtaining regional and continental scale measurements of climate at regular intervals for real-time epidemiological applications such as disease early warning systems. The potential of Pathfinder AVHRR Land (PAL) data to provide surrogate variables for near-surface air temperature and vapour pressure deficit (VPD) over Africa and Europe were assessed in this context. For the years 1988-1990 and 1992, correlations were examined between meteorological ground measurements (monthly mean air temperature and VPD(grd)) and variables derived from Advanced Very High Resolution Radiometer (AVHRR) data (LST and VPD(sat)). The AVHRR indices were derived from both daily and composite PAL data so that their relative performance could be determined. Furthermore, the ground observations were divided into African and European subsets, so that the relative performance of the satellite data at tropical/sub-tropical and temperate latitudes could be assessed.Significant correlations were shown between air temperature and LST in all months. Temporal variability existed in the strength of correlations throughout any twelve-month period, with the pattern of variability consistent between years. The adjusted r(2) values increased when elevation and the Normalised Difference Vegetation Index (NDVI) were included, in addition to LST, as predictor variables of air temperature. Attempts to derive monthly estimates of atmospheric moisture availability resulted in an over-estimation of VPD(sat) compared to ground observations, VPD(grd). The use of daily PAL data to derive monthly mean climatic indices was shown to be more accurate than those obtained using monthly maximum values from 10-day composite data. A subset of the 1992 data was then used to build linear regression models for the direct retrieval of monthly mean air temperature from PAL data. The accuracy of retrieved estimates was greatest when NDVI was included with LST as predictor variables, with root mean square errors varying from 1.83°C to 3.18 °C with a mean of 2.38 °C over the twelve months.     

Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data

Vapor Pressure Deficit (VPD) is a principle mediator of global terrestrial CO₂ uptake and water vapor loss through plant stomata. As such, methods to estimate VPD accurately and efficiently are critical for ecosystem and climate modeling efforts. Based on prior work relating energy partitioning, remotely sensed land surface temperature (LST), and VPD, we developed simple linear models to predict VPD using saturated vapor pressure calculated from MODIS LST at a number of different temporal and spatial resolutions. We developed and assessed the LST-VPD models using three data sets: (1) instantaneous and daytime average ground-based VPD and radiometric temperature from the Soil Moisture Experiments in 2002 (SMEX02); (2) daytime average VPD from AmeriFlux eddy covariance flux tower observations; and (3) estimated daytime average VPD from Global Surface Summary of Day (GSSD) observations. We estimated model parameters for VPD estimation both regionally (MOD11 A2) and globally (MOD11 C2) with RMSE values ranging from .32 to .38 kPa. VPD was overestimated along coastlines and underestimated in arid regions with low vegetation cover. Also, residuals were larger with higher VPDs because of the non-linear function of saturation vapor pressure with LST. Linear relationships were seen at multiple scales and appear useful for estimation purposes within a range of 0 to 2.5 kPa.     

Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation

Surface urban heat island (SUHI) is a phenomenon of both high spatial and temporal variability. In this context, studying and monitoring the SUHIs of urban areas through the satellite remote sensing technology, requires land surface temperature (LST) image data from satellite-borne thermal sensors of high spatial resolution as well as temporal resolution. However, due to technical constrains, satellite-borne thermal sensors yield a trade-off between their spatial and temporal resolution; a high spatial resolution is associated with a low temporal resolution and vice versa. To resolve this drawback, we applied in this study four downscaling techniques using different scaling factors to downscale 1-km LST image data provided by the Advanced Very High Resolution Radiometer (AVHRR) sensor, given that AVHRR can offer the highest temporal resolution currently available. The city of Athens in Greece was used as the application site. Downscaled 120-m AVHRR LSTs simulated by the downscaling techniques, were then used for SUHI intensity estimation based on LST differences observed between the main urban land covers of Athens and the city's rural background. For the needs of the study, land cover information for Athens was obtained from the Corine Land Cover (CLC) 2000 database for Greece. Validation of the downscaled 120-m AVHRR LSTs as well of the retrieved SUHI intensities was performed by comparative analysis with time-coincident observations of 120-m LST and SUHI intensities generated from the band 6 of the Thermal Mapper (TM) sensor onboard the Landsat 5 platform. The spatial pattern of the downscaled AVHRR LST was found to be visually improved when compared to that of the original AVHRR LST and to resemble more that of TM6 LST. Statistical results indicated that, when compared to 120-m TM6 LST, the root mean square error (RMSE) in 120-m AVHRR LST generated by the downscaling techniques ranged from 4.9 to 5.3 °C. However, the accuracy in SUHI intensity was found to have significantly improved, with a RMSE value decreasing from 2.4 °C when the original AVHRR LST was utilized, down to 0.94 °C in case that downscaling was applied.     

