Rice Yield Estimation by Integrating Remote Sensing with Rice Growth Simulation Model

Since remote sensing can provide information on the actual status of an agricultural crop, the integration between remote sensing data and crop growth simulation models has become an important trend for yield estimation and prediction.The main objective of this research was to combine a rice growth simulation model with remote sensing data to estimate rice grain yield for different growing seasons leading to an assessment of rice yield at regional levels. Integration between NOAA (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) data and the rice growth simulation model ORYZA1 to develop a new software, which was named as Rice-SRS Model, resulted in accurate estimates for rice yield in Shaoxing, China, with an estimation error reduced to 1.03% and 0.79% over-estimation and 0.79% under-estimation for early, single and late season rice, respectively. Selecting suitable dates for remote sensing images was an important factor which could influence estimation accuracy. Thus, given the different growing periods for each rice season, four images were needed for early and late rice, while five images were preferable for single season rice.Estimating rice yield using two or three images was possible, however, if images were obtained during the panicle initiation and heading stages.

Regionalization for Rice Yield Estimation by Remote Sensing in Zhejiang Province

In order to provide a scientific basis for rice yield estimation and improve the accuracy of yield estimation in Zhejiang Province, regionalization indices for rice yield estimation by remote sensing (RS) in the province were determined by considering the special features of yield estimation by RS, and based on analysis of the natural conditions of Zhejiang Province. The indices determined included rice cropping system, agroclimate, landform, surface feature structure and rice yield level, where rice planting system was considered as the main one. Then regionalization for rice yield estimation by RS was completed by spatial neighboring analysis with the Geographical information System (GIS) technology combined with using of tree algorithm. The province was divided into two regions, i. e., the single-cropping rice region which was subdivided into 3 regions including those in mountains of northwest Zhejiang, water network area of north Zhejiang and mountains of south Zhejiang, and double-cropping rice region which was subdivided into 5 regions including those on plain of north Zhejiang, coastal plains and hills of southeast Zhejiang, Jin-Qu Basin of middle Zhejiang, hills of east Zhejiang, and hills and mountains of northwest Zhejiang. This regionalization took the county borders as the region boundaries, kept the regions connective and made the administrative regions integrity and, then, could meet the requirements of rice yield estimation by RS, showing that the results were quite satisfying.

REMOTE SENSING BASED ESTIMATION SYSTEM FOR WINTER WHEAT YIELD IN NORTH CHINA PLAIN

This paper presents the applications of Landsat Thematic Mapper (TM) data and Advanced Very High Resolution Radiometer (AVHRR) time series data for winter wheat production estimation in North China Plain. The keytechniques are described systematically about winter wheat yield estimation system, including automatically extractingwheat area, simulating and monitoring wheat growth situation, building wheat unit yield model of large area and forecasting wheat production. Pattern recognition technique was applied to extract sown area using TM data. Temporal NDVI(Normal Division Vegetation Index) profiles were produced from 8 - 12 times AVHRR data during wheat growth dynamically. A remote sensing yield model for large area was developed based on greenness accumulation, temperature andgreenness change rate. On the basis of the solution of key problems, an operational system for winter wheat yield estimation in North China Plain using remotely sensed data was established and has operated since 1993, which consists of 4 subsystems, namely databases management, image processing, models bank management and production prediction system.The accuracy of wheat production prediction exceeded 96 per cent compared with on the spot measurement.

STUDY ON GIS FOR YIELD ESTIMATION BY REMOTE SENSING IN JILIN MAIZE BELT

The integration of remote sensing and geographical information system (GIS) technology is an optimal method for maize yield estimation, because it needs the support of various data including remote sensing information and others.This paper introduces the objective, components, data acquisition and implementing way of maize yield estimation information system, and uses it in the study on maize acreage calculation, growth vigour monitoring, regional soil moisture content assessment and final yield forecast.

Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer

Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change, which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer （MODIS） weekly normalized difference vegetation index （NDVI） product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels （〉90%） masked by the United States Department of Agriculture （USDA） Cropland Data Layer （CDL）. The NDVI-based time-series difference between flood years and normal year （median of years 2000-2014） was used to detect flood occur- rences. To further measure the impact of the flood on corn yield, regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R2 value of 0.85, the model indicates statistically significant linear relation between the NDVI and corn yield. Testing the predictability of the model with 10 new cases, the average relative error of the model was only 4.47%. Furthermore, small error （4.8%） of leave-one-out cross validation （LOOCV） along with smaller statistical error indicators （root mean square error （RMSE）, mean absolute error （MAE）, and mean bias error （MBE））, further validated the accuracy of the model. Utilizing the linear regression approach, change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally, with the 250 m MODIS-based NDVI, these yield losses can be estimated up to field level.

