PhenoCrop: An integrated satellite-based framework to estimate physiological growth stages of corn and soybeans     

《International Journal of Applied Earth Observation and Geoinformation》2020

The accurate and timely estimates of crop physiological growth stages are essential for efficient crop management and precise modeling of agricultural systems. Satellite remote sensing has been widely used to retrieve vegetation phenology metrics at local to global scales. However, most of these phenology metrics (e.g., green-up) are different from crop growth stages (e.g., emergence) used in crop management and modeling. As such, an integrated framework referred to as PhenoCrop was developed to: 1) establish a connection between remote sensing-derived phenology metrics and key crop growth stages based on Wang and Engle plant phenology model and 2) use fused MODIS-Landsat 30 m 8-day reflectance data generated using Kalman Filter-based data fusion technique to produce onset dates of key growth stages of corn (Zea mays L.) and soybeans (Glycine max L.) at 30 m spatial resolution. In this paper, we described the PhenoCrop framework, and tested its performance for the State of Nebraska for 2012–2016 by comparison to observations of estimated key growth stages at four experimental sites, and state-level statistical data from Crop Progress Reports (CPRs) published by the United States Department of Agriculture’s (USDA) National Agricultural Statistical Services (NASS). In addition, to evaluate the suitability of using coarse or high spatial resolution satellite imagery, fused MODIS-Landsat-based estimates were compared with those produced using EOS MODIS 250 m (MOD9Q1) reflectance data.The PhenoCrop estimates captured the typical spatial trends of gradual delay in the progression of the growing season from southeast to northwest Nebraska. Also inter-annual differences due to factors such as weather fluctuations and change in management strategies (e.g., early season in 2012) were evident in the estimates. Validation results revealed that average root mean square error (RMSE) of the state-level estimates of corn and soybean growth stages ranged from 1.10 to 4.20 days and from 3.81 to 7.89 days, respectively, while pixel level estimates had a RMSE ranging from 3.72 to 8.51 days for corn and 4.76–9.51 days for soybean growth stages. Although MODIS 250 m based estimates showed similar general spatial patterns observed in the fused MODIS-Landsat based estimates, the accuracy and ability to capture field scale variations was improved with fused MODIS-Landsat data. Overall, results showed the ability of PhenoCrop framework to provide reliable estimates of crop growth stages that can be highly useful in crop modeling and crop management during the growing season.  相似文献   

Crop characterization using an improved scattering power decomposition technique for compact polarimetric SAR data     

《International Journal of Applied Earth Observation and Geoinformation》2020

Crop characterization using Compact-Pol Synthetic Aperture Radar (CP-SAR) data is of prime interest with the rapid advancements of SAR systems towards operational applications. It is noteworthy that as a good compromise between the dual and quad-polarized SAR systems, the CP-SAR offer advantages in terms of the larger swath and lower data rate. The m − χ CP decomposition considers two out of the three Stokes child parameters: degree of polarization (m), ellipticity (χ), and orientation angle (ψ) to describe the polarized part of the quasi-monochromatic partially polarized wave. An improvement in the scattering powers was proposed in the S − Ω decomposition, which takes into accounts both the transmitted and received wave ellipticities (χ_t, χ_r) and the orientation angles (ψ_t, ψ_r). In this decomposition, S denotes the Stokes vector and Ω is the polarized power fraction. However, it may be noted that the S − Ω decomposition intrinsically ignores dominance in the target scattering mechanism while calculating the powers. In this work, improvement is proposed for the S − Ω decomposition by utilizing the degree of dominance in the scattering mechanism. The improved S − Ω (named as iS − Ω) decomposition powers are first compared with the existing m − χ and S − Ω powers for elementary (viz., trihedral and dihedral corner reflectors) and distributed targets using simulated CP-SAR data from quad-pol RADARSAT-2 data. An increase of ∼2% for odd and even-bounce powers obtained from the iS − Ω decomposition is observed for the trihedral and dihedral corner reflectors respectively. The analysis of the scattering powers for distributed targets shows that an increase of 15% and 12% in the even and odd-bounce powers is observed from iS − Ω for urban and bare soil areas respectively as compared to the m − χ and S − Ω decompositions. Besides, temporal variations in the scattering powers obtained from the iS − Ω decomposition are also analyzed for rice, cotton, and sugarcane crops at different growth stages.  相似文献   

