Accurate assessment of paddy rice planted area is challenging in tropical regions where the availability of optical satellite data and samples is limited due to the high prevalence of cloudy and rainy periods. This study, carried out in Vietnam, presents a novel two-step method to estimate annual paddy rice planted area at the 10-m spatial resolution overcoming the limitations of scarce optical remote sensing data and ground-truth samples. First, an easy and effective visualization method was designed to collect rice and nonrice samples based on the significant color difference of consecutive three monthly VH composite data between paddy rice fields and other land covers during the flooding to transplanting stage. Second, rice spatial distribution information was extracted by taking full advantage of coarse optical imagery in a spatialtemporal continuum and Sentinel-1/2 images combined with rice potential zone (RPZ) generation by threshold and rice fine classification by random forest. Third, the cropping intensity of paddy rice fields was identified from monthly VH bands smoothed by harmonic analysis of time series (HANTS) using the quadratic difference method. The utilization of RPZ improved$R^{2}$of the paddy rice fields’ identification accuracy from 0.78 to 0.97 and reduced the root mean square error (RMSE) from$69.28\times 10^{3}$to$31.54\times 10^{3}$ha. Results indicate that in 2020, the paddy rice planted area of Vietnam was estimated as$6264.9\times 10^{3}$ha, strongly corresponding to the estimation in the statistical yearbook by province ($R^{2} =0.97$). The study demonstrated the effectiveness of Sentinel-1 synthetic aperture radar (SAR) data in the estimation of annual paddy rice planted areas in frequently cloudy and rainy tropical regions where optical data are scarce.

In remote sensing earth observation applications, there is often a trade-off between automated processing and improved observation accuracy. A key issue in this contradiction is the underutilization of rich knowledge embedded in historical remote sensing data. To address this, this paper proposes an object-level multi-source transfer learning method (OMSTL) that effectively leverages the knowledge in historical remote sensing data for unsupervised land object discrimination. This method effectively integrates multi-source heterogeneous features and represents land object knowledge, enabling reliable knowledge transfer. Multi-source transfer learning utilizes knowledge from remote sensing images at different times and locations, significantly enhancing transfer stability. The method's effectiveness and potential were demonstrated through experiments using five optical remote sensing images (GF-1B, GF-1D, GF-6, GF-7, ZY-3) and their corresponding SAR images (Sentinel-1).

Current agricultural field (AF) delineation methods using high-resolution remote sensing (RS) images encounter two primary challenges: 1) the interactions between boundary features and field features are often overlooked, resulting in imprecise field boundaries and 2) the efficacy of delineating AFs with fuzzy boundaries, irregular shapes, and small sizes in mountainous and hilly regions necessitates systematic evaluation. To deal with these challenges, we propose a boundary-field interaction network, namely, BFINet, leveraging multitask learning techniques for AF delineation. BFINet comprises two branches: a core branch for AF delineation and an auxiliary branch for boundary prediction that furnishes fine-grained boundary information to enhance geometric feature learning. To model the interactions between boundary semantics derived from low- and high-level semantics obtained from the field extraction branch, we introduce a semantic interaction module (SIM), facilitating AF identification in complex terrains. A boundary enhancement module is integrated to refine spatial details of high-level semantics, thereby alleviating boundary fuzziness. We also design a dense multibranch fusion module (DMFM) to augment the semantic representation of BFINet’s multiscale information for various fields. Extensive experiments were conducted on these datasets from Hunan (HN) and Chongqing (CQ) in China, featuring small AFs with irregular shapes and fuzzy boundaries, and compared BFINet with five existing AF delineation methods. Our findings indicate that BFINet excelled in identifying diverse AFs, achieving superior attribute and geometric accuracies over competing methods. It effectively delineated AFs with various sizes and shapes while minimizing interference from other ground objects.

Synthetic aperture radar (SAR) plays an essential role in earth observation and projection due to its capability to penetrate clouds, which makes it possible to monitor terrestrial surfaces under all weather conditions. Multiplicative noise often occurs in the SAR signal, hampering the retrieval of information from SAR imagery. Convolutional neural networks (CNNs) have been used in many computer vision tasks and are helpful in image denoising. However, current CNN-based denoising approaches inevitably lead to a “washed out” effect that loses spatial details. Another limitation is that most typical CNN-based denoising models require a noise-free image for training. To address these issues, we propose a novel end-to-end self-supervised SAR denoising model: Enhanced Noise2Noise (EN2N), which can be trained without a noise-free image. To enhance the quality of the result images, the perceptual features from a pre-learned CNN are introduced to restore the spatial details by a hybrid loss function. Experiments show that our proposed method outperforms the typical denoising methods in terms of noise reduction and feature preservation based on image quality metrics. Also, the new hybrid loss could enhance the spatial details significantly. The good performance maintains the robustness throughout time, which reduces the uncertainty in time-series SAR caused by random noise. Benefiting from optimization of graphics processing unit (GPU) and multi-threading, the proposed method has higher computation efficiency than traditional methods. This study demonstrates the great potential of using our self-supervised deep learning approaches for SAR image denoising in the future.