Implementation of GOES‐based land surface temperature diurnal cycle to AVHRR

To enable frequent estimates of land surface temperature (LST) from satellite measurements, and to characterize the land surface temperature diurnal (LSTD) cycle, two new LST retrieval algorithms are applied to observations from the Geostationary Operational Environmental Satellite (GOES). Evaluation against the atmospheric radiation measurement (ARM) observations indicates that LST can be determined from the real‐time GOES‐8 observations within r.m.s. accuracy of about 2 K. In order to combine the advantages of geostationary and polar orbiting instruments, the LSTD estimated from GOES can be incorporated into LST retrievals from polar orbiting imager National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) using a newly proposed fitting algorithm, with r.m.s. errors close to those obtained directly from GOES‐8.     

Spatial analysis of the homogeneity of the land surface temperature in three Spanish test sites

Calibration and validation (cal/val) are key activities to test the data quality acquired from satellite-based instruments, as well as to report the accuracy of derived products such as the land surface temperature (LST). Calibration of thermal infrared (TIR) data and validation of LST products at low spatial resolution requires the identification of large and homogeneous areas, which is a difficult task. In this work, spatial and temporal homogeneity of LST was analysed over three Spanish regions: the agricultural area of Barrax, Doñana National Park, and Cabo de Gata Natural Park. For this purpose, very high spatial resolution (approximately 3 m) imagery acquired with the Airborne Hyperspectral Scanner (AHS) in the framework of different field campaigns and high–medium spatial resolution (approximately 100 m) imagery acquired with the Landsat-8 (L8) TIR sensor (TIRS) have been used to retrieve homogeneity of high–medium and low spatial resolution sensors, respectively. Different LST retrieval algorithms were applied to AHS and TIRS to compare the LST for a given pixel against the LST of neighbour pixels through the computation of the root mean square error (RMSE). The results obtained from the analysis of LST derived from AHS data over Barrax and Doñana test sites show that part of these regions have an RMSE lower than 1 K, which is consistent with the accuracy of the LST validation (between 0.5 and 1.5 K). The analysis of LST derived from the TIRS shows that some parts of Doñana and Cabo de Gata sites have a mean RMSE of 1 K over the period of a year, with maximal homogeneity in autumn and winter (lower than 1 K) and minimal in spring and summer (around 2 K). These results are lower than the accuracy of the LST validation (approximately 2 K). The results show the usefulness of these three test sites to perform cal/val activities for both low and high spatial resolution sensors. The methodology presented in this study also allows the identification of suitable areas for future cal/val activities.     

利用MODIS数据反演地表温度的研究

地表温度(LST)是气象、水文、生态等研究中一个重要的参数,目前国内的研究大多使用NOAA/AVHRR数据来获取地表温度,应用MODIS数据获取LST基本上还是空白。MODISLST反演算法精度较高但是计算复杂,在很大程度上限制了其应用。采用简单的统计方法和神经网络方法,得出了内蒙古东北地区的LST计算公式。该公式计算简单而且精度很高,完全能够满足一般的研究需要。     

Evaluation of AVHRR PAL and GIMMS 10‐day composite NDVI time series products using SPOT‐4 vegetation data for the African continent

