Inversion of Different Cultivated Soil Types’ Salinity Using Hyperspectral Data and Machine Learning

Soil salinization is one of the main causes of global desertification and soil degradation. Although previous studies have investigated the hyperspectral inversion of soil salinity using machine learning, only a few have been based on soil types. Moreover, agricultural fields can be improved based on the accurate estimation of the soil salinity, according to the soil type. We collected field data relating to six salinized soils, Haplic Solonchaks (HSK), Stagnic Solonchaks (SSK), Calcic Sonlonchaks (CSK), Fluvic Solonchaks (FSK), Haplic Sonlontzs (HSN), and Takyr Solonetzs (TSN), in the Hetao Plain of the upper reaches of the Yellow River, and measured the in situ hyperspectral, pH, and electrical conductivity (EC) values of a total of 231 soil samples. The two-dimensional spectral index, topographic factors, climate factors, and soil texture were considered. Several models were used for the inversion of the saline soil types: partial least squares regression (PLSR), random forest (RF), extremely randomized trees (ERT), and ridge regression (RR). The spectral curves of the six salinized soil types were similar, but their reflectance sizes were different. The degree of salinization did not change according to the spectral reflectance of the soil types, and the related properties were inconsistent. The Pearson’s correlation coefficient (PCC) between the two-dimensional spectral index and the EC was much greater than that between the reflectance and EC in the original band. In the two-dimensional index, the PCC of the HSK-NDI was the largest (0.97), whereas in the original band, the PCC of the SSK400 nm was the largest (0.70). The two-dimensional spectral index (NDI, RI, and DI) and the characteristic bands were the most selected variables in the six salinized soil types, based on the variable projection importance analysis (VIP). The best inversion model for the HSK and FSK was the RF, whereas the best inversion model for the CSK, SSK, HSN, and TSN was the ERT, and the CSK-ERT had the best performance (R2 = 0.99, RMSE = 0.18, and RPIQ = 6.38). This study provides a reference for distinguishing various salinization types using hyperspectral reflectance and provides a foundation for the accurate monitoring of salinized soil via multispectral remote sensing

Optimal Irrigation Scheduling for Summer Maize Crop: Based on GIS and CROPWAT Application in Hetao District; Inner Mongolia Autonomous Region, China

Net Irrigation Water Requirement was estimated using GIS and CROPWAT software. The study aims to develop an optimal irrigation scheduling in summer, to increase crop yield under water scarcity conditions. The proportion of rainwater evaporated over year 2008 “868.6 mm/dec” was used to compare with the water requirements of the maize crop “734.1 mm/dec” The results showed deficits ranging between 30.7 mm/month and 200.8 mm/month, for the period between April and September. In addition, there was uneven distribution of precipitation and an irregular basis during the agricultural year (2000-2008). The ET0 was between 0.47mm and 3.08mm, and net irrigation water requirement was 833.4 mm for the maize crop. On refilling soil to field capacity with irrigation at critical depletion, 70% field efficiency was achieved which correspond to optimal condition, while adapting fixed interval per stage gave a yield reduction of about 2.5 %. Keywords: Evapotranspiration; ArcGIS; CROPWAT; Optimal Irrigation Scheduling; Cor

Land Degradation Assessment with Earth Observation

This Special Issue (SI) on “Land Degradation Assessment with Earth Observation” comprises 17 original research papers with a focus on land degradation in arid, semiarid and dry-subhumid areas (i.e., desertification) in addition to temperate rangelands, grasslands, woodlands and the humid tropics. The studies cover different spatial, spectral and temporal scales and employ a wealth of different optical and radar sensors. Some studies incorporate time-series analysis techniques that assess the general trend of vegetation or the timing and duration of the reduction in biological productivity caused by land degradation. As anticipated from the latest trend in Earth Observation (EO) literature, some studies utilize the cloud-computing infrastructure of Google Earth Engine to cope with the unprecedented volume of data involved in current methodological approaches. This SI clearly demonstrates the ever-increasing relevance of EO technologies when it comes to assessing and monitoring land degradation. With the recently published IPCC Reports informing us of the severe impacts and risks to terrestrial and freshwater ecosystems and the ecosystem services they provide, the EO scientific community has a clear obligation to increase its efforts to address any remaining gaps—some of which have been identified in this SI—and produce highly accurate and relevant land-degradation assessment and monitoring tools

Quantitatively estimating main soil water-soluble salt ions content based on Visible-near infrared wavelength selected using GC, SR and VIP