CropWatch, which was developed by the Institute of Remote Sensing and Digital Earth (RADI), Chinese Academy of Sciences (CAS), has achieved breakthrough results in the integration of methods, independence of the assessments and support to emergency response by periodically releasing global agricultural information. Taking advantages of the multisource remote sensing data and the openness of the data sharing policies, CropWatch group reported their monitoring results by publishing four bulletins one year. The cloud technology is an effective tool for people to produce, to browse and analysis the information anywhere. To better produce and generate the bulletin and provide an alternative way to access agricultural monitoring indicators and results in CropWatch, a new simple all-inone solution platform named CropWatch online system based on the cloud techniques has been developed. CropWatch online system provide four main components: CropWatch Processing, CropWatch Explore, CropWatch Analysis and CropWatch Bulletin. CropWatch Processing provides an online interface for people producing CropWatch indicators at anytime and anywhere from county to global scales. By taking advantages of Alibaba cloud flexible system with parallel computing's architecture, the data production efficiency for each bulletin increased by 50 times and no consumption in the idle time. CropWatch Explore provides a visualization web service for our users to smoothly explore CropWatch data including agro-climatic indicators, agronomic indicators related with crop area, crop yield and crop production. Information or indicators are visualized using vector or raster according to their features. Vector mode provides the statistic value for all the indicators over each monitoring units (from MRU, Main producing zone, county level to national or regional scale) which allows users to compare current situation with historical values (average, maximum, etc.). Raster mode provides pixel based anomaly of CropWatch indicators globally. In this mode, users can zoom in to the regions where the notable anomaly was identified from statistic values in vector mode. CropWatch Analysis is cloud collaboration documentation tool for the CropWatch teams or invited people from over the world analyzing their CropWatch indicators anywhere. It provides create document, allocate and manage tasks, monitor schedule and publish the document online functions which let people over the world finish their documents together on the cloud platform. CropWatch Bulletin publishes the CropWatch bulletin for each season, CropWatch updates for each month and global crop information on http://www.cropwatch.com.cn. Currently, based on Digital Belt and Road (DBAR) Initiative, the Cropwatch team formed a DBAR agricultural working group which cooperate with the international organizations and countries along the Belt and Road. Thanks to the good scalability of Cropwatch cloud system, the working group intends to build a new open-sourced data-driven agricultural resource monitoring technology system based on Cropwatch. The system is mainly used for monitoring the status of agriculture in the key countries or areas along the belt and road and aims to assess the potential and sustainability of the reserve agricultural land resources.

Land cover is an important part of ecosystem change and driving factors, while above ground biomass also plays an important role in carbon cycling. Supported by object-oriented classification technology, the land cover data of China (ChinaCover) in 2010 have been produced using Landsat TM/ETM and HJ-1 satellite data of 30m resolution, combined with a large number of data of field investigation. At the same time, we select 6 typical study sites for collecting airborne LiDAR data, high resolution images and hyperspectral data, and estimate the vegetation aboveground biomass, then scale to the 30m scale by Landsat TM data and 250m scale based on MODIS, canopy height, vegetation coverage and vegetation type classification data. The final models were applied to output China's vegetation above ground biomass map in 2010.

Monitoring forest biodiversity is essential to the conservation and management of forest resource. A new method called “spectranomics” that map forest species richness based on leaf biochemical and spectroscopic traits using imaging spectroscopy has been developed. In this study, we use this method combined with the airborne imaging spectroscopy (PHI-3 with 1m spatial resolution) data to detect the relationship among the spectral, biochemical and taxonomic diversity of tree species based on 20 dominant canopy species collected in the Longmenhe Forest Nature Reserve of China. Seven optimal biochemical components (chlorophyll, carotenoid, water, specific leaf area, nitrogen, cellulose, and lignin) are selected (R2>0.58, P4 points/m2). Finally, a self-adaptive Fuzzy C-Means (FCM) clustering algorithm is applied to determine the optimal clustering numbers (i.e. species richness) and Shannon-Wiener for each 30×30m window based on the isolated individual tree height and 7 biochemical indices. According to total 22 sample plots, the mapping results show that the predicted species richness is close to the field measurements (R2=0.65,P2=0.83, P<;0.01) than the species richness.

Aerodynamic roughness (AR) is an important parameter affecting land-atmosphere interactions. Previous studies on inversions based on bidirectional reflectance distribution functions (BRDFs) have focused on spatial analysis over desert and gobi, which are vegetation-free areas, due to the lack of wind gradient data for continuous time periods over vegetated surfaces. This study uses meteorological gradient data from a new long-term site that is supported by the Heihe Watershed Allied Telemetry Experimental Research project and is located in an irrigated farmland area in the Heihe River Basin of northwestern China. These data are used to calculate a field AR time series over maize using an iterative computation method based on Monin-Obukhov similarity theory. A linear relationship is demonstrated between the field AR and the BRDF parameters derived from multiband Moderate Resolution Imaging Spectroradiometer data. The correlation analysis for the near-infrared and shortwave bands reveals R2values of 0.8739 and 0.8833, respectively; however, R2is only 0.0146 for the visible band. Various band combinations do not improve the outcome. Thus, the near-infrared and shortwave parameters have the potential to be used to infer AR and its related evapotranspiration at more extensive temporal and spatial scales.

The time series of the fraction of absorbed photosynthetically active radiation (FPAR) derived by remote sensing are widely used to monitor vegetation and estimate vegetation productivity. This study, the Wide Dynamic Range Vegetation Index (WDRVI) based on the 16-day NDVI of MODIS product was employed to estimate time series of FPAR in China from 2000 to 2011. In addition the MODIS collection 5 products of FPAR (MOD15A2) in China were used to compare in two different views: the spatial patterns analysis and the linear relationship analysis. An application was also used to analyze the relationship between the WDRVI FPAR and crop yield derived from China's CropWatch System in China. The result showed that there were similar spatial patterns and good relationships between the MODIS FPAR and WDRVI FPAR among most vegetation type although the MODIS FPAR was slightly larger than the WDRVI FPAR. A good relationship between the trend of WDRVI FPAR and the trend of crop yield has been observed which demonstrated that it had a potential for crop yield estimation.

The monitoring of crop production is an important component of the assessment of the economic impact on agriculture in global markets. This paper investigates the suitability of COSMOS-kyMed X-band and Radarsat-2 C-band data fused with THAICHOTE optical imagery to estimate above-ground biomass of rice over the whole growing period, and eventually yield, in irrigated areas of Central Thailand. Ground-based plots were measured in parallel with Synthetic Aperture Radar (SAR) derived variables such as plant density, height, water level, Leaf Area Index (LAI) and biomass. Biophysical characteristics of rice and COSMO-SkyMed as well as Radarsat-2 backscattering coefficients are analyzed. The resultant fusion technique is compared to national agricultural statistical data. Results show that X-band HH backscatters and Optical Vegetation Indices are a promising method to estimate rice yield.

