农田多源信息获取与空间变异表征研究
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摘要
农田多源信息的获取是进行农田土壤、作物与环境等信息空间变异特性研究的基础。随着新一代高分辨率主被动遥感卫星的发射和各类近地传感器的快速发展,利用各自的优势进行农田多源信息获取、空间变异表征与数字制图研究,已成为当前国内外研究的热点。特别是针对土壤信息快速获取与空间变异研究,国际土壤学界给予了极大的关注。国际土壤学会在2002年第17届国际土壤科学大会上新成立了计量土壤学专业委员会(Pedometrics, Commission1.5),提出利用数学和统计学方法研究土壤的分布和发生。2008年又成立了土壤近地传感(Proximal Soil Sensing)工作组,发展各种土壤信息室内外近地快速获取的方法和手段,推进传统土壤理化测试分析向土壤野外实时监测方向发展。本论文围绕上述研究热点,以浙江省杭州湾滨海围垦试验田为样区,针对土壤盐分、水分等关键影响因子,利用近地传感器和主被动遥感等多种手段开展农田信息快速获取和解译、土壤采样方法、农田管理分区与数字土壤制图的研究,为研究区进行土壤科学改良和农田精确管理提供技术支撑与辅助决策指导。主要研究结果包括以下四个方面：
(1)基于近地传感器数据的土壤盐分时空变异研究
在海涂围垦区,土壤盐分是影响作物生长的一个重要因素,本研究利用2009-2011年实地测量的土壤表观电导率(ECa)数据,结合传统统计和地统计方法进行土壤盐分的时空变异研究,揭示土壤盐分的空问变异情况,以期为作物种植和农田土壤管理提供依据。空间分析表明,土壤盐分含量高的区域位于研究田块的中部,土壤盐分含量低的区域位于田块的周围。时序上的变异性分析表明,随着围垦利用年限的增加,土壤含盐量逐步减少,年季减少的幅度在降低,年季之间多重比较分析差异显著。时序的稳定性分析表明,在中部土壤盐分含量高的区域,具有时序变异方向上的稳定性,而在土壤盐分含量较低的周围区域,时序稳定性相对较差。这对于农业生产者了解围垦区土壤盐分的时空变化规律,科学指导土壤改良和农业生产具有重要意义。
(2)基于星地数据的RSM土壤采样设计研究
从土壤采样设计两个基本原则——样点个数最小化,差异最大化出发,针对当前存在的问题——样点个数和样点位置如何确定,利用ECa数据和后向散射系数(σ0)数据,结合方差四叉树法(VQT)和曲面响应采样设计(RSM)进行土壤肥力高效采样方法的研究。本研究田块中,先采用VQT法得到合理的样点数目为12个,再采用RSM方法进行样点位置的确定和优化,从而确定12个土壤采样点。与传统的42个规则网格采样点比较,两种采样方法具有统计上的相似特征,t-检验的Tukey-Kramer HSD比较表明,差异不显著。说明本方法与传统网格采样方法相比,样点设计的采样效率更高,这对于土壤采样策略的进展具有重要的推动作用。
(3)基于星地多源数据的农田田间管理分区与数字制图研究
随着遥感技术和近地传感技术的发展,高分辨率遥感影像和农田快速实时获取的信息在土壤学科中的应用越来越广泛。本研究综合利用反映海涂围垦区土壤盐分的土壤电导率信息,反映作物长势的归一化植被指数(NDVI)和反映土壤水分特征的后向散射系数(G0)作为输入变量,采用模糊κ-均值聚类算法来定义田间管理分区,并利用模糊性能指数(FPI)、改进分类熵(MPE)和聚类独立性指数(S)有效地确定了最佳聚类效果和分区数目,结果表明,最佳分区数目为3。用混淆指数(CI)评价单个管理单元分区效果的好坏。通过划分管理分区来指导土壤采样,能通过少量的土壤样品调查获取各管理分区的土壤养分变异特征；另一方面利用地统计学方法进行制图,进行土壤空间变异田间尺度的表达,给农户提供最直观的数字土壤图,可以为分区进行精准农业管理提供决策依据,具有重要的理论和应用价值。
(4)基于原位可见-近红外(vis-NIR)高光谱技术的土壤制图研究
vis-NIR光谱技术是近地传感技术中的一个重要分支,与常规土壤理化分析相比,可见-近红外光谱测量技术具有快速、无损、高通量、低成本等特点。本研究利用ASD FieldSpec Pro FR野外型光谱仪获取的vis-NIR数据,进行土壤光谱数据处理、预测建模和数字制图方法研究。研究中,将野外现场获取的土壤光谱经过去噪和倒数的对数转换(Log (1/R))为吸收光谱后,利用逐步回归分析法寻找表征土壤属性的特征波段,然后利用偏最小二乘法(PLSR)进行土壤属性的预测与建模,结果表明,采用逐步回归法提取特征波段,利用PLSR可以对土壤有机质(SOM)、总氮(TN)、CEC、速效氮(AN)、速效磷(AP)和速效钾(AK)进行准确的预测。例如,对AP,模型可以解释98.9%的变异,RMSE和RPD的值分别为1.03和9.98。即使最小的解释变异模型(TN),也可以解释87.4%的变异,RMSE和RPD的值分别为0.56和3.15。最后利用土壤光谱数据和实测土壤属性数据(SOM、TN、CEC、AP和AK)分别进行克里格插值制图,两组土壤属性空间分布特征基本相同,说明野外光谱测量手段在农田土壤属性快速获取与数字制图方面具有很大的应用潜力。
本研究基本完成了研究内容,达到了预期的研究目标,取得了以下新进展：
(1)利用遥感和近地传感器技术各自优势获取的多种农田信息,综合运用在土壤空间变异研究、土壤采样研究和农田管理分区中,有新意。在土壤肥力采样方法的研究中,以围垦区土壤限制因子——水分和盐分数据为基础,提出了VQT和RSM方法相结合,从土壤空间变异特性出发来解决土壤采样问题的方法。一方面解决了采样点位置的问题,另一方面也考虑了数据的空间位置,同时打破了VQT方法中采样形状是矩形的限制。与传统的网格采样方法进行比较,RSM采样方法速度快,效率高。
(2)针对土壤制图中的田间尺度制图问题,利用多源数据进行土壤空间变异制图分析。如针对田间管理分区及制图问题,综合利用围垦区土壤限制因子盐分、水分信息结合作物长势信息,采用模糊κ-均值聚类进行管理分区划分并制图,为精准农业管理提供最为直观的依据。针对原位信息采集制图问题,利用vis-NIR原位测量的土壤高光谱信息,进行土壤属性预测特征波段的筛选、预测和制图,预测结果良好,为利用野外光谱测量手段进行实时快速的获取土壤属性空间分布信息提供理论支撑,同时也为农户的田间管理提供最为直观的图形数据,对国内进行土壤光谱的原位测量与建模研究具有重要的意义。
