A Benchmark Test for Ecohydrological Models of Interannual Variability of NDVI in Semi-arid Tropical Grasslands

Pulses of aboveground net primary productivity (ANPP) in response to discrete precipitation events are an integral feature of ecosystem functioning in arid and semi-arid lands. Yet, the usefulness of nonlinear, ecohydrological pulse response functions to predict regional-scale patterns of annual ANPP at decadal scales remains unclear. Here, we assessed how different pulse response (PR) models compete with simple linear statistical models to capture variability in yearly integrated values of Normalized Difference Vegetation Index (NDVI_int), a remotely sensed proxy of annual ANPP. We examined 24-year-long time series of NDVI_int calculated from Advanced Very High Resolution Radiometer (AVHRR) NDVI for 350,000 km² of tropical grasslands in northern Australia. Based on goodness-of-fit statistics, PR models clearly outperformed statistical models when parameters were optimized for each site but all models showed the same error magnitude when all sites were combined in ensemble simulations or when the models were evaluated outside the calibration period. PR models were less biased and their performance did not deteriorate in the driest areas compared to linear models. Increasing the complexity of PR models to provide a better representation of soil water balance and its feedback with plant growth did not improve model performance in ensemble simulations. When error magnitude, bias, and sensitivity to parameter uncertainty were all considered, we concluded that a low-dimensional PR model was the most robust to capture NDVI_int variability. This study shows the potential of long time series of AVHRR NDVI to benchmark process-oriented models of interannual variability of NDVI_int in water-controlled ecosystems. This opens new avenues to examine at the global scale and over several decades the causal relationships between climate and leaf dynamics in the grassland biome.     

Detecting Leaf Pulvinar Movements on NDVI Time Series of Desert Trees: A New Approach for Water Stress Detection

Heliotropic leaf movement or leaf ‘solar tracking’ occurs for a wide variety of plants, including many desert species and some crops. This has an important effect on the canopy spectral reflectance as measured from satellites. For this reason, monitoring systems based on spectral vegetation indices, such as the normalized difference vegetation index (NDVI), should account for heliotropic movements when evaluating the health condition of such species. In the hyper-arid Atacama Desert, Northern Chile, we studied seasonal and diurnal variations of MODIS and Landsat NDVI time series of plantation stands of the endemic species Prosopis tamarugo Phil., subject to different levels of groundwater depletion. As solar irradiation increased during the day and also during the summer, the paraheliotropic leaves of Tamarugo moved to an erectophile position (parallel to the sun rays) making the NDVI signal to drop. This way, Tamarugo stands with no water stress showed a positive NDVI difference between morning and midday (ΔNDVI_mo-mi) and between winter and summer (ΔNDVI_W-S). In this paper, we showed that the ΔNDVI_mo-mi of Tamarugo stands can be detected using MODIS Terra and Aqua images, and the ΔNDVI_W-S using Landsat or MODIS Terra images. Because pulvinar movement is triggered by changes in cell turgor, the effects of water stress caused by groundwater depletion can be assessed and monitored using ΔNDVI_mo-mi and ΔNDVI_W-S. For an 11-year time series without rainfall events, Landsat ΔNDVI_W-S of Tamarugo stands showed a positive linear relationship with cumulative groundwater depletion. We conclude that both ΔNDVI_mo-mi and ΔNDVI_W-S have potential to detect early water stress of paraheliotropic vegetation.     

晋南地区冬小麦生育期气候变化特征及其对产量的影响

明确气候变化特征及其对小麦生产的影响,有助于为农业适应气候变化方案的制定提供理论依据.本研究分析了晋南小麦主产区临汾、运城市1957-2015年降水量、气温、日照时长、潜在蒸散量、干燥度等变化及其对冬小麦生育期及产量的影响.结果 表明:1957-2015年,临汾、运城市全年降水量和冬小麦生育期日照时长均呈下降趋势,冬小...     