The aerodynamic roughness is one of the major parameters in the evapotranspiration (ET) calculation, which affects the turbulent exchange process between terrestrial and atmosphere. The ETWatch system is developed by integrating SEBS, SEBAL and the improvements to the retrieval of important parameters discussed in the introduction section. At present, the aerodynamic roughness calculation has mainly considered two factors, including NDVI and topography. However, when mapping ET in a large scale with multi-temporal, unavoidably, the situation of flat geographic region in winter season will be met, in which the two above factors both lose effects. In this paper, the satellite-based radar data of ENVISAT ASAR backscattering information in winter was introduced into ETWATCH system to calculate the ET in Guantao County in winter season. The field EC (Eddy Covariance System) and LAS (Large Aperture Scintillometers) measurements data were used to validate the ET results before and after introducing radar-based aerodynamic roughness. The results indicate that the radar-based aerodynamic roughness information is essential for ET calculation in winter season in the flat region. It can decrease the original ET to a proper extent, which is more agreeable to field measurements.

Based on high resolution remote sensing imagery and in combination with multi-temporal imagery, this article classified land cover of Miyun reservoir area using the object-oriented classification method. The results showed that the deciduous broad-leaved shrub, deciduous broad-leaved forest, dry land, and grass accounted for 92% of the total area. The study proved that multi-temporal satellite images are critical in grouping those objects with different spectrum together, like the cultivated land. Moreover, plenty of ancillary data is good to distinguish the different objects with same spectrum.

In order to solve the problem of data shortages resulting from long revisit period and cloud contamination in high resolution imagery, this paper proposed a fusion model for generating high spatial and temporal resolution images based on semi-physical method and improved ESTARFM(enhanced spatial and temporal adaptive reflectance fusion model). The result shows that the new model can produce more precise reflectance than ESTARFM and take full use of high spatial resolution data.

Crop residue, as one of important factors of eco-agricultural system, can influence the flow of nutrients, carbon, water, and energy in terrestrial ecosystems. Remote sensing approaches have been investigated to obtain the crop residue cover (CRC) over large areas rapidly and objectively. While Cellulose Absorption Index (CAI) is a reliable indicator for crop residue cover estimation, it can only be applied to hyperspectral sensor, which usually has a narrow swath width. A new approach utilizing the shape of the spectrum of consecutive bands of MODIS data was introduced to distinguishing soil, crop residue and live crops. Since Landsat 5 TM's band configuration is similar to MODIS and provides fine spatial resolution (30 m) for agriculture in China, it offers an opportunity to assess crop residue cover over large areas. The objective of this research is to evaluate the Landsat-based angle indices for estimating CRC. Four spectral angle indices were derived by surface reflectance of Landsat TM. The results showed that shortwave angle slope index (SASI) is an ideal index for crop residue cover estimation. Simple linear regression reveals a coefficient of determination (R2) of 0.7895 between measured CRC and SASI for the calibration dataset and RMSE of 0.0886 between measured and predicted CRC for validation dataset. Crop residue cover for Hongxing farm was estimated using Landsat-based SASI and sorted into three categories, corresponding to intensive (15% residue cover), reduced (15-30% cover) and conservation (>;30% cover) tillage. Results in this study demonstrated that Landsat-based SASI was capable of crop residue cover estimation.

Opium is a narcotic obtained from opium poppy and is the raw material of heroin for the illegal drug trade. Monitoring the illegal concentrated cultivation of opium poppy in major regions is critical for the understanding by governments and international communities of the scale of illegal drug trade. This paper investigates whether opium poppy can be discriminated from its coexisting plants using analytical-spectral-device field spectrometer data in the visible to short-wave infrared spectral range. Canopy spectral measurements were conducted during three different growth periods of opium poppy. A synthetic method with three analysis levels was applied to discriminate opium poppy from other species and to select optimal bands for opium poppy discrimination. First, the Mann-WhitneyU-test method was used to test the spectral reflectance difference between opium poppy and coexisting crops at each wavelength. Then, the Jeffries-Matusita distance and band correlation analysis were conducted to select the optimal wavebands for discriminating opium poppy using the significant wavebands from the test results. Finally, classification and regression tree analysis was employed to validate the classification accuracy based on the selected optimal wavebands. The results indicated that the spectral reflectance of opium poppy was significantly different from that of coexisting crops in many surveyed wavebands, and opium poppy could be discriminated using a field survey spectrum at canopy level. The best time for discriminating opium poppy from coexisting crops was around flowering time. This paper provided the prerequisite for monitoring opium poppy using satellite remote sensing data in some regions of concern.

Crop growth means the growth of cereal crop seedlings, as well as the status and trend of their growth. It has been one of the most important aspects of agricultural remote sensing in the last twenty years. Evaluation of crop growing condition in large scope before harvesting could be helpful for field management, as well as provide important information for early crop production estimation. For a country with a population of 1.2 billion it is important to know not only the domestic crop growth, but also the crop condition in other big agricultural countries that have food trade with China. This paper introduced the design, methods used and implementation of a global crop growth monitoring system, which satisfies the need of the global crop monitoring in the world. The system uses two methods of monitoring, which are real-time crop growth monitoring and crop growing process monitoring. Real-time crop growth monitoring could get the crop growing status for certain period by comparing the remote sensed data (NDVI, for example) of the period with the data of the period in the history (last year, mostly). The differential result was classified into several categories to reflect the condition at difference level of crop growing. This analysis result allows users to quickly assess how much and where conditions have either deteriorated, remained unchanged or improved. The crop growing process monitoring is the contrast between year and year for crop growth profile, which reflects the crop growing continuance at time during crop growing season. Time series of NDVI during the crop season are used and crop growth profiles are formed by getting statistical average of time series NDVI image for plowland in certain regions such as a state. Eigenvalues such as growing rate, peak value, average and so on were gotten from the crop growing profile to estimate crop growing status which concerns the whole growing process. Based on the above monitoring methods, we developed a crop growth monitoring system based on remote sensing, which provides a monitoring and analyzing environment for real-time crop growth monitoring and crop growing process monitoring to all the users. The system was developed in C/S pattern and includes five main functional modules, which are real-time crop growth monitoring module, crop growing process monitoring module, results visualization module, business management module and system configuring module. The structure and the function are particularly described in the paper. In the system, both real-time crop growth monitoring and crop growing process monitoring are carried out at three scales, which are state (province) scale, country scale and continent scale. While in most of the countries in the world the monitoring was carried out at country scale, monitoring in large agricultural countries such as USA, Canada, India, etc., was carried out at both state (province) scale and country scale. This can provide more detailed crop growth information in these countries. Much more macro crop growth information was supplied by running the system at continent scale such as North America, Europe, Africa and South East Asia, etc. Taken 10-day (16-day for MODIS) composite NDVI products of NOAA/AVHRR, SPOT/VEGETATION and MODIS as data source, the system has been run successfully for more than a year, which provides decision-supporting information on crop condition to more than a dozen of ministries and commissions in China.

