Land surface emissivity (LSE) is the most critical factor affecting land surface temperature (LST) retrieval. Understanding its variation characteristics is essential, as this knowledge provides fundamental prior constraints for the LST retrieval process. This study utilizes thermal infrared emissivity and hyperspectral data collected from diverse underlying surfaces from 2017 to 2024 to analyze LSE variation characteristics across different surface types, spectral bands, and temporal scales. Key influencing factors are quantified to establish empirical relationships between LSE dynamics and environmental variables. Furthermore, the impact of LSE models on diurnal LST retrieval accuracy is systematically evaluated through comparative experiments, emphasizing the necessity of integrating time-dependent LSE corrections into radiative transfer equations. The results indicate that LSE in the 8–11 µm band is highly sensitive to surface composition, with distinct dual-valley absorption features observed between 8 and 9.5 µm across different soil types, highlighting spectral variability. The 9.6 µm LSE exhibits strong sensitivity to crop growth dynamics, characterized by pronounced absorption valleys linked to vegetation biochemical properties. Beyond soil composition, LSE is significantly influenced by soil moisture, temperature, and vegetation coverage, emphasizing the need for multi-factor parameterization. LSE demonstrates typical diurnal variations, with an amplitude reaching an order of magnitude of 0.01, driven by thermal inertia and environmental interactions. A diurnal LSE retrieval model, integrating time-averaged LSE and diurnal perturbations, was developed based on underlying surface characteristics. This model reduced the root mean square error (RMSE) of LST retrieved from geostationary satellites from 6.02 °C to 2.97 °C, significantly enhancing retrieval accuracy. These findings deepen the understanding of LSE characteristics and provide a scientific basis for refining LST/LSE separation algorithms in thermal infrared remote sensing and for optimizing LSE parameterization schemes in land surface process models for climate and hydrological simulations.

As a fundamental parameter of the surface radiation process, land surface emissivity (LSE) is the most direct factor affecting the land surface temperature (LST) retrieval accuracy. Based on the measured data from 2017 to 2023, the characteristics of LSE changes were analysed, different timescale LSE retrieval models were developed, and the impact of LSE on pixel scale observational LST acquisition was evaluated by using unmanned aerial vehicle (UAV) remote sensing images. The results show that with changes in atmospheric environmental conditions, the LSE changes are relatively less pronounced at 3.99 mu m and 4.02 mu m in the mid-infrared band and at 10.8 mu m and 11.8 mu m in the thermal infrared band than in the other bands. At the timescale above day, the LSE is significant correlation with the normalized differential vegetation index (NDVI). Vegetation is the main parameter that dominates the LSE change under heterogeneous underlying surfaces. On a daily timescale, LSE exhibits typical diurnal variation characteristics, and the amplitude of diurnal variation is influenced mainly by shallow soil water and heat conditions. The developed LSE model based on Fengyun-4A (FY-4A) satellite remote-sensing data can effectively reproduce the daily variation characteristics of LST and avoid the 'jumping' phenomenon that may occur in the NDVI threshold method. The root-mean-square error (RMSE) between the retrieved LST and in situ data is reduced to 3.78 degrees C, and the retrieval accuracy is better than that of the published LST product. When using UAVs to obtain pixel-scale LSTs, dry bare soil is more sensitive to LSE. When the LSE increases from 0.95 to 1.0, the average LST obtained by the UAV deviates by approximately 1.0 degrees C. This study preliminarily provides some spatiotemporal variation patterns of LSE and lays a foundation for the later-modified LST retrieval algorithms.