Acquisition of multi-sources field information is the premise to conduct studies on spatial variability of soil, crops and the environment. Especially, with the rapid development of a new generation of high-resolution active and passive remote sensing satellite and soil proximal sensor, outstanding advantages of the method to obtain the multi-source farmland information for analyzing soil spatial variability and digital mapping has attracted more and more attention. In this regard, the International Union of Soil Science (IUSS) also gave a great concern on the quick access to field information and soil spatial variability. In2002,17th World Congress of Soil Science set up a Pedometrics, Commission1.5which suggested approaching the soil distribution and pedogenesis with mathematical and geostatistical methods. Consequently in2008, the Proximal Soil Sensing group was formed to focus on the research and development of proximal sensors to acquire soil information inside and outside the room, advancing the approaches of soil science from lab analysis to field monitoring in-stiu. In the present study, the reclaimed coastal fields in Hangzhou gulf, Zhejiang Province, were selected as study area to carry out the research on quick access to field information and soil spatial variability, where soil salinity/moisture has a decisive role in soil quality and crop planting. As such, researches based on the critical factors (i.e. soil salinity, soil moisture) in reclaimed coastal areas, including multi-source information acquisition and processing, soil sampling, soil management zoning and digital mapping were implemented to provide ancillary technical information for soil improvement and precision agriculture. The main results are as follows:
(1) Sensor-directed spatio-temporal variability of soil salinity in coastal areas
Soil salinity is crucial variable which influences the soil quality and agricultural productivity in reclaimed areas. Accurately characterizing the spatial variability is critical for the rational development and utilization of tideland resources. In the present study, we employed soil apparent electrical conductivity (ECa) acquired by EM38from2009to2011to carry out spatio-temporal variability analysis integrating traditional and geostatistical analyses. The analyses of the spatial distribution and trend maps revealed the relatively high salinity in the middle, which reasoned by farming and water table fluctuation; low soil salinity in the surroundings may arise of the drainage ditches surrounded the field (i.e. ridge building in the surroundings, irrigation and drainage for rice). In addition, soil salinity decreased from2009to2011with significant difference between years by the calculated Tukey-Kramer means comparisons. Coefficient of variation over time at each measurement showed that the middle region with a high salinity content displayed temporal stability, while the surrounding region of a lower salinity level showed temporal instability. These results could be used for knowing of the change discipline of soil salinity in reclaimed soil, directing the farmer to plant, or scheduling the variable rate application.