This paper describes the characteristics of vegetation condition index (VCI), temperature condition index (TCI), normalized differential water index (NDWI) and vegetation health index (VHI), and uses the relationship between above indices and relative soil moisture to evaluate the index adaptation. The analysis means that different indices have different adaptation. NDWI is not sensitive to the drought. VCI is sensitive to the drought only in the growing time, but has a lag of 10 days. TCI is more sensitive (the max R2=0.52) to drought and can reflect drought without lag, but not stable. However, in the growing time, VHI is the most sensitive to drought (the max R2=0.56) and stable. Finally, this paper analysis such uncertain factors as measurement condition, data quality and index calculation, which affects the adaptation of index. The result shows that VHI has a good adaptation in the growing time and has a nice prospect in drought monitoring.

In this paper, the potential of using three existing models coupled to estimate the regional evapotranspiration (ET) in the Haihe basin (China) is studied. The Surface Energy Balance System (SEBS) is used to determining the spatio-temporal variability in regional evapotranspiration condition though 2002-2004 using more than 200 NOAA AVHRR images per year. The surface energy balance algorithm for land (SEBAL) is used to describe transpiration ability in the main days of crop growing season on various crop types at the field scale using LANDSAT TM and ETM data. And the Penman-Monteith equation is used for integrating the previous high and low resolution ET results to finally estimate the field scale water usage in the whole growing season, which has great benefit to policy-maker for agriculture management. The adaptation of this approach is evaluated over various land cover types at 250 m spatial resolution by analyzing the error between daily ET estimation and in-situ measurements. These comparisons reveal that: considering the underlying complexity in Haihe basin, the ET estimates can be accepted in the vegetation-covered fields after validated by limited scintillometer measurements (24.4% average deviation for daily estimates, and less than 7% for the yearly period), for whole-regional validation, more measurements are needed.

The Tarim River is the longest continental river in China, and also one of the longest rivers in the world. The ecological environment of Tarim River drainage area has been gradually deteriorating in recent twenty years. By using 1 km NDVI data from Advanced Very High Resolution Radiometer (AVHRR) and introducing an integrated vegetation index to the region of interest, conclusion was gotten that the whole ecological environment of the Tarim River drainage area is deteriorating rapidly and the ecological environments of upper, middle and down streams are changing differently. While the ecological environment of the upper stream is meliorating slowly, the ecological environment of middle stream is maintaining its actuality and the ecological environment of the down stream is deteriorating rapidly. After we analyzed the causes of the changes by using the monitoring data of the river discharge from hydrographic stations in the research area, practical suggestions were made for improving the ecological environment in the aspects of water conservancy developments, the exploitation of water resources, etc.

The generation of meteorological surfaces from point-source data is an overwork in computing evapotranspiration in Haihe river basin. To date, the software in common use can't meet this demand. Here we offer a high efficient, flexible, integrated interpolation system that employs a database to store climate weather data, a choice of interpolation methods, validation and carry on batch processing. We performed validations for five meteorological variables (minimum and maximum temperature, relative humidity, average wind speed and air pressure) with inverse distance weighting (IDW), thin plate smoothing spline (SPLINE) and ordinary kriging (OK) and achieved comparable success among all interpolation methods. IDW and OK precision are superior to SPLINE, but SPLINE is more effective and smoothing. There is not too big difference between IDW and OK, and the precision obviously improves after temperature and pressure are revised with the terrain.

It is of great significance to obtain the information of crop production objectively, accurately and in time. Crop yield estimation per unit is one of the keys of total production estimation. As a result, crop yield estimating, models directly influences the precision and credibility of total production estimation because of their rationality and scientificalness. In china, Research of crop yield estimating models has already developed more than twenty years. The precision and effectiveness of estimation models have improved greatly, especially with the advent of crop yield estimation based on remote sensing data. And it has already showed the following trend and characteristic: The theory starting point of yield models establishment is becoming more rational and scientific. The mathematical methods of setting up crop yield models are more advanced. Taking remote sensing information as the main information source, synthetic yield estimation model will become dominant for the future. The remote sensing information of high time resolution and low space resolution is still the main information source of estimation models of crops yield.