Soil moisture is the most direct evaluation index for agricultural drought. It is not only directly affected by meteorological conditions such as precipitation and temperature but is also indirectly influenced by environmental factors such as climate zone, surface vegetation type, soil type, elevation, and irrigation conditions. These influencing factors have a complex, nonlinear relationship with soil moisture. It is difficult to accurately describe this non-linear relationship using a single indicator constructed from meteorological data, remote sensing data, and other data. It is also difficult to fully consider environmental factors using a single drought index on a large scale. Machine learning (ML) models provide new technology for nonlinear problems such as soil moisture retrieval. Based on the multi-source drought indexes calculated by meteorological, remote sensing, and land surface model data, and environmental factors, and using the Cubist algorithm based on a classification decision tree (CART), a comprehensive agricultural drought monitoring model at 10 cm, 20 cm, and 50 cm depth in Gansu Province is established. The influence of environmental factors and meteorological factors on the accuracy of the comprehensive model is discussed, and the accuracy of the comprehensive model is evaluated. The results show that the comprehensive model has a significant improvement in accuracy compared to the single variable model, which is a decrease of about 26% and 28% in RMSE and MAPE, respectively, compared to the best MCI model. Environmental factors such as season, DEM, and climate zone, especially the DEM, play a crucial role in improving the accuracy of the integrated model. These three environmental factors can comprehensively reduce the average RMSE of the comprehensive model by about 25%. Compared to environmental factors, meteorological factors have a slightly weaker effect on improving the accuracy of comprehensive models, which is a decrease of about 6.5% in RMSE. The fitting accuracy of the comprehensive model in humid and semi-humid areas, as well as semi-arid and semi-humid areas, is significantly higher than that in arid and semi-arid areas. These research results have important guiding significance for improving the accuracy of agricultural drought monitoring in Gansu Province.

According to the spectral and plant physiological parameters, combined with the PROSAIL model, the equivalent water thickness canopy（E） sensitivity index corresponding to the spectral curve was simulated, and it was compared with the E sensitivity spectral index of the actual spectral curve: the PROSAIL model had high precision in simulating spectral curve, and R~2＞ 0.8. The single factor and overall sensitivity analysis of the E sensitive band of the spectrum showed that the E sensitive band was1 240, 1 450 and 1 650 nm respectively. Through the inversion of alfalfa E by water index, moisture stress index（MSI）, normalized difference infrared index（NDII） and four generalized normalized difference water indexes（NDWI） were combined with the E sensitive band: it was concluded that the seven indexes had a good inversion effect, R~2＞0.8, and the root mean square error was between 0.001 0-0.001 9 g/cm~2. Of them, the inversion effect of E-MSI, E-NDII, E-NDWI （860,1240） and E-NDWI （1240,1650） based on generalized spectral index was the best. The E of alfalfa can be retrieved accurately, therefore providing a reference for the evaluation of alfalfa water deficit.

The surface radiation and energy flux in the source area of the Yellow River are estimated by using the Moderate Resolution Imaging Spectroradiometer products from 2000 to 2019 and verified through the in situ observed data. The changes in land cover type and its effect on water exchange and climate are analyzed. Results show that the latent heat estimated by Priestley-Taylor formula in the energy limited area is close to the measured value, and the root mean square error is 17.8 W m−2. From 2000 to 2019, the daytime land surface temperature (LST) in the source area of the Yellow River mainly shows a negative change trend and decreases significantly in some areas, about − 0.2 °C/a. The nighttime LST shows a positive change trend, and the trend is mainly concentrated at 0–0.15 °C/a. The trend of average land surface temperature calculated from daytime and nighttime LST is mainly concentrated between ± 0.05 °C/a, with a weak warming trend. The LST difference between day and night decreases significantly, and the variation values are mainly concentrated in the range of 0–0.3 °C/a. The albedo in the study area mainly shows a positive trend, and the change value is mainly concentrated between − 0.002 and 0.004/a. The net radiation (Rn) decreases, and the sensible heat (Hs) and latent heat (LE) show a negative trend. The change in land cover type in the source area of the Yellow River mainly occurred in 2016 and 2017. The conversion between grasslands and barren accounts for 70% of the total conversion times. The conversion between grasslands and croplands accounts for 11%. The conversion between water body and barren accounts for 5.9%. The conversion between grasslands and water body accounts for 5.1%, and the other thirty conversions of land cover type account for 8% of the total conversion times. In the dominant land cover conversion type, the mutual conversion between grasslands and barren amplifies the reduction of daytime LST, decelerates the increase in average land surface temperature, and inhibits the water exchange between land and atmosphere because the cooling effect caused by the conversion from barren to grasslands is significantly stronger than the warming effect caused by the conversion from grasslands to barren. Grassland protection is still an important task of ecological protection in the source area of the Yellow River.