Global 8 km resolution AVHRR (advanced very high resolution radiometer) NDVI (normalized difference vegetation index) 10‐day composite data sets have been used for numerous local to global scale vegetation time series studies during recent years. AVHRR Pathfinder (PAL) NDVI was available from 1981 until 2001, and the new AVHRR GIMMS NDVI was available from 1981 to the present time. A number of aspects potentially introduce noise in the NDVI data set due to the AVHRR sensor design and data processing. NDVI from SPOT‐4 VGT data is considered an improvement over AVHRR, and for this reason it is important to examine how and if the differences in sensor design and processing influence continental scale NDVI composite products. In this study, the quality of these AVHRR NDVI time series are evaluated by the continental scale 1 km resolution SPOT‐4 vegetation (VGT) 10‐day composite (S10) NDVI data. Three years of AVHRR PAL (1998–2000) and seven years of GIMMS (1998–2004) have been compared to 8 km resampled SPOT‐4 VGT (1998–2004) data. The dynamic range of SPOT‐4 VGT NDVI tends to be higher than the AVHRR PAL NDVI, whereas there is an exact match between AVHRR GIMMS NDVI and SPOT‐4 VGT NDVI. Ortho‐regression analysis on annually integrated values of AVHRR PAL/GIMMS and SPOT‐4 VGT on a continental scale reveals high correlations amongst the AVHRR and the SPOT data set, with lowest RMSE (root mean square error) on the GIMMS/SPOT‐4 VGT compared to the PAL/SPOT‐4 VGT.

Analyses on decade data likewise show that a linear relation exists between Spot‐4 VGT NDVI and the two AVHRR composite products; GIMMS explaining most of the Spot‐4 VGT NDVI variance compared to PAL. These results show that the AVHRR GIMMS NDVI is more consistent with Spot‐4 VGT NDVI compared to AVHRR PAL versus Spot‐4 VGT NDVI (in terms of RMSE and dynamic range) and can therefore be considered the more accurate long time AVHRR data record. Analyses performed on monthly maximum composites and decade composite data, however, reveal intra‐annual variations in the correlation between SPOT‐4 VGT and the two AVHRR data sets, which are attributed to different cloud masking algorithms. The SPOT‐4 VGT cloud‐screening algorithm is insufficient, thereby suppressing the rainy season NDVI.     

Quantitative comparison of the operational NOAA-AVHRR LST product of DLR and the MODIS LST product V005

Land surface temperature (LST) products of two different sensors – the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) – were cross-compared. The analysis was conducted on a daily basis for four different years. Only pixels that stuck to certain homogeneity criteria were chosen. Furthermore, a time criterion defining the maximal time difference between two acquisitions was introduced.

The differences between the two products showed both a diurnal and an annual pattern, with LST of AVHRR being higher than that of MODIS at high surface temperatures and AVHRR LST being lower than MODIS LST at lower temperatures. Additionally, some irregular patterns were identified and attributed to the different algorithm approaches. Mean annual absolute differences were relatively low: only 2.2 K for the daytime and 1.4 K for the night-time scenes, speaking for a general good agreement between the two products. The coefficient of determination (R ²) between the LST of AVHRR and MODIS of both day and night scenes was 0.99.     

Effects of orbital drift on land surface temperature measured by AVHRR thermal sensors

The NOAA series of meteorological satellites that carry the Advanced Very High Resolution Radiometer (AVHRR) suffer from orbital drift so that during each satellite's duty period the overpass time occurs later in the day. Replacement satellites restore the overpass time temporarily, but then it gradually decays. The goals of this paper are to document the effects of variable observation time owing to orbital drift on brightness temperatures (BT) and land surface temperature (LST) calculated from them in the NOAA/NASA Pathfinder AVHRR Land (PAL) data set and to consider possible corrections for the resulting trends and discontinuities in the PAL BT data. The drift effects were found to be greater for bare ground than for vegetated land cover classes, however, significant effects were found for most vegetated classes. The magnitude of the orbital drift effect for most global cover types was at least as large as the other errors that affect LST measurement. A simple empirical correction for observation time based on solar zenith angle (SZA) was used to correct the PAL BT time series following Gutman [Int. J. Remote Sens. 20 (1999a) 3407]. The correction from this method was compared with that predicted by a physically based model and was found to differ in the early part of each satellite's duty cycle. Finally, the impacts of correction on the effective observation time are analyzed and the simple statistical correction was found to suffer from greater variability than has hitherto been recognized. A modification to the statistical correction to adjust the effective observation time is described.     