Water is held in the soil pores by weaker capillary forces. When a soil is sufficiently wet, its capillary forces can hold no more water and the soil is at field capacity. Field capacity reflects the water holding capacity of the soil. Most agricultural crops require irrigation through the dry season, harvest, and after harvest. Specific water needs can be very different from one crop to another and, for the same crop, depend on soil type and climate. Some crops benefit in the quality of fruit produced when the soil water content is regulated during a specific growth stage of the plant. Some crops are sensitive to too much water in the soil, while other crops are sensitive to minor fluctuations in soil water content in the root zone during the growing season. Before a crop’s water needs can be addressed, and a specific water management program can be established, several important plant and soil parameters need to be determined. Field capacity of the soil is one of the important parameters. As a soil begins to dry out, however, increasingly stronger forces hold the pore water until a point is reached when plants can no longer extract any water from the soil. This state of soil moisture is the wilting coefficient of a soil. The wilting coefficient for a crop in a particular soil means that the water in the soil can no longer be easily extracted by plant and the plant begins to wilt. The method of field capacity is douche of sample in the field usually. The sample in the field was saturated with water, and was allowed to gravity drain for a few days. Then you can obtain the soil water content in the balance of the soil water. This method is more fit to the real condition of the field, but not fit to the soil in which the osmosis is very bad. The method of wilting coefficient is biological method usually. The sample in the field was used to plant the crop in the lab until the crop begins to wilt because of the deficit of the water. The measurements of the two soil parameters are very complicated. coefficient because of many influencing factors, such as differences in soil texture, content of organic matter and the mode of the use of soil. Field capacity and wilting coefficient of a soil vary with each factor. Soil texture is a physical property of soil and is determined by the percentages of soil separates. Content of organic matter will affect the hole of soil and the arranging mode of soil grains. The mode of planting will affect the physical and chemic character of soil. Usually we think the two soil parameters are relative changeless year by year, they fluctuate from year to year in a specific range in fact. Because of complicated determine method of soil parameters and too much influencing factors, some scholars put forward models to calculate field capacity, but quantifying each factor is very difficult, so the model may be adaptive to a section. Researches usually use the observed data in the field. The observed data are not easily collected and not uniform, so the data researches used may be observed long before. Water in the soil between field capacity and wilting coefficient is the plant available water. Once you know the soil’s field capacity and wilting coefficient, you will know the soil’s total amount of plant available water. Soil water condition combined with the plant available water is used to master the law of variety in the soil water, and the intensity of transpiration and evaporation combined with the plant available water is used to analyze available water in the soil for some crop in different period of growth, then you can make irrigation schedule, regulated deficit irrigation, water conservation in agriculture. Drought could be reflected through monitoring the growth condition of the crop, while the growth condition of the crop rests with the soil available water. When the water extracted from soil can’t meet the requirement of the crop, the crop would be in the condition of drought and the growth would be affected. Knowing the two soil parameters, you can calculate the relative soil moisture through monitoring the real soil water, and analyze whether the soil water can meet the need of crop, then you can judge whether the drought for a crop could happen. Considering the need and broad application of the two soil parameters, mapping the countrywide field capacity and wilting coefficient are proposed.

Micro-watershed or sub watershed is the unit of soil and water conservation practices in China. Every year the Chinese government devotes fund and manpower in regional soil and water conservation practices. How to get the information about regional devotion objectively becomes a problem. The management organization needs a new tool to learn the spatial pattern of certain conservation technique and ecological change. High spatial resolution image data is very expensive in cost to monitor every Micro-watershed. So depending on the exiting Micro-watershed distribution map, the attribute information in tables about soil and water conservation practices, road information in GIS, Landsat TM data and GPS, a field survey and sampling framework strategy of soil and water conservation practices on regional scale was developed in this paper and applied to the upstream Guan Ting reservoir.

A soil and water conservation-oriented small watershed management information system had been developed based on geographic information system and remote sensing technology. It is a sub-system of soil and water conservation monitoring system in the upper basin of Miyun and Guanting reservoir, and its main objective lays a strong emphasis on the analysis of soil erosion spatial-temporal change and the benefit evaluation of soil and water conservation measures for each small watershed in the region. The number of small watersheds, especially for those which had been taken soil and water conservation measures by projects, is more than 100. The main functions of the system include data management, data query, information analysis and decision support with functionalities of information abstract and measures effect evaluation et al. The procedures developed will contribute to the decision support for the soil and water conservation planning. The system adopted the client/server mode and the database was built on the support of Oracle and ESRI ArcSDE technology. Based on the COM technology, Arcobject and ArcGIS desktop product, ArcMap, the system was developed with the MS Visual Basic for Applications Development Environment. Simultaneously, DLL was built to implement special functions with the language of MS Visual Basic. Based on the above, a small watershed-oriented management information system really was realized.

Determining precisely what portion of upwelling water leaving radiance attributed to chlorophyll-a pigment alone is a complex undertaking. The suspended sediment reflect more radiance than chlorophyll-a normally. The calculation of chlorophyll-a using maximum ratio of SeaWIFS band 2/5, band 3/5 and band 4/5 according to OC4 algorithm gets low accuracy on coastal zone, which is rich suspended sediment distribution impact. An improved approach is developed to remove the impact of suspended sediment and dissolved organism on chlorophyll-a pigment estimate. The SeaWIFS band 6 with spectrum 660-680 mum, on which the low reflectance of chlorophyll-a pigment is distributed, is applied to detect suspended sediment, the spectral curve model of different suspended sediment concentration is measured in situ and demonstrates that different suspended sediments have a prominent spectral curve between 550-750 mum and varied spectral curve slope, these curve model has been added to the chlorophyll-a concentration calculation algorithm. The correction evaluation of suspended sediment for chlorophyll-a calculation is made to assess the correction quality, the in situ samples of chlorophyll-a are compared with uncorrected chlorophyll-a map and corrected chlorophyll-a map, the results shows that suspended sediment for chlorophyll-a has improved about 5% of accuracy of chlorophyll-a concentration

Using remote sensing to monitoring grassland for urban area is one of the major environmental applications today. Experts urge that there should be more nature grassland than artificial grassland in urban area, because the former is much important than the latter. It is difficult to identify them using normal methods such as pixel-based classification because both grasslands have similar spectral information. Going far beyond the methodical limits of pixel-based and manual interpretation approaches; object-oriented image analysis approaches are used for extracting information. This paper presents a snapshot of work to detect different grassland information in Daqing city using this patented technique. The result of grassland information extraction is promising and the precision of classification is higher than other conventional processes. It is obvious that this new image analysis approach offers a satisfying solution to extract information quickly and efficiently.

Miyun reservoir is an important water supplier of Beijing, China, therefore soil erosion of this area is very critical and must be paid sufficient attention. To monitor and manage soil erosion information in Miyun reservoir area, a monitoring information system that is dynamic, interactive and Internet-based was developed. The paper describes how the WebGIS application was developed, implemented and used. The overall monitoring information system is multi-scale, multi-source, flexible and geographically organized. It uses an Internet-based GIS ("WebGIS") technology, and has obtained information about soil erosion in Miyun reservoir area through four methods: RUSLE model, TM images visual interpretation, model based on vegetation cover and slope, and data fusion of three results. The decision-making officers can access and analyze these data more effectively and conveniently via the Internet.