Analysis of spatial and temporal patterns of aboveground net primary productivity in the Eurasian steppe region from 1982 to 2013

To explore the importance of the Eurasian steppe region (EASR) in global carbon cycling, we analyzed the spatiotemporal dynamics of the aboveground net primary productivity (ANPP) of the entire EASR from 1982 to 2013. The ANPP in the EASR was estimated from the Integrated ANPP_NDVI model, which is an empirical model developed based on field‐observed ANPP and long‐term normalized difference vegetation index (NDVI) data. The optimal composite period of NDVI data was identified by considering spatial heterogeneities across the study area in the Integrated ANPP_NDVI model. EASR's ANPP had apparent zonal patterns along hydrothermal gradients, and the mean annual value was 43.78 g C m^?2 yr^?1, which was lower than the global grasslands average. Compared to other important natural grasslands, EASR's ANPP was lower than the North American, South American, and African grasslands. The total aboveground net primary productivity (TANPP) was found to be 378.97 Tg C yr^?1, which accounted for 8.18%–36.03% of the TANPP for all grasslands. In addition, EASR's TANPP was higher than that of the grasslands in North America, South America, and Africa. The EASR's TANPP increased in a fluctuating manner throughout the entire period of 1982–2013. The increasing trend was greater than that for North American and South American and was lower than that for African grasslands over the same period. The years 1995 and 2007 were two turning points at which trends in EASR's TANPP significantly changed. Our analysis demonstrated that the EASR has been playing a substantial and progressively more important role in global carbon sequestration. In addition, in the development of empirical NDVI‐based ANPP models, the early–middle growing season averaged NDVI, the middle–late growing season averaged NDVI and the annual maximum NDVI are recommended for use for semi‐humid regions, semi‐arid regions, and desert vegetation in semi‐arid regions, respectively.     

Use of climatic data and satellite imagery to model the abundance of Culicoides imicola, the vector of African horse sickness virus, in Morocco

African horse sickness (AHS) is a vector-borne, infectious disease of equids caused by African horse sickness virus (AHSV) . The only proven field vector of the virus is the biting midge Culicoides imicola. Following a recent epizootic (1989–91) of AHS in Morocco, light traps and automatic weather stations were operated for 2 years at twenty-two sites distributed over much of the country. The annually-averaged mean daily trap catch of C. imicola at these sites was negatively correlated with wind speed, and positively correlated with the average and mean annual minimum NDVI (Normalized Difference Vegetation Index, a remotely sensed measure of vegetation activity). There were no significant correlations between the mean daily trap catch and air temperature, soil temperature, relative humidity, saturation deficit, rainfall, altitude or the mean annual maximum or range of NDVI. The best two-variable model, which combined Windspeed_MnAvMn (the average daily minimum wind speed of the least windy month) and NDVI_min (the average annual minimum NDVI) as predictors, explained over 50% of the variance in the annually-averaged mean daily trap catch of C. imicola. There was a significant, positive correlation between minimum wind speed at night and the daily mortality rate of adult female C. imicola and it is suggested that the relationship between wind speed and the abundance of C. imicola arises from effects on adult mortality or dispersal. Considering several climatic variables, in North Africa NDVI_min was most significantly correlated with total annual rainfall. It is suggested that the relationship between NDVI_min and the abundance of C. imicola arises from the impact of soil moisture on both. It is proposed that areas of Morocco with higher levels of soil moisture in late summer or autumn provide more, larger and/or more enduring breeding sites for C. imicola, as well as supporting more photosynthetically active vegetation and hence having higher NDVI.     