Crop classification from full-year fully-polarimetric L-band UAVSAR time-series using the Random Forest algorithm     

《International Journal of Applied Earth Observation and Geoinformation》2020

Accurate and timely information on the distribution of crop types is vital to agricultural management, ecosystem services valuation and food security assessment. Synthetic Aperture Radar (SAR) systems have become increasingly popular in the field of crop monitoring and classification. However, the potential of time-series polarimetric SAR data has not been explored extensively, with several open scientific questions (e.g. the optimal combination of image dates for crop classification) that need to be answered. In this research, the usefulness of full year (both 2011 and 2014) L-band fully-polarimetric Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) data in crop classification was fully investigated over an agricultural region with a heterogeneous distribution of crop categories. In total, 11 crop classes including tree crops (almond and walnut), forage crops (grass, alfalfa, hay, and clover), a spring crop (winter wheat), and summer crops (corn, sunflower, tomato, and pepper), were discriminated using the Random Forest (RF) algorithm. The SAR input variables included raw linear polarization channels as well as polarimetric parameters derived from Cloude-Pottier (CP) and Freeman-Durden (FD) decompositions. Results showed clearly that the polarimetric parameters yielded much higher classification accuracies than linear polarizations. The combined use of all variables (linear polarizations and polarimetric parameters) produced the maximum overall accuracy of 90.50 % and 84.93 % for 2011 and 2014, respectively, with a significant increase of approximately 8 percentage points compared with linear polarizations alone. The variable importance provided by the RF illustrated that the polarimetric parameters had a far greater influence than linear polarizations, with the CP parameters being much more important than the FD parameters. The most important acquisitions were the images dated during the peak biomass stage (July and August) when the differences in structural characteristics between most crops were the largest. At the same time, the images in spring (April and May) and autumn (October) also contributed to the crop classification since they respectively provided unique information for discriminating fruit crops (almond and walnut) as well as summer crops (corn, sunflower, and tomato). As a result, the combined use of only four acquisitions (dated May, July, August, and October for 2011 and April, June, August, and October for 2014) was adequate to achieve a nearly-optimal overall accuracy. In light of the promising classification accuracies demonstrated in this research, it becomes increasingly viable to provide accurate and up-to-date crops inventories over large areas based solely on multitemporal polarimetric SAR.  相似文献   

基于空间抽样的农作物面积遥感测量   总被引：2，自引：0，他引：2  

徐迪峰  徐锐 《北京测绘》2009,(4):40-42,55

传统的农作物面积统计通常采用人工统计的方法,费时费力,并且精确度不高,实时性不强。为了快速、准确地获取农作物面积,本文根据实际业务需求,充分利用遥感技术,同时结合地理信息技术,与基于PPS二阶空间抽样主法提出了一套切实可行的农作物面积遥感测量方法和流程。  相似文献   

冬小麦生长模式及其在干旱影响评估中的应用   总被引：11，自引：3，他引：8       下载免费PDF全文

王石立 《应用气象学报》1998,9(1):15-23

在前人理论研究和田间试验的基础上,考虑水分胁迫影响的后效性及作物不同发育阶段对水分胁迫的敏感性,研制出实际水分条件下的冬小麦生长模拟模式。经与不同水分处理的实测资料对比,模拟效果基本令人满意,平均误差为10%左右。利用生长模式得到实际水分条件下的干物重减少率,进行了干旱影响实时评估的尝试。并分别在返青后、拔节后和成熟前展望了干旱对最终生物量的可能影响。  相似文献   

Integrating age in the detection and mapping of incongruous patches in coffee (Coffea arabica) plantations using multi-temporal Landsat 8 NDVI anomalies     