When drought occurs in different regions, evapotranspiration (ET) changes differently with the process of drought. To achieve an accurate monitoring of large-scale drought using remote sensing, it is particularly necessary to clarify the temporal and spatial characteristics of ET changes with soil water content (SWC). Firstly, based on the measured data, combined with the artificial intelligence particle swarm optimization (PSO) algorithm, an empirical model of ET retrieval by FY–4A satellite data was established and the spatial–temporal characteristics of ET changes with SWC were further analyzed. Lastly, different ET regulation regions were distinguished to achieve the remote sensing monitoring of large-scale drought based on SWC. The main results are as follows: (1) The correlation coefficient between the ET estimated by the empirical model and the measured value was 0.48 and the root mean square error was 24 W·m−2. (2) In the areas with extreme water shortage, water limits the conversion rate of net radiation (Rn) to ET (ECR) and surpasses Rn to become the determinative factor of ET. (3) In extreme arid areas, ET has a significant positive correlation with WVP and SWC. In other precipitation areas, ET has a significant linear correlation with WVP, but the slope of the linear fitting line is different for precipitation. The relationship between ET and SWC is more complex. In areas with precipitation exceeding 800 mm, the correlation between SWC and ET is not significant. In areas with precipitation between 200 mm and 800 mm or in alpine regions, SWC and ET have a quadratic relationship. (4) ECR has quadratic correlations with WVP and SWC, and ECR reaches the maximum when WVP = 0.182 kPa and SWC = 0.217 m3∙m−3. ET may be inhibited for water shortage or water supersaturation. (5) In areas where SWC determines ET, the ET stress index (ESI) is inversely proportional to SWC, and in areas where heat affects ET, the ESI is directly proportional to SWC. Therefore, for the accurate monitoring of large-scale drought, various drought monitoring criteria should be determined in different areas and periods, considering information on precipitation, the underlying surface type, and digital elevation.

Understanding the spectral characteristics of moso bamboo leaves damaged by Pantana phyllostachysae Chao can provide theoretical guidance for developing applicable and effective technologies to monitor the ecological safety of the bamboo forest. Compared with the traditional multispectral data, hyperspectral remote sensing can sense the subtle changes of host spectrum among different severity of Pantana phyllostachysae Chao. However, the related researches were still rare, and the spectral change mechanism of the host needs to be further summarized. Therefore, this study analyzed the spectral differences among healthy, damaged and off-yearmoso bamboo leaves based on 552 field measured spectrums. The characteristic variables that can act as indicators of leaves health status were selected. Finally, the model for monitoring the damage of leaves caused by Pantana phyllostachysae Chao was established using the XGBoost algorithm. The results show that: (1) with the increase of pest damage, the reflectance of damaged leaves gradually appeared "green low and red high" in visible-band, while the reflectance noticeably decreased in near-infrared band, and the reflectance of damaged leaves in shortwave infrared band was significantly higher than that of healthy leaves, especially in the two typical water vapor absorption bands (1 450 and 1 940 nm); (2) the reflectance of off-year leaves in visible and near infrared bands was significantly higher than healthy and damaged leaves; (3) the spectral characteristics of indentation-only leaves only slightly changed in comparison with healthy leaves, while the red band reflectance of leaves with red-brown disease spots increased to some extent, andthe leaves with gray-white disease spots completely lost the basic spectral characteristics of vegetation; (4) according to the feature importance score determined by the XGBoost algorithm, the contribution of each characteristic variable was PRI>FDVI576, 717 > NPCI>DSWI> VOG 1> RVSI> NDWI; (5) the overall average accuracy of the model to detect the damage by the pest was 74. 39%, and the accuracy for healthy, mild damaged, severe damaged, off-year, and moderate damaged leaves was 94. 55%, 74. 93%, 84. 12%, 71. 10%, and 33. 48% respectively.