NDVI saturation adjustment: A new approach for improving cropland performance estimates in the Greater Platte River Basin,USA

In this study, we developed a new approach that adjusted normalized difference vegetation index (NDVI) pixel values that were near saturation to better characterize the cropland performance (CP) in the Greater Platte River Basin (GPRB), USA. The relationship between NDVI and the ratio vegetation index (RVI) at high NDVI values was investigated, and an empirical equation for estimating saturation-adjusted NDVI (NDVI_sat__adjust) based on RVI was developed. A 10-year (2000–2009) NDVI_sat__adjust data set was developed using 250-m 7-day composite historical eMODIS (expedited Moderate Resolution Imaging Spectroradiometer) NDVI data. The growing season averaged NDVI (GSN), which is a proxy for ecosystem performance, was estimated and long-term NDVI non-saturation- and saturation-adjusted cropland performance (CP_non__sat__adjust, CP_sat__adjust) maps were produced over the GPRB. The final CP maps were validated using National Agricultural Statistics Service (NASS) crop yield data. The relationship between CP_sat__adjust and the NASS average corn yield data (r = 0.78, 113 samples) is stronger than the relationship between CP_non__sat__adjust and the NASS average corn yield data (r = 0.67, 113 samples), indicating that the new CP_sat__adjust map reduces the NDVI saturation effects and is in good agreement with the corn yield ground observations. Results demonstrate that the NDVI saturation adjustment approach improves the quality of the original GSN map and better depicts the actual vegetation conditions of the GPRB cropland systems.     

Accuracy of the AVHRR vegetation index as a predictor of biomass,primary productivity and net CO2 flux

The Normalized Difference Vegetation Index (NDVI) or greenness index, based on the Advanced Very High Resolution Radiometer (AVHRR) aboard the NOAA-7 satellite, has been widely interpreted as a measure of regional to global vegetation patterns. This study provides the first rigorous, quantitative evaluation of global relationships between the NDVI and geographically representative vegetation data-bases, including field metabolic measurements and carbon-balance results from global simulation models. Geographic reliability of the NDVI is judged by comparing NDVI values for different surface types with a general global trend and by statistical analysis of relationships to biomass amounts, net and gross primary productivity, and actual evapotranspiration. NDVI data appear to be relatively reliable predictors of primary productivity except in areas of complex terrain, for seasonal values at high latitudes, and in extreme deserts. The strength of the NDVI-productivity relationship seems comparable to that of earlier climate-based productivity models. Little consistent relationship was found, across different vegetation types, between NDVI and biomass amounts or net biospheric CO₂ flux.Abbreviations AET= Actual Evapotranspiration - AVHRR= Advanced Very High Resolution Radiometer - GPP= Gross Primary Production - GVI= Global Vegetation Index - NDVI= Normalized Difference Vegetation Index - NPP= Net Primary Production     

Supplementation of isonitrogenous oil seed cakes in cactus (Opuntia ficus-indica)–tef straw (Eragrostis tef) based feeding of Tigray Highland sheep

The experiment was conducted at Maichew Agricultural Technical Vocational Education and Training College, Ethiopia. Twenty four male yearling Tigray Highland sheep with mean body weight (BW) of 21 ± 2.6 kg (mean ± S.D.) were used to investigate the effect of different protein sources on feed intake, nutrient digestibility, BW change and carcass parameters in a study comprising of 90 days feeding trial, followed by 7 days of digestibility trial and evaluation of carcass parameters. Six individually fed animals were used per treatment in a randomized complete block design. The treatments consisted of ad libitum feeding of tef (Eragrostis tef) straw plus 172 g dry matter (DM) of cactus (Opuntia ficus-indica) pear (T1, control) and supplementation with 145 g DM cotton seed cake (CSC) (T2), 195 g DM noug seed cake (NSC) (T3) or 149 g DM peanut cake (PNC) (T4) per head per day. The quantity of the supplements was set to supply 62.5 g crude protein (CP). Tef straw DM intake was depressed (P<0.01) as the result of NSC supplementation. Sheep supplemented with CSC and PNC had higher (P<0.001) total DM intake than the control and NSC supplemented ones. Supplementation with NSC and PNC also resulted in higher (P<0.01) apparent digestibility of DM and OM compared to the control treatment. Supplementation with CSC and PNC resulted in better daily BW gain (P<0.001), feed conversion efficiency (FCE) and dressed carcass weight (P<0.01) compared to the non-supplemented diet. Dressing percentage on slaughter weight base was higher (P<0.01) in supplemented sheep than in the non-supplemented ones. Supplementation with PNC also promoted higher (P<0.05) rib-eye muscle area than in the non-supplemented ones. It was concluded that supplementation with 145 g DM CSC and 149 g DM PNC resulted in better feed intake, BW gain and carcass traits in cactus–tef straw based feeding of sheep.     