In this study, three Landsat ETM images obtained at different period of the growth of seed rape and winter wheat are used in a study of analyzing the potential of multi-date ETM in crop discrimination for an area near Kaifeng city in Henan province, China. We compared the spectrum development between winter wheat and seed rape before, during and after the flowering stage of seed rape. Then, we provide two different classification schemas to discriminate the two crops depend on different image combination. The first one using images between the jointing stage and flowering stage of seed rape, the other one using images between the jointing stage and leguminous stage. At last, we selected two ETM images, on March 8 2001 and April 1 in 2001, to build a decision tree to discriminate seed rape and winter wheat. And the result was compared with that from IKONOS image classification and proved that the discrimination accuracy was 87% high. And the results also told us that discrimination errors were happened in two kinds of regions. One is the regions that crop growing status were very poor, another is that the planting patches were very thin or small

Drought is one of the major environmental disasters in north China, and it is very important to detect and monitor drought periodically at large scale for decision making. The Normalized Difference Vegetation Index (NDVI) has been widely used to monitor moisture-related vegetation condition. To better understand the relationship between vegetation vigor and moisture availability, the Normalized Difference Water Index (NDWI) was calculated in addition to the NDVI. In this study, the analysis was conducted on time series compositing NDVI and NDWI of the period of ten days. With the support of land use map and soil humidity of crop for the growing season, we build the simple model of northern China for crop drought monitoring. The results of July 2002 show that the large scale temporal and spatial characteristics of drought in Northern China can be effectively detected by this way. Based on this method, we have developed a operational crop drought monitoring system for whole China land areas.

In the present study an attempt has been made to evaluate the value of vegetations' air purification supported by Geographic Information System. Based on the theory and model about valuation of vegetations' air purification services, the material quantities and value of vegetations' air purification in Xi'an city were achieved. The results showed as the follows. Vegetations' air purification services included CO/sub 2/ fixation, O/sub 2/ release, pollutants absorption (SO/sub 2/, HF, NO/sub X/), dust retention, and sterilization. The total value of vegetations' air purification was 3426.35 million Yuan per year, which was six percent of Xian's GDP in 1999. Its value of CO/sub 2/ fixation and O/sub 2/ release was 2718.31 million Yuan per year, and that of absorbing the three pollutants, SO/sub 2/, HF, NO/sub X/, was 182.9031 million Yuan. Their dust retention and sterilization value were 182.51 and 342.64 million Yuan respectively. Using GIS's capabilities for inputting, storage, mapping, analysis and display of spatial data, we obtained spatial map of vegetations' air purification value in Xi'an and discussed the discrepancies of different vegetation types' air purification services.

With the great agriculture population and limited cropland, it is very important to estimate the output of grain produce in China by remote sensing technology. However, the smallholders of cropland can plant what they like, thus it is difficult to monitor the crop planting proportion with only RS images, even with IKONOS/QUICKBIRD data. In GVG agro-status sampling system, the video camera connected with a notebook by a video capture card and GPS receive card are integrated into the GIS environment. GVG is fixed on a motor and restore the crop pictures and their GPS data when the car is moving along the country road derived from the linear sampling frame in a plantation division unit. A great many of crop pictures along the sample lines are obtained on the field in a limited time, and then all pictures with geographical data are interpreted to calculate the ratio of each type of crop plantation. The crop proportion of plantation division unit is estimated by all picture's plantation ratio of sample line due to this unit. This literature review has demonstrated the GVG systems hardware's constitute, working principle and the case studies. GVG agro-status sampling system not only can be acquire every crop's planting proportion of large areas in short time, but also can check up the results, which get from remote sensing crop classification.

Vegetation fraction is an input parameter to scale, the vegetation cover on the ground for soil loss equation. In order to calculate the yearly volume of losing soil in the Upper Basin of Miyun Reservoir in the north of Beijing, China, it is necessary to develop a model to estimate vegetation fraction using remote sensing technology. Based on the analysis on the existing methods of measuring vegetation fraction, an improved Dimidiate Pixel Model was developed to estimate vegetation fraction from normalized difference vegetation index (NDVI) derived from Landsat TM images, and a new method was also brought forward to quantify NDVI thresholds of soil and vegetation. The. vegetation fraction data have been estimated using improved model in the study area, and was validated with the field survey data. The result shows that the improved model could satisfy with the need for quantifying vegetation cover parameter front TM imagery for soil loss equation.

Three Gorges Project is a great water resource project. After the project is completed, it will partially change Yangtze's water regime and affect ecology and environment of region along Yangtze River, from reservoir region, middle reaches, lower reaches to estuary. As a result, it is of great significance to monitor Yangtze's ecology and environment element, which take place since 1996 with 19 themes over the whole river. This paper introduces the ecology and environment information system for Three Gorges Project. The architecture and working principles of the system is introduced firstly. Then, system functions and key implementation technology are stated. The system is unique and complicate mainly because its monitoring contents are wide, which cover water pollution, agricultural ecology, terraneous ecology, hydrophytic ecology, local climate, social environment and so on. The application of the system will provide decision support for sustainable development of Yangtze's drainage area.

Miyun Reservoir is located in the northeast of Beijing and it is the most important drinking water resource of the city. Soil and water loss in this area directly affects local eco-environment and people's life. The soil and water conservation project has been launched out to combat the degrading environment in the upper reach of Miyun Reservoir basin. A ruled-based approach based on the objective of catchments, which is the unit of most soil conservation project, was applied to evaluate soil loss. For each heterogeneous hilly valley in the study area, a set of knowledge-based rules was formulated with remotely sensed images, land use map, DEM and ground investigated data. The relevant parameters, such as slope, vegetation fraction, ravine density, and rainfall distribution, which are also the input parameters of the widely applied universal soil loss equation (USLE), were scaling to the object properties to count this ruled-based model. Finally, all catchments were grouped into four grades according to the soil loss intensity, namely very severe, severe, moderate and slight, the result of the study was practicable to support to make the soil conservation planning.