Soil salinization is the primary obstacle to the sustainable development of agriculture and eco-environment in arid regions. The accurate inversion of the major water-soluble salt ions in the soil using visible-near infrared (VIS-NIR) spectroscopy technique can enhance the effectiveness of saline soil management. However, the accuracy of spectral models of soil salt ions turns out to be affected by high dimensionality and noise information of spectral data. This study aims to improve the model accuracy by optimizing the spectral models based on the exploration of the sensitive spectral intervals of different salt ions. To this end, 120 soil samples were collected from Shahaoqu Irrigation Area in Inner Mongolia, China. After determining the raw reflectance spectrum and content of salt ions in the lab, the spectral data were pre-treated by standard normal variable (SNV). Subsequently the sensitive spectral intervals of each ion were selected using methods of gray correlation (GC), stepwise regression (SR) and variable importance in projection (VIP). Finally, the performance of both models of partial least squares regression (PLSR) and support vector regression (SVR) was investigated on the basis of the sensitive spectral intervals. The results indicated that the model accuracy based on the sensitive spectral intervals selected using different analytical methods turned out to be different: VIP was the highest, SR came next and GC was the lowest. The optimal inversion models of different ions were different. In general, both PLSR and SVR had achieved satisfactory model accuracy, but PLSR outperformed SVR in the forecasting effects. Great difference existed among the optimal inversion accuracy of different ions: the predicative accuracy of Ca2+, Na+, Cl−, Mg2+ and SO42− was very high, that of CO32− was high and K+ was relatively lower, but HCO3− failed to have any predicative power. These findings provide a new approach for the optimization of the spectral model of water-soluble salt ions and improvement of its predicative precision

Preliminary Strategic Environmental Assessment of the Great Western Development Strategy: Safeguarding Ecological Security for a New Western China

The Great Western Development Strategy (GWDS) is a long term national campaign aimed at boosting development of the western area of China and narrowing the economic gap between the western and the eastern parts of China. The Strategic Environmental Assessment (SEA) procedure was employed to assess the environmental challenges brought about by the western development plans. These plans include five key developmental domains (KDDs): water resource exploitation and use, land utilization, energy generation, tourism development, and ecological restoration and conservation. A combination of methods involving matrix assessment, incorporation of expert judgment and trend analysis was employed to analyze and predict the environmental impacts upon eight selected environmental indicators: water resource availability, soil erosion, soil salinization, forest destruction, land desertification, biological diversity, water quality and air quality. Based on the overall results of the assessment, countermeasures for environmental challenges that emerged were raised as key recommendations to ensure ecological security during the implementation of the GWDS. This paper is intended to introduce a consensus-based process for evaluating the complex, long term pressures on the ecological security of large areas, such as western China, that focuses on the use of combined methods applied at the strategic level

Local and regional desertification indicators in a global perspective: Seminar proceedings

This volume contains the proceedings of the International Seminar on Local and Regional Desertification Indicators in a Global Perspective held in Beijing, China, in May 2005. Aim of the seminar was to provide a precious opportunity to exchange information and experiences about the identification and use of desertification B&I among representatives of UNCCD Annexes, while contributing to strengthen linkages among them and exploring possible synergies. The seminar was organised in the framework of the AIDCCD project (Active Exchange of Experiences on Indicators and Development of Perspective in the Context of UNCCD), aiming at developing and co-ordinating exchange of experience across the world among institutions involved in the implementation of the UNCCD regional Annexes

Lake dynamics in Central Asia in the past 30 years

Water is a key resource in arid Central Asia (CA) and is heavily affected by climate change and human activities. Temperature across the region has increased drastically especially in the mountain region while precipitation change is less homogeneous. The increased temperature has caused increased melting of glacier and snow which has a large contribution to the runoff in rivers. Human activities such as agriculture irrigation and reservoir management also affect water availability. In the Soviet era, agriculture in CA expanded continuously and large amount of water was extracted from rivers for irrigation. This has caused the catastrophic decline of the Aral Sea. In the post-Soviet era, countries in CA have reorganized their agriculture structure to be self-sufficient. It is important to understand how these changes affect water availability in CA especially under climate change. This dissertation uses lakes as proxy indicators of water availability and assesses how climate and human activities have affected lakes in CA. Seventeen lakes located in three former Soviet republics and western China from seven basins are examined using remote sensing and hydrologic modeling to estimate their changes in area, water level and volume. Agriculture area changes in these basins from seven countries are also examined using remote sensing. It is found that 1) lakes located in the mountains have generally expanded due to the melting glaciers and snow; 2) lakes located in the lowlands have remained relatively stable due to the relative stability of agriculture area; 3) reservoirs exhibit different seasonal patterns due to their major function as power generation reservoirs release water during the winter while irrigation reservoirs release water during the summer; 4) agriculture area in the former Soviet Central Asia republics is highly dependent on precipitation due to the lack of efficient irrigation infrastructure while agriculture in China has continuously expanded due to the adoption of drip irrigation and groundwater extraction. In conclusion, climate is the more dominant factor affecting water availability especially in the mountains causing the lakes to expand while agriculture irrigation has offset some of the surplus in the lowlands causing the lakes to remain relatively stable