Assessing the performance of NDVI as a proxy for plant biomass using non-linear models: a case study on the Kerguelen archipelago

Numerous ecological studies, including of the polar environment, are now using the remotely sensed normalized difference vegetation index (NDVI, e.g. PAL-NDVI or MODIS-NDVI) as a proxy of vegetation productivity rather than performing direct vegetation assessments. Even though previous data strongly suggested a saturation of NDVI at high biomass values, few studies have explicitly included this characteristic in the modelling process. Here, we developed a generalized non-linear model to explicitly model the relationship between temporal variations of NDVI (Pathfinder AVHRR Land 8 km dataset) and empirical field data. We illustrated our approach on the Kerguelen archipelago by using a green biomass index (point-intercept protocol) sampled at a small scale relative to PAL-NDVI data, and in presence of spatial (water) and temporal (cloud contamination, snow) heterogeneity, i.e. field conditions encountered in many ecological studies. We showed a strong relationship (r _pred.obs = 0.89 [0.77; 0.95]_95%) between this index and the seasonal component of NDVI time series (NDVI_comp). Despite the absence of lignified species in the stand, the NDVI_comp reached an asymptote (0.54 ± 0.05) for high values of green biomass index stressing the need to account for non-linearity when relating NDVI and plant measurements. We provided here a new methodological framework to standardize comparisons between studies assessing performance of NDVI as a proxy of vegetation data.

北京城区道路系统路网空间特征及其与LST和NDVI的相关性

城市道路系统是人类活动最为频繁的场所之一,研究路网空间特征与相关环境指标的关系,可为定量分析城市建设过程对城市生态环境的影响以及为城市基础设施规划管理提供方法。对北京市五环内各级道路抽样调查及面向对象的高分辨率遥感影像识别,建立道路空间信息数据库,运用TM影像反演获得地表温度(LST)及归一化植被指数(NDVI),研究4种不同分析网格下城市路网特征与两者的空间相关性。结果表明,在较大分析网格下(4×4),路网空间指标与LST mean及NDVI mean相关性显著;中等网格下,除LST与NDVI的平均值外,路网空间指标还可与两者最小值建立相关性;在较小窗口下(32×32),除平均值与最小值,还可建立特定道路指标与LST max及NDVI max的联系;各路网指标中,单位面积道路总长与LST及NDVI相关性最强;加权道路结点数在8×8和32×32网格分析中,与NDVI的相关性高于该指标未加权值,而结点指标均不适合与LST max建立联系;在各分析网格下,4项道路指标均与NDVI mean呈极显著负相关,表明道路绿地建设规模还不足以对NDVI的强度及分布产生较大影响。     

Asynchronous Response of Tropical Forest Leaf Phenology to Seasonal and El Ni?o-Driven Drought

The Hawaiian Islands are an ideal location to study the response of tropical forests to climate variability because of their extreme isolation in the middle of the Pacific, which makes them especially sensitive to El Niño-Southern Oscillation (ENSO). Most research examining the response of tropical forests to drought or El Niño have focused on rainforests, however, tropical dry forests cover a large area of the tropics and may respond very differently than rainforests. We use satellite-derived Normalized Difference Vegetation Index (NDVI) from February 2000-February 2009 to show that rainforests and dry forests in the Hawaiian Islands exhibit asynchronous responses in leaf phenology to seasonal and El Niño-driven drought. Dry forest NDVI was more tightly coupled with precipitation compared to rainforest NDVI. Rainforest cloud frequency was negatively correlated with the degree of asynchronicity (Δ_NDVI) between forest types, most strongly at a 1-month lag. Rainforest green-up and dry forest brown-down was particularly apparent during the 2002–003 El Niño. The spatial pattern of NDVI response to the NINO 3.4 Sea Surface Temperature (SST) index during 2002–2003 showed that the leeward side exhibited significant negative correlations to increased SSTs, whereas the windward side exhibited significant positive correlations to increased SSTs, most evident at an 8 to 9-month lag. This study demonstrates that different tropical forest types exhibit asynchronous responses to seasonal and El Niño-driven drought, and suggests that mechanisms controlling dry forest leaf phenology are related to water-limitation, whereas rainforests are more light-limited.     