Research advances of SAR remote sensing for agriculture applications: A review

Synthetic aperture radar(SAR) is an effective and important technique in monitoring crop and other agricultural targets because its quality does not depend on weather conditions. SAR is sensitive to the geometrical structures and dielectric properties of the targets and has a certain penetration ability to some agricultural targets. The capabilities of SAR for agriculture applications can be organized into three main categories: crop identification and crop planting area statistics, crop and cropland parameter extraction, and crop yield estimation. According to the above concepts, this paper systematically analyses the recent progresses, existing problems and future directions in SAR agricultural remote sensing. In recent years, with the remarkable progresses in SAR remote sensing systems, the available SAR data sources have been greatly enriched. The accuracies of the crop classification and parameter extraction by SAR data have been improved progressively. But the development of modern agriculture has put forwarded higher requirements for SAR remote sensing. For instance, the spatial resolution and revisiting cycle of the SAR sensors, the accuracy of crop classification, the whole phenological period monitoring of crop growth status, the soil moisture inversion under the condition of high vegetation coverage, the integrations of SAR remote sensing retrieval information with hydrological models and/or crop growth models, and so on, still need to be improved. In the future, the joint use of optical and SAR remote sensing data, the application of multi-band multi-dimensional SAR, the precise and high efficient modeling of electromagnetic scattering and parameter extraction of crop and farmland composite scene, the development of light and small SAR systems like those onboard unmanned aerial vehicles and their applications will be active research areas in agriculture remote sensing. This paper concludes that SAR remote sensing has great potential and will play a more significant role in the various fields of agricultural remote sensing.

基于Terra与Aqua MODIS增强型植被指数的县级水稻总产遥感估算

以湖南省醴陵市为研究区域,在比较分析Terra卫星与Aqua卫星中分辨率成像光谱仪增强型植被指数的基础上,结合这两个卫星提供的空间分辨率为250m、时间分辨率为16d的植被指数产品,建立水稻各主要生育期增强型植被指数平均值乘以水稻面积的结果与乡镇级水稻总产的一次线性、二次非线性及逐步回归模型。通过误差分析,选择最优遥感拟合模型,并在此基础上,预测下一年水稻总产。结果显示,水稻种植区Terra卫星与Aqua卫星中分辨率成像光谱仪增强型植被指数有超过50%的偏差绝对值小于0.03;93.22%、99.50%的偏差绝对值分别小于0.08、0.10。水稻遥感拟合模型模拟结果相对误差小于0.1%,预测模型的估产结果比遥感拟合模型的拟合结果的误差大,但相对误差仍然小于5%。

Remote sensing-based estimation of rice yields using various models:A critical review

Reliable estimation of region-wide rice yield is vital for food security and agricultural management.Field-scale models have increased our understanding of rice yield and its estimation under theoretical environmental conditions.However,they offer little infor-mation on spatial variability effects on farm-scale yield.Remote Sensing(RS)is a useful tool to upscale yield estimates from farm scales to regional levels.Much research used RS with rice models for reliable yield estimation.As several countries start to operatio-nalize rice monitoring systems,it is needed to synthesize current literature to identify knowledge gaps,to improve estimation accuracies,and to optimize processing.This paper critically reviewed significant developments in using geospatial methods,imagery,and quantitative models to estimate rice yield.First,essential characteristics of rice were discussed as detected by optical and radar sensors,band selection,sensor configuration,spatial resolution,mapping methods,and biophysical variables of rice derivable from RS data.Second,various empirical,process-based,and semi-empirical models that used RS data for spatial estimation of yield were critically assessed-discussing how major types of models,RS platforms,data assimilation algorithms,canopy state variables,and RS variables can be integrated for yield estimation.Lastly,to overcome current constraints and to improve accuracies,several possibilities were suggested-adding new modeling modules,using alternative canopy variables,and adopting novel modeling approaches.As rice yields are expected to decrease due to global warming,geospatial rice yield estimation techniques are indispensable tools for climate change assessments.Future studies should focus on resolving the current limitations of estimation by precise delineation of rice cultivars,by incorporating dynamic harvesting indices based on climatic drivers,using innovative modeling approaches with machine learning.