《International Journal of Applied Earth Observation and Geoinformation》2017

The development of cost-effective, reliable and easy to implement crop condition monitoring methods is urgently required for perennial tree crops such as coffee (Coffea arabica), as they are grown over large areas and represent long term and higher levels of investment. These monitoring methods are useful in identifying farm areas that experience poor crop growth, pest infestation, diseases outbreaks and/or to monitor response to management interventions. This study compares field level coffee mean NDVI and LSWI anomalies and age-adjusted coffee mean NDVI and LSWI anomalies in identifying and mapping incongruous patches across perennial coffee plantations. To achieve this objective, we first derived deviation of coffee pixels from the global coffee mean NDVI and LSWI values of nine sequential Landsat 8 OLI image scenes. We then evaluated the influence of coffee age class (young, mature and old) on Landsat-scale NDVI and LSWI values using a one-way ANOVA and since results showed significant differences, we adjusted NDVI and LSWI anomalies for age-class. We then used the cumulative inverse distribution function (α ≤ 0.05) to identify fields and within field areas with excessive deviation of NDVI and LSWI from the global and the age-expected mean for each of the Landsat 8 OLI scene dates spanning three seasons. Results from accuracy assessment indicated that it was possible to separate incongruous and healthy patches using these anomalies and that using NDVI performed better than using LSWI for both global and age-adjusted mean anomalies. Using the age-adjusted anomalies performed better in separating incongruous and healthy patches than using the global mean for both NDVI (Overall accuracy = 80.9% and 68.1% respectively) and for LSWI (Overall accuracy = 68.1% and 48.9% respectively). When applied to other Landsat 8 OLI scenes, the results showed that the proportions of coffee fields that were modelled incongruent decreased with time for the young age category and while it increased for the mature and old age classes with time. We concluded that the method could be useful for the identification of anomalous patches using Landsat scale time series data to monitor large coffee plantations and provide an indication of areas requiring particular field attention.  相似文献   

Hyperspectral-based predictive modelling of grapevine water status in the Portuguese Douro wine region     

《International Journal of Applied Earth Observation and Geoinformation》2017

In this study, hyperspectral reflectance (HySR) data derived from a handheld spectroradiometer were used to assess the water status of three grapevine cultivars in two sub-regions of Douro wine region during two consecutive years. A large set of potential predictors derived from the HySR data were considered for modelling/predicting the predawn leaf water potential (Ψ_pd) through different statistical and machine learning techniques. Three HySR vegetation indices were selected as final predictors for the computation of the models and the in-season time trend was removed from data by using a time predictor. The vegetation indices selected were the Normalized Reflectance Index for the wavelengths 554 nm and 561 nm (NRI_554;561), the water index (WI) for the wavelengths 900 nm and 970 nm, and the D1 index which is associated with the rate of reflectance increase in the wavelengths of 706 nm and 730 nm. These vegetation indices covered the green, red edge and the near infrared domains of the electromagnetic spectrum. A large set of state-of-the-art analysis and statistical and machine-learning modelling techniques were tested. Predictive modelling techniques based on generalized boosted model (GBM), bagged multivariate adaptive regression splines (B-MARS), generalized additive model (GAM), and Bayesian regularized neural networks (BRNN) showed the best performance for predicting Ψ_pd, with an average determination coefficient (R²) ranging between 0.78 and 0.80 and RMSE varying between 0.11 and 0.12 MPa. When cultivar Touriga Nacional was used for training the models and the cultivars Touriga Franca and Tinta Barroca for testing (independent validation), the models performance was good, particularly for GBM (R² = 0.85; RMSE = 0.09 MPa). Additionally, the comparison of Ψ_pd observed and predicted showed an equitable dispersion of data from the various cultivars. The results achieved show a good potential of these predictive models based on vegetation indices to support irrigation scheduling in vineyard.  相似文献   

