scholarly journals Developing a process-based and remote sensing driven crop yield model for maize (PRYM–Maize) and its validation over the Northeast China Plain

2021 ◽  
Vol 20 (2) ◽  
pp. 408-423 ◽  
Author(s):  
Sha ZHANG ◽  
Yun BAI ◽  
Jia-hua ZHANG ◽  
Shahzad ALI
Keyword(s):  
Remote Sensing ◽  
Crop Yield ◽  
Northeast China ◽  
Yield Model

scholarly journals Water Stress Regime of Irrigated Crops Based on Remote Sensing and Ground-Based Data

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1117
Author(s):  
Anatoly Mikhailovich Zeyliger ◽  
Olga Sergeevna Ermolaeva
Keyword(s):  
Remote Sensing ◽  
Water Stress ◽  
Crop Yield ◽  
Satellite Monitoring ◽  
Agricultural Crop ◽  
Crop Water ◽  
Stress Regime ◽  
Irrigation Water Use ◽  
Crop Water Stress ◽  
Crop Irrigation

In the past few decades, combinations of remote sensing technologies with ground-based methods have become available for use at the level of irrigated fields. These approaches allow an evaluation of crop water stress dynamics and irrigation water use efficiency. In this study, remotely sensed and ground-based data were used to develop a method of crop water stress assessment and analysis. Input datasets of this method were based on the results of ground-based and satellite monitoring in 2012. Required datasets were collected for 19 irrigated alfalfa crops in the second year of growth at three study sites located in Saratovskoe Zavolzhie (Saratov Oblast, Russia). Collected datasets were applied to calculate the dynamics of daily crop water stress coefficients for all studied crops, thereby characterizing the efficiency of crop irrigation. Accordingly, data on the crop yield of three harvests were used. An analysis of the results revealed a linear relationship between the crop yield of three cuts and the average value of the water stress coefficient. Further application of this method may be directed toward analyzing the effectiveness of irrigation practices and the operational management of agricultural crop irrigation.

Study on the Wetland Change in Northeast China Based on RS and GIS Technology

2011 ◽  
Vol 356-360 ◽  
pp. 2886-2891
Author(s):  
Han Wen Cui ◽  
Qi Gang Jiang
Keyword(s):  
Remote Sensing ◽  
Current Distribution ◽  
Constructed Wetland ◽  
Human Activities ◽  
Northeast China ◽  
Transition Matrix ◽  
Gis Technology ◽  
Wetland Area ◽  
Rs And Gis ◽  
Data Source

Based on the RS and GIS technology, the remote sensing imageries MSS in 1975, ETM in 2000 and CBERS-2 in 2007 have been used as main data source in this paper. Wetland current distribution, spatiotemporal change principle and transition matrix have been analyzed in order to realize the wetland change situation in Northeast China during the 30 years. The results show that the wetland area in Northeast China, on the whole, is decreased first and then increased. The dramatic change happened in mire and constructed wetland. Mire is decreased first and then increased, but the whole is still decreased. While, constructed wetland is increased continuously. Constructed wetland increased owing to the transition from mire and non-wetland. The level of the transition from mire to constructed wetland is lower. In Northeast China, human activities have a great impact on wetland change than nature factors.

scholarly journals Prediction Potential of Remote Sensing-Related Variables in the Topsoil Organic Carbon Density of Liaohekou Coastal Wetlands, Northeast China

2021 ◽  
Vol 13 (20) ◽  
pp. 4106
Author(s):  
Shuai Wang ◽  
Mingyi Zhou ◽  
Qianlai Zhuang ◽  
Liping Guo
Keyword(s):  
Remote Sensing ◽  
Organic Carbon ◽  
Coastal Wetlands ◽  
Environmental Variables ◽  
Northeast China ◽  
Vegetation Index ◽  
Coastal Wetland ◽  
Wetland Ecosystems ◽  
Flat Terrain ◽  
Soc Density