Use of a super‐resolution method in interpretation of forests from multiple NOAA/AVHRR images

Super‐resolution methods take several images from the same location and fuse them into one, called a super‐resolution image. If the fusion is carried out correctly, it is possible to recognize details from the super‐resolution image which are not visible in the original images. In this study, a super‐resolution technique is applied to sequences of National Oceanic and Atmospheric Administration, Advanced Very High Resolution Radiometer (NOAA/AVHRR) scenes using a varying number of images and acquisition times, in order to determine whether it is possible to recognize forested and non‐forested areas from NOAA/AVHRR images more accurately using a super‐resolution method than with the original images. The overall proportion of forest and the proportion of forest with a growing stock volume of over 50 m³/ha were calculated for each pixel, and the results evaluated using landscape indices, r.m.s. error (RMSE) and a parameter showing how large a proportion of the estimates are closer to the ground truth in the original image sequence than in the corresponding super‐resolution image. The results showed the super‐resolution estimates to be better in all cases than those based on the original image material, but the improvement was marginal. Neither the number of images nor the image acquisition time had any obvious effect on the results.     

Validation of a temperature emissivity separation hybrid method from airborne hyperspectral scanner data and ground measurements in the SEN2FLEX field campaign

This paper presents an assessment of the performance of a hybrid method that allows a simultaneous retrieval of land‐surface temperature (LST) and emissivity (LSE) from remotely‐sensed data. The proposed method is based on a synergistic usage of the split‐window (SW) algorithm and the two‐temperature method (TTM) and combines the advantages of both procedures while mitigating their drawbacks. The method was implemented for thermal channels 76 (10.56 µm) and 78 (11.72 µm) of the Airborne Hyperspectral Scanner (AHS), which was flown over the Barrax test site (Albacete, Spain) in the second week of July 2005, within the framework of the Sentinel‐2 and Fluorescence Experiment (SEN2FLEX) field campaign. A set of radiometric measurements was performed in the thermal infrared region in coincidence with aircraft overpasses for different surface types, e.g. bare soil, water body, corn, wheat, grass. The hybrid method was tested and compared with a standard SW algorithm and the results obtained show that the hybrid method is able to provide better estimates of LST, with values of bias (RMSE) of the order of 0.8 K (1.9 K), i.e. about one third (one half) of the corresponding values of 2.7 K (3.4 K) that were obtained for bias (RMSE) when using the SW algorithm. These figures provide a sound indication that the developed hybrid method is particularly useful for surface and atmospheric conditions where SW algorithms cannot be accurately applied.     

Comparison of modelling ANN and ELM to estimate solar radiation over Turkey using NOAA satellite data

In this study, solar radiation (SR) is estimated at 61 locations with varying climatic conditions using the artificial neural network (ANN) and extreme learning machine (ELM). While the ANN and ELM methods are trained with data for the years 2002 and 2003, the accuracy of these methods was tested with data for 2004. The values for month, altitude, latitude, longitude, and land-surface temperature (LST) obtained from the data of the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA-AVHRR) satellite are chosen as input in developing the ANN and ELM models. SR is found to be the output in modelling of the methods. Results are then compared with meteorological values by statistical methods. Using ANN, the determination coefficient (R²), mean bias error (MBE), root mean square error (RMSE), and Willmott’s index (WI) values were calculated as 0.943, ?0.148 MJ m^?2, 1.604 MJ m^?2, and 0.996, respectively. While R² was 0.961, MBE, RMSE, and WI were found to be in the order 0.045 MJ m^?2, 0.672 MJ m^?2, and 0.997 by ELM. As can be understood from the statistics, ELM is clearly more successful than ANN in SR estimation.     