(2) Soil sampling strategy based on radar imagery and proximally sensed data
On the basis of the two basic principles of soil sampling design-minimized the number of samples and maximized the difference between sampling points, we focused on how to determine the number of samples and sample location herein. ECa and the backscattering coefficient (σ0) data were employed, combining with the variance quadtree method (VQT) and response surface methodology (RSM) to seek the efficient soil sampling. In this specific field, VQT method generated a reasonable number of samples of12, and then we used RSM to determine the sampling locations. The validation indicated that the RSM sampling strategy was highly effective at characterizing spatial variability of various topsoil chemical properties (e.g. soil organic matter (SOM), available nitrogen (AN), and available potassium (AP) compared with the traditional-grid sampling design. The two sampling methods have similar characteristics, t-test with Tukey-Kramer HSD comparison showed that the difference was not significant. The results would play an important role in promoting the progress of soil sampling strategy aided with auxiliary data.
(3) Management zone and digital soil mapping based on multi-source data of high resolution remote sensing imagery and proximally sensed data
With the development of a new generation of high resolution remote sensing and proximal soil sensing technology, the usage of high-resolution remote sensing images and real-time access to field information is becoming more and more popular in soil science. In this regard, ECa indicating soil salinity, the normalized difference vegetation index (NDVI) reflecting the crop vigour and the backscattering coefficient (σ0) characterizing the soil moisture were selected as input variables, fuzzy k-means clustering algorithm was used to partition the field management zone. Fuzzy performance index(FPI), modified partition entropy (MPE) and separate clustering independence index (S) were adopted to determine the best clustering and the number of partitions. The results indicated that the optimal number of partitions was3. Afterwards, confusion index (CI) was employed to evaluate the effect of each unit. In light of the characteristics of each zone, corresponding management practices should be adopted to ameliorate the soil and implement precision farmland management. The zoning in useful not only for guiding soil sampling, but also to recommendation of variable input and precision fertilization, and on the other hand, the digital mapping of the management zone can provide the most intuitive basis for precision agriculture, which are of important theoretical and practical value.