STUDY ON MODEL FOR REMOTE SENSING ESTIMATION OF MAIZE YIELD

Through analysis of perpendicular vegetation index (PVI) from combination of visible and nearinfrared spectrums reflecting the feature of crop reflectance, we come to the conclusion that the index can better indicate crop instantaneous photosynthesis whereas people generally regard it as the representation of crop leaf area index(LAI). Exploration of crop photosynthesis within a day and its period of duration leads to production of photosynthetic vegetation index (PST) that can reflect the whole crop accumulated photosynthesis, which means the total biomass produced by crop, moreover the method simulating PST is put forward by employment of multitemporal spectrum parameters. On the basis of the achievements mentioned above, a new comprehensive model for remote sensing estimation of maize yield is established, which can comprehensively show major physiological actions of maize and the course of its yield formation, organically integrate various effective ways of crop yield estimation. It lays a solid foundation for carrying out remote sensing estimation of maize yield on a large scale.

Corn Yield Forecasting in Northeast China Using Remotely Sensed Spectral Indices and Crop Phenology Metrics

Early crop yield forecasting is important for food safety as well as large-scale food related planning. The phenology-adjusted spectral indices derived from Moderate Resolution Imaging Spectroradiometer （MODIS） data were used to develop liner regression models with the county-level corn yield data in Northeast China. We also compared the different spectral indices in predicting yield. The results showed that, using Enhanced Vegetation Index （EVI）, Normalized Difference Water Index （NDWI） and Land Surface Water Index （LSWI）, the best time to predict corn yields was 55-60 days after green-up date. LSWI showed the strongest correlation （R2=0.568）, followed by EVI （R2=0.497） and NDWI （R2=0.495）. The peak correlation between Wide Dynamic Range Vegetation Index （WDRVI） and yield was detected 85 days after green-up date （RZ=0.506）. The correlation was generally low for Normalized Difference Vegetation Index （NDVI） （R2=0.385） and no obvious peak correlation existed for NDVI. The coefficients of determination of the different spectral indices varied from year to year, which were greater in 2001 and 2004 than in other years. Leave-one-year-out approach was used to test the performance of the model. Normalized root mean square error （NRMSE） ranged from 7.3 to 16.9% for different spectral indices. Overall, our results showed that crop phenology-tuned spectral indices were feasible and helpful for regional corn yield forecasting.

Estimating Rice Yield by HJ-1A Satellite Images

As illustrated by the case of Xuyi County, Jinhu County and Hongze County in Jiangsu Province, China, monitoring and forecasting of rice production were carried out by using HJ-1A satellite remote sensing images. The handhold GPS machines were used to measure the geographical position and some other information of these samples such as area shape. The GPS data and the interpretation marks were used to correct H J-1 image, assist human-computer interactive interpretation, and other operations. The test data had been participated in the whole classification process. The accuracy of interpreted information on rice planting area was more than 90% By using the leaf area index from the normalized difference vegetation index inversion, the biomass from the ratio vegetation index inversion, and combined with the rice yield estimation model, the rice yield was estimated. Further, the thematic map of rice production classification was made based on the rice yield data. According to the comparison results between measured and fitted values of yields and areas of sampling sites, the accuracy of the yield estimation was more than 85%. The results suggest that HJ-A/B images could basically meet the demand of rice growth monitoring and yield forecasting, and could be widely applied to rice production monitoring.

A Remote Sensing Model to Estimate Sunshine Duration in the Ningxia Hui Autonomous Region,China

Sunshine duration（SD） is strongly correlated with solar radiation, and is most widely used to estimate the latter. This study builds a remote sensing model on a 100 m × 100 m spatial resolution to estimate SD for the Ningxia Hui Autonomous Region, China. Digital elevation model（DEM） data are employed to reflect topography, and moderate-resolution imaging spectroradiometer（MODIS） cloud products（Aqua MYD06-L2 and Terra MOD06-L2） are used to estimate sunshine percentage. Based on the terrain（e.g.,slope, aspect, and terrain shadowing degree） and the atmospheric conditions（e.g., air molecules, aerosols,moisture, cloud cover, and cloud types）, observation data from weather stations are also incorporated into the model. Verification results indicate that the model simulations match reasonably with the observations,with the average relative error of the total daily SD being 2.21%. Further data analysis reveals that the variation of the estimated SD is consistent with that of the maximum possible SD; its spatial variation is so substantial that the estimated SD differs significantly between the south-facing and north-facing slopes,and its seasonal variation is also large throughout the year.