Terra and Aqua MODIS inter‐comparison of three reflective solar bands using AVHRR onboard the NOAA‐KLM satellites

Cross‐sensor inter‐comparison is important to assess calibration quality and consistency and ensure continuity of observational datasets. This study conducts an inter‐comparison of Terra and Aqua MODIS (the MODerate Resolution Imaging Spectroradiometer) to examine the overall calibration consistency of the reflective solar bands. Observations obtained from AVHRR (the Advanced Very High Resolution Radiometer) onboard the NOAA‐KLM series of satellites are used as a transfer radiometer to examine three MODIS bands at 0.65 (visible), 0.85 (near‐IR) and 1.64 µm (far near‐IR) that match spectrally with AVHRR channels. Coincident events are sampled at a frequency of about once per month with each containing at least 3000 pixel‐by‐pixel matched data points. Multiple AVHRR sensors on‐board NOAA‐15 to 18 satellites are used to check the repeatability of the Terra/Aqua MODIS inter‐comparison results. The same approach applied in previous studies is used with defined criteria to generate coincident and co‐located near nadir MODIS and AVHRR pixel pairs matched in footprint. Terra and Aqua MODIS to AVHRR reflectance ratios are derived from matched pixel pairs with the same AVHRR used as a transfer radiometer. The ratio differences between Terra and Aqua MODIS/AVHRR give an indication of the calibration biases between the two MODIS instruments. Effects due to pixel footprint mismatch, band spectral differences and surface and atmospheric bi‐directional reflectance distributions (BRDFs) are discussed. Trending results from 2002 to 2006 show that Terra and Aqua MODIS reflectances agree with each other within 2% for the three reflective solar bands.     

Synergistic use of the two-temperature and split-window methods for land-surface temperature retrieval

A strategy is presented with the aim of achieving an operational accuracy of 2.0 K in land-surface temperature (LST) from METEOSAT Second Generation (MSG)/Spinning Enhanced Visible and Infrared Imager (SEVIRI) data. The proposed method is based on a synergistic usage of the split-window (SW) and the two-temperature method (TTM) and consists in combining the use of a priori land-surface emissivity (LSE) estimates from emissivity maps with LST estimates obtained from SW method with the endeavour of defining narrower and more reliable ranges of admissible solutions before applying TTM. The method was tested for different surface types, according to SEVIRI spatial resolution, and atmospheric conditions occurring within the MSG disc. Performance of the method was best in the case of relatively dry atmospheres (water-vapour content less than 3 g cm^?2), an important feature since in this case SW algorithms provide the worst results because of their sensitivity to uncertainties in surface emissivity. The hybrid method was also applied using real MSG/SEVIRI data and then validated with the Moderate resolution Imaging Spectroradiometer (MODIS)/Terra LST/LSE Monthly Global 0.05° geographic climate modeling grid (CMG) product (MOD11C3) generated by the day/night algorithm. The LST and LSE retrievals from the hybrid-method agree well (bias and root mean square error (RMSE) of??0.2 K and 1.4 K for LST, and around 0.003–0.02 and 0.009–0.02 for LSE) with the MOD11C3 product. These figures are also in conformity with the MOD11C3 performance at a semi-desert where LST (LSE) values is 1–1.7 K (0.017) higher (less) than the ground-based measurements.     

Derivation of land surface temperatures from MODIS data using the general split‐window technique

Fast Atmospheric Signature Code (FASCODE), a line‐by‐line radiative transfer programme, was used to simulate Moderate Resolution Imaging Spectroradiometer (MODIS) data at wavelengths 11.03 and 12.02 µm to ascertain how accurately the land surface temperature (LST) can be inferred, by the split‐window technique (SWT), for a wide range of atmospheric and terrestrial conditions. The approach starts from the Ulivieri algorithm, originally applied to Advanced Very High Resolution Radiometer (AVHRR) channels 4 and 5. This algorithm proved to be very accurate compared to several others and takes into account the atmospheric effects, in particular the water vapour column (WVC) amount and a non‐unitary surface emissivity. Extended simulations allowed the determination of new coefficients of this algorithm appropriate to MODIS bands 31 and 32, using different atmospheric conditions. The algorithm was also improved by removing some of the hypothesis on which its original expression was based. This led to the addition of a new corrective term that took into account the interdependence between water vapour and non‐unitary emissivity values and their effects on the retrieved surface temperature. The LST products were validated within 1 K with in situ LSTs in 11 cases.     