(4) Using visible and near infrared (vis-NIR) reflectance spectroscopy for simultaneous assessment and digital mapping of selected soil properties
vis-NIR (100-2500mm) technology is a rapid, proximal sensing method that has great potential for quantifying constituents of dried/ground soil samples. It is becoming increasingly important to improve the soil properties prediction and spatial resolution of soil maps as a fundamental information layer for soil classification, mapping and so on. In the present study, vis-NIR reflectance spectra collected in-situ were employed as an indicator for the prediction of some soil properties. Stepwise regression method was used to search for all possible subsets and sort out the best ones from a large number of spectral variables. Afterwards the PLSR approach was adopted to build up the predicted model for some soil properties. Our work demonstrated that vis-NIR spectroscopy has the potential to predict the SOM, TN. CEC, AN, AP and AK from the field measurements. In this instance, the largest explanation proportion for AP achieved98.9%with RMSE of1.03, and the RPD value of9.98, even the smallest one for TN can explain87.4%with RMSE and RPD of0.56and3.15, indicating good prediction. Digital mapping of the measured and predicted soil properties showed similar patterns and value ranges, though there are some minor differences between them. The results demonstrated the ability to use this methodology as an indicator for rapid and reliable soil assessment and mapping. The use of these techniques will promote the implementation of assessment and digital mapping of soil properties by a rapid and reliable approach from lab to field.
The innovations or new development were made as follows:
(1) A novel idea was proposed to study the soil spatial variability, soil sampling strategy and soil management zoning based on the outstanding advantages of multi-sources information collected by remotely and proximally sensed techniques. For the study of soil sampling algorithm, the limited factors of soil salinity and soil moisture in the reclaimed soil were used to calibrate and validate the method we proposed. That is, integrating the VQT and RSM to solve the key problem of soil sampling number and location departure from the soil spatial variability. As such, we eliminated two important aspects, one was confirming the accurate soil sampling location; the other was the breaking of the limitation of the rectangular in shape restriction in consideration of the spatial position of sampling strategy. Compared with grid sampling method, RSM has strong points of high speed and efficiency.
(2) Focused on the field-scale digital soil mapping, we used the multi-source information to conduct soil spatial variability analysis and soil mapping. Firstly, integrating the information of soil salinity, soil moisture and crop vigour in the reclaimed area, fuzzy k-means clustering algorithm was adopted to define the management zones, providing the most intuitive basis for precision agriculture. Secondly, acquisition of high resolution soil vis-NIR spectra in-situ measurement was applied to establish the prediction model, and for soil mapping based on the screened characteristic spectral bands with stepwise regression method and PLSR. Good predictions were gained, which provided theoretical support with the regard of in-situ measurements, and played an important role in the prediction and soil mapping on the basis of in-situ measurement in China.