基于MODIS-NDVI的区域冬小麦遥感估产——以山东省济宁市为例

以黄淮海平原冬小麦主产区山东省济宁市为研究实例,利用遥感方法,采用250 m分辨率经过Savitzky-Golay滤波技术平滑处理的MODIS-NDVI遥感数据对冬小麦产量进行预测.研究选取了冬小麦关键生育期内0.2～0.8范围的旬NDVI数据,并建立了其与冬小麦产量的关系.同时,采用逐步回归方法筛选建立冬小麦关键生育期旬NDVI与冬小麦产量间关系的估产模型.利用地面实测冬小麦产量数据,对所建的估产模型进行精度检验,结果表明,估产相对误差在-3.6％～3.9％之间.表明利用Savitzky-Golay滤波技术平滑后的作物关键生育期内MODIS-NDVI遥感数据进行冬小麦估产,其方法精度较高,具有一定的可行性.     

平稳小波变换在冬小麦SPAD高光谱监测中的应用

在2010与2011年度冬小麦生长季通过大田小区试验,利用ASD便携式野外光谱仪和SPAD 502叶绿素计实测冬小麦冠层的高光谱反射率与SPAD值.分析不同SPAD值下的冬小麦冠层光谱特征,建立了基于归一化植被指数（NDVI）与比值植被指数（RVI）、小波能量系数的不同生育期冬小麦SPAD估算模型.结果表明： 随着SPAD值的增大,“绿峰”与“红谷”特征愈加明显.在冬小麦返青期、拔节期、抽穗期、灌浆期NDVI估算SPAD的效果较好,估算模型的R²分别为0.7957、0.8096、0.7557、0.5033.小波能量系数回归模型可以提高冬小麦SPAD的估算精度,在返青期、拔节期、抽穗期、灌浆期以高频、低频小波能量系数为自变量的冬小麦SPAD估算模型的R²分别达到0.9168、0.9154、0.8802、0.9087.     

黄土高原不同植被覆被类型NDVI对气候变化的响应

植被与气候是目前研究生态与环境的重要内容。为探究黄土高原地区植被与气候因子之间的响应机制,利用线性趋势分析、Pearson相关分析、多元线性回归模型以及通径分析的方法,对黄土高原2000—2015年全区和不同植被覆被类型区内NDVI与气候因子的变化趋势以及相互作用关系进行分析。植被覆被分类数据和植被指数数据分别来源于ESA CCI-LC(The European Space Agency Climate Change Initiative Land Cover)以及MODND1T/NDVI(Normalized Difference Vegetation Index)。结果表明:(1) 2000—2015年黄土高原全区植被年NDVI_(max)显著增加的区域占总面积的74.25%,不同植被覆被类型年NDVI_(max)分别为常绿阔叶林常绿针叶林落叶阔叶林落叶针叶林镶嵌草地农田镶嵌林地草地灌木,并且都呈显著增加趋势,其中常绿阔叶林和农田增加幅度最大,为0.012/a。(2)黄土高原全区NDVI与气温、日照、降水和相对湿度等气候因子之间没有显著相关性,但在不同植被覆被类型区,气候因子对NDVI存在显著作用,且不同植被覆被类型差异明显。(3)在全区和不同植被覆被类型区NDVI仅对降水的响应比较一致,气温无论在整个区域尺度还是不同植被覆被类型区对植被的影响均不显著。(4)常绿阔叶林、落叶阔叶林、常绿针叶林及镶嵌林地等以乔木为主的植被覆被类型受年均相对湿度和年总日照时数的显著负效应驱动,草地、镶嵌草地等以草本为主的植被覆被类型则受到年总降水量的显著正效应影响。这说明对植被类型进行区分,更有利于揭示气候对植被的作用机制。     