简单回顾了卫星遥感在可见光、热红外、微波、重力卫星、高光谱、叶绿素荧光、无人机以及多源遥感技术和大数据挖掘等领域的干旱监测技术;对各种指数(模型)应用中存在的问题进行评述;指出卫星遥感干旱面临的主要技术问题,并结合国家和气象行业需求提出研究对策及学科建议.

Drought impact is closely related to water deficit phases within the hydrological cycle, and to drought propagation (the complex evolutions from meteorological drought to hydrological drought and to soil moisture drought). Besides the qualitative description, the quantitative analysis of drought propagation is still limited so far. Therefore, in this study, the propagation from meteorological to hydrological and to soil moisture droughts have been quantitatively analyzed. In particular, run theory has been utilized for quantifying key drought features including its duration and magnitude. The two available land assimilation datasets from 1981-2018 were selected for comparison purpose in Northern China Plain. Our results showed that although drought events identified by two datasets were not the same, both datasets revealed that meteorological drought occurred more frequently than hydrological and soil moisture droughts. And both demonstrated meteorological drought, hydrological drought and soil moisture drought were not synchronous. In particular, more than 91.89% of meteorological droughts led to hydrological droughts, and more than 87.10% of hydrological droughts caused soil moisture droughts. Furthermore, linear models showed the best for drought duration and magnitude. We found when meteorological drought was prolonged or shortened by one month, more than 91.89% probability it would lead to the extension or shortening of hydrological drought by 0.992 months with 1.687 unit in magnitude. And if the duration of hydrological drought changed by one month, more than 80.56% probability of the soil moisture drought duration would change by 1.006 months with 0.992 unit in magnitude. The results of duration and magnitude fitted well across the whole study area. Building on the above drought evolution information, practical drought mitigation measures and early warning system could be established.

为预测未来青海云杉在不同海拔梯度上的分布范围,基于FAREAST模型,对祁连山西部、中部和东部3个站点的青海云衫(Picea crassifolia)中-幼龄林(0～60 a)生物量碳的海拔分布特征进行模拟.结果表明:(1)在同一站点,青海云杉幼苗幼树生物量碳在中间海拔分布最多,集中在海拔2800～3100 m之间,此范围以外,生物量碳随之减少.(2)不同站点比较,青海云杉幼苗幼树平均生物量碳在祁连山中部最高,达到27.48±5.51 t·C·hm-2,其次为东部的24.56±3.50 t·C·hm-2和西部的23.80±2.07 t·C·hm-2.(3)青海云杉幼苗幼树分布的海拔范围约在2500～3400 m之间,但不同站点间存在差异.模拟得出,祁连山区青海云杉幼苗幼树生物量碳分布存在最佳海拔区间2800～3100 m,高于或低于该区间时,青海云杉的生长和更新过程将会受到限制.祁连山中部青海云杉幼苗幼树生物量碳高于东部和西部,表明中部是青海云杉生长和潜在分布的最佳区域,导致东、西部区域更新较差的原因可能是由于东部受人类活动的影响更加频繁,而西部山区则可能更易受干旱胁迫的影响.