(1)基于近地传感器数据的土壤盐分时空变异研究
在海涂围垦区,土壤盐分是影响作物生长的一个重要因素,本研究利用2009-2011年实地测量的土壤表观电导率(ECa)数据,结合传统统计和地统计方法进行土壤盐分的时空变异研究,揭示土壤盐分的空问变异情况,以期为作物种植和农田土壤管理提供依据。空间分析表明,土壤盐分含量高的区域位于研究田块的中部,土壤盐分含量低的区域位于田块的周围。时序上的变异性分析表明,随着围垦利用年限的增加,土壤含盐量逐步减少,年季减少的幅度在降低,年季之间多重比较分析差异显著。时序的稳定性分析表明,在中部土壤盐分含量高的区域,具有时序变异方向上的稳定性,而在土壤盐分含量较低的周围区域,时序稳定性相对较差。这对于农业生产者了解围垦区土壤盐分的时空变化规律,科学指导土壤改良和农业生产具有重要意义。
(2)基于星地数据的RSM土壤采样设计研究
从土壤采样设计两个基本原则——样点个数最小化,差异最大化出发,针对当前存在的问题——样点个数和样点位置如何确定,利用ECa数据和后向散射系数(σ0)数据,结合方差四叉树法(VQT)和曲面响应采样设计(RSM)进行土壤肥力高效采样方法的研究。本研究田块中,先采用VQT法得到合理的样点数目为12个,再采用RSM方法进行样点位置的确定和优化,从而确定12个土壤采样点。与传统的42个规则网格采样点比较,两种采样方法具有统计上的相似特征,t-检验的Tukey-Kramer HSD比较表明,差异不显著。说明本方法与传统网格采样方法相比,样点设计的采样效率更高,这对于土壤采样策略的进展具有重要的推动作用。
(3)基于星地多源数据的农田田间管理分区与数字制图研究
随着遥感技术和近地传感技术的发展,高分辨率遥感影像和农田快速实时获取的信息在土壤学科中的应用越来越广泛。本研究综合利用反映海涂围垦区土壤盐分的土壤电导率信息,反映作物长势的归一化植被指数(NDVI)和反映土壤水分特征的后向散射系数(G0)作为输入变量,采用模糊κ-均值聚类算法来定义田间管理分区,并利用模糊性能指数(FPI)、改进分类熵(MPE)和聚类独立性指数(S)有效地确定了最佳聚类效果和分区数目,结果表明,最佳分区数目为3。用混淆指数(CI)评价单个管理单元分区效果的好坏。通过划分管理分区来指导土壤采样,能通过少量的土壤样品调查获取各管理分区的土壤养分变异特征；另一方面利用地统计学方法进行制图,进行土壤空间变异田间尺度的表达,给农户提供最直观的数字土壤图,可以为分区进行精准农业管理提供决策依据,具有重要的理论和应用价值。
(4)基于原位可见-近红外(vis-NIR)高光谱技术的土壤制图研究
vis-NIR光谱技术是近地传感技术中的一个重要分支,与常规土壤理化分析相比,可见-近红外光谱测量技术具有快速、无损、高通量、低成本等特点。本研究利用ASD FieldSpec Pro FR野外型光谱仪获取的vis-NIR数据,进行土壤光谱数据处理、预测建模和数字制图方法研究。研究中,将野外现场获取的土壤光谱经过去噪和倒数的对数转换(Log (1/R))为吸收光谱后,利用逐步回归分析法寻找表征土壤属性的特征波段,然后利用偏最小二乘法(PLSR)进行土壤属性的预测与建模,结果表明,采用逐步回归法提取特征波段,利用PLSR可以对土壤有机质(SOM)、总氮(TN)、CEC、速效氮(AN)、速效磷(AP)和速效钾(AK)进行准确的预测。例如,对AP,模型可以解释98.9%的变异,RMSE和RPD的值分别为1.03和9.98。即使最小的解释变异模型(TN),也可以解释87.4%的变异,RMSE和RPD的值分别为0.56和3.15。最后利用土壤光谱数据和实测土壤属性数据(SOM、TN、CEC、AP和AK)分别进行克里格插值制图,两组土壤属性空间分布特征基本相同,说明野外光谱测量手段在农田土壤属性快速获取与数字制图方面具有很大的应用潜力。
本研究基本完成了研究内容,达到了预期的研究目标,取得了以下新进展：
(1)利用遥感和近地传感器技术各自优势获取的多种农田信息,综合运用在土壤空间变异研究、土壤采样研究和农田管理分区中,有新意。在土壤肥力采样方法的研究中,以围垦区土壤限制因子——水分和盐分数据为基础,提出了VQT和RSM方法相结合,从土壤空间变异特性出发来解决土壤采样问题的方法。一方面解决了采样点位置的问题,另一方面也考虑了数据的空间位置,同时打破了VQT方法中采样形状是矩形的限制。与传统的网格采样方法进行比较,RSM采样方法速度快,效率高。
(2)针对土壤制图中的田间尺度制图问题,利用多源数据进行土壤空间变异制图分析。如针对田间管理分区及制图问题,综合利用围垦区土壤限制因子盐分、水分信息结合作物长势信息,采用模糊κ-均值聚类进行管理分区划分并制图,为精准农业管理提供最为直观的依据。针对原位信息采集制图问题,利用vis-NIR原位测量的土壤高光谱信息,进行土壤属性预测特征波段的筛选、预测和制图,预测结果良好,为利用野外光谱测量手段进行实时快速的获取土壤属性空间分布信息提供理论支撑,同时也为农户的田间管理提供最为直观的图形数据,对国内进行土壤光谱的原位测量与建模研究具有重要的意义。
Acquisition of multi-sources field information is the premise to conduct studies on spatial variability of soil, crops and the environment. Especially, with the rapid development of a new generation of high-resolution active and passive remote sensing satellite and soil proximal sensor, outstanding advantages of the method to obtain the multi-source farmland information for analyzing soil spatial variability and digital mapping has attracted more and more attention. In this regard, the International Union of Soil Science (IUSS) also gave a great concern on the quick access to field information and soil spatial variability. In2002,17th World Congress of Soil Science set up a Pedometrics, Commission1.5which suggested approaching the soil distribution and pedogenesis with mathematical and geostatistical methods. Consequently in2008, the Proximal Soil Sensing group was formed to focus on the research and development of proximal sensors to acquire soil information inside and outside the room, advancing the approaches