秸秆还田条件下灌水模式对冬小麦产量和水肥利用效率的影响

于2008-2010年,在山西省临汾市尧都区半干旱、半湿润季风气候区,通过大田试验研究了玉米秸秆连续还田条件下灌水模式对冬小麦籽粒产量、干物质转移及水肥利用效率的影响.结果表明:浇越冬水可促进小麦分蘖;浇拔节水可提高分蘖成穗率,增加成穗数;浇孕穗水可促进穗部干物质积累,提高千粒重.浇2水时,推迟第2次浇水时期使叶片干物质转移量和穗粒数增加;浇2水比浇l水的肥料表观利用率高,可促进穗部干物质积累.越冬水灌水量和总灌水量对分蘖、穗部干物质积累的影响较小;拔节期或孕穗期增加灌水量则更有利于养分吸收及干物质积累与转移,提高籽粒水分利用效率,产量构成因素协调,增产效果明显.因此,确保越冬水可实现稳产,在越冬水基础上,拔节期增量灌水(900 m3·hm-2)可满足冬小麦中后期生长发育的需要,提高籽粒水分利用效率,实现节水高产栽培.     

水分胁迫下不同抗旱类型品种对氮素营养反应的比较研究

实验结果表明,小麦受到水分胁迫时,游离脯氨酸大量积累,胁迫缓慢加重时,渗透调节能力增强,适当的氮素营养,可增强渗透调节作用,有助于干旱下维持膨压,提高NR酶活性和净光合速率。水地型品种对水分和氮素营养都较敏感,叶片水势、饱和渗透势、NR酶活性、游离脯氨酸含量的变化都较旱地型品种大,旱地型品种在受旱时水分状况较稳定,生理代谢变化幅度较小,但两类旱地品种对氮素营养的反应不同。旱肥型较旱薄型敏感。水分胁迫下,水地型品种渗透势下降较多,渗透调节幅度较大,是对逆境更敏感或实际受到的胁迫较重的表现。     

基于植被/温度特征的黄土高原地表水分季节变化

地表水分是监测土地退化的一个重要指标,是气候、水文、生态、农业等领域的主要参数。选择陕北黄土高原地区作为研究区域,首先采用基于植被覆盖特征的植被状态指数（Vegetation Condition Index,简称VCI）和基于地表温度特征（Land Surface Temperature,简称Ts）的温度状态指数（Temperature Condition Index,简称TCI）分别评价了区域地表水分状况的季节变化。在此基础上分析了植被指数与地表温度特征线性关系的季节变化规律,计算了基于两者经验关系的地表干燥度指数（Temperature Vegetation Dryness Index,TVDI）。该指数对Ts／NDVI特征空间的生态特征的解释,对土壤和作物的水分含量具有综合的指示意义。文中利用该指数综合评价了研究区域地表水分状况的时空分布差异,进一步对VCI、TCI与TVDI相关关系的季节变化进行比较分析,并结合气候因子进行了相关验证,从而对不同指数的应用范围做出判定。研究结果表明,单独采用TCI或VCI表征地表水分会受到明显的季节影响,而TVDI能在不同季节综合体现植被覆盖和地表温度特征对地表水分的影响,从而能较好的反映区域地表水分状况的时空变化特征。各区域的TVDI值季节分布上皆为4—7月份高于10-翌年1月份,但各区TVDI值的季节变化则存在显著不同,而各流域内部TVDI值的空间变异性也存在季节差异,其中在10月份较为显著。     