Estimation of Grassland Production in Central and Eastern Mongolia from 2006 to 2015 via Remote Sensing

Mongolia is an important part of the Belt and Road Initiative"China-Mongolia-Russia Economic Corridor"and a region that has been severely affected by global climate change.Changes in grassland production have had a profound impact on the sustainable development of the region.Our study explored an optimal model for estimating grassland production in Mongolia and discovered its temporal and spatial distributions.Three estimation models were established using a statistical analysis method based on EVI,MSAVI,NDVI,and PsnNet from Moderate Resolution Imaging Spectroradiometer(MODIS)remote sensing data and measured data.A model evaluation and accuracy comparison showed that an exponential model based on MSAVI was the best simulation(model accuracy 78%).This was selected to estimate the grassland production in central and eastern Mongolia from 2006 to 2015.The results show that the grassland production in the study area had a significantly fluctuating trend for the decade study;a slight overall increasing trend was observed.For the first five years,the grassland production decreased slowly,whereas in the latter five years,significant fluctuations were observed.The grassland production(per unit yield)gradually increased from the southwest to northeast.In most provinces of the study area,the production was above 1000 kg ha with the largest production in Hentiy,at 3944.35 kg ha.The grassland production(total yield)varied greatly among the provinces,with Kent showing the highest production,2341.76x1〇4 t.Results also indicate that the trend in grassland production along the China-Mongolia railway was generally consistent with that of the six provinces studied.

Relationship between MODIS-NDVI data and wheat yield: A case study in Northern Buenos Aires province, Argentina

In countries like Argentina,whose economy depends heavily on crop production,the estimation of harvests is an elementary requirement.Besides providing objectivity,the use of remote sensing allows estimating yield in advance.Since the time of maximum leaf area in wheat corresponds with the critical period of the crop,a good relationship is expected between the Normalized Difference Vegetation Index(NDVI)and yield.The present study was carried out in the North of Buenos Aires province,Argentina.Based on the type of soil,the study area can be divided into two homogeneous subzones:a subzone with lower clay content in the southwestand a subzone with higher clay content in the northeast.Nine growing seasons(2003–2011)were studied.In the first five years,an empirical model was calibrated and validated with field-observed wheat yields and MOD13q1 product-NDVI data,whereas in the other four years,the calibrated model was applied by means of yield maps and by comparing with official yields.The MOD13q1 image corresponding to Julian day 289 showed the best fit between NDVI and yield to estimate wheat yield early.Through yield maps,better weather conditions showedhigher yields and higher soil productivity presented a greater proportion of the area occupied by higher yields.At department level,an R2 value of 0.75 was found after relating the estimation of the calibrated empirical model with official yields.The method used allows predicting wheat yield 30 days before harvest.Through yield maps,the NDVI perceived the temporal and spatial variability in the study area.

Modeling grass yields in Qinghai Province,China,based on MODIS NDVI data--an empirical comparison

Qinghai Province is one of the four largest pastoral regions in China.Timely monitoring of grass growth and accurate estimation of grass yields are essential for its ecological protection and sustainable development.To estimate grass yields in Qinghai,we used the normalized difference vegetation index(NDVI)time-series data derived from the Moderate-resolution Imaging Spectroradiometer(MODIS)and a pre-existing grassland type map.We developed five estimation approaches to quantify the overall accuracy by combining four data pre-processing techniques(original,Savitzky-Golay(SG),Asymmetry Gaussian(AG)and Double Logistic(DL)),three metrics derived from NDVI time series(VImax,VIseason and VImean)and four fitting functions(linear,second-degree polynomial,power function,and exponential function).The five approaches were investigated in terms of overall accuracy based on 556 ground survey samples in 2016.After assessment and evaluation,we applied the best estimation model in each approach to map the fresh grass yields over the entire Qinghai Province in 2016.Results indicated that:1)For sample estimation,the crossvalidated overall accuracies increased with the increasing flexibility in the chosen fitting variables,and the best estimation accuracy was obtained by the so called“fully flexible model”with R2 of 0.57 and RMSE of 1140 kg/ha.2)Exponential models generally outperformed linear and power models.3)Although overall similar,strong local discrepancies were identified between the grass yield maps derived from the five approaches.In particular,the two most flexible modeling approaches were too sensitive to errors in the pre-existing grassland type map.This led to locally strong overestimations in the modeled grass yields.