of soil science from lab analysis to field monitoring in-stiu. In the present study, the reclaimed coastal fields in Hangzhou gulf, Zhejiang Province, were selected as study area to carry out the research on quick access to field information and soil spatial variability, where soil salinity/moisture has a decisive role in soil quality and crop planting. As such, researches based on the critical factors (i.e. soil salinity, soil moisture) in reclaimed coastal areas, including multi-source information acquisition and processing, soil sampling, soil management zoning and digital mapping were implemented to provide ancillary technical information for soil improvement and precision agriculture. The main results are as follows:
(1) Sensor-directed spatio-temporal variability of soil salinity in coastal areas
Soil salinity is crucial variable which influences the soil quality and agricultural productivity in reclaimed areas. Accurately characterizing the spatial variability is critical for the rational development and utilization of tideland resources. In the present study, we employed soil apparent electrical conductivity (ECa) acquired by EM38from2009to2011to carry out spatio-temporal variability analysis integrating traditional and geostatistical analyses. The analyses of the spatial distribution and trend maps revealed the relatively high salinity in the middle, which reasoned by farming and water table fluctuation; low soil salinity in the surroundings may arise of the drainage ditches surrounded the field (i.e. ridge building in the surroundings, irrigation and drainage for rice). In addition, soil salinity decreased from2009to2011with significant difference between years by the calculated Tukey-Kramer means comparisons. Coefficient of variation over time at each measurement showed that the middle region with a high salinity content displayed temporal stability, while the surrounding region of a lower salinity level showed temporal instability. These results could be used for knowing of the change discipline of soil salinity in reclaimed soil, directing the farmer to plant, or scheduling the variable rate application.
(2) Soil sampling strategy based on radar imagery and proximally sensed data
On the basis of the two basic principles of soil sampling design-minimized the number of samples and maximized the difference between sampling points, we focused on how to determine the number of samples and sample location herein. ECa and the backscattering coefficient (σ0) data were employed, combining with the variance quadtree method (VQT) and response surface methodology (RSM) to seek the efficient soil sampling. In this specific field, VQT method generated a reasonable number of samples of12, and then we used RSM to determine the sampling locations. The validation indicated that the RSM sampling strategy was highly effective at characterizing spatial variability of various topsoil chemical properties (e.g. soil organic matter (SOM), available nitrogen (AN), and available potassium (AP) compared with the traditional-grid sampling design. The two sampling methods have similar characteristics, t-test with Tukey-Kramer HSD comparison showed that the difference was not significant. The results would play an important role in promoting the progress of soil sampling strategy aided with auxiliary data.