It is important to understand the propagation of an agricultural drought, which is crucial for early warning. Recent studies have partly revealed this hidden process and regarded it as another critical feature of drought, but the relevant studies are still limited. Here, we propose a quantitative method to explore the full propagation process of agricultural drought by using cross-wavelets combined with multiple drought indices and spatial autocorrelation methods. The Standardized Precipitation Index (SPI), Standardized Runoff Index (SRI), Standardized Soil Moisture Index (SSI) and Vegetation Health Index (VHI) were adopted to characterize meteorological, hydrological, soil moisture and vegetation droughts, respectively. The propagation time of agricultural drought was investigated by the cross wavelet analysis. The spatial relationship of those droughts was examined by spatial autocorrelation method. Results demonstrated that the propagation time was within one month from meteorological to hydrological drought, and within two months from hydrological to soil moisture drought, and between two to three months from hydrological to vegetation drought in most areas of Yangtze River Basin, respectively. It was also found the meteorological and hydrological droughts, hydrological and soil moisture droughts, hydrological and vegetation droughts were all characterized by statistical linkages on both long and short time scales. The global Moran's Index of SPI, SRI and SSI were higher than 0.7 and the local Moran's Index were mainly High-High and Low-Low clustering, indicating those subtype droughts were closely associated with the neighboring regions. This study clearly revealed the full propagation of agricultural drought in Yangtze River Basin both from spatial and temporal perspective for the first time, which provides valuable knowledge for understanding and predicting agricultural drought.

The eastern part of Northwest China, located on the edge of the East Asian monsoon region with low precipitation and high variability, is one of the regions with the most frequent droughts in China. The drought causes huge agriculture losses in this region. We created agricultural drought remote sensing monitoring and analysis platform(ADMP) in Northwest China base on FY-3 Data in order to improve the universality, comparability and automation degree of agricultural drought monitoring based on remote sensing in Northwest China, meanwhile, an optimization scheme for drought monitoring based on the coupling of climate division and vegetation types was proposed according to the applicability evaluation of multiple drought monitoring models, for giving full play to the role of FY-3 series satellites. The ADMP consists of three parts, namely data support system, monitoring and analysis system and product release and display system. The function of these systems is achieved using plug-in design concept, which build server-side plug-in framework, formulate business plug-in integration specification as well as carry out modular packaging and service transformation of algorithms and data resources, Therefore, It realizes the configurable, expandable, pluggable goals of the new business modules, and effectively avoids repeated construction caused by the addition of new applications in the traditional mode.The ADMP is composed of three subsystems and twelve sub-functional modules. It can automatically produce seven drought indices products with multiple time scales (day, five days, ten days and month). Furthermore, the ADMP products include thematic maps, statistical tables and monitoring reports at the four levels of the northwest region, province, city, and county. The ADMP products consistent with the actual situation in drought range and intensity through the monitoring lots of drought cases.

A major experimental drought research project entitled "Mechanisms and Early Warning of Drought Disasters over Northern China" (DroughtEX_China) was launched by the Ministry of Science and Technology of China in 2015. The objective of DroughtEX_China is to investigate drought disaster mechanisms and provide early-warning information via multisource observations and multiscale modeling. Since the implementation of DroughtEX_China, a comprehensive V-shape in situ observation network has been established to integrate different observational experiment systems for different landscapes, including crops in northern China. In this article, we introduce the experimental area, observational network configuration, ground- and air-based observing/testing facilities, implementation scheme, and data management procedures and sharing policy. The preliminary observational and numerical experimental results show that the following are important processes for understanding and modeling drought disasters over arid and semiarid regions: 1) the soil water vapor-heat interactions that affect surface soil moisture variability, 2) the effect of intermittent turbulence on boundary layer energy exchange, 3) the drought-albedo feedback, and 4) the transition from stomatal to nonstomatal control of plant photosynthesis with increasing drought severity. A prototype of a drought monitoring and forecasting system developed from coupled hydroclimate prediction models and an integrated multisource drought information platform is also briefly introduced. DroughtEX_China lasted for four years (i.e., 2015-18) and its implementation now provides regional drought monitoring and forecasting, risk assessment information, and a multisource data-sharing platform for drought adaptation over northern China, contributing to the global drought information system (GDIS).