REGIONALIZATION FOR LARGE AREA CROP ESTIMATES BY REMOTE SENSING——A Case Study of Chinese Wheat

Ｔｈｅｃｒｏｐｅｓｔｉｍａｔｅｓｂｙｒｅｍｏｔｅｓｅｎｓｉｎｇ，ｄｅｖｅｌｏｐｉｎｇｑｕｉｃｋｌｙｉｎｒｅｃｅｎｔｄｅｃａｄｅｓ，ｉｓａｕｐｔｏｄａｔｅｔｅｃｈｎｉｑｕｅ．Ｓｏｍｅｓｙｓｔｅｍｓｏｆｃｒｏｐｅｓｔｉｍａｔｅｓｂｙｒｅｍｏｔｅｓｅｎ...

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250m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．

基于MODIS数据的松嫩草原产草量遥感估算模型与空间反演

根据NASA/MODIS遥感数据获得NDVI、RVI、MSAVI和EVI4种植被指数,结合草原产草量地面实测数据,利用统计分析方法建立松嫩草原产草量遥感估算模型,并进行产草量空间反演和验证,为该区草原产草量合理估算和草原资源管理提供科学依据。相关分析结果表明,前期和同期的4种植被指数均与草原产草量显著相关,其中NDVI相关性最高,EVI相关性最低;松嫩草原产草量最优模拟模型为基于NDVI的S曲线模型,模拟精度达78%。利用该模型反演得到2009年松嫩草原平均鲜草单产为5717kg/hm2,折合干草单产1687kg/hm2,鲜草总产量为1885万t,干草总产量为589万t。其中,黑龙江省部分的鲜草总产量为1356万t,折合干草424万t;吉林省部分的鲜草和干草总产量分别为531万t和166万t。利用植被指数预报未来16d草原产草量效果较好,其中基于NDVI的幂指数模型预报精度为74%。研究表明,基于植被指数进行松嫩草原产草量研究是可行的。

基于MODIS与TM时序插补的省域尺度玉米遥感估产

针对省域尺度作物估产中的TM影像时相不一致和覆盖能力不足的问题,以山东省2008年玉米产量为研究对象,在6景不同玉米物候期的TM影像和长时间序列的MODIS全覆盖影像的支持下,构建基于玉米生长过程的时序插补模型,将不同物候期的TM影像插补为玉米乳熟期的同期数据集,并通过地面实割实测样本数据,建立地面-TM、TM-MODIS的两阶段遥感估产模型,开展省域尺度玉米产量全覆盖遥感估测方法研究。结果表明,基于时序插补的省域尺度玉米遥感估产方法能充分发挥TM和MODIS影像的各自优势,有效地避免TM影像时相不同所造成的归一化植被指数(NDVI)区域差异,获得较高精度的省域尺度玉米单产估测结果,为开展省级作物遥感估产提供了一种新的技术方法。

MODIS EVI时间序列数据和光谱角聚类的冬小麦遥感估产分区方法研究

农作物遥感估产区划是农作物遥感估产的基础,它为估产研究和实践提供了重要的科学依据。以冬小麦生育期内的MODIS EVI时间序列作为分区数据,选择江苏省为试验区,探讨了一种改进的光谱角制图和K均值聚类相结合(光谱角聚类)的分区方法,并在冬小麦遥感估产中进行了试验。结果表明:光谱角聚类分区方法充分利用了MODIS时间序列数据所反映的农作物生长进程,可以充分体现气候差异所带来的冬小麦区域差异;与传统分区相比,基于光谱角聚类分区方法所得到的遥感估产结果具有较高的决定系数R2(0.702 6比0.624 8)和较低的均方根误差RMSE(343.34比381.34kg.hm-2),体现了该分区方法在冬小麦遥感估产中的优势。光谱角聚类分区方法仅以获取便利的低分辨率时间序列遥感数据为分区数据,且能很好的将冬小麦划分为特征性质一致的区域,所得遥感估产模型的精度和稳定性也较好,为冬小麦遥感估产分区提供了一种有效方法,有利于进行冬小麦遥感估产研究。