黄土高原冬小麦田光谱特征变化及其与二氧化碳日收支的相关分析

根据光谱辐射仪对黄土高原冬小麦整个生育期光谱反射率的连续观测数据及CO₂通量观测数据,对冬小麦田光谱特征变化及其与CO₂日收支的相关性进行了分析.结果表明：冬小麦田不同波长光谱反射率和归一化植被指数（NDVI）呈现明显的日变化和季节变化.同一天内,反射率随太阳高度角的变化而变化,变化最大的波段（550 nm左右、700～1 050 nm）表现为峰.不同生育期同一时刻,可见光波段（350～670 nm）反射率变化不大,近红外波段（700～1 050 nm）出现较大差异,在出苗期、分蘖期和越冬期后红边位置向长波方向“红移”;越冬期前出现向短波方向“蓝移” 的现象;但成熟期“蓝移”现象不明显,表现为突变;其他生育时期没有观测到波谱位移.NDVI的日变化呈U型,13：00左右最低,16：00后出现较大波动,与抛物线有较好的拟合效果,小麦生长旺盛时期,对其地面遥感观测应选择在NDVI变化不大的13：00左右进行;整个冬小麦生长季11:00反射率及NDVI以播种后第140天为中心对称,NDVI的季节变化表现为M型,可用四次多项式拟合;在整个小麦生育期中NDVI与CO₂的日收支呈极显著负相关,但正午左右的相关性稍差.     

Estimating soil respiration using spectral vegetation indices and abiotic factors in irrigated and rainfed agroecosystems

Aims

Our aims were to identify the primary factors involved in soil respiration (R_s) variability and the role that spectral vegetation indices played in R_s estimation in irrigated and rainfed agroecosystems during the growing season.

Methods

We employed three vegetation indices [i.e., normalized difference vegetation index (NDVI), green edge chlorophyll index (CI_{green edge}) and enhanced vegetation index (EVI)] derived from the Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance product as approximations of crop gross primary production (GPP) for R_s estimation. Different statistical models were used to analyze the dependencies of R_s on soil temperature, soil water content and plant photosynthesis, and accuracy of these models were compared in the irrigated and rainfed agroecosystems.

Results

The results demonstrated that a model based only on abiotic factors (e.g., soil temperature and soil water content) failed to describe part of the growing-season variability in R_s. Residual analysis indicated that R_s was influenced by a short-term gross primary production (GPP) and a longer-term (≥3 days) accumulated GPP in the irrigated and rainfed agroecosystems. Therefore, photosynthesis dependency of R_s should be included in the R_s model to describe the growing-season dynamics of R_s. Among the three VIs, CI_{green edge} showed generally better correlations with GPP at different cumulative times and canopy green leaf area index than EVI and NDVI. Adding the CI_{green edge} into the model considering only soil temperature and soil water content significantly improved the simulation accuracy of R_s.

Conclusions

Our results suggest that spectral vegetation index from remote sensing could be used to estimate R_s, which will be helpful for the development of a future R_s model over a large spatial scale.     

Spatial Analysis and Modeling Tool (SAMT): 2. Applications

The Spatial Analysis and Modeling Tool (SAMT) is a GIS-based analytical software for land use study. This paper demonstrates the capabilities of SAMT as applied in studies of land use and landscape change in the catchment area of the River Quillow, North-East Germany. Neural network and fuzzy models are important features that were incorporated in SAMT. The region of study for this application is a catchment area of about 170 km² and is subdivided into 54,000 grid cells for spatial simulations.Three different applications of SAMT are discussed in this article. The first application involves assessment of the potential soil erosion risk on arable land. This user-defined application combines an economic model (LP model), a temperature-driven crop coverage model and a soil erosion risk model (Universal Soil Loss Equation) using data from tables and map information. The second and third applications are winter wheat yield estimates using, respectively, the fuzzy toolbox SAMT_FUZZY in conjunction with expert knowledge, and using the neural network toolbox SAMT_NN based on observations.