The global version of the 1km spatial resolution land cover map have been finished at the end of 2003, which is initiated by the European Commission's Joint Research Center, named Global Land Cover 2000 Project (GLC-2000). As a part of GLC2000, the China window has been developed with the 10-day composite SPOT VGT NDVI data over a period of 01 January 2000 to 31 December 2000, DEM and the Meteorological data (Multi-annual average temperature, multi-annual average precipitation data) collected from 313 weather stations distributed over the China from 1971 to 2000. In order to remove cloud contamination and interpolate the missing data masked by cloud, the Harmonic Analysis of Time Series (HANTS) was applied to NDVI data. With the assistance of Erdas ISODATA algorithm, the classification has been carried out, and 22 types of land cover has labeled in the whole China by interpreting according to the Land Cover Classification System (LCCS) developed by the UN Food and Agriculture Organization (FAO) in the framework of the AFRICOVER project. Preliminary comparisons with the statistic data from Chinese Statistics Bureau and TM data show very promising results, and the accuracv assessment of the GLC-2000 is underway.

The China Brazil Earth Resources Satellite (CBERS)-2 CCD has 4 bands of 19.5m spatial resolutions in the visible/near-infrared wavelength regions and 1 pan band of 19.5m spatial resolutions. These bands (except pan) have the same spectral zones as Landsat7 ETM+ multispectral bands. We compared the performance of CCD image with ETM image from 4 key aspects in order to accelerate its application for agriculture monitoring. These 4 key aspects are geometric correction, typical surface features identification, land target area measurement, image classification and interpretation. The results show that CCD image can be geometrically corrected with high accuracy, is better than ETM+ image for typical surface features identification, records the small land targets in detail, can be more suit for recognition by eyes, can be used for measuring land targets area with high accuracy, has the same good performance as ETM image for image classification and interpretation. CBERS-2 CCD shows great potential for the applications of agricultural monitoring.

National key projects of eco-environment conservation have been carried out to combat the environmental degradation in the fragile ecological zone in China since 1998. To evaluate the project's effect on eco-environment restoration, five cases, locating in different eco-environmental zones and representing the different types of practiced measures, were selected to map land cover for each sample area, respectively, using Landsat TM images in 1997 and 2002. The mapping coverage of each sample site was confined within the overlaying extent of two images with the same path and row. And then the land cover changes were detected using overlaying technology in GIS. The results show that the land cover changes of each case site except for the site E located in the northeastern China characterized by farmland decreasing and natural or seminatural vegetation increasing, and that a serious of practiced measures adopted by the conservation projects have an effect on the rehabilitation of ecological environment.

By interactive interpretation method with the assistance of GIS and RS, soil and water loss classification information in the upper basin of Miyun reservoir was derived from Landsat Enhanced Thematic Mapper (ETM) images and other relevant datum. The ecological and environmental background database was built in the region, which consists of the controlling factors, such as vegetation fraction, ravine density, isohyet map, to the intensity of soil and water loss. Although these factors, directly or indirectly influencing the process of soil erosion, are the key parameters in the widely applied revised universal soil loss equation (RUSLE), they may have the different contribution to the soil erosion at the diverse spatial scale. The correlation between these environmental factors and soil erosion intensity was researched at a regional scale in This work. The purpose of the paper is to bring the reference of the case study for the application of the revised universal soil loss equation in the same region, as well as to verify these factors data, and the veracity of the result of RUSLE application.

The ability to map and monitor the spatial extent of the built environment, and associated temporal changes, has important societal and economic meaning. In This work, the high spatial resolution of the image - Quick Bird was used to create a detailed land cover maps of Taigu region, Shanxi province, China. Adopting object-oriented image segmentation and classification which is based on fuzzy logic allows the integration of a broad spectrum of different object features, such as spectral values, shape and texture. In this study we use not only image object's attributes, but also the relationship between networked image objects, it can perform sophisticated classification and get satisfied classification result. The aim of this work was to develop an object-oriented segmentation and classification approach for operational land cover mapping.

In China, crop structure adjustment policy has brought great change of different breed's planting area in different years. Government managers of agricultural industry need timely crop structure information to monitor the performance of the crop structure adjustment policy. The objective of this research was to evaluate the synergistic effects of multitemporal RADAR SAT synthetic aperture radar (SAR) and Land sat ETM+ data for extracting agricultural crops structure using an object-oriented classification approach. This work instructs and analyses the crop structure near the Kaifeng city area in 2002. Four crop types were extracted: corn, soybean, cotton, and peanut. With the object-oriented classification approach, the overall accuracy of crop structure extracting from two-date F5 mode's SAR data (mid- to last-season) and two-date Land sat ETM+ is over 90%.

There is an urgent need to monitor and evaluate the forest ecosystem that supports richer assemblages of biological species in order to preserve the habitats and protect the greatest number of species. Remotely sensed data hold tremendous potential for mapping species habitats and indicators of biological diversity, such as species richness. The objective of this research was to develop the empirical relationships between Landsat-5 TM spectral response and broad-leaved forest inventory parameter of species in the Longmenhe preserve of China. The regression analysis had been used to link the field-measured species richness index and remote sensing data. The accuracy of the resulting mapping was assessed by the field inventory. The results demonstrated that plant diversity can be predicted from satellite remotely sensed data, and the species richness index was highly correlated to TM Tasseled Cap wetness

The primary objective of this study was to assess the trends of vegetation changes in west of China since 1998 to 2002 with the 1 km/sup 2/-resolution SPOT-Vegetation 10-day maximum-value synthesized normalized difference vegetation index (NDVI) data. The framework for the analysis is the use of the coefficient of variation (COV) of the decadal NDVI as a measure of vegetative biomass change. A higher NDVI COV for a given pixel represents a greater change in vegetation biomass in the ground area represented by that pixel. And then a linear regression was used to determine the trend of COV value for each pixel over the 5-year period. The slope of the linear regression can be gained and acted as the criterion for the change direction. Pixels with a negative slope are considered to represent ground areas with decreasing amounts of vegetation, vice versa. Results showed vegetation ecosystems in west of China are undergoing accelerated change due to natural and anthropogenic disturbances since 1998 had the increase trends in the five years.