Cyberinfrastructure plays an important role in the collection, management, and dissemination of drought information in agricultural activities, especially when the activities involve a variety of facilities, data sources, and communities. The challenge of coordinating tremendous sources of data and systems becomes paramount. Some key questions require additional attention if analyzing agricultural drought in a large social-environmental context: preprocessing observation into analysis-ready format, integrate vegetation/soil observations across platforms, and assess potential risks on the crop yield and environment. Cyberinfrastructure capable of accepting data from either research and monitoring networks or professionals in agricultural activities, must be built to achieve these goals. The cyberinfrastructure design generally consists of four components: data source, standardized web service, application service, and client interface. This study introduces a cloud-based global agricultural drought monitoring and forecasting system (GADMFS) which provides scalable vegetation-based drought indicators derived from satellite-, and model-based vegetation condition datasets. The provided datasets include global historical drought severity data from the monitoring component. The system is a significant extension to current capabilities and datasets from global drought assessment and early warning. The experiment results show that GADMFS successfully captured the major drought events in history and reflected the high-resolution spatial distribution which can specifically assist agriculture stakeholders to make informative decisions and take proactive drought management actions.

The non-rectangular hyperbola (NRH) equation is the most popular method that plots the photosynthetic light-response (PLR) curve and helps to identify plant photosynthetic capability. However, the PLR curve can't be plotted well by the NRH equation at different plant growth phases due to the variations of plant development. Recently, plant physiological parameters have been considered into the NRH equation to establish the modified NRH equation, but plant height (H), an important parameter in plant growth phases, is not taken into account. In this study, H was incorporated into the NRH equation to establish the modified NRH equation, which could be used to estimate photosynthetic capability of herbage at different growth phases. To explore photosynthetic capability of herbage, we selected the dominant herbage species Potentilla anserina L. and Elymus nutans Griseb. in the Heihe River Basin, Northwest China as the research materials. Totally, twenty-four PLR curves and H at different growth phases were measured during the growing season in 2016. Results showed that the maximum net photosynthetic rate and the initial slope of PLR curve linearly increased with H. The modified NRH equation, which is established by introducing H and an H-based adjustment factor into the NRH equation, described better the PLR curves of P. anserina and E. nutans than the original ones. The results may provide an effective method to estimate the net primary productivity of grasslands in the study area.

Precise monitoring of the yield of crops is critical for growth diagnosis and precision management. Many spectral indices have been developed for yield estimation. The objective of this study was to determine the most suitable model for the rational estimation of spring wheat yield using canopy hyperspectral reflectance data of spring wheat. Canopy hyperspectral datasets were obtained during the whole growth stage from the seeding to mature stage in semi-arid rained region of Loess Plateau in Northwest China. On the basis of the correlation analysis of the hyperspectral data and the yield data, the study compared potentials and limitations of hyperspectral indices from the canopy level, and analyzed the relationship between hyperspectral indices and yield in different grown stage. The spectral indices REP in booting stage and heading stage are sensitive to the yield. We proposed a compound regression equation from the spectral index of booting and heading stage, the R 2 is 0.875, the combined multiphase phase spectral index is better for yield estimation. The results from field experiment are limited to large-scale experiments and would provide a good basis for remote sensing of yield estimation in a wide range of vegetation.

As an important component of the Earth's ecosystem, soil moisture plays a vital role in the global water cycle and serves as an important parameter in the study of hydrology, meteorology, and agroecology. Based on the energy balance theory of underlying surface, the atmospheric temperature data recorded by an automatic weather station as well as unmanned aerial vehicle (UAV)-borne thermal infrared and multispectral remote-sensing data were used to establish inversion models of relative soil moisture at different depths based on remote-sensing UAV data and date from near-ground quadrats, respectively. Spatial differences and data accuracy verification were then performed using the 2017 spring wheat moisture data as a control. The results showed that: (1) the relative moisture of farmland soil can be effectively estimated using the proposed soil moisture inversion model. In terrestrial ecosystems, the ratio of actual to potential evapotranspiration, which is often used to characterize potential drought levels, is linearly correlated to soil moisture at different depths; (2) during the inversion of farmland soil moisture, the UAV-based observation method is superior to the near-ground quadrat observation method in both efficiency and accuracy. In addition, the relative soil moisture estimation model based on UAV data has a high accuracy, with R-2 reaching 0.629, and a root mean square error (RMSE) of <0.100; and (3) the number and size of quadrats are important factors affecting the inversion accuracy. The data collected by the UAVs covered a wide range and had high spatial matching degree at the field scale. Especially, during estimation of the relative moisture of surface soil (0 to 10 and 0 to 20 cm), the linear fitting between the inversion model based on UAV data and the measured value was optimal. The error was minimal (RMSE <0.07) and R-2 was >0.714, so this method is more suitable for estimating and dynamically monitoring relative soil moisture of farmland at the field scale. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)