Efficiency Research of Planted Rice Area Estimation From NOAA一AVHRR Data in Taihu Domain

运用NOAA-AVHRR资料估算水稻种植面积，是遥感应用领域中一个新的研究方向，结合国家“八五”攻关项目“太湖地区遥感话产”的要求，在太湖地区进行了初步的尝试:(1)根据估产精度要求和NOAA一AVHRR资料校正精度，探讨了运用NOAA一AVHRR资料估产所需的最小区域范围。(2)针对太湖地区的具休地理环境设计了提取水稻种植曲积的技术方案，并在试验区取得了初步成果。     

A three-channel algorithm for retrieving night-time land surface temperature from MODIS data under thin cirrus clouds

Thin cirrus clouds are dominated by non-spherical ice crystals with an effective emissivity of less than 0.5. Until now, the influences of clouds were not commonly considered in the development of algorithms for retrieving land-surface temperature (LST). However, numerical simulations showed that the influence of thin cirrus clouds could lead to a maximum LST retrieval error of more than 14 K at night if the cirrus optical depth (COD) at 12 μm was equal to 0.7 (cirrus emissivity equivalent to 0.5). To obtain an accurate estimate of the LST under thin cirrus using satellite infrared data, a nonlinear three-channel LST retrieval algorithm was proposed based on a widely used two-channel algorithm for clear-sky conditions. The variations in the cloud top height, COD, and effective radius of cirrus clouds were considered in this three-channel LST retrieval algorithm. Using Moderate Resolution Imaging Spectroradiometer (MODIS) channels 20, 31, and 32 (centred at 3.8, 11.0, and 12.0 μm, respectively) and the corresponding land surface emissivities (LSEs), the simulated data showed that this algorithm could obtain LSTs with root mean square errors (RMSEs) of less than 2.8 K when the COD at 12 μm is less than 0.7 and the viewing zenith angle (VZA) is less than 60°. In addition, a sensitivity analysis of the proposed algorithm showed that the total LST errors, including errors from the uncertainties in input parameters and algorithm error, were nearly the same as the algorithm error itself. Some lake surface water temperatures measured in Lake Superior and Lake Erie were used to test the performance of the proposed LST retrieval algorithm. The results showed that the proposed nonlinear three-channel algorithm could be used for estimating LST under thin cirrus with an RMSE of less than 2.8 K.     

The relationship between land-surface temperature and rice quality in Tottori prefecture,Japan

Numerous studies have suggested that rice quality in Japan is affected by high temperatures during the ripening period, especially in summer. On the assumption that land-surface temperature (LST) can be substituted for air temperature, we examined rice quality using LST satellite data from the Moderate Resolution Imaging Spectroradiometer (MODIS) during the ripening stage (August) in Tottori prefecture. Rice quality in Tottori was very low compared with Japan as a whole and to neighbouring prefectures. LST was correlated with minimum and average air temperatures in August at six meteorological stations in Tottori. Rice quality decreased with increasing LST, and the threshold LST value when the quality of rice was less than 50% was 307 K (33.9 °C). The spatial distribution of LST in August indicated that LST values over 307 K were widespread, especially in coastal and lowland areas, and areas with the highest rice quality corresponded with intermountain regions that had LSTs less than 307 K.     

雪盖卫星遥感信息的提取方法探讨

着重论述了从卫星遥感资料中提取雪盖信息的一些方法,结果表明,利用积雪阈值参数从ＮＯＡＡ／ＡＶＨＲＲ图象中提取雪盖信息方法和利用积雪指数（ＮＤＳＩ）从陆地卫星ＴＭ图象中提取雪盖面积的技术,以及利用ＮＯＡＡ／ＡＶＨＲＲ和ＴＭ信息复合的技术,可提高信息获取的精度,具有实用价值。