The land desertification is one of the major environmental problems in the Source Region of the Yangtze River, Qinghai Province. With the Three Gorges Dam construction, more and more attention is paid to restoring the degraded eco-environment of the region. In this study, the sandy land was classified into three types, and the desertified land was also inventoried correspondingly into three levels. Through interpreting TM images in 1986 and 2000, the databases of sandy land at two times had been established, respectively. And then the desertified land data was derived through overlaying the databases of sandy land. The result shows that there is 1,459,365 ha of sandy land in the region in 2000, accounting for 9.89% of all study area. There is 11,877 ha of newly sandy land resulted from land desertification, and sandy land has increased by 7.35% during about 14 years in the end of the 20th century.

The process of soil erosion is complex. Among these factors that influence the process of water and soil erosion, vegetation cover and slope steepness are selected. The vegetation cover data in the Upper Basin of Miyun Reservoir have been estimated from normalized difference vegetation index derived from Landsat-7 ETM+ images. Based on the pixel of DEM slope, steepness is computed. Vegetation cover and topographic factor can be combined as a crosstab model. A soil erosion risk map with six grades can be drawn. Using the method the probability and the extent of soil loss can be measured. This information is very useful to watershed manager. This case study describes and assesses soil erosion in the Upper Basin of Miyun Reservoir

With the assistance of GIS and RS technology, it is extremely crucial to manage the huge volume, multisource and multiscale spatial data for the design and development of geographic information system. The database and its client management system have been developed to act as a data management and integrating platform in the soil and water loss monitoring system in the upper basin of Miyun reservoir. This work expounded the detail about the design technique and framework of the database in practice, and focused on manipulating the huge volume spatial data management, which was based on the software of Oracle9i and ArcSDE version 8.2 and the support of object-oriented technology. The paper approached to put forward a feasible and practical technology project reference for the construction of the applied GIS on the aspect of system database construction, and especially for which needs to manage both spatial data and nonspatial data.

ASTER has 3 bands of 15 m spatial resolution in the visible and near-infrared wavelength regions. These three bands have the same spectral zones as Landsat7 ETM+ multispectral bands. We compared the performance of the ASTER image with that of the ETM fusion image from 4 key aspects in order to accelerate its application for agriculture monitoring. These 4 key aspects are geometric correction, typical surface features identification, land target area measurement, image classification and interpretation. The results show that the ASTER image can be geometrically corrected with high accuracy, is better than the ETM+ fusion image for typical surface features identification, records the small land targets in detail, can be more suitable for recognition by eye, can be used for measuring land target area with high accuracy, and has the same good performance as the ETM image for image classification and interpretation. ASTER shows great potential for the applications of agricultural monitoring.

RADARSAT-1 data has been widely used to monitor paddy rice in the world since RADARSAT-1 satellite was launched in 1995. In China, Crop structure adjustment policy has brought great change of rice area. Government managers of agricultural industry need timely crop structure information to monitor the performance of implementing the crop structure adjustment policy. But crop structure information based on statistical data from reporting system cannot meet the urgent and timely needs of governors for rice structure information. RADARSAT-1 brought a new data source for extracting rice information with high accuracy. Using multitemporal RADARSAT data can obtain all rice area with different season in a farming year in certain area. This paper presents the methods of remote sensing for study on rice structure using multitemporal RADARSAT data. Then, this paper instructs and analyses the rice structure in the Wuhan city area in 2002.

Information extraction encountered a new challenge while the spatial resolution is increasing quickly. People suppose that the higher the spatial resolution is, the better is the result of classification. To prove this guess we use two approaches: pixel-based classification and object-oriented classification. The former site test shows one class has different accuracy from various resolution images. The object-oriented approach is an advanced solution for image analysis. The accuracy of object-oriented approach is much higher than those of the pixel-based approach. The site result shows that each class has its optimal image segmentation scale.

In this paper we developed an operational approach using time series Normal Difference Vegetation Index (NDVI) derived from SPOT VGT for crop yield forecasting in China during a five-year span (1988-2002). In order to increase the information content extracted from NDVI profiles, we compose our NDVI profile only in arable area. Thanks to extract the characteristics of the vegetation dynamics, the harmonic analysis of time series algorithm is performed on the NDVI data. We extract our analytical indicators from the time series NDVI profiles, and then remove the trend from the yield series using a linear upward trend function. The difference between the year and last year of residuals and corresponding NDVI indicators were related analysis, we get our predicting indicators by selecting those parameters that had the highest correlation coefficient. At last, we build our yield estimation model based on linear regression analysis between the indicators and residuals just mentioned. These are combined with trend yield, last year residuals yield and satellite based estimation to forecast yields. This year, we used our model in forecasting winter wheat production in China. The resulting productivity could be consistent with other existing data. Our results were well received by the local authorities.

Presents a series of methods that monitor crops in China by remote sensing. First the paper describes the methodology used to integrate high resolution land cover data and 1 km/sup 2/-resolution SPOT-Vegetation 10-day images for the extraction of NDVI profiles. That can increase the information content of the time series of NDVI. It is proved that information extracted this way is closer to crop behavior than a simple mixed vegetation profile. Next for cloud masking, we use the HANTS (harmonic analysis of time series) algorithm. This is because time series of NDVI data have a periodic characteristic that describes the yearly cycle of the vegetation dynamics. HANTS can analyze the time signal for each individual pixel and use the series of harmonic sine and cosine waves to reconstruct wave fits into the total period. Results showed that cloud affected data are recognized and replaced successfully by reconstruction of seasonal NDVI profiles. The application and the results fit the objective of the crop monitoring system in China, which is used during the cropping season to monitor crop growth and identify possible food shortages in real time.