(3) Management zone and digital soil mapping based on multi-source data of high resolution remote sensing imagery and proximally sensed data
With the development of a new generation of high resolution remote sensing and proximal soil sensing technology, the usage of high-resolution remote sensing images and real-time access to field information is becoming more and more popular in soil science. In this regard, ECa indicating soil salinity, the normalized difference vegetation index (NDVI) reflecting the crop vigour and the backscattering coefficient (σ0) characterizing the soil moisture were selected as input variables, fuzzy k-means clustering algorithm was used to partition the field management zone. Fuzzy performance index(FPI), modified partition entropy (MPE) and separate clustering independence index (S) were adopted to determine the best clustering and the number of partitions. The results indicated that the optimal number of partitions was3. Afterwards, confusion index (CI) was employed to evaluate the effect of each unit. In light of the characteristics of each zone, corresponding management practices should be adopted to ameliorate the soil and implement precision farmland management. The zoning in useful not only for guiding soil sampling, but also to recommendation of variable input and precision fertilization, and on the other hand, the digital mapping of the management zone can provide the most intuitive basis for precision agriculture, which are of important theoretical and practical value.
(4) Using visible and near infrared (vis-NIR) reflectance spectroscopy for simultaneous assessment and digital mapping of selected soil properties
vis-NIR (100-2500mm) technology is a rapid, proximal sensing method that has great potential for quantifying constituents of dried/ground soil samples. It is becoming increasingly important to improve the soil properties prediction and spatial resolution of soil maps as a fundamental information layer for soil classification, mapping and so on. In the present study, vis-NIR reflectance spectra collected in-situ were employed as an indicator for the prediction of some soil properties. Stepwise regression method was used to search for all possible subsets and sort out the best ones from a large number of spectral variables. Afterwards the PLSR approach was adopted to build up the predicted model for some soil properties. Our work demonstrated that vis-NIR spectroscopy has the potential to predict the SOM, TN. CEC, AN, AP and AK from the field measurements. In this instance, the largest explanation proportion for AP achieved98.9%with RMSE of1.03, and the RPD value of9.98, even the smallest one for TN can explain87.4%with RMSE and RPD of0.56and3.15, indicating good prediction. Digital mapping of the measured and predicted soil properties showed similar patterns and value ranges, though there are some minor differences between them. The results demonstrated the ability to use this methodology as an indicator for rapid and reliable soil assessment and mapping. The use of these techniques will promote the implementation of assessment and digital mapping of soil properties by a rapid and reliable approach from lab to field.
The innovations or new development were made as follows:
(1) A novel idea was proposed to study the soil spatial variability, soil sampling strategy and soil management zoning based on the outstanding advantages of multi-sources information collected by remotely and proximally sensed techniques. For the study of soil sampling algorithm, the limited factors of soil salinity and soil moisture in the reclaimed soil were used to calibrate and validate the method we proposed. That is, integrating the VQT and RSM to solve the key problem of soil sampling number and location departure from the soil spatial variability. As such, we eliminated two important aspects, one was confirming the accurate soil sampling location; the other was the breaking of the limitation of the rectangular in shape restriction in consideration of the spatial position of sampling strategy. Compared with grid sampling method, RSM has strong points of high speed and efficiency.
(2) Focused on the field-scale digital soil mapping, we used the multi-source information to conduct soil spatial variability analysis and soil mapping. Firstly, integrating the information of soil salinity, soil moisture and crop vigour in the reclaimed area, fuzzy k-means clustering algorithm was adopted to define the management zones, providing the most intuitive basis for precision agriculture. Secondly, acquisition of high resolution soil vis-NIR spectra in-situ measurement was applied to establish the prediction model, and for soil mapping based on the screened characteristic spectral bands with stepwise regression method and PLSR. Good predictions were gained, which provided theoretical support with the regard of in-situ measurements, and played an important role in the prediction and soil mapping on the basis of in-situ measurement in China.
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