In order to verify the applicability of different triangular methods for evapotranspiration (ET) estimation and the effect of spatial resolution on triangular methods, the applicability of normalized-difference vegetation index-land surface temperature (NDVI-LST) and NDVI-albedo triangular methods was validated based on the enhanced thematic mapper (ETM)+moderateresolution imaging spectrometer (MODIS) data. Considering the effecting of soil moisture on ET, a new triangular method was developed by using the perpendicular drought index (PDI). Compared to the measured values, the result showed that LSTs retrieved by a single-channel method using ETM+ data were closed to the measured values, with a root-mean-square error (RMSE) of 5.7 K. Given the inhomogeneity of the underlying surface, the remote-sensing data related to the low spatial resolution blur the between-pixel differences. A higher spatial resolution of the remote sensing (RS) data corresponds to a greater homogeneity of the distribution of scatter plots in the eigenspace and greater differences between pixels, particularly in the NDVI-LST eigenspace. The eigenspace formed by the PDI and the NDVI possess distinct triangular characteristics, particularly the inversion results of the ETM+ data, with an mean absolute percent error of 14% and an RMSE of 103 W.m(-2). The dry-edge slope introduced by the PDI in the expression increases the accuracy of the estimated ET. Compared to the measured data, the RMSEs of ET estimated by the NDVI-PDI using the ETM+ and MODIS data were reduced to 92 and 121 W.m(-2), respectively. The regional distribution of ET inverted by the NDVI-PDI method significantly coincided with the actual scenario of the underlying surface.

Statistically relating vegetation index to yield is a common wheat yield estimation method. In this paper, we investigate the relationship between a variety of spectral vegetation indices and yield factors for spring wheat in a semi-arid, rain-fed, agricultural region under different meteorological conditions on the basis of relevant ground observations. We also analyze the yield estimation factor of remote sensing for spring wheat by regression method under different meteorological conditions. Results are as follows. 1) The theoretical yield per unit area, thousand-kernel weight, grains per ear, and number of productive tillers per square meters at the milk stage of maturity are relatively small. These data exhibit flat variation trends with spectral vegetation indices under drought conditions. By contrast, the trends under non-drought conditions are significantly changing. 2) Meanwhile, the spectral vegetation indices under drought and non-drought conditions are appreciably associated with the theoretical yields at the booting (0.01) and heading stages (0.001). 3) In the meteorological droughts, the aggregate value of the semi-arid water index at the booting and heading stages is suitable for use as the yield estimation factor for spring wheat. However, under the meteorological non-droughts, the RVI(p780/p1750) at the booting stage is used as the yield estimation factor for spring wheat. The mean absolute percentage errors of the yield estimations in the two cases are 70.9% and 84.2%, respectively.

Based on the Penman-Monteith formula recommended by FAO (Food and Agriculture Organization), calculated the potential evapotranspiration of all stations in Heihe River basin in 49 years. The research data included he monthly average temperatures, average maximum temperature, average minimum temperature, wind speed, relative humidity, wind speed, hours of sunshine and solar radiation from 1960 to 2009. The change characteristics of evapotranspiration were analyzed by using trend analysis and Mann-Kendall abrupt tests. The results showed that the ET0 is increasing in the upper reaches and fluctuating in middle and lower reaches. The abrupt point is 1979 in middle reaches and in 1983 in middle reaches. While there is no obvious abrupt point in upper reaches.