Wetland ecosystems contain large amounts of soil organic carbon. Their natural environment is often both at the junction of land and water with good conditions for carbon sequestration. Therefore, the study of accurate prediction of soil organic carbon (SOC) density in coastal wetland ecosystems of flat terrain areas is the key to understanding their carbon cycling. This study used remote sensing data to study SOC density potentials of coastal wetland ecosystems in Northeast China. Eleven environmental variables including normalized difference vegetation index (NDVI), difference vegetation index (DVI), soil adjusted vegetation index (SAVI), renormalization difference vegetation index (RDVI), ratio vegetation index (RVI), topographic wetness index (TWI), elevation, slope aspect (SA), slope gradient (SG), mean annual temperature (MAT), and mean annual precipitation (MAP) were selected to predict SOC density. A total of 193 soil samples (0–30 cm) were divided into two parts, 70% of the sampling sites data were used to construct the boosted regression tree (BRT) model containing three different combinations of environmental variables, and the remaining 30% were used to test the predictive performance of the model. The results show that the full variable model is better than the other two models. Adding remote sensing-related variables significantly improved the model prediction. This study revealed that SAVI, NDVI and DVI were the main environmental factors affecting the spatial variation of topsoil SOC density of coastal wetlands in flat terrain areas. The mean (±SD) SOC density of full variable models was 18.78 (±1.95) kg m−2, which gradually decreased from northeast to southwest. We suggest that remote sensing-related environmental variables should be selected as the main environmental variables when predicting topsoil SOC density of coastal wetland ecosystems in flat terrain areas. Accurate prediction of topsoil SOC density distribution will help to formulate soil management policies and enhance soil carbon sequestration.

Effect of conservation tillage on crop yield and soil organic carbon in Northeast China: A meta‐analysis

2021 ◽  
Author(s):  
Cong He ◽  
Jia‐Rui Niu ◽  
Cheng‐Tang Xu ◽  
Shou‐Wei Han ◽  
Wei Bai ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Crop Yield ◽  
Northeast China ◽  
Conservation Tillage ◽  
Meta Analysis

Techniques to Predict Agricultural Droughts

2005 ◽  
Author(s):  
Zekai Şen
Keyword(s):  
Crop Yield ◽  
Satellite Data ◽  
Wheat Yield ◽  
Weather Variables ◽  
Short Term ◽  
Yield Model ◽  
Regression Techniques ◽  
The Difference ◽  
Effects Of Drought

In general, the techniques to predict drought include statistical regression, time series, stochastic (or probabilistic), and, lately, pattern recognition techniques. All of these techniques require that a quantitative variable be identified to define drought, with which to begin the process of prediction. In the case of agricultural drought, such a variable can be the yield (production per unit area) of the major crop in a region (Kumar, 1998; Boken, 2000). The crop yield in a year can be compared with its long-term average, and drought intensity can be classified as nil, mild, moderate, severe, or disastrous, based on the difference between the current yield and the average yield. Regression techniques estimate crop yields using yield-affecting variables. A comprehensive list of possible variables that affect yield is provided in chapter 1. Usually, the weather variables routinely available for a historical period that significantly affect the yield are included in a regression analysis. Regression techniques using weather data during a growing season produce short-term estimates (e.g., Sakamoto, 1978; Idso et al., 1979; Slabbers and Dunin, 1981; Diaz et al., 1983; Cordery and Graham, 1989; Walker, 1989; Toure et al., 1995; Kumar, 1998). Various researchers in different parts of the world (see other chapters) have developed drought indices that can also be included along with the weather variables to estimate crop yield. For example, Boken and Shaykewich (2002) modifed the Western Canada Wheat Yield Model (Walker, 1989) drought index using daily temperature and precipitation data and advanced very high resolution radiometer (AVHRR) satellite data. The modified model improved the predictive power of the wheat yield model significantly. Some satellite data-based variables that can be used to predict crop yield are described in chapters 5, 6, 9, 13, 19, and 28. The short-term estimates are available just before or around harvest time. But many times long-term estimates are required to predict drought for next year, so that long-term planning for dealing with the effects of drought can be initiated in time.

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