Changes in crop type distribution in Zhangye City of the Heihe River Basin,China     

《Applied geography (Sevenoaks, England)》2016

With rapid economic development in China, crops have undergone remarkable changes in both their type and spatial pattern. Timely and accurate information of crop type distribution will help government and agricultural producers quickly understand regional agricultural production conditions to better facilitate appropriate adjustments in planting patterns and policies. Another benefit of acquiring such knowledge of crops is that it should enhance regional agricultural competitiveness, optimize resource allocations, and further guarantee national food security. Towards this end, and using the Zhangye City in the Heihe River Basin as a study area, the present research elaborated upon a methodology to classify crop type distribution based on multi-temporal Thematic Mapper and Enhanced Thematic Mapper Plus (TM/ETM+) images. Using this methodology we achieved the spatial distributions of crop types in Zhangye City in 2007 and 2012, and analyzed changes in their distributions over this period. In addition, some landscape indices were calculated to clarify the landscape pattern of crops. The crop conversion potentials in 2017 were modeled using four conversion sub-models of the Multi-Layer Perceptron (MLP) neural network. Generally, the overall accuracy of crop classification in Zhangye was high, at 89.38%. From 2007 to 2012, the cultivated land area in Zhangye increased from 463.81 × 10³ ha to 493.89 × 10³ ha. The sowing area of corn and oilseed rape increased by 39.21 × 10³ ha and 5.99 × 10³ ha, respectively, while for wheat and barley the sowing area decreased by 3.61 × 10³ ha and 9.14 × 10³ ha, respectively. Considering other crop types as a group, their sowing area decreased by only 2.37 × 10³ ha. The increase in corn sowing area mainly came from the conversion of other crops to corn, which accounted for 43.09% of its total sowing area in 2012. Furthermore, corn and oilseed rape showed a tendency of intensive sowing, whereas for wheat and barley the tendency was towards scattered sowing. For the future, corn has high conversion potential in Linze and Gaotai counties of Zhangye, while wheat, barley and oilseed rape have high conversion potentials in Minle and Shandan counties.  相似文献   

阿克苏河灌区作物需水量对气候变化的敏感性分析     

王志成 《沙漠与绿洲气象(新疆气象)》2018,12(3):33-39

阿克苏河灌区是中纬度干旱区典型的绿洲灌溉系统,同时也是新疆第二大灌区,了解灌区作物需水量可为灌区种植结构调整、水资源优化配置提供科学依据。本研究基于联合国粮农组织(FAO)的Penman-Monteith蒸散发模型,结合作物系数法估算了阿克苏灌区作物需水量的时空变化及其对气候因子和作物种植结构的敏感性。结果表明,1960—2015年阿克苏灌区多年平均作物需水量为586 mm,且呈显著上升趋势,上升速率为38.43 mm/10 a。随着气候变化和作物种植结构的改变,1990—2015年间作物需水量急剧增加,增加速率高达99.37 mm/10 a。对于不同作物类型,果林的需水量最大,高达829.8 mm,其次是棉花、水稻和玉米,小麦需水量最低。阿克苏灌区的作物需水量对日最高气温和日照时数较为敏感,而对最低气温、风速和水汽压的敏感度较低。当日最高气温升高2℃时,作物需水量增加4%,当日照时数增加10%时,作物需水量将增加3.2%。另外,作物需水量对作物种植结构非常敏感,当果林的种植面积比例增加10%时,作物需水量增加了12.1%。  相似文献   

Quantifying winter wheat residue biomass with a spectral angle index derived from China Environmental Satellite data     

《International Journal of Applied Earth Observation and Geoinformation》2014

Quantification of crop residue biomass on cultivated lands is essential for studies of carbon cycling of agroecosystems, soil-atmospheric carbon exchange and Earth systems modeling. Previous studies focus on estimating crop residue cover (CRC) while limited research exists on quantifying crop residue biomass. This study takes advantage of the high temporal resolution of the China Environmental Satellite (HJ-1) data and utilizes the band configuration features of HJ-1B data to establish spectral angle indices to estimate crop residue biomass. Angles formed at the NIR_IRS vertex by the three vertices at R, NIR_IRS, and SWIR (ANIR_IRS) of HJ-1B can effectively indicate winter wheat residue biomass. A coefficient of determination (R²) of 0.811 was obtained between measured winter wheat residue biomass and ANIR_IRS derived from simulated HJ-1B reflectance data. The ability of ANIR_IRS for quantifying winter wheat residue biomass using HJ-1B satellite data was also validated and evaluated. Results indicate that ANIR_IRS performed well in estimating winter wheat residue biomass with different residue treatments; the root mean square error (RMSE) between measured and estimated residue biomass was 0.038 kg/m². ANIR_IRS is a potential method for quantifying winter wheat residue biomass at a large scale due to wide swath width (350 km) and four-day revisit rate of the HJ-1 satellite. While ANIR_IRS can adequately estimate winter wheat residue biomass at different residue moisture conditions, the feasibility of ANIR_IRS for winter wheat residue biomass estimation at different fractional coverage of green vegetation and different environmental conditions (soil type, soil moisture content, and crop residue type) needs to be